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1.  Underlying genetic models of inheritance in established type 2 diabetes associations 
American journal of epidemiology  2009;170(5):537-545.
For most associations of common polymorphisms with common diseases, the genetic model of inheritance is unknown. We extended and applied a Bayesian meta-analysis approach to data from 19 studies on 17 replicated associations for type 2 diabetes. For 13 polymorphisms, the data fit very well to an additive model, for 4 polymorphisms the data were consistent with either an additive or dominant model, and for 2 polymorphisms with an additive or recessive model of inheritance for the diabetes risk allele. Results were robust to using different priors and after excluding data where index polymorphisms had been examined indirectly through proxy markers. The Bayesian meta-analysis model yielded point estimates for the genetic effects that are very similar to those previously reported based on fixed or random effects models, but uncertainty about several of the effects was substantially larger. We also examined the extent of between-study heterogeneity in the genetic model and found generally small values of the between-study deviation for the genetic model parameter. Heterosis could not be excluded in 4 SNPs. Information on the genetic model of robustly replicated GWA-derived association signals may be useful for predictive modeling, and for designing biological and functional experiments.
doi:10.1093/aje/kwp145
PMCID: PMC2732984  PMID: 19602701
2.  Continuous Subcutaneous Insulin Infusion (CSII) Pumps for Type 1 and Type 2 Adult Diabetic Populations 
Executive Summary
In June 2008, the Medical Advisory Secretariat began work on the Diabetes Strategy Evidence Project, an evidence-based review of the literature surrounding strategies for successful management and treatment of diabetes. This project came about when the Health System Strategy Division at the Ministry of Health and Long-Term Care subsequently asked the secretariat to provide an evidentiary platform for the Ministry’s newly released Diabetes Strategy.
After an initial review of the strategy and consultation with experts, the secretariat identified five key areas in which evidence was needed. Evidence-based analyses have been prepared for each of these five areas: insulin pumps, behavioural interventions, bariatric surgery, home telemonitoring, and community based care. For each area, an economic analysis was completed where appropriate and is described in a separate report.
To review these titles within the Diabetes Strategy Evidence series, please visit the Medical Advisory Secretariat Web site, http://www.health.gov.on.ca/english/providers/program/mas/mas_about.html,
Diabetes Strategy Evidence Platform: Summary of Evidence-Based Analyses
Continuous Subcutaneous Insulin Infusion Pumps for Type 1 and Type 2 Adult Diabetics: An Evidence-Based Analysis
Behavioural Interventions for Type 2 Diabetes: An Evidence-Based Analysis
Bariatric Surgery for People with Diabetes and Morbid Obesity: An Evidence-Based Summary
Community-Based Care for the Management of Type 2 Diabetes: An Evidence-Based Analysis
Home Telemonitoring for Type 2 Diabetes: An Evidence-Based Analysis
Application of the Ontario Diabetes Economic Model (ODEM) to Determine the Cost-effectiveness and Budget Impact of Selected Type 2 Diabetes Interventions in Ontario
Objective
The objective of this analysis is to review the efficacy of continuous subcutaneous insulin infusion (CSII) pumps as compared to multiple daily injections (MDI) for the type 1 and type 2 adult diabetics.
Clinical Need and Target Population
Insulin therapy is an integral component of the treatment of many individuals with diabetes. Type 1, or juvenile-onset diabetes, is a life-long disorder that commonly manifests in children and adolescents, but onset can occur at any age. It represents about 10% of the total diabetes population and involves immune-mediated destruction of insulin producing cells in the pancreas. The loss of these cells results in a decrease in insulin production, which in turn necessitates exogenous insulin therapy.
Type 2, or ‘maturity-onset’ diabetes represents about 90% of the total diabetes population and is marked by a resistance to insulin or insufficient insulin secretion. The risk of developing type 2 diabetes increases with age, obesity, and lack of physical activity. The condition tends to develop gradually and may remain undiagnosed for many years. Approximately 30% of patients with type 2 diabetes eventually require insulin therapy.
CSII Pumps
In conventional therapy programs for diabetes, insulin is injected once or twice a day in some combination of short- and long-acting insulin preparations. Some patients require intensive therapy regimes known as multiple daily injection (MDI) programs, in which insulin is injected three or more times a day. It’s a time consuming process and usually requires an injection of slow acting basal insulin in the morning or evening and frequent doses of short-acting insulin prior to eating. The most common form of slower acting insulin used is neutral protamine gagedorn (NPH), which reaches peak activity 3 to 5 hours after injection. There are some concerns surrounding the use of NPH at night-time as, if injected immediately before bed, nocturnal hypoglycemia may occur. To combat nocturnal hypoglycemia and other issues related to absorption, alternative insulins have been developed, such as the slow-acting insulin glargine. Glargine has no peak action time and instead acts consistently over a twenty-four hour period, helping reduce the frequency of hypoglycemic episodes.
Alternatively, intensive therapy regimes can be administered by continuous insulin infusion (CSII) pumps. These devices attempt to closely mimic the behaviour of the pancreas, continuously providing a basal level insulin to the body with additional boluses at meal times. Modern CSII pumps are comprised of a small battery-driven pump that is designed to administer insulin subcutaneously through the abdominal wall via butterfly needle. The insulin dose is adjusted in response to measured capillary glucose values in a fashion similar to MDI and is thus often seen as a preferred method to multiple injection therapy. There are, however, still risks associated with the use of CSII pumps. Despite the increased use of CSII pumps, there is uncertainty around their effectiveness as compared to MDI for improving glycemic control.
Part A: Type 1 Diabetic Adults (≥19 years)
An evidence-based analysis on the efficacy of CSII pumps compared to MDI was carried out on both type 1 and type 2 adult diabetic populations.
Research Questions
Are CSII pumps more effective than MDI for improving glycemic control in adults (≥19 years) with type 1 diabetes?
Are CSII pumps more effective than MDI for improving additional outcomes related to diabetes such as quality of life (QoL)?
Literature Search
Inclusion Criteria
Randomized controlled trials, systematic reviews, meta-analysis and/or health technology assessments from MEDLINE, EMBASE, CINAHL
Adults (≥ 19 years)
Type 1 diabetes
Study evaluates CSII vs. MDI
Published between January 1, 2002 – March 24, 2009
Patient currently on intensive insulin therapy
Exclusion Criteria
Studies with <20 patients
Studies <5 weeks in duration
CSII applied only at night time and not 24 hours/day
Mixed group of diabetes patients (children, adults, type 1, type 2)
Pregnancy studies
Outcomes of Interest
The primary outcomes of interest were glycosylated hemoglobin (HbA1c) levels, mean daily blood glucose, glucose variability, and frequency of hypoglycaemic events. Other outcomes of interest were insulin requirements, adverse events, and quality of life.
Search Strategy
The literature search strategy employed keywords and subject headings to capture the concepts of:
1) insulin pumps, and
2) type 1 diabetes.
The search was run on July 6, 2008 in the following databases: Ovid MEDLINE (1996 to June Week 4 2008), OVID MEDLINE In-Process and Other Non-Indexed Citations, EMBASE (1980 to 2008 Week 26), OVID CINAHL (1982 to June Week 4 2008) the Cochrane Library, and the Centre for Reviews and Dissemination/International Agency for Health Technology Assessment. A search update was run on March 24, 2009 and studies published prior to 2002 were also examined for inclusion into the review. Parallel search strategies were developed for the remaining databases. Search results were limited to human and English-language published between January 2002 and March 24, 2009. Abstracts were reviewed, and studies meeting the inclusion criteria outlined above were obtained. Reference lists were also checked for relevant studies.
Summary of Findings
The database search identified 519 relevant citations published between 1996 and March 24, 2009. Of the 519 abstracts reviewed, four RCTs and one abstract met the inclusion criteria outlined above. While efficacy outcomes were reported in each of the trials, a meta-analysis was not possible due to missing data around standard deviations of change values as well as missing data for the first period of the crossover arm of the trial. Meta-analysis was not possible on other outcomes (quality of life, insulin requirements, frequency of hypoglycemia) due to differences in reporting.
HbA1c
In studies where no baseline data was reported, the final values were used. Two studies (Hanaire-Broutin et al. 2000, Hoogma et al. 2005) reported a slight reduction in HbA1c of 0.35% and 0.22% respectively for CSII pumps in comparison to MDI. A slightly larger reduction in HbA1c of 0.84% was reported by DeVries et al.; however, this study was the only study to include patients with poor glycemic control marked by higher baseline HbA1c levels. One study (Bruttomesso et al. 2008) showed no difference between CSII pumps and MDI on Hba1c levels and was the only study using insulin glargine (consistent with results of parallel RCT in abstract by Bolli 2004). While there is statistically significant reduction in HbA1c in three of four trials, there is no evidence to suggest these results are clinically significant.
Mean Blood Glucose
Three of four studies reported a statistically significant reduction in the mean daily blood glucose for patients using CSII pump, though these results were not clinically significant. One study (DeVries et al. 2002) did not report study data on mean blood glucose but noted that the differences were not statistically significant. There is difficulty with interpreting study findings as blood glucose was measured differently across studies. Three of four studies used a glucose diary, while one study used a memory meter. In addition, frequency of self monitoring of blood glucose (SMBG) varied from four to nine times per day. Measurements used to determine differences in mean daily blood glucose between the CSII pump group and MDI group at clinic visits were collected at varying time points. Two studies use measurements from the last day prior to the final visit (Hoogma et al. 2005, DeVries et al. 2002), while one study used measurements taken during the last 30 days and another study used measurements taken during the 14 days prior to the final visit of each treatment period.
Glucose Variability
All four studies showed a statistically significant reduction in glucose variability for patients using CSII pumps compared to those using MDI, though one, Bruttomesso et al. 2008, only showed a significant reduction at the morning time point. Brutomesso et al. also used alternate measures of glucose variability and found that both the Lability index and mean amplitude of glycemic excursions (MAGE) were in concordance with the findings using the standard deviation (SD) values of mean blood glucose, but the average daily risk range (ADRR) showed no difference between the CSII pump and MDI groups.
Hypoglycemic Events
There is conflicting evidence concerning the efficacy of CSII pumps in decreasing both mild and severe hypoglycemic events. For mild hypoglycemic events, DeVries et al. observed a higher number of events per patient week in the CSII pump group than the MDI group, while Hoogma et al. observed a higher number of events per patient year in the MDI group. The remaining two studies found no differences between the two groups in the frequency of mild hypoglycemic events. For severe hypoglycemic events, Hoogma et al. found an increase in events per patient year among MDI patients, however, all of the other RCTs showed no difference between the patient groups in this aspect.
Insulin Requirements and Adverse Events
In all four studies, insulin requirements were significantly lower in patients receiving CSII pump treatment in comparison to MDI. This difference was statistically significant in all studies. Adverse events were reported in three studies. Devries et al. found no difference in ketoacidotic episodes between CSII pump and MDI users. Bruttomesso et al. reported no adverse events during the study. Hanaire-Broutin et al. found that 30 patients experienced 58 serious adverse events (SAEs) during MDI and 23 patients had 33 SAEs during treatment out of a total of 256 patients. Most events were related to severe hypoglycemia and diabetic ketoacidosis.
Quality of Life and Patient Preference
QoL was measured in three studies and patient preference was measured in one. All three studies found an improvement in QoL for CSII users compared to those using MDI, although various instruments were used among the studies and possible reporting bias was evident as non-positive outcomes were not consistently reported. Moreover, there was also conflicting results in two of the studies using the Diabetes Treatment Satisfaction Questionnaire (DTSQ). DeVries et al. reported no difference in treatment satisfaction between CSII pump users and MDI users while Brutomesso et al. reported that treatment satisfaction improved among CSII pump users.
Patient preference for CSII pumps was demonstrated in just one study (Hanaire-Broutin et al. 2000) and there are considerable limitations with interpreting this data as it was gathered through interview and 72% of patients that preferred CSII pumps were previously on CSII pump therapy prior to the study. As all studies were industry sponsored, findings on QoL and patient preference must be interpreted with caution.
Quality of Evidence
Overall, the body of evidence was downgraded from high to low due to study quality and issues with directness as identified using the GRADE quality assessment tool (see Table 1) While blinding of patient to intervention/control was not feasible in these studies, blinding of study personnel during outcome assessment and allocation concealment were generally lacking. Trials reported consistent results for the outcomes HbA1c, mean blood glucose and glucose variability, but the directness or generalizability of studies, particularly with respect to the generalizability of the diabetic population, was questionable as most trials used highly motivated populations with fairly good glycemic control. In addition, the populations in each of the studies varied with respect to prior treatment regimens, which may not be generalizable to the population eligible for pumps in Ontario. For the outcome of hypoglycaemic events the evidence was further downgraded to very low since there was conflicting evidence between studies with respect to the frequency of mild and severe hypoglycaemic events in patients using CSII pumps as compared to CSII (see Table 2). The GRADE quality of evidence for the use of CSII in adults with type 1 diabetes is therefore low to very low and any estimate of effect is, therefore, uncertain.
GRADE Quality Assessment for CSII pumps vs. MDI on HbA1c, Mean Blood Glucose, and Glucose Variability for Adults with Type 1 Diabetes
Inadequate or unknown allocation concealment (3/4 studies); Unblinded assessment (all studies) however lack of blinding due to the nature of the study; No ITT analysis (2/4 studies); possible bias SMBG (all studies)
HbA1c: 3/4 studies show consistency however magnitude of effect varies greatly; Single study uses insulin glargine instead of NPH; Mean Blood Glucose: 3/4 studies show consistency however magnitude of effect varies between studies; Glucose Variability: All studies show consistency but 1 study only showed a significant effect in the morning
Generalizability in question due to varying populations: highly motivated populations, educational component of interventions/ run-in phases, insulin pen use in 2/4 studies and varying levels of baseline glycemic control and experience with intensified insulin therapy, pumps and MDI.
GRADE Quality Assessment for CSII pumps vs. MDI on Frequency of Hypoglycemic
Inadequate or unknown allocation concealment (3/4 studies); Unblinded assessment (all studies) however lack of blinding due to the nature of the study; No ITT analysis (2/4 studies); possible bias SMBG (all studies)
Conflicting evidence with respect to mild and severe hypoglycemic events reported in studies
Generalizability in question due to varying populations: highly motivated populations, educational component of interventions/ run-in phases, insulin pen use in 2/4 studies and varying levels of baseline glycemic control and experience with intensified insulin therapy, pumps and MDI.
Economic Analysis
One article was included in the analysis from the economic literature scan. Four other economic evaluations were identified but did not meet our inclusion criteria. Two of these articles did not compare CSII with MDI and the other two articles used summary estimates from a mixed population with Type 1 and 2 diabetes in their economic microsimulation to estimate costs and effects over time. Included were English articles that conducted comparisons between CSII and MDI with the outcome of Quality Adjusted Life Years (QALY) in an adult population with type 1 diabetes.
From one study, a subset of the population with type 1 diabetes was identified that may be suitable and benefit from using insulin pumps. There is, however, limited data in the literature addressing the cost-effectiveness of insulin pumps versus MDI in type 1 diabetes. Longer term models are required to estimate the long term costs and effects of pumps compared to MDI in this population.
Conclusions
CSII pumps for the treatment of adults with type 1 diabetes
Based on low-quality evidence, CSII pumps confer a statistically significant but not clinically significant reduction in HbA1c and mean daily blood glucose as compared to MDI in adults with type 1 diabetes (>19 years).
CSII pumps also confer a statistically significant reduction in glucose variability as compared to MDI in adults with type 1 diabetes (>19 years) however the clinical significance is unknown.
There is indirect evidence that the use of newer long-acting insulins (e.g. insulin glargine) in MDI regimens result in less of a difference between MDI and CSII compared to differences between MDI and CSII in which older insulins are used.
There is conflicting evidence regarding both mild and severe hypoglycemic events in this population when using CSII pumps as compared to MDI. These findings are based on very low-quality evidence.
There is an improved quality of life for patients using CSII pumps as compared to MDI however, limitations exist with this evidence.
Significant limitations of the literature exist specifically:
All studies sponsored by insulin pump manufacturers
All studies used crossover design
Prior treatment regimens varied
Types of insulins used in study varied (NPH vs. glargine)
Generalizability of studies in question as populations were highly motivated and half of studies used insulin pens as the mode of delivery for MDI
One short-term study concluded that pumps are cost-effective, although this was based on limited data and longer term models are required to estimate the long-term costs and effects of pumps compared to MDI in adults with type 1 diabetes.
Part B: Type 2 Diabetic Adults
Research Questions
Are CSII pumps more effective than MDI for improving glycemic control in adults (≥19 years) with type 2 diabetes?
Are CSII pumps more effective than MDI for improving other outcomes related to diabetes such as quality of life?
Literature Search
Inclusion Criteria
Randomized controlled trials, systematic reviews, meta-analysis and/or health technology assessments from MEDLINE, Excerpta Medica Database (EMBASE), Cumulative Index to Nursing & Allied Health Literature (CINAHL)
Any person with type 2 diabetes requiring insulin treatment intensive
Published between January 1, 2000 – August 2008
Exclusion Criteria
Studies with <10 patients
Studies <5 weeks in duration
CSII applied only at night time and not 24 hours/day
Mixed group of diabetes patients (children, adults, type 1, type 2)
Pregnancy studies
Outcomes of Interest
The primary outcome of interest was a reduction in glycosylated hemoglobin (HbA1c) levels. Other outcomes of interest were mean blood glucose level, glucose variability, insulin requirements, frequency of hypoglycemic events, adverse events, and quality of life.
Search Strategy
A comprehensive literature search was performed in OVID MEDLINE, MEDLINE In-Process and Other Non-Indexed Citations, EMBASE, CINAHL, The Cochrane Library, and the International Agency for Health Technology Assessment (INAHTA) for studies published between January 1, 2000 and August 15, 2008. Studies meeting the inclusion criteria were selected from the search results. Data on the study characteristics, patient characteristics, primary and secondary treatment outcomes, and adverse events were abstracted. Reference lists of selected articles were also checked for relevant studies. The quality of the evidence was assessed as high, moderate, low, or very low according to the GRADE methodology.
Summary of Findings
The database search identified 286 relevant citations published between 1996 and August 2008. Of the 286 abstracts reviewed, four RCTs met the inclusion criteria outlined above. Upon examination, two studies were subsequently excluded from the meta-analysis due to small sample size and missing data (Berthe et al.), as well as outlier status and high drop out rate (Wainstein et al) which is consistent with previously reported meta-analyses on this topic (Jeitler et al 2008, and Fatourechi M et al. 2009).
HbA1c
The primary outcome in this analysis was reduction in HbA1c. Both studies demonstrated that both CSII pumps and MDI reduce HbA1c, but neither treatment modality was found to be superior to the other. The results of a random effects model meta-analysis showed a mean difference in HbA1c of -0.14 (-0.40, 0.13) between the two groups, which was found not to be statistically or clinically significant. There was no statistical heterogeneity observed between the two studies (I2=0%).
Forrest plot of two parallel, RCTs comparing CSII to MDI in type 2 diabetes
Secondary Outcomes
Mean Blood Glucose and Glucose Variability
Mean blood glucose was only used as an efficacy outcome in one study (Raskin et al. 2003). The authors found that the only time point in which there were consistently lower blood glucose values for the CSII group compared to the MDI group was 90 minutes after breakfast. Glucose variability was not examined in either study and the authors reported no difference in weight gain between the CSII pump group and MDI groups at the end of study. Conflicting results were reported regarding injection site reactions between the two studies. Herman et al. reported no difference in the number of subjects experiencing site problems between the two groups, while Raskin et al. reported that there were no injection site reactions in the MDI group but 15 such episodes among 8 participants in the CSII pump group.
Frequency of Hypoglycemic Events and Insulin Requirements
All studies reported that there were no differences in the number of mild hypoglycemic events in patients on CSII pumps versus MDI. Herman et al. also reported no differences in the number of severe hypoglycemic events in patients using CSII pumps compared to those on MDI. Raskin et al. reported that there were no severe hypoglycemic events in either group throughout the study duration. Insulin requirements were only examined in Herman et al., who found that daily insulin requirements were equal between the CSII pump and MDI treatment groups.
Quality of Life
QoL was measured by Herman et al. using the Diabetes Quality of Life Clinical Trial Questionnaire (DQOLCTQ). There were no differences reported between CSII users and MDI users for treatment satisfaction, diabetes impact, and worry-related scores. Patient satisfaction was measured in Raskin et al. using a patient satisfaction questionnaire, whose results indicated that patients in the CSII pump group had significantly greater improvement in overall treatment satisfaction at the end of the study compared to the MDI group. Although patient preference was also reported, it was only examined in the CSII pump group, thus results indicating a greater preference for CSII pumps in this groups (as compared to prior injectable insulin regimens) are biased and must be interpreted with caution.
Quality of Evidence
Overall, the body of evidence was downgraded from high to low according to study quality and issues with directness as identified using the GRADE quality assessment tool (see Table 3). While blinding of patient to intervention/control is not feasible in these studies, blinding of study personnel during outcome assessment and allocation concealment were generally lacking. ITT was not clearly explained in one study and heterogeneity between study populations was evident from participants’ treatment regimens prior to study initiation. Although trials reported consistent results for HbA1c outcomes, the directness or generalizability of studies, particularly with respect to the generalizability of the diabetic population, was questionable as trials required patients to adhere to an intense SMBG regimen. This suggests that patients were highly motivated. In addition, since prior treatment regimens varied between participants (no requirement for patients to be on MDI), study findings may not be generalizable to the population eligible for a pump in Ontario. The GRADE quality of evidence for the use of CSII in adults with type 2 diabetes is, therefore, low and any estimate of effect is uncertain.
GRADE Quality Assessment for CSII pumps vs. MDI on HbA1c Adults with Type 2 Diabetes
Inadequate or unknown allocation concealment (all studies); Unblinded assessment (all studies) however lack of blinding due to the nature of the study; ITT not well explained in 1 of 2 studies
Indirect due to lack of generalizability of findings since participants varied with respect to prior treatment regimens and intensive SMBG suggests highly motivated populations used in trials.
Economic Analysis
An economic analysis of CSII pumps was carried out using the Ontario Diabetes Economic Model (ODEM) and has been previously described in the report entitled “Application of the Ontario Diabetes Economic Model (ODEM) to Determine the Cost-effectiveness and Budget Impact of Selected Type 2 Diabetes Interventions in Ontario”, part of the diabetes strategy evidence series. Based on the analysis, CSII pumps are not cost-effective for adults with type 2 diabetes, either for the age 65+ sub-group or for all patients in general. Details of the analysis can be found in the full report.
Conclusions
CSII pumps for the treatment of adults with type 2 diabetes
There is low quality evidence demonstrating that the efficacy of CSII pumps is not superior to MDI for adult type 2 diabetics.
There were no differences in the number of mild and severe hypoglycemic events in patients on CSII pumps versus MDI.
There are conflicting findings with respect to an improved quality of life for patients using CSII pumps as compared to MDI.
Significant limitations of the literature exist specifically:
All studies sponsored by insulin pump manufacturers
Prior treatment regimens varied
Types of insulins used in study varied (NPH vs. glargine)
Generalizability of studies in question as populations may not reflect eligible patient population in Ontario (participants not necessarily on MDI prior to study initiation, pen used in one study and frequency of SMBG required during study was high suggesting highly motivated participants)
Based on ODEM, insulin pumps are not cost-effective for adults with type 2 diabetes either for the age 65+ sub-group or for all patients in general.
PMCID: PMC3377523  PMID: 23074525
3.  Inflammation, Insulin Resistance, and Diabetes—Mendelian Randomization Using CRP Haplotypes Points Upstream 
PLoS Medicine  2008;5(8):e155.
Background
Raised C-reactive protein (CRP) is a risk factor for type 2 diabetes. According to the Mendelian randomization method, the association is likely to be causal if genetic variants that affect CRP level are associated with markers of diabetes development and diabetes. Our objective was to examine the nature of the association between CRP phenotype and diabetes development using CRP haplotypes as instrumental variables.
Methods and Findings
We genotyped three tagging SNPs (CRP + 2302G > A; CRP + 1444T > C; CRP + 4899T > G) in the CRP gene and measured serum CRP in 5,274 men and women at mean ages 49 and 61 y (Whitehall II Study). Homeostasis model assessment-insulin resistance (HOMA-IR) and hemoglobin A1c (HbA1c) were measured at age 61 y. Diabetes was ascertained by glucose tolerance test and self-report. Common major haplotypes were strongly associated with serum CRP levels, but unrelated to obesity, blood pressure, and socioeconomic position, which may confound the association between CRP and diabetes risk. Serum CRP was associated with these potential confounding factors. After adjustment for age and sex, baseline serum CRP was associated with incident diabetes (hazard ratio = 1.39 [95% confidence interval 1.29–1.51], HOMA-IR, and HbA1c, but the associations were considerably attenuated on adjustment for potential confounding factors. In contrast, CRP haplotypes were not associated with HOMA-IR or HbA1c (p = 0.52–0.92). The associations of CRP with HOMA-IR and HbA1c were all null when examined using instrumental variables analysis, with genetic variants as the instrument for serum CRP. Instrumental variables estimates differed from the directly observed associations (p = 0.007–0.11). Pooled analysis of CRP haplotypes and diabetes in Whitehall II and Northwick Park Heart Study II produced null findings (p = 0.25–0.88). Analyses based on the Wellcome Trust Case Control Consortium (1,923 diabetes cases, 2,932 controls) using three SNPs in tight linkage disequilibrium with our tagging SNPs also demonstrated null associations.
Conclusions
Observed associations between serum CRP and insulin resistance, glycemia, and diabetes are likely to be noncausal. Inflammation may play a causal role via upstream effectors rather than the downstream marker CRP.
Using a Mendelian randomization approach, Eric Brunner and colleagues show that the associations between serum C-reactive protein and insulin resistance, glycemia, and diabetes are likely to be noncausal.
Editors' Summary
Background.
Diabetes—a common, long-term (chronic) disease that causes heart, kidney, nerve, and eye problems and shortens life expectancy—is characterized by high levels of sugar (glucose) in the blood. In people without diabetes, blood sugar levels are controlled by the hormone insulin. Insulin is released by the pancreas after eating and “instructs” insulin-responsive muscle and fat cells to take up the glucose from the bloodstream that is produced by the digestion of food. In the early stages of type 2 diabetes (the commonest type of diabetes), the muscle and fat cells become nonresponsive to insulin (a condition called insulin resistance), and blood sugar levels increase. The pancreas responds by making more insulin—people with insulin resistance have high blood levels of both insulin and glucose. Eventually, however, the insulin-producing cells in the pancreas start to malfunction, insulin secretion decreases, and frank diabetes develops.
Why Was This Study Done?
Globally, about 200 million people have diabetes, but experts believe this number will double by 2030. Ways to prevent or delay the onset of diabetes are, therefore, urgently needed. One major risk factor for insulin resistance and diabetes is being overweight. According to one theory, increased body fat causes mild, chronic tissue inflammation, which leads to insulin resistance. Consistent with this idea, people with higher than normal amounts of the inflammatory protein C-reactive protein (CRP) in their blood have a high risk of developing diabetes. If inflammation does cause diabetes, then drugs that inhibit CRP might prevent diabetes. However, simply measuring CRP and determining whether the people with high levels develop diabetes cannot prove that CRP causes diabetes. Those people with high blood levels of CRP might have other unknown factors in common (confounding factors) that are the real causes of diabetes. In this study, the researchers use “Mendelian randomization” to examine whether increased blood CRP causes diabetes. Some variants of CRP (the gene that encodes CRP) increase the amount of CRP in the blood. Because these variants are inherited randomly, there is no likelihood of confounding factors, and an association between these variants and the development of insulin resistance and diabetes indicates, therefore, that increased CRP levels cause diabetes.
What Did the Researchers Do and Find?
The researchers measured blood CRP levels in more than 5,000 people enrolled in the Whitehall II study, which is investigating factors that affect disease development. They also used the “homeostasis model assessment-insulin resistance” (HOMA-IR) method to estimate insulin sensitivity from blood glucose and insulin measurements, and measured levels of hemoglobin A1c (HbA1c, hemoglobin with sugar attached—a measure of long-term blood sugar control) in these people. Finally, they looked at three “single polynucleotide polymorphisms” (SNPs, single nucleotide changes in a gene's DNA sequence; combinations of SNPs that are inherited as a block are called haplotypes) in CRP in each study participant. Common haplotypes of CRP were related to blood serum CRP levels and, as previously reported, increased blood CRP levels were associated with diabetes and with HOMA-IR and HbA1c values indicative of insulin resistance and poor blood sugar control, respectively. By contrast, CRP haplotypes were not related to HOMA-IR or HbA1c values. Similarly, pooled analysis of CRP haplotypes and diabetes in Whitehall II and another large study on health determinants (the Northwick Park Heart Study II) showed no association between CRP variants and diabetes risk. Finally, data from the Wellcome Trust Case Control Consortium also showed no association between CRP haplotypes and diabetes risk.
What Do These Findings Mean?
Together, these findings suggest that increased blood CRP levels are not responsible for the development of insulin resistance or diabetes, at least in European populations. It may be that there is a causal relationship between CRP levels and diabetes risk in other ethnic populations—further Mendelian randomization studies are needed to discover whether this is the case. For now, though, these findings suggest that drugs targeted against CRP are unlikely to prevent or delay the onset of diabetes. However, they do not discount the possibility that proteins involved earlier in the inflammatory process might cause diabetes and might thus represent good drug targets for diabetes prevention.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050155.
This study is further discussed in a PLoS Medicine Perspective by Bernard Keavney
The MedlinePlus encyclopedia provides information about diabetes and about C-reactive protein (in English and Spanish)
US National Institute of Diabetes and Digestive and Kidney Diseases provides patient information on all aspects of diabetes, including information on insulin resistance (in English and Spanish)
The International Diabetes Federation provides information about diabetes, including information on the global diabetes epidemic
The US Centers for Disease Control and Prevention provides information for the public and professionals on all aspects of diabetes (in English and Spanish)
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.0050155
PMCID: PMC2504484  PMID: 18700811
4.  GUESS-ing Polygenic Associations with Multiple Phenotypes Using a GPU-Based Evolutionary Stochastic Search Algorithm 
PLoS Genetics  2013;9(8):e1003657.
Genome-wide association studies (GWAS) yielded significant advances in defining the genetic architecture of complex traits and disease. Still, a major hurdle of GWAS is narrowing down multiple genetic associations to a few causal variants for functional studies. This becomes critical in multi-phenotype GWAS where detection and interpretability of complex SNP(s)-trait(s) associations are complicated by complex Linkage Disequilibrium patterns between SNPs and correlation between traits. Here we propose a computationally efficient algorithm (GUESS) to explore complex genetic-association models and maximize genetic variant detection. We integrated our algorithm with a new Bayesian strategy for multi-phenotype analysis to identify the specific contribution of each SNP to different trait combinations and study genetic regulation of lipid metabolism in the Gutenberg Health Study (GHS). Despite the relatively small size of GHS (n = 3,175), when compared with the largest published meta-GWAS (n>100,000), GUESS recovered most of the major associations and was better at refining multi-trait associations than alternative methods. Amongst the new findings provided by GUESS, we revealed a strong association of SORT1 with TG-APOB and LIPC with TG-HDL phenotypic groups, which were overlooked in the larger meta-GWAS and not revealed by competing approaches, associations that we replicated in two independent cohorts. Moreover, we demonstrated the increased power of GUESS over alternative multi-phenotype approaches, both Bayesian and non-Bayesian, in a simulation study that mimics real-case scenarios. We showed that our parallel implementation based on Graphics Processing Units outperforms alternative multi-phenotype methods. Beyond multivariate modelling of multi-phenotypes, our Bayesian model employs a flexible hierarchical prior structure for genetic effects that adapts to any correlation structure of the predictors and increases the power to identify associated variants. This provides a powerful tool for the analysis of diverse genomic features, for instance including gene expression and exome sequencing data, where complex dependencies are present in the predictor space.
Author Summary
Nowadays, the availability of cheaper and accurate assays to quantify multiple (endo)phenotypes in large population cohorts allows multi-trait studies. However, these studies are limited by the lack of flexible models integrated with efficient computational tools for genome-wide multi SNPs-traits analyses. To overcome this problem, we propose a novel Bayesian analysis strategy and a new algorithmic implementation which exploits parallel processing architecture for fully multivariate modeling of groups of correlated phenotypes at the genome-wide scale. In addition to increased power of our algorithm over alternative Bayesian and well-established non-Bayesian multi-phenotype methods, we provide an application to a real case study of several blood lipid traits, and show how our method recovered most of the major associations and is better at refining multi-trait polygenic associations than alternative methods. We reveal and replicate in independent cohorts new associations with two phenotypic groups that were not detected by competing multivariate approaches and not noticed by a large meta-GWAS. We also discuss the applicability of the proposed method to large meta-analyses involving hundreds of thousands of individuals and to diverse genomic datasets where complex dependencies in the predictor space are present.
doi:10.1371/journal.pgen.1003657
PMCID: PMC3738451  PMID: 23950726
5.  Power to Detect Risk Alleles Using Genome-Wide Tag SNP Panels 
PLoS Genetics  2007;3(10):e170.
Advances in high-throughput genotyping and the International HapMap Project have enabled association studies at the whole-genome level. We have constructed whole-genome genotyping panels of over 550,000 (HumanHap550) and 650,000 (HumanHap650Y) SNP loci by choosing tag SNPs from all populations genotyped by the International HapMap Project. These panels also contain additional SNP content in regions that have historically been overrepresented in diseases, such as nonsynonymous sites, the MHC region, copy number variant regions and mitochondrial DNA. We estimate that the tag SNP loci in these panels cover the majority of all common variation in the genome as measured by coverage of both all common HapMap SNPs and an independent set of SNPs derived from complete resequencing of genes obtained from SeattleSNPs. We also estimate that, given a sample size of 1,000 cases and 1,000 controls, these panels have the power to detect single disease loci of moderate risk (λ ∼ 1.8–2.0). Relative risks as low as λ ∼ 1.1–1.3 can be detected using 10,000 cases and 10,000 controls depending on the sample population and disease model. If multiple loci are involved, the power increases significantly to detect at least one locus such that relative risks 20%–35% lower can be detected with 80% power if between two and four independent loci are involved. Although our SNP selection was based on HapMap data, which is a subset of all common SNPs, these panels effectively capture the majority of all common variation and provide high power to detect risk alleles that are not represented in the HapMap data.
Author Summary
Advances in high-throughput genotyping technology and the International HapMap Project have enabled genetic association studies at the whole-genome level. Our paper describes two genome-wide SNP panels that contain tag SNPs derived from the International HapMap Project. Tag SNPs are proxies for groups of highly correlated SNPs. Information can be captured for the entire group of correlated SNPs by genotyping only one representative SNP, the tag SNP. These whole-genome SNP panels also contain additional content thought to be overrepresented in disease, such as amino acid–changing nonsynonymous SNPs and mitochondrial SNPs. We show that these panels cover the genome with very high efficiency as measured by coverage of all HapMap SNPs and a set of SNPs derived from completely resequenced genes from the Seattle SNPs database. We also show that these panels have high power to detect disease risk alleles for both HapMap and non-HapMap SNPs. In complex disease where multiple risk alleles are believed to be involved, we show that the ability to detect at least one risk allele with the tag SNP panels is also high.
doi:10.1371/journal.pgen.0030170
PMCID: PMC2000969  PMID: 17922574
6.  Geographic Differences in Genetic Susceptibility to IgA Nephropathy: GWAS Replication Study and Geospatial Risk Analysis 
PLoS Genetics  2012;8(6):e1002765.
IgA nephropathy (IgAN), major cause of kidney failure worldwide, is common in Asians, moderately prevalent in Europeans, and rare in Africans. It is not known if these differences represent variation in genes, environment, or ascertainment. In a recent GWAS, we localized five IgAN susceptibility loci on Chr.6p21 (HLA-DQB1/DRB1, PSMB9/TAP1, and DPA1/DPB2 loci), Chr.1q32 (CFHR3/R1 locus), and Chr.22q12 (HORMAD2 locus). These IgAN loci are associated with risk of other immune-mediated disorders such as type I diabetes, multiple sclerosis, or inflammatory bowel disease. We tested association of these loci in eight new independent cohorts of Asian, European, and African-American ancestry (N = 4,789), followed by meta-analysis with risk-score modeling in 12 cohorts (N = 10,755) and geospatial analysis in 85 world populations. Four susceptibility loci robustly replicated and all five loci were genome-wide significant in the combined cohort (P = 5×10−32–3×10−10), with heterogeneity detected only at the PSMB9/TAP1 locus (I2 = 0.60). Conditional analyses identified two new independent risk alleles within the HLA-DQB1/DRB1 locus, defining multiple risk and protective haplotypes within this interval. We also detected a significant genetic interaction, whereby the odds ratio for the HORMAD2 protective allele was reversed in homozygotes for a CFHR3/R1 deletion (P = 2.5×10−4). A seven–SNP genetic risk score, which explained 4.7% of overall IgAN risk, increased sharply with Eastward and Northward distance from Africa (r = 0.30, P = 3×10−128). This model paralleled the known East–West gradient in disease risk. Moreover, the prediction of a South–North axis was confirmed by registry data showing that the prevalence of IgAN–attributable kidney failure is increased in Northern Europe, similar to multiple sclerosis and type I diabetes. Variation at IgAN susceptibility loci correlates with differences in disease prevalence among world populations. These findings inform genetic, biological, and epidemiological investigations of IgAN and permit cross-comparison with other complex traits that share genetic risk loci and geographic patterns with IgAN.
Author Summary
IgA nephropathy (IgAN) is the most common cause of kidney failure in Asia, has lower prevalence in Europe, and is very infrequent among populations of African ancestry. A long-standing question in the field is whether these differences represent variation in genes, environment, or ascertainment. In a recent genome-wide association study of 5,966 individuals, we identified five susceptibility loci for this trait. In this paper, we study the largest IgAN case-control cohort reported to date, composed of 10,775 individuals of European, Asian, and African-American ancestry. We confirm that all five loci are significant contributors to disease risk across this multi-ethnic cohort. In addition, we identify two novel independent susceptibility alleles within the HLA-DQB1/DRB1 locus and a new genetic interaction between loci on Chr.1p36 and Chr.22q22. We develop a seven–SNP genetic risk score that explains nearly 5% of variation in disease risk. In geospatial analysis of 85 world populations, the genetic risk score closely parallels worldwide patterns of disease prevalence. The genetic risk score also predicts an unsuspected Northward risk gradient in Europe. This genetic prediction is verified by examination of registry data demonstrating, similarly to other immune-mediated diseases such as multiple sclerosis and type I diabetes, a previously unrecognized increase in IgAN–attributable kidney failure in Northern European countries.
doi:10.1371/journal.pgen.1002765
PMCID: PMC3380840  PMID: 22737082
7.  Mixture SNPs effect on phenotype in genome-wide association studies 
BMC Genomics  2015;16(1):3.
Background
Recently mixed linear models are used to address the issue of “missing" heritability in traditional Genome-wide association studies (GWAS). The models assume that all single-nucleotide polymorphisms (SNPs) are associated with the phenotypes of interest. However, it is more common that only a small proportion of SNPs have significant effects on the phenotypes, while most SNPs have no or very small effects. To incorporate this feature, we propose an efficient Hierarchical Bayesian Model (HBM) that extends the existing mixed models to enforce automatic selection of significant SNPs. The HBM models the SNP effects using a mixture distribution of a point mass at zero and a normal distribution, where the point mass corresponds to those non-associative SNPs.
Results
We estimate the HBM using Gibbs sampling. The estimation performance of our method is first demonstrated through two simulation studies. We make the simulation setups realistic by using parameters fitted on the Framingham Heart Study (FHS) data. The simulation studies show that our method can accurately estimate the proportion of SNPs associated with the simulated phenotype and identify these SNPs, as well as adapt to certain model mis-specification than the standard mixed models. In addition, we analyze data from the FHS and the Health and Retirement Study (HRS) to study the association between Body Mass Index (BMI) and SNPs on Chromosome 16, and replicate the identified genetic associations. The analysis of the FHS data identifies 0.3% SNPs on Chromosome 16 that affect BMI, including rs9939609 and rs9939973 on the FTO gene. These two SNPs are in strong linkage disequilibrium with rs1558902 (Rsq =0.901 for rs9939609 and Rsq =0.905 for rs9939973), which has been reported to be linked with obesity in previous GWAS. We then replicate the findings using the HRS data: the analysis finds 0.4% of SNPs associated with BMI on Chromosome 16. Furthermore, around 25% of the genes that are identified to be associated with BMI are common between the two studies.
Conclusions
The results demonstrate that the HBM and the associated estimation algorithm offer a powerful tool for identifying significant genetic associations with phenotypes of interest, among a large number of SNPs that are common in modern genetics studies.
doi:10.1186/1471-2164-16-3
PMCID: PMC4417323  PMID: 25649116
Bayesian variable selection; Genome-wide association studies; Gibbs sampling
8.  Genome-wide association with diabetes-related traits in the Framingham Heart Study 
BMC Medical Genetics  2007;8(Suppl 1):S16.
Background
Susceptibility to type 2 diabetes may be conferred by genetic variants having modest effects on risk. Genome-wide fixed marker arrays offer a novel approach to detect these variants.
Methods
We used the Affymetrix 100K SNP array in 1,087 Framingham Offspring Study family members to examine genetic associations with three diabetes-related quantitative glucose traits (fasting plasma glucose (FPG), hemoglobin A1c, 28-yr time-averaged FPG (tFPG)), three insulin traits (fasting insulin, HOMA-insulin resistance, and 0–120 min insulin sensitivity index); and with risk for diabetes. We used additive generalized estimating equations (GEE) and family-based association test (FBAT) models to test associations of SNP genotypes with sex-age-age2-adjusted residual trait values, and Cox survival models to test incident diabetes.
Results
We found 415 SNPs associated (at p < 0.001) with at least one of the six quantitative traits in GEE, 242 in FBAT (18 overlapped with GEE for 639 non-overlapping SNPs), and 128 associated with incident diabetes (31 overlapped with the 639) giving 736 non-overlapping SNPs. Of these 736 SNPs, 439 were within 60 kb of a known gene. Additionally, 53 SNPs (of which 42 had r2 < 0.80 with each other) had p < 0.01 for incident diabetes AND (all 3 glucose traits OR all 3 insulin traits, OR 2 glucose traits and 2 insulin traits); of these, 36 overlapped with the 736 other SNPs. Of 100K SNPs, one (rs7100927) was in moderate LD (r2 = 0.50) with TCF7L2 (rs7903146), and was associated with risk of diabetes (Cox p-value 0.007, additive hazard ratio for diabetes = 1.56) and with tFPG (GEE p-value 0.03). There were no common (MAF > 1%) 100K SNPs in LD (r2 > 0.05) with ABCC8 A1369S (rs757110), KCNJ11 E23K (rs5219), or SNPs in CAPN10 or HNFa. PPARG P12A (rs1801282) was not significantly associated with diabetes or related traits.
Conclusion
Framingham 100K SNP data is a resource for association tests of known and novel genes with diabetes and related traits posted at . Framingham 100K data replicate the TCF7L2 association with diabetes.
doi:10.1186/1471-2350-8-S1-S16
PMCID: PMC1995610  PMID: 17903298
9.  Generalization and Dilution of Association Results from European GWAS in Populations of Non-European Ancestry: The PAGE Study 
PLoS Biology  2013;11(9):e1001661.
A multi-ethnic study demonstrates that the extrapolation of genetic disease risk models from European populations to other ethnicities is compromised more strongly by genetic structure than by environmental or global genetic background in differential genetic risk associations across ethnicities.
The vast majority of genome-wide association study (GWAS) findings reported to date are from populations with European Ancestry (EA), and it is not yet clear how broadly the genetic associations described will generalize to populations of diverse ancestry. The Population Architecture Using Genomics and Epidemiology (PAGE) study is a consortium of multi-ancestry, population-based studies formed with the objective of refining our understanding of the genetic architecture of common traits emerging from GWAS. In the present analysis of five common diseases and traits, including body mass index, type 2 diabetes, and lipid levels, we compare direction and magnitude of effects for GWAS-identified variants in multiple non-EA populations against EA findings. We demonstrate that, in all populations analyzed, a significant majority of GWAS-identified variants have allelic associations in the same direction as in EA, with none showing a statistically significant effect in the opposite direction, after adjustment for multiple testing. However, 25% of tagSNPs identified in EA GWAS have significantly different effect sizes in at least one non-EA population, and these differential effects were most frequent in African Americans where all differential effects were diluted toward the null. We demonstrate that differential LD between tagSNPs and functional variants within populations contributes significantly to dilute effect sizes in this population. Although most variants identified from GWAS in EA populations generalize to all non-EA populations assessed, genetic models derived from GWAS findings in EA may generate spurious results in non-EA populations due to differential effect sizes. Regardless of the origin of the differential effects, caution should be exercised in applying any genetic risk prediction model based on tagSNPs outside of the ancestry group in which it was derived. Models based directly on functional variation may generalize more robustly, but the identification of functional variants remains challenging.
Author Summary
The number of known associations between human diseases and common genetic variants has grown dramatically in the past decade, most being identified in large-scale genetic studies of people of Western European origin. But because the frequencies of genetic variants can differ substantially between continental populations, it's important to assess how well these associations can be extended to populations with different continental ancestry. Are the correlations between genetic variants, disease endpoints, and risk factors consistent enough for genetic risk models to be reliably applied across different ancestries? Here we describe a systematic analysis of disease outcome and risk-factor–associated variants (tagSNPs) identified in European populations, in which we test whether the effect size of a tagSNP is consistent across six populations with significant non-European ancestry. We demonstrate that although nearly all such tagSNPs have effects in the same direction across all ancestries (i.e., variants associated with higher risk in Europeans will also be associated with higher risk in other populations), roughly a quarter of the variants tested have significantly different magnitude of effect (usually lower) in at least one non-European population. We therefore advise caution in the use of tagSNP-based genetic disease risk models in populations that have a different genetic ancestry from the population in which original associations were first made. We then show that this differential strength of association can be attributed to population-dependent variations in the correlation between tagSNPs and the variant that actually determines risk—the so-called functional variant. Risk models based on functional variants are therefore likely to be more robust than tagSNP-based models.
doi:10.1371/journal.pbio.1001661
PMCID: PMC3775722  PMID: 24068893
10.  Simultaneous Discovery, Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model 
PLoS Genetics  2015;11(4):e1004969.
Gene discovery, estimation of heritability captured by SNP arrays, inference on genetic architecture and prediction analyses of complex traits are usually performed using different statistical models and methods, leading to inefficiency and loss of power. Here we use a Bayesian mixture model that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. We apply the method to simulated data of quantitative traits and Welcome Trust Case Control Consortium (WTCCC) data on disease and show that it provides accurate estimates of SNP-based heritability, produces unbiased estimators of risk in new samples, and that it can estimate genetic architecture by partitioning variation across hundreds to thousands of SNPs. We estimated that, depending on the trait, 2,633 to 9,411 SNPs explain all of the SNP-based heritability in the WTCCC diseases. The majority of those SNPs (>96%) had small effects, confirming a substantial polygenic component to common diseases. The proportion of the SNP-based variance explained by large effects (each SNP explaining 1% of the variance) varied markedly between diseases, ranging from almost zero for bipolar disorder to 72% for type 1 diabetes. Prediction analyses demonstrate that for diseases with major loci, such as type 1 diabetes and rheumatoid arthritis, Bayesian methods outperform profile scoring or mixed model approaches.
Author Summary
Most genome-wide association studies performed to date have focused on testing individual genetic markers for associations with phenotype. Recently, methods that analyse the joint effects of multiple markers on genetic variation have provided further insights into the genetic basis of complex human traits. In addition, there is increasing interest in using genotype data for genetic risk prediction of disease. Often disparate analytical methods are used for each of these tasks. We propose a flexible novel approach that simultaneously performs identification of susceptibility loci, inference on the genetic architecture and provides polygenic risk prediction in the same statistical model. We illustrate the broad applicability of the approach by considering both simulated and real data. In the analysis of seven common diseases we show large differences in the proportion of genetic variation due to loci with different effect sizes and differences in prediction accuracy between complex traits. These findings are important for future studies and the understanding of the complex genetic architecture of common diseases.
doi:10.1371/journal.pgen.1004969
PMCID: PMC4388571  PMID: 25849665
11.  Simultaneous Analysis of All SNPs in Genome-Wide and Re-Sequencing Association Studies 
PLoS Genetics  2008;4(7):e1000130.
Testing one SNP at a time does not fully realise the potential of genome-wide association studies to identify multiple causal variants, which is a plausible scenario for many complex diseases. We show that simultaneous analysis of the entire set of SNPs from a genome-wide study to identify the subset that best predicts disease outcome is now feasible, thanks to developments in stochastic search methods. We used a Bayesian-inspired penalised maximum likelihood approach in which every SNP can be considered for additive, dominant, and recessive contributions to disease risk. Posterior mode estimates were obtained for regression coefficients that were each assigned a prior with a sharp mode at zero. A non-zero coefficient estimate was interpreted as corresponding to a significant SNP. We investigated two prior distributions and show that the normal-exponential-gamma prior leads to improved SNP selection in comparison with single-SNP tests. We also derived an explicit approximation for type-I error that avoids the need to use permutation procedures. As well as genome-wide analyses, our method is well-suited to fine mapping with very dense SNP sets obtained from re-sequencing and/or imputation. It can accommodate quantitative as well as case-control phenotypes, covariate adjustment, and can be extended to search for interactions. Here, we demonstrate the power and empirical type-I error of our approach using simulated case-control data sets of up to 500 K SNPs, a real genome-wide data set of 300 K SNPs, and a sequence-based dataset, each of which can be analysed in a few hours on a desktop workstation.
Author Summary
Tests of association with disease status are normally conducted one SNP at a time, ignoring the effects of all other genotyped SNPs. We developed a computationally efficient method to simultaneously analyse all SNPs, either in a genome-wide association (GWA) study, or a fine-mapping study based on re-sequencing and/or imputation. The method selects a subset of SNPs that best predicts disease status, while controlling the type-I error of the selected SNPs. This brings many advantages over standard single-SNP approaches, because the signal from a particular SNP can be more clearly assessed when other SNPs associated with disease status are already included in the model. Thus, in comparison with single-SNP analyses, power is increased and the false positive rate is reduced because of reduced residual variation. Localisation is also greatly improved. We demonstrate these advantages over the widely used single-SNP Armitage Trend Test using GWA simulation studies, a real GWA dataset, and a sequence-based fine-mapping simulation study.
doi:10.1371/journal.pgen.1000130
PMCID: PMC2464715  PMID: 18654633
12.  EMR-linked GWAS study: investigation of variation landscape of loci for body mass index in children 
Frontiers in Genetics  2013;4:268.
Common variations at the loci harboring the fat mass and obesity gene (FTO), MC4R, and TMEM18 are consistently reported as being associated with obesity and body mass index (BMI) especially in adult population. In order to confirm this effect in pediatric population five European ancestry cohorts from pediatric eMERGE-II network (CCHMC-BCH) were evaluated.
Method: Data on 5049 samples of European ancestry were obtained from the Electronic Medical Records (EMRs) of two large academic centers in five different genotyped cohorts. For all available samples, gender, age, height, and weight were collected and BMI was calculated. To account for age and sex differences in BMI, BMI z-scores were generated using 2000 Centers of Disease Control and Prevention (CDC) growth charts. A Genome-wide association study (GWAS) was performed with BMI z-score. After removing missing data and outliers based on principal components (PC) analyses, 2860 samples were used for the GWAS study. The association between each single nucleotide polymorphism (SNP) and BMI was tested using linear regression adjusting for age, gender, and PC by cohort. The effects of SNPs were modeled assuming additive, recessive, and dominant effects of the minor allele. Meta-analysis was conducted using a weighted z-score approach.
Results: The mean age of subjects was 9.8 years (range 2–19). The proportion of male subjects was 56%. In these cohorts, 14% of samples had a BMI ≥95 and 28 ≥ 85%. Meta analyses produced a signal at 16q12 genomic region with the best result of p = 1.43 × 10-7 [p(rec) = 7.34 × 10-8) for the SNP rs8050136 at the first intron of FTO gene (z = 5.26) and with no heterogeneity between cohorts (p = 0.77). Under a recessive model, another published SNP at this locus, rs1421085, generates the best result [z = 5.782, p(rec) = 8.21 × 10-9]. Imputation in this region using dense 1000-Genome and Hapmap CEU samples revealed 71 SNPs with p < 10-6, all at the first intron of FTO locus. When hetero-geneity was permitted between cohorts, signals were also obtained in other previously identified loci, including MC4R (rs12964056, p = 6.87 × 10-7, z = -4.98), cholecystokinin CCK (rs8192472, p = 1.33 × 10-6, z = -4.85), Interleukin 15 (rs2099884, p = 1.27 × 10-5, z = 4.34), low density lipoprotein receptor-related protein 1B [LRP1B (rs7583748, p = 0.00013, z = -3.81)] and near transmembrane protein 18 (TMEM18) (rs7561317, p = 0.001, z = -3.17). We also detected a novel locus at chromosome 3 at COL6A5 [best SNP = rs1542829, minor allele frequency (MAF) of 5% p = 4.35 × 10-9, z = 5.89].
Conclusion: An EMR linked cohort study demonstrates that the BMI-Z measurements can be successfully extracted and linked to genomic data with meaningful confirmatory results. We verified the high prevalence of childhood rate of overweight and obesity in our cohort (28%). In addition, our data indicate that genetic variants in the first intron of FTO, a known adult genetic risk factor for BMI, are also robustly associated with BMI in pediatric population.
doi:10.3389/fgene.2013.00268
PMCID: PMC3847941  PMID: 24348519
BMI; obesity; polymorphism; GWAS
13.  Investigating the Causal Relationship of C-Reactive Protein with 32 Complex Somatic and Psychiatric Outcomes: A Large-Scale Cross-Consortium Mendelian Randomization Study 
PLoS Medicine  2016;13(6):e1001976.
Background
C-reactive protein (CRP) is associated with immune, cardiometabolic, and psychiatric traits and diseases. Yet it is inconclusive whether these associations are causal.
Methods and Findings
We performed Mendelian randomization (MR) analyses using two genetic risk scores (GRSs) as instrumental variables (IVs). The first GRS consisted of four single nucleotide polymorphisms (SNPs) in the CRP gene (GRSCRP), and the second consisted of 18 SNPs that were significantly associated with CRP levels in the largest genome-wide association study (GWAS) to date (GRSGWAS). To optimize power, we used summary statistics from GWAS consortia and tested the association of these two GRSs with 32 complex somatic and psychiatric outcomes, with up to 123,865 participants per outcome from populations of European ancestry. We performed heterogeneity tests to disentangle the pleiotropic effect of IVs. A Bonferroni-corrected significance level of less than 0.0016 was considered statistically significant. An observed p-value equal to or less than 0.05 was considered nominally significant evidence for a potential causal association, yet to be confirmed.
The strengths (F-statistics) of the IVs were 31.92–3,761.29 and 82.32–9,403.21 for GRSCRP and GRSGWAS, respectively. CRP GRSGWAS showed a statistically significant protective relationship of a 10% genetically elevated CRP level with the risk of schizophrenia (odds ratio [OR] 0.86 [95% CI 0.79–0.94]; p < 0.001). We validated this finding with individual-level genotype data from the schizophrenia GWAS (OR 0.96 [95% CI 0.94–0.98]; p < 1.72 × 10−6). Further, we found that a standardized CRP polygenic risk score (CRPPRS) at p-value thresholds of 1 × 10−4, 0.001, 0.01, 0.05, and 0.1 using individual-level data also showed a protective effect (OR < 1.00) against schizophrenia; the first CRPPRS (built of SNPs with p < 1 × 10−4) showed a statistically significant (p < 2.45 × 10−4) protective effect with an OR of 0.97 (95% CI 0.95–0.99). The CRP GRSGWAS showed that a 10% increase in genetically determined CRP level was significantly associated with coronary artery disease (OR 0.88 [95% CI 0.84–0.94]; p < 2.4 × 10−5) and was nominally associated with the risk of inflammatory bowel disease (OR 0.85 [95% CI 0.74–0.98]; p < 0.03), Crohn disease (OR 0.81 [95% CI 0.70–0.94]; p < 0.005), psoriatic arthritis (OR 1.36 [95% CI 1.00–1.84]; p < 0.049), knee osteoarthritis (OR 1.17 [95% CI 1.01–1.36]; p < 0.04), and bipolar disorder (OR 1.21 [95% CI 1.05–1.40]; p < 0.007) and with an increase of 0.72 (95% CI 0.11–1.34; p < 0.02) mm Hg in systolic blood pressure, 0.45 (95% CI 0.06–0.84; p < 0.02) mm Hg in diastolic blood pressure, 0.01 ml/min/1.73 m2 (95% CI 0.003–0.02; p < 0.005) in estimated glomerular filtration rate from serum creatinine, 0.01 g/dl (95% CI 0.0004–0.02; p < 0.04) in serum albumin level, and 0.03 g/dl (95% CI 0.008–0.05; p < 0.009) in serum protein level. However, after adjustment for heterogeneity, neither GRS showed a significant effect of CRP level (at p < 0.0016) on any of these outcomes, including coronary artery disease, nor on the other 20 complex outcomes studied. Our study has two potential limitations: the limited variance explained by our genetic instruments modeling CRP levels in blood and the unobserved bias introduced by the use of summary statistics in our MR analyses.
Conclusions
Genetically elevated CRP levels showed a significant potentially protective causal relationship with risk of schizophrenia. We observed nominal evidence at an observed p < 0.05 using either GRSCRP or GRSGWAS—with persistence after correction for heterogeneity—for a causal relationship of elevated CRP levels with psoriatic osteoarthritis, rheumatoid arthritis, knee osteoarthritis, systolic blood pressure, diastolic blood pressure, serum albumin, and bipolar disorder. These associations remain yet to be confirmed. We cannot verify any causal effect of CRP level on any of the other common somatic and neuropsychiatric outcomes investigated in the present study. This implies that interventions that lower CRP level are unlikely to result in decreased risk for the majority of common complex outcomes.
Using genetic instruments, Behrooz Z. Alizadeh and colleagues examine the hypothesis that increased CRP levels play a causal role in common somatic and psychiatric conditions.
Editors' Summary
Background
Inflammation is an important part of the human immune response, the network of cells and molecules that protects the body from attack by pathogens (infectious organisms) and from harmful substances and foreign particles (for example, splinters). When human cells are attacked by pathogens or injured by trauma or chemicals, molecules called inflammatory mediators induce fluid leakage from the blood vessels into the damaged tissue and attract “phagocytes” (a type of immune cell) to the site of infection or injury to “eat” the germs and dead or damaged cells. The end result is inflammation, which is characterized by swelling, redness, heat, and pain. The inflammatory response, although unpleasant, limits the damage caused by foreign invaders or chemicals by preventing further contact with body tissues. Sometimes, however, inflammation can be harmful. Persistent dysregulation of the inflammatory response is implicated in numerous somatic disorders (diseases that affect the body, such as cardiovascular disease) and neuropsychiatric disorders (mental disorders attributable to diseases of the nervous system, such as schizophrenia).
Why Was This Study Done?
Observational studies suggest that increased blood levels of C-reactive protein (CRP, an inflammatory protein) are associated with certain somatic and neuropsychiatric disorders. But observational studies cannot prove that changes in CRP levels actually cause any of these disorders. It could be that the individuals who develop a specific disease and who have a high CRP level also share another unknown characteristic that is actually responsible for disease development (confounding). Alternatively, it could be that the disease itself increases CRP levels (reverse causation). It is important to know whether CRP is causally involved in the development of specific diseases because it might then be possible to prevent or treat these diseases using drugs that control CRP levels. Here, the researchers undertake a Mendelian randomization study to determine whether CRP has a causal relationship with 32 common complex somatic and neuropsychiatric outcomes. Because gene variants are inherited randomly, they are not prone to confounding and are free from reverse causation. So, if CRP levels actually cause a specific somatic or neuropsychiatric disease, genetic variants that affect CRP levels should be associated with an altered risk for that disease.
What Did the Researchers Do and Find?
The researchers used data collected by several consortia involved in large genome-wide association studies (studies that ask whether specific genetic changes across the whole human genome, or blueprint, are associated with specific diseases) to look for associations between 32 somatic and neuropsychiatric outcomes and two genetic risk scores (GRSs) for CRP level. GRSCRP consisted of four single nucleotide polymorphisms (SNPs; a type of genetic variant) in the gene encoding CRP; GRSGWAS consisted of 18 SNPs that were associated with CRP level in a genome-wide association study. The researchers report that a genetically increased CRP level was significantly associated with a reduced risk of schizophrenia (a significant association is one unlikely to have arisen by chance). In addition, they found a nominally significant association (an association that needs to be confirmed) between genetically increased CRP levels and an increased risk of knee osteoarthritis, raised diastolic and systolic blood pressure, and bipolar disorder. Notably, there was no evidence for an effect of genetically increased CRP levels on any of the other 27 outcomes studied.
What Do These Findings Mean?
These findings suggest that genetically raised levels of CRP are causally associated with protection against schizophrenia, an unexpected finding given other recent studies that suggest that raised CRP levels and brain inflammation predispose individuals to schizophrenia. The findings also provide preliminary evidence that genetically raised levels of CRP may be causally associated with an increased risk of raised blood pressure, knee arthritis, and bipolar disorder. The lack of any association between genetically raised levels of CRP and the other outcomes studied suggests, however, that many previously identified disease-associated rises in CRP levels might be a response to the disease process rather than a cause of these diseases. Like all Mendelian randomization studies, the reliability of these findings depends on the validity of several assumptions made by the researchers and on the ability of the GRSs used in the study to explain variations in CRP level. Importantly, however, these findings suggest that interventions designed to lower CRP level are unlikely to decrease the risk of people developing the majority of common complex somatic and neuropsychiatric outcomes.
Additional Information
This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001976.
Wikipedia has pages on inflammation, C-reactive protein, and Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
The MedlinePlus encyclopedia has a page on C-reactive protein (in English and Spanish)
The American Heart Association provides a short article on inflammation and heart disease
A UK National Health Service “Behind the Headlines” article explains a recent study that found an association between immune activity in the brain and schizophrenia
doi:10.1371/journal.pmed.1001976
PMCID: PMC4915710  PMID: 27327646
14.  Replication of genetic loci for ages at menarche and menopause in the multi-ethnic Population Architecture using Genomics and Epidemiology (PAGE) study 
Human Reproduction (Oxford, England)  2013;28(6):1695-1706.
STUDY QUESTION
Do genetic associations identified in genome-wide association studies (GWAS) of age at menarche (AM) and age at natural menopause (ANM) replicate in women of diverse race/ancestry from the Population Architecture using Genomics and Epidemiology (PAGE) Study?
SUMMARY ANSWER
We replicated GWAS reproductive trait single nucleotide polymorphisms (SNPs) in our European descent population and found that many SNPs were also associated with AM and ANM in populations of diverse ancestry.
WHAT IS KNOWN ALREADY
Menarche and menopause mark the reproductive lifespan in women and are important risk factors for chronic diseases including obesity, cardiovascular disease and cancer. Both events are believed to be influenced by environmental and genetic factors, and vary in populations differing by genetic ancestry and geography. Most genetic variants associated with these traits have been identified in GWAS of European-descent populations.
STUDY DESIGN, SIZE, DURATION
A total of 42 251 women of diverse ancestry from PAGE were included in cross-sectional analyses of AM and ANM.
MATERIALS, SETTING, METHODS
SNPs previously associated with ANM (n = 5 SNPs) and AM (n = 3 SNPs) in GWAS were genotyped in American Indians, African Americans, Asians, European Americans, Hispanics and Native Hawaiians. To test SNP associations with ANM or AM, we used linear regression models stratified by race/ethnicity and PAGE sub-study. Results were then combined in race-specific fixed effect meta-analyses for each outcome. For replication and generalization analyses, significance was defined at P < 0.01 for ANM analyses and P < 0.017 for AM analyses.
MAIN RESULTS AND THE ROLE OF CHANCE
We replicated findings for AM SNPs in the LIN28B locus and an intergenic region on 9q31 in European Americans. The LIN28B SNPs (rs314277 and rs314280) were also significantly associated with AM in Asians, but not in other race/ethnicity groups. Linkage disequilibrium (LD) patterns at this locus varied widely among the ancestral groups. With the exception of an intergenic SNP at 13q34, all ANM SNPs replicated in European Americans. Three were significantly associated with ANM in other race/ethnicity populations: rs2153157 (6p24.2/SYCP2L), rs365132 (5q35/UIMC1) and rs16991615 (20p12.3/MCM8). While rs1172822 (19q13/BRSK1) was not significant in the populations of non-European descent, effect sizes showed similar trends.
LIMITATIONS, REASONS FOR CAUTION
Lack of association for the GWAS SNPs in the non-European American groups may be due to differences in locus LD patterns between these groups and the European-descent populations included in the GWAS discovery studies; and in some cases, lower power may also contribute to non-significant findings.
WIDER IMPLICATIONS OF THE FINDINGS
The discovery of genetic variants associated with the reproductive traits provides an important opportunity to elucidate the biological mechanisms involved with normal variation and disorders of menarche and menopause. In this study we replicated most, but not all reported SNPs in European descent populations and examined the epidemiologic architecture of these early reported variants, describing their generalizability and effect size across differing ancestral populations. Such data will be increasingly important for prioritizing GWAS SNPs for follow-up in fine-mapping and resequencing studies, as well as in translational research.
STUDY FUNDING/COMPETING INTEREST(S)
The Population Architecture Using Genomics and Epidemiology (PAGE) program is funded by the National Human Genome Research Institute (NHGRI), supported by U01HG004803 (CALiCo), U01HG004798 (EAGLE), U01HG004802 (MEC), U01HG004790 (WHI) and U01HG004801 (Coordinating Center), and their respective NHGRI ARRA supplements. The authors report no conflicts of interest.
doi:10.1093/humrep/det071
PMCID: PMC3657124  PMID: 23508249
menopause; menarche; genome-wide association study; race/ethnicity; single nucleotide polymorphism
15.  Novel Meta-Analysis-Derived Type 2 Diabetes Risk Loci Do Not Determine Prediabetic Phenotypes 
PLoS ONE  2008;3(8):e3019.
Background
Genome-wide association (GWA) studies identified a series of novel type 2 diabetes risk loci. Most of them were subsequently demonstrated to affect insulin secretion of pancreatic β-cells. Very recently, a meta-analysis of GWA data revealed nine additional risk loci with still undefined roles in the pathogenesis of type 2 diabetes. Using our thoroughly phenotyped cohort of subjects at an increased risk for type 2 diabetes, we assessed the association of the nine latest genetic variants with the predominant prediabetes traits, i.e., obesity, impaired insulin secretion, and insulin resistance.
Methodology/Principal Findings
One thousand five hundred and seventy-eight metabolically characterized non-diabetic German subjects were genotyped for the reported candidate single nucleotide polymorphisms (SNPs) JAZF1 rs864745, CDC123/CAMK1D rs12779790, TSPAN8/LGR5 rs7961581, THADA rs7578597, ADAMTS9 rs4607103, NOTCH2 rs10923931, DCD rs1153188, VEGFA rs9472138, and BCL11A rs10490072. Insulin sensitivity was derived from fasting glucose and insulin concentrations, oral glucose tolerance test (OGTT), and hyperinsulinemic-euglycemic clamp. Insulin secretion was estimated from OGTT data. After appropriate adjustment for confounding variables and Bonferroni correction for multiple comparisons (corrected α-level: p = 0.0014), none of the SNPs was reliably associated with adiposity, insulin sensitivity, or insulin secretion (all p≥0.0117, dominant inheritance model). The risk alleles of ADAMTS9 SNP rs4607103 and VEGFA SNP rs9472138 tended to associate with more than one measure of insulin sensitivity and insulin secretion, respectively, but did not reach formal statistical significance. The study was sufficiently powered (1-β = 0.8) to detect effect sizes of 0.19≤d≤0.25 (α = 0.0014) and 0.13≤d≤0.16 (α = 0.05).
Conclusions/Significance
In contrast to the first series of GWA-derived type 2 diabetes candidate SNPs, we could not detect reliable associations of the novel risk loci with prediabetic phenotypes. Possible weak effects of ADAMTS9 SNP rs4607103 and VEGFA SNP rs9472138 on insulin sensitivity and insulin secretion, respectively, await further confirmation by larger studies.
doi:10.1371/journal.pone.0003019
PMCID: PMC2500187  PMID: 18714373
16.  Genome-Wide Association and Trans-ethnic Meta-Analysis for Advanced Diabetic Kidney Disease: Family Investigation of Nephropathy and Diabetes (FIND) 
PLoS Genetics  2015;11(8):e1005352.
Diabetic kidney disease (DKD) is the most common etiology of chronic kidney disease (CKD) in the industrialized world and accounts for much of the excess mortality in patients with diabetes mellitus. Approximately 45% of U.S. patients with incident end-stage kidney disease (ESKD) have DKD. Independent of glycemic control, DKD aggregates in families and has higher incidence rates in African, Mexican, and American Indian ancestral groups relative to European populations. The Family Investigation of Nephropathy and Diabetes (FIND) performed a genome-wide association study (GWAS) contrasting 6,197 unrelated individuals with advanced DKD with healthy and diabetic individuals lacking nephropathy of European American, African American, Mexican American, or American Indian ancestry. A large-scale replication and trans-ethnic meta-analysis included 7,539 additional European American, African American and American Indian DKD cases and non-nephropathy controls. Within ethnic group meta-analysis of discovery GWAS and replication set results identified genome-wide significant evidence for association between DKD and rs12523822 on chromosome 6q25.2 in American Indians (P = 5.74x10-9). The strongest signal of association in the trans-ethnic meta-analysis was with a SNP in strong linkage disequilibrium with rs12523822 (rs955333; P = 1.31x10-8), with directionally consistent results across ethnic groups. These 6q25.2 SNPs are located between the SCAF8 and CNKSR3 genes, a region with DKD relevant changes in gene expression and an eQTL with IPCEF1, a gene co-translated with CNKSR3. Several other SNPs demonstrated suggestive evidence of association with DKD, within and across populations. These data identify a novel DKD susceptibility locus with consistent directions of effect across diverse ancestral groups and provide insight into the genetic architecture of DKD.
Author Summary
Type 2 diabetes is the most common cause of severe kidney disease worldwide and diabetic kidney disease (DKD) associates with premature death. Individuals of non-European ancestry have the highest burden of type 2 DKD; hence understanding the causes of DKD remains critical to reducing health disparities. Family studies demonstrate that genes regulate the onset and progression of DKD; however, identifying these genes has proven to be challenging. The Family Investigation of Diabetes and Nephropathy consortium (FIND) recruited a large multi-ethnic collection of individuals with type 2 diabetes with and without kidney disease in order to detect genes associated with DKD. FIND discovered and replicated a DKD-associated genetic locus on human chromosome 6q25.2 (rs955333) between the SCAF8 and CNKSR genes. Findings were supported by significantly different expression of genes in this region from kidney tissue of subjects with, versus without DKD. The present findings identify a novel kidney disease susceptibility locus in individuals with type 2 diabetes which is consistent across subjects of differing ancestries. In addition, FIND results provide a rich catalogue of genetic variation in DKD patients for future research on the genetic architecture regulating this common and devastating disease.
doi:10.1371/journal.pgen.1005352
PMCID: PMC4549309  PMID: 26305897
17.  Re-Ranking Sequencing Variants in the Post-GWAS Era for Accurate Causal Variant Identification 
PLoS Genetics  2013;9(8):e1003609.
Next generation sequencing has dramatically increased our ability to localize disease-causing variants by providing base-pair level information at costs increasingly feasible for the large sample sizes required to detect complex-trait associations. Yet, identification of causal variants within an established region of association remains a challenge. Counter-intuitively, certain factors that increase power to detect an associated region can decrease power to localize the causal variant. First, combining GWAS with imputation or low coverage sequencing to achieve the large sample sizes required for high power can have the unintended effect of producing differential genotyping error among SNPs. This tends to bias the relative evidence for association toward better genotyped SNPs. Second, re-use of GWAS data for fine-mapping exploits previous findings to ensure genome-wide significance in GWAS-associated regions. However, using GWAS findings to inform fine-mapping analysis can bias evidence away from the causal SNP toward the tag SNP and SNPs in high LD with the tag. Together these factors can reduce power to localize the causal SNP by more than half. Other strategies commonly employed to increase power to detect association, namely increasing sample size and using higher density genotyping arrays, can, in certain common scenarios, actually exacerbate these effects and further decrease power to localize causal variants. We develop a re-ranking procedure that accounts for these adverse effects and substantially improves the accuracy of causal SNP identification, often doubling the probability that the causal SNP is top-ranked. Application to the NCI BPC3 aggressive prostate cancer GWAS with imputation meta-analysis identified a new top SNP at 2 of 3 associated loci and several additional possible causal SNPs at these loci that may have otherwise been overlooked. This method is simple to implement using R scripts provided on the author's website.
Author Summary
As next-generation sequencing (NGS) costs continue to fall and genome-wide association study (GWAS) platform coverage improves, the human genetics community is positioned to identify potentially causal variants. However, current NGS or imputation-based studies of either the whole genome or regions previously identified by GWAS have not yet been very successful in identifying causal variants. A major hurdle is the development of methods to distinguish disease-causing variants from their highly-correlated proxies within an associated region. We show that various common factors, such as differential sequencing or imputation accuracy rates and linkage disequilibrium patterns, with or without GWAS-informed region selection, can substantially decrease the probability of identifying the correct causal SNP, often by more than half. We then describe a novel and easy-to-implement re-ranking procedure that can double the probability that the causal SNP is top-ranked in many settings. Application to the NCI Breast and Prostate Cancer (BPC3) Cohort Consortium aggressive prostate cancer data identified new top SNPs within two associated loci previously established via GWAS, as well as several additional possible causal SNPs that had been previously overlooked.
doi:10.1371/journal.pgen.1003609
PMCID: PMC3738448  PMID: 23950724
18.  Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD 
PLoS Genetics  2016;12(8):e1006011.
Implementing precision medicine for complex diseases such as chronic obstructive lung disease (COPD) will require extensive use of biomarkers and an in-depth understanding of how genetic, epigenetic, and environmental variations contribute to phenotypic diversity and disease progression. A meta-analysis from two large cohorts of current and former smokers with and without COPD [SPIROMICS (N = 750); COPDGene (N = 590)] was used to identify single nucleotide polymorphisms (SNPs) associated with measurement of 88 blood proteins (protein quantitative trait loci; pQTLs). PQTLs consistently replicated between the two cohorts. Features of pQTLs were compared to previously reported expression QTLs (eQTLs). Inference of causal relations of pQTL genotypes, biomarker measurements, and four clinical COPD phenotypes (airflow obstruction, emphysema, exacerbation history, and chronic bronchitis) were explored using conditional independence tests. We identified 527 highly significant (p < 8 X 10−10) pQTLs in 38 (43%) of blood proteins tested. Most pQTL SNPs were novel with low overlap to eQTL SNPs. The pQTL SNPs explained >10% of measured variation in 13 protein biomarkers, with a single SNP (rs7041; p = 10−392) explaining 71%-75% of the measured variation in vitamin D binding protein (gene = GC). Some of these pQTLs [e.g., pQTLs for VDBP, sRAGE (gene = AGER), surfactant protein D (gene = SFTPD), and TNFRSF10C] have been previously associated with COPD phenotypes. Most pQTLs were local (cis), but distant (trans) pQTL SNPs in the ABO blood group locus were the top pQTL SNPs for five proteins. The inclusion of pQTL SNPs improved the clinical predictive value for the established association of sRAGE and emphysema, and the explanation of variance (R2) for emphysema improved from 0.3 to 0.4 when the pQTL SNP was included in the model along with clinical covariates. Causal modeling provided insight into specific pQTL-disease relationships for airflow obstruction and emphysema. In conclusion, given the frequency of highly significant local pQTLs, the large amount of variance potentially explained by pQTL, and the differences observed between pQTLs and eQTLs SNPs, we recommend that protein biomarker-disease association studies take into account the potential effect of common local SNPs and that pQTLs be integrated along with eQTLs to uncover disease mechanisms. Large-scale blood biomarker studies would also benefit from close attention to the ABO blood group.
Author Summary
Precision medicine is an emerging approach that takes into account variability in genes, gene and protein expression, environment and lifestyle. Recent advances in high-throughput genome-wide genotyping, genomics, and proteomics coupled with the creation of large, highly-phenotyped clinical cohorts now allows for integration of these molecular data sets at the individual level. Here we use genome-wide genotyping and blood measurements of 88 biomarkers in 1,340 subjects from two large NIH-supported clinical cohorts of smokers (SPIROMICS and COPDGene) to identify more than 300 novel DNA variants that influence measurement of blood protein levels (pQTLs). We find that many DNA variants explain a large portion of the variability of measured protein expression in blood. Furthermore, we show that integration of DNA variants with blood biomarker levels can improve the ability of predictive models to reflect the relationship between biomarker and disease features (e.g., emphysema) within chronic obstructive pulmonary disease (COPD).
doi:10.1371/journal.pgen.1006011
PMCID: PMC4988780  PMID: 27532455
19.  Meta-Analysis of Genome-Wide Association Studies in African Americans Provides Insights into the Genetic Architecture of Type 2 Diabetes 
Ng, Maggie C. Y. | Shriner, Daniel | Chen, Brian H. | Li, Jiang | Chen, Wei-Min | Guo, Xiuqing | Liu, Jiankang | Bielinski, Suzette J. | Yanek, Lisa R. | Nalls, Michael A. | Comeau, Mary E. | Rasmussen-Torvik, Laura J. | Jensen, Richard A. | Evans, Daniel S. | Sun, Yan V. | An, Ping | Patel, Sanjay R. | Lu, Yingchang | Long, Jirong | Armstrong, Loren L. | Wagenknecht, Lynne | Yang, Lingyao | Snively, Beverly M. | Palmer, Nicholette D. | Mudgal, Poorva | Langefeld, Carl D. | Keene, Keith L. | Freedman, Barry I. | Mychaleckyj, Josyf C. | Nayak, Uma | Raffel, Leslie J. | Goodarzi, Mark O. | Chen, Y-D Ida | Taylor, Herman A. | Correa, Adolfo | Sims, Mario | Couper, David | Pankow, James S. | Boerwinkle, Eric | Adeyemo, Adebowale | Doumatey, Ayo | Chen, Guanjie | Mathias, Rasika A. | Vaidya, Dhananjay | Singleton, Andrew B. | Zonderman, Alan B. | Igo, Robert P. | Sedor, John R. | Kabagambe, Edmond K. | Siscovick, David S. | McKnight, Barbara | Rice, Kenneth | Liu, Yongmei | Hsueh, Wen-Chi | Zhao, Wei | Bielak, Lawrence F. | Kraja, Aldi | Province, Michael A. | Bottinger, Erwin P. | Gottesman, Omri | Cai, Qiuyin | Zheng, Wei | Blot, William J. | Lowe, William L. | Pacheco, Jennifer A. | Crawford, Dana C. | Grundberg, Elin | Rich, Stephen S. | Hayes, M. Geoffrey | Shu, Xiao-Ou | Loos, Ruth J. F. | Borecki, Ingrid B. | Peyser, Patricia A. | Cummings, Steven R. | Psaty, Bruce M. | Fornage, Myriam | Iyengar, Sudha K. | Evans, Michele K. | Becker, Diane M. | Kao, W. H. Linda | Wilson, James G. | Rotter, Jerome I. | Sale, Michèle M. | Liu, Simin | Rotimi, Charles N. | Bowden, Donald W.
PLoS Genetics  2014;10(8):e1004517.
Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15×10−94
Author Summary
Despite the higher prevalence of type 2 diabetes (T2D) in African Americans than in Europeans, recent genome-wide association studies (GWAS) were examined primarily in individuals of European ancestry. In this study, we performed meta-analysis of 17 GWAS in 8,284 cases and 15,543 controls to explore the genetic architecture of T2D in African Americans. Following replication in additional 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry, we identified two novel and three previous reported T2D loci reaching genome-wide significance. We also examined 158 loci previously reported to be associated with T2D or regulating glucose homeostasis. While 56% of these loci were shared between African Americans and the other populations, the strongest associations in African Americans are often found in nearby single nucleotide polymorphisms (SNPs) instead of the original SNPs reported in other populations due to differential genetic architecture across populations. Our results highlight the importance of performing genetic studies in non-European populations to fine map the causal genetic variants.
doi:10.1371/journal.pgen.1004517
PMCID: PMC4125087  PMID: 25102180
The lancet. Respiratory medicine  2013;1(4):309-317.
Summary
Background
Idiopathic pulmonary fibrosis (IPF) is a devastating disease that probably involves several genetic loci. Several rare genetic variants and one common single nucleotide polymorphism (SNP) of MUC5B have been associated with the disease. Our aim was to identify additional common variants associated with susceptibility and ultimately mortality in IPF.
Methods
First, we did a three-stage genome-wide association study (GWAS): stage one was a discovery GWAS; and stages two and three were independent case-control studies. DNA samples from European-American patients with IPF meeting standard criteria were obtained from several US centres for each stage. Data for European-American control individuals for stage one were gathered from the database of genotypes and phenotypes; additional control individuals were recruited at the University of Pittsburgh to increase the number. For controls in stages two and three, we gathered data for additional sex-matched European-American control individuals who had been recruited in another study. DNA samples from patients and from control individuals were genotyped to identify SNPs associated with IPF. SNPs identified in stage one were carried forward to stage two, and those that achieved genome-wide significance (p<5 × 10−8) in a meta-analysis were carried forward to stage three. Three case series with follow-up data were selected from stages one and two of the GWAS using samples with follow-up data. Mortality analyses were done in these case series to assess the SNPs associated with IPF that had achieved genome-wide significance in the meta-analysis of stages one and two. Finally, we obtained gene-expression profiling data for lungs of patients with IPF from the Lung Genomics Research Consortium and analysed correlation with SNP genotypes.
Findings
In stage one of the GWAS (542 patients with IPF, 542 control individuals matched one-by-one to cases by genetic ancestry estimates), we identified 20 loci. Six SNPs reached genome-wide significance in stage two (544 patients, 687 control individuals): three TOLLIP SNPs (rs111521887, rs5743894, rs5743890) and one MUC5B SNP (rs35705950) at 11p15.5; one MDGA2 SNP (rs7144383) at 14q21.3; and one SPPL2C SNP (rs17690703) at 17q21.31. Stage three (324 patients, 702 control individuals) confirmed the associations for all these SNPs, except for rs7144383. Linkage disequilibrium between the MUC5B SNP (rs35705950) and TOLLIP SNPs (rs111521887 [r2=0.07], rs5743894 [r2=0.16], and rs5743890 [r2=0.01]) was low. 683 patients from the GWAS were included in the mortality analysis. Individuals who developed IPF despite having the protective TOLLIP minor allele of rs5743890 carried an increased mortality risk (meta-analysis with fixed-effect model: hazard ratio 1.72 [95% CI 1.24–2.38]; p=0.0012). TOLLIP expression was decreased by 20% in individuals carrying the minor allele of rs5743890 (p=0.097), 40% in those with the minor allele of rs111521887 (p=3.0 × 10−4), and 50% in those with the minor allele of rs5743894 (p=2.93 × 10−5) compared with homozygous carriers of common alleles for these SNPs.
Interpretation
Novel variants in TOLLIP and SPPL2C are associated with IPF susceptibility. One novel variant of TOLLIP, rs5743890, is also associated with mortality. These associations and the reduced expression of TOLLIP in patients with IPF who carry TOLLIP SNPs emphasise the importance of this gene in the disease.
Funding
National Institutes of Health; National Heart, Lung, and Blood Institute; Pulmonary Fibrosis Foundation; Coalition for Pulmonary Fibrosis; and Instituto de Salud Carlos III.
doi:10.1016/S2213-2600(13)70045-6
PMCID: PMC3894577  PMID: 24429156
PLoS Medicine  2015;12(8):e1001865.
Background
Observational epidemiological studies indicate that maternal height is associated with gestational age at birth and fetal growth measures (i.e., shorter mothers deliver infants at earlier gestational ages with lower birth weight and birth length). Different mechanisms have been postulated to explain these associations. This study aimed to investigate the casual relationships behind the strong association of maternal height with fetal growth measures (i.e., birth length and birth weight) and gestational age by a Mendelian randomization approach.
Methods and Findings
We conducted a Mendelian randomization analysis using phenotype and genome-wide single nucleotide polymorphism (SNP) data of 3,485 mother/infant pairs from birth cohorts collected from three Nordic countries (Finland, Denmark, and Norway). We constructed a genetic score based on 697 SNPs known to be associated with adult height to index maternal height. To avoid confounding due to genetic sharing between mother and infant, we inferred parental transmission of the height-associated SNPs and utilized the haplotype genetic score derived from nontransmitted alleles as a valid genetic instrument for maternal height. In observational analysis, maternal height was significantly associated with birth length (p = 6.31 × 10−9), birth weight (p = 2.19 × 10−15), and gestational age (p = 1.51 × 10−7). Our parental-specific haplotype score association analysis revealed that birth length and birth weight were significantly associated with the maternal transmitted haplotype score as well as the paternal transmitted haplotype score. Their association with the maternal nontransmitted haplotype score was far less significant, indicating a major fetal genetic influence on these fetal growth measures. In contrast, gestational age was significantly associated with the nontransmitted haplotype score (p = 0.0424) and demonstrated a significant (p = 0.0234) causal effect of every 1 cm increase in maternal height resulting in ~0.4 more gestational d. Limitations of this study include potential influences in causal inference by biological pleiotropy, assortative mating, and the nonrandom sampling of study subjects.
Conclusions
Our results demonstrate that the observed association between maternal height and fetal growth measures (i.e., birth length and birth weight) is mainly defined by fetal genetics. In contrast, the association between maternal height and gestational age is more likely to be causal. In addition, our approach that utilizes the genetic score derived from the nontransmitted maternal haplotype as a genetic instrument is a novel extension to the Mendelian randomization methodology in casual inference between parental phenotype (or exposure) and outcomes in offspring.
Using a Mendelian randomization approach, Ge Zhang and colleagues examine the causal relationship between maternal height, birth size, and gestational age at birth.
Editors' Summary
Background
Soon after the birth of a baby, doting parents send messages to friends and relatives or post information on social media sites to let everyone know when their new baby boy or girl was born. They may also post information about how heavy he/she was at birth and his/her length. These pregnancy outcomes, together with gestational age at birth (the length of time that a baby has spent developing in its mother’s womb), affect the baby’s immediate health and survival. Importantly, however, these pregnancy outcomes are also associated with the risk of long-term adverse health outcomes such as obesity, cardiometabolic disorders (heart disease and conditions such as diabetes that affect how the body makes energy from food), and neuropsychiatric conditions (mental disorders attributable to diseases of the nervous system, such as depression). For example, some studies have shown an association between low birth weight and an increased risk of type 2 diabetes later in life.
Why Was This Study Done?
Identification of the environmental and genetic factors that causally influence gestational age, length, and weight at birth would improve our understanding of why these pregnancy outcomes are associated with disease during adulthood and could help in the design of strategies to prevent these diseases. Epidemiological studies (investigations that examine disease patterns in populations) suggest that, compared to tall mothers, short mothers tend to deliver their babies at earlier gestational ages, with lower birth weights and lengths. Epidemiological studies cannot show, however, whether variations in maternal height cause variations in pregnancy outcomes. Other characteristics shared by tall mothers might actually determine the size and gestational age of their offspring (confounding). Here, the researchers use “Mendelian randomization” to assess the causal effect of maternal height on the size and gestational age at birth of babies. Because gene variants are inherited randomly, they are not prone to confounding. So, if maternal height actually affects gestational age and size at birth, genetic variants (instruments) that affect maternal height should be associated with differences in gestational age and size at birth, provided confounding due to the transmission of parental alleles (variant forms of genes; people have two alleles of every gene, one inherited from each parent) is avoided by adjusting for the baby’s genotype.
What Did the Researchers Do and Find?
The researchers used phenotype data (observable characteristics such as maternal height and birth weight of the baby) and single nucleotide polymorphism (SNP; a type of genetic variant) data obtained from 3,486 Nordic mother/baby pairs. Analysis of the phenotype data indicated that maternal height was significantly associated with length, weight, and gestational age at birth (a significant association is unlikely to have arisen by chance). For their Mendelian randomization analysis, the researchers constructed a genetic score based on 697 SNPs known to be associated with adult height. To avoid confounding due to genetic sharing between the mother and baby, they determined which of the height-associated alleles each baby had inherited from its mother and used the nontransmitted haplotype score as a genetic instrument for maternal height (a haplotype is a set of DNA variations that are inherited together). Birth length and weight were significantly associated with both the maternal and paternal transmitted haplotype scores but not with the maternal nontransmitted haplotype score. However, gestational age was significantly but modestly associated with the maternal nontransmitted haplotype score.
What Do These Findings Mean?
The validity of the assumptions that underlie the Mendelian randomization approach and the design of the studies supplying the data for this analysis may affect the accuracy of the findings reported here. Nevertheless, these findings suggest that the observed association between maternal height and fetal growth measurements is mainly determined by the genetics of the baby. That is, differences in maternal height do not cause differences in birth weight or length. Rather, some of the gene variants that the baby inherits from its mother determine both its size and its mother’s height. These findings also provide weak evidence that the association between maternal height and gestational age is causal. Maternal height might, for example, causally influence gestational age by limiting the space available for the baby’s growth before birth. Finally, these findings introduce an extension to the Mendelian randomization approach that can be used to investigate causal associations between parental characteristics and offspring outcomes.
Additional Information
This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001865.
The March of Dimes, a not-for-profit organization for pregnancy and baby health, provides information about low birth weight and its consequences
Nemours, a not-for-profit organization for child health, provides information about the weight of newborn babies (in English and Spanish)
Wikipedia has pages on birth weight, gestational age, and Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
MedlinePlus provides information and links to additional resources about birth weight and a brief explanation of gestational age (in English and Spanish)
doi:10.1371/journal.pmed.1001865
PMCID: PMC4540580  PMID: 26284790
PLoS Genetics  2016;12(9):e1006186.
Primary open angle glaucoma (POAG) is a complex disease and is one of the major leading causes of blindness worldwide. Genome-wide association studies have successfully identified several common variants associated with glaucoma; however, most of these variants only explain a small proportion of the genetic risk. Apart from the standard approach to identify main effects of variants across the genome, it is believed that gene-gene interactions can help elucidate part of the missing heritability by allowing for the test of interactions between genetic variants to mimic the complex nature of biology. To explain the etiology of glaucoma, we first performed a genome-wide association study (GWAS) on glaucoma case-control samples obtained from electronic medical records (EMR) to establish the utility of EMR data in detecting non-spurious and relevant associations; this analysis was aimed at confirming already known associations with glaucoma and validating the EMR derived glaucoma phenotype. Our findings from GWAS suggest consistent evidence of several known associations in POAG. We then performed an interaction analysis for variants found to be marginally associated with glaucoma (SNPs with main effect p-value <0.01) and observed interesting findings in the electronic MEdical Records and GEnomics Network (eMERGE) network dataset. Genes from the top epistatic interactions from eMERGE data (Likelihood Ratio Test i.e. LRT p-value <1e-05) were then tested for replication in the NEIGHBOR consortium dataset. To replicate our findings, we performed a gene-based SNP-SNP interaction analysis in NEIGHBOR and observed significant gene-gene interactions (p-value <0.001) among the top 17 gene-gene models identified in the discovery phase. Variants from gene-gene interaction analysis that we found to be associated with POAG explain 3.5% of additional genetic variance in eMERGE dataset above what is explained by the SNPs in genes that are replicated from previous GWAS studies (which was only 2.1% variance explained in eMERGE dataset); in the NEIGHBOR dataset, adding replicated SNPs from gene-gene interaction analysis explain 3.4% of total variance whereas GWAS SNPs alone explain only 2.8% of variance. Exploring gene-gene interactions may provide additional insights into many complex traits when explored in properly designed and powered association studies.
Author Summary
The complex nature of primary-open angle glaucoma (POAG) has left researchers exploring the genetic architecture and searching for the missing heritability using a number of different study designs. Over the past decade, many studies have been conducted to explain the etiology of POAG; however, a high proportion of estimated heritability still remains unexplained. GWA studies for POAG have identified significant associations but these associations have only explained a small proportion of the genetic risk (odds ratios range between 1–3). In this paper, we sought to confirm the primary genome-wide significant associations that have been discovered so far for glaucoma in phenotypes developed from EMR data in an effort to show that EMR data can be a powerful resource for finding genetic variants influencing POAG susceptibility. Next, we tested for statistical interactions, which can be presented as an important tool in an attempt to explain POAG heritability. We used a reduced list of variants filtered by marginal main effect analysis to look for epistatic interactions. We present our results from replication of gene-based interaction analyses performed in eMERGE and the NEIGHBOR consortium data. Using expression data and annotations from various publicly available databases, the most significant genes that replicated in our analyses show expression in the eye and trabecular meshwork. Analysis for estimation of genetic variance explained by significant associations from previous GWAS and replicated variants from gene-based interactions suggest that these explain 5.6% of variance in eMERGE dataset and also explain 3.4% variance in NEIGHBOR dataset.
doi:10.1371/journal.pgen.1006186
PMCID: PMC5021356  PMID: 27623284
PLoS Genetics  2011;7(9):e1002293.
Diabetes impacts approximately 200 million people worldwide, of whom approximately 10% are affected by type 1 diabetes (T1D). The application of genome-wide association studies (GWAS) has robustly revealed dozens of genetic contributors to the pathogenesis of T1D, with the most recent meta-analysis identifying in excess of 40 loci. To identify additional genetic loci for T1D susceptibility, we examined associations in the largest meta-analysis to date between the disease and ∼2.54 million SNPs in a combined cohort of 9,934 cases and 16,956 controls. Targeted follow-up of 53 SNPs in 1,120 affected trios uncovered three new loci associated with T1D that reached genome-wide significance. The most significantly associated SNP (rs539514, P = 5.66×10−11) resides in an intronic region of the LMO7 (LIM domain only 7) gene on 13q22. The second most significantly associated SNP (rs478222, P = 3.50×10−9) resides in an intronic region of the EFR3B (protein EFR3 homolog B) gene on 2p23; however, the region of linkage disequilibrium is approximately 800 kb and harbors additional multiple genes, including NCOA1, C2orf79, CENPO, ADCY3, DNAJC27, POMC, and DNMT3A. The third most significantly associated SNP (rs924043, P = 8.06×10−9) lies in an intergenic region on 6q27, where the region of association is approximately 900 kb and harbors multiple genes including WDR27, C6orf120, PHF10, TCTE3, C6orf208, LOC154449, DLL1, FAM120B, PSMB1, TBP, and PCD2. These latest associated regions add to the growing repertoire of gene networks predisposing to T1D.
Author Summary
Despite the fact that there is clearly a large genetic component to type 1 diabetes (T1D), uncovering the genes contributing to this disease has proven challenging. However, in the past three years there has been relatively major progress in this regard, with advances in genetic screening technologies allowing investigators to scan the genome for variants conferring risk for disease without prior hypotheses. Such genome-wide association studies have revealed multiple regions of the genome to be robustly and consistently associated with T1D. More recent findings have been a consequence of combining of multiple datasets from independent investigators in meta-analyses, which have more power to pick up additional variants contributing to the trait. In the current study, we describe the largest meta-analysis of T1D genome-wide genotyped datasets to date, which combines six large studies. As a consequence, we have uncovered three new signals residing at the chromosomal locations 13q22, 2p23, and 6q27, which went on to be replicated in independent sample sets. These latest associated regions add to the growing repertoire of gene networks predisposing to T1D.
doi:10.1371/journal.pgen.1002293
PMCID: PMC3183083  PMID: 21980299
BMC Genomics  2009;10(Suppl 2):S2.
Background
The genome sequence and a high-density SNP map are now available for the chicken and can be used to identify genetic markers for use in marker-assisted selection (MAS). Effective MAS requires high linkage disequilibrium (LD) between markers and quantitative trait loci (QTL), and sustained marker-QTL LD over generations. This study used data from a 3,000 SNP panel to assess the level and consistency of LD between single nucleotide polymorphisms (SNPs) over consecutive years in two egg-layer chicken lines, and analyzed one line by two methods (SNP-wise association and genome-wise Bayesian analysis) to identify markers associated with egg-quality and egg-production phenotypes.
Results
The LD between markers pairs was high at short distances (r2 > 0.2 at < 2 Mb) and remained high after one generation (correlations of 0.80 to 0.92 at < 5 Mb) in both lines. Single- and 3-SNP regression analyses using a mixed model with SNP as fixed effect resulted in 159 and 76 significant tests (P < 0.01), respectively, across 12 traits. A Bayesian analysis called BayesB, that fits all SNPs simultaneously as random effects and uses model averaging procedures, identified 33 SNPs that were included in the model >20% of the time (φ > 0.2) and an additional ten 3-SNP windows that had a sum of φ greater than 0.35. Generally, SNPs included in the Bayesian model also had a small P-value in the 1-SNP analyses.
Conclusion
High LD correlations between markers at short distances across two generations indicate that such markers will retain high LD with linked QTL and be effective for MAS. The different association analysis methods used provided consistent results. Multiple single SNPs and 3-SNP windows were significantly associated with egg-related traits, providing genomic positions of QTL that can be useful for both MAS and to identify causal mutations.
doi:10.1186/1471-2164-10-S2-S2
PMCID: PMC2966334  PMID: 19607653
BMC Genetics  2016;17:116.
Background
Genome-wide association studies of Single Nucleotide Polymorphisms (SNPs) have identified 55 SNPs associated with lung function. However, little is known about the effect of copy number variants (CNVs) on lung function, although CNVs represent a significant proportion of human genetic polymorphism. To assess the effect of CNVs on lung function quantitative traits, we measured copy number at 2788 previously characterised, common copy number variable regions in 6 independent cohorts (n = 24,237) using intensity data from SNP genotyping experiments. We developed a pipeline for genome-wide association analysis and meta-analysis of CNV genotypes measured across multiple studies using SNP genotype array intensity data from different platform technologies. We then undertook cohort-level genome-wide association studies of CNV with lung function in a subset of 4 cohorts (n < =12,403) with lung function measurements and meta-analysed the results. Follow-up was undertaken for CNVs which were well tagged by SNPs, in up to 146,871 individuals.
Results
We generated robust copy number calls for 1962 out of 2788 (70 %) known CNV regions genome-wide, with 1103 measured with compatible class frequencies in at least 2 cohorts. We report a novel CNV association (discovery P = 0.0007) with Forced Vital Capacity (FVC) downstream of BANP on chromosome 16 that shows evidence of replication by a tag SNP in two independent studies (replication P = 0.004). In addition, we provide suggestive evidence (discovery P = 0.0002) for a role of complex copy number variation at a previously reported lung function locus, containing the rootletin gene CROCC, that is not tagged by SNPs.
Conclusions
We demonstrate how common CNV regions can be reliably and consistently called across cohorts, using an existing calling algorithm and rigorous quality control steps, using SNP genotyping array intensity data. Although many common biallelic CNV regions were well-tagged by common SNPs, we also identified associations with untagged mulitallelic CNV regions thereby illustrating the potential of our approach to identify some of the missing heritability of complex traits.
Electronic supplementary material
The online version of this article (doi:10.1186/s12863-016-0423-0) contains supplementary material, which is available to authorized users.
doi:10.1186/s12863-016-0423-0
PMCID: PMC4981989  PMID: 27514831
Copy number variation; Lung function; Genome-wide association study

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