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1.  Detailed Investigation of the Role of Common and Low-Frequency WFS1 Variants in Type 2 Diabetes Risk 
Diabetes  2009;59(3):741-746.
Wolfram syndrome 1 (WFS1) single nucleotide polymorphisms (SNPs) are associated with risk of type 2 diabetes. In this study we aimed to refine this association and investigate the role of low-frequency WFS1 variants in type 2 diabetes risk.
For fine-mapping, we sequenced WFS1 exons, splice junctions, and conserved noncoding sequences in samples from 24 type 2 diabetic case and 68 control subjects, selected tagging SNPs, and genotyped these in 959 U.K. type 2 diabetic case and 1,386 control subjects. The same genomic regions were sequenced in samples from 1,235 type 2 diabetic case and 1,668 control subjects to compare the frequency of rarer variants between case and control subjects.
Of 31 tagging SNPs, the strongest associated was the previously untested 3′ untranslated region rs1046320 (P = 0.008); odds ratio 0.84 and P = 6.59 × 10−7 on further replication in 3,753 case and 4,198 control subjects. High correlation between rs1046320 and the original strongest SNP (rs10010131) (r2 = 0.92) meant that we could not differentiate between their effects in our samples. There was no difference in the cumulative frequency of 82 rare (minor allele frequency [MAF] <0.01) nonsynonymous variants between type 2 diabetic case and control subjects (P = 0.79). Two intermediate frequency (MAF 0.01–0.05) nonsynonymous changes also showed no statistical association with type 2 diabetes.
We identified six highly correlated SNPs that show strong and comparable associations with risk of type 2 diabetes, but further refinement of these associations will require large sample sizes (>100,000) or studies in ethnically diverse populations. Low frequency variants in WFS1 are unlikely to have a large impact on type 2 diabetes risk in white U.K. populations, highlighting the complexities of undertaking association studies with low-frequency variants identified by resequencing.
PMCID: PMC2828659  PMID: 20028947
2.  Inherited common variants in mitochondrial DNA and invasive serous epithelial ovarian cancer risk 
BMC Research Notes  2013;6:425.
Mitochondria are the site of oxidative phosphorylation, a process which generates reactive oxygen species (ROS). Elevated ROS levels can lead to oxidative stress, a cellular state implicated in carcinogenesis. It is hypothesized that alternations in mitochondrial (MT) DNA, including heritable MT single nucleotide polymorphisms (MT-SNPs), have the potential to change the capacity of MT function, leading to increased oxidative stress and cancer risk. We investigated if common MT-SNPs and/or haplogroups and are associated with invasive serous ovarian cancer (OvCa) risk.
A panel of 64 MT-SNPs designed to tag all common variation in the European MT genome (minor allele frequency (MAF) >1%, r^2 >0.8) was genotyped in study participants of European descent using the Sequenom MassARRAY iPlex Gold® system (Sequenom Inc, CA, USA). Invasive serous OvCa cases (n = 405) and frequency age-matched controls (n = 445) were drawn from a population-based case-control study of OvCa in western Canada. Binary logistic regression was used to estimate the odds ratio (OR) and 95% confidence intervals (C.I.) for carriage of the minor versus major allele by case-control status. MitoTool was used to test the relationship between European haplogroup status and case-control status using Fisher’s exact test.
The most significant disease-SNP association was for rs2857285, a synonymous MT-SNP in ND4 (OR = 4.84, 95% CI: 1.03–22.68, P = 0.045). After adjustment for multiple testing using a Bonferroni correction of the Type 1 error this MT-SNP was not significant. No other MT-SNP had a P-value < 0.05. European haplogroup status was not associated with case status. Most MT-SNPs (73%) genotyped had a MAF <5%.
Common European MT-SNPs (MAF > 5%) and haplogroups were not associated with invasive serous OvCa risk in this study; however, most European MT-SNPs have a low MAF (<5%), which we were underpowered to adequately assess. Larger studies are needed to clarify the role of low MAF MT-SNPs (MAF < 5%) in invasive serous OvCa risk.
PMCID: PMC3854008  PMID: 24148579
Mitochondrial DNA; Single nucleotide polymorphism; Epithelial ovarian cancer; Serous ovarian cancer; Haplogroup; Heritable risk; European
3.  Resequencing and Analysis of Variation in the TCF7L2 Gene in African Americans Suggests That SNP rs7903146 Is the Causal Diabetes Susceptibility Variant 
Diabetes  2011;60(2):662-668.
Variation in the transcription factor 7-like 2 (TCF7L2) locus is associated with type 2 diabetes across multiple ethnicities. The aim of this study was to elucidate which variant in TCF7L2 confers diabetes susceptibility in African Americans.
Through the evaluation of tagging single nucleotide polymorphisms (SNPs), type 2 diabetes susceptibility was limited to a 4.3-kb interval, which contains the YRI (African) linkage disequilibrium (LD) block containing rs7903146. To better define the relationship between type 2 diabetes risk and genetic variation we resequenced this 4.3-kb region in 96 African American DNAs. Thirty-three novel and 13 known SNPs were identified: 20 with minor allele frequencies (MAF) >0.05 and 12 with MAF >0.10. These polymorphisms and the previously identified DG10S478 microsatellite were evaluated in African American type 2 diabetic cases (n = 1,033) and controls (n = 1,106).
Variants identified from direct sequencing and databases were genotyped or imputed. Fifteen SNPs showed association with type 2 diabetes (P < 0.05) with rs7903146 being the most significant (P = 6.32 × 10−6). Results of imputation, haplotype, and conditional analysis of SNPs were consistent with rs7903146 being the trait-defining SNP. Analysis of the DG10S478 microsatellite, which is outside the 4.3-kb LD block, revealed consistent association of risk allele 8 with type 2 diabetes (odds ratio [OR] = 1.33; P = 0.022) as reported in European populations; however, allele 16 (MAF = 0.016 cases and 0.032 controls) was strongly associated with reduced risk (OR = 0.39; P = 5.02 × 10−5) in contrast with previous studies.
In African Americans, these observations suggest that rs7903146 is the trait-defining polymorphism associated with type 2 diabetes risk. Collectively, these results support ethnic differences in type 2 diabetes associations.
PMCID: PMC3028368  PMID: 20980453
4.  Replication of the association between variants in the WFS1 gene and risk of type 2 diabetes in European populations 
Diabetologia  2007;51(3):458-463.
Mutations at the Wolframin encoding gene, WFS1, cause Wolfram syndrome, a rare neurological condition. Associations between single nucleotide polymorphisms (SNPs) at WFS1 and type 2 diabetes have recently been reported. In the present study, we sought to replicate those associations in a northern Swedish case-control study for type 2 diabetes. We also meta-analyzed published and previously unpublished data from Sweden, Finland and France to obtain updated summary effect estimates.
Four WFS1 SNPs (rs10010131, rs6446482, rs752854, rs734312 [R611H]) were genotyped in a type 2 diabetes case-control study (N=1,296/1,412) of Swedish adults. Logistic regression was used to assess the association between each WFS1 SNP and type 2 diabetes, following adjustment for age, sex, and body mass index. We then performed a meta-analysis of 11 studies of type 2 diabetes, comprising up to 14,139 cases and 16,109 controls, to obtain a summary effect estimate for the WFS1 variants.
In the northern Swedish study, the minor allele at rs752854 was associated with reduced type 2 diabetes risk (OR=0.85; 95% CI=0.75-0.96; p=0.010). Borderline statistical associations were observed for the remaining SNPs. The meta-analysis of the four independent replication studies for SNP rs10010131, or its proxy variants, showed evidence for statistical association (OR=0.87; 95% CI=0.82-0.93; p=4.5×10−5). In an updated meta-analysis of all 11 studies, comprising 14,139 cases and 16,109 controls, strong evidence for statistical association was also observed (OR=0.89; 95% CI=0.86-0.92; p=4.9×10−11).
In this study of WFS1 gene variants and type 2 diabetes risk, we have replicated the previously reported associations between SNPs at this locus and risk of type 2 diabetes.
PMCID: PMC2670195  PMID: 18040659
Wolfram Syndrome; WFS1; Genetic; Replication; Type 2 diabetes; Association study; Swedish; Meta-analysis
5.  The Effect of Chromosome 9p21 Variants on Cardiovascular Disease May Be Modified by Dietary Intake: Evidence from a Case/Control and a Prospective Study 
PLoS Medicine  2011;8(10):e1001106.
Ron Do and colleagues find that a prudent diet high in raw vegetables may modify the increased genetic risk of cardiovascular disease conferred by the chromosome 9p21 SNP.
One of the most robust genetic associations for cardiovascular disease (CVD) is the Chromosome 9p21 region. However, the interaction of this locus with environmental factors has not been extensively explored. We investigated the association of 9p21 with myocardial infarction (MI) in individuals of different ethnicities, and tested for an interaction with environmental factors.
Methods and Findings
We genotyped four 9p21 SNPs in 8,114 individuals from the global INTERHEART study. All four variants were associated with MI, with odds ratios (ORs) of 1.18 to 1.20 (1.85×10−8≤p≤5.21×10−7). A significant interaction (p = 4.0×10−4) was observed between rs2383206 and a factor-analysis-derived “prudent” diet pattern score, for which a major component was raw vegetables. An effect of 9p21 on MI was observed in the group with a low prudent diet score (OR = 1.32, p = 6.82×10−7), but the effect was diminished in a step-wise fashion in the medium (OR = 1.17, p = 4.9×10−3) and high prudent diet scoring groups (OR = 1.02, p = 0.68) (p = 0.014 for difference). We also analyzed data from 19,129 individuals (including 1,014 incident cases of CVD) from the prospective FINRISK study, which used a closely related dietary variable. In this analysis, the 9p21 risk allele demonstrated a larger effect on CVD risk in the groups with diets low or average for fresh vegetables, fruits, and berries (hazard ratio [HR] = 1.22, p = 3.0×10−4, and HR = 1.35, p = 4.1×10−3, respectively) compared to the group with high consumption of these foods (HR = 0.96, p = 0.73) (p = 0.0011 for difference). The combination of the least prudent diet and two copies of the risk allele was associated with a 2-fold increase in risk for MI (OR = 1.98, p = 2.11×10−9) in the INTERHEART study and a 1.66-fold increase in risk for CVD in the FINRISK study (HR = 1.66, p = 0.0026).
The risk of MI and CVD conferred by Chromosome 9p21 SNPs appears to be modified by a prudent diet high in raw vegetables and fruits.
Please see later in the article for the Editors' Summary
Editors' Summary
Cardiovascular diseases (CVDs)—diseases that affect the heart and/or the blood vessels—are a leading cause of illness and death worldwide. In the United States, for example, the leading cause of death is coronary heart disease, a CVD in which narrowing of the heart's blood vessels by fatty deposits slows the blood supply to the heart and may eventually cause a heart attack (myocardial infarction, or MI); the third leading cause of death in the US is stroke, a CVD in which the brain's blood supply is interrupted. Environmental factors such as diet, physical activity, and smoking alter a person's risk of developing CVD. In addition, certain genetic variants (alterations in the DNA that forms the body's blueprint; DNA is packed into structures called chromosomes) alter the risk of developing CVD and are passed from parent to child. Thus, in CVD, as in most common diseases, both genetics and the environment play a role.
Why Was This Study Done?
Recent studies have identified several genetic variants that are associated with an increased risk of developing CVD. One of the most robust of these genetic associations is a cluster of single nucleotide polymorphisms (SNPs, differences in a single DNA building block) in a chromosomal region (locus) called 9p21. So far, this association has been mainly studied in European populations. Moreover, the interaction of this locus with environmental factors has not been extensively studied. A better understanding of how 9p21 variants affect CVD risk in people of different ethnicities and of the interaction between this locus and environmental factors could allow the development of targeted strategies for the prevention of CVD. In this study, the researchers investigate the association of 9p21 risk variants with CVD in people of different ethnicities and test for an interaction between this locus and environmental factors.
What Did the Researchers Do and Find?
The researchers assessed four 9p21 SNPs in people enrolled in the INTERHEART study, a global retrospective case-control study that investigated potential MI risk factors by comparing people who had had an acute non-fatal MI with similar people without heart disease. All four SNP risk variants increased the risk of MI by about a fifth. However, the effect of the SNPs on MI was influenced by the “prudent” diet pattern score of the INTERHEART participants, a score that includes fresh fruit and vegetable intake as recorded in food frequency questionnaires. That is, the risk of MI in people carrying SNP risk variants was influenced by their diet. The strongest interaction was seen with an SNP called rs2383206, but although rs2383206 carriers who ate a diet poor in fruits and vegetables had a higher risk of MI than people with a similar diet who did not carry this SNP, rs2383206 carriers and non-carriers who ate a fruit- and vegetable-rich diet had a comparable MI risk. Overall, the combination of the least “prudent” diet and two copies of the risk variant (human cells contain two complete sets of chromosomes) was associated with a two-fold increase in risk for MI in the INTERHEART study. Additionally, data collected in the FINRISK study, which characterized healthy individuals living in Finland at baseline and then followed them to see whether they developed CVD, revealed a similar interaction between diet and 9p21 SNPs.
What Do These Findings Mean?
These findings suggest that the risk of CVD conferred by chromosome 9p21 SNPs may be influenced by diet in multiple ethnic groups. Importantly, they suggest that the deleterious effect of 9p21 SNPs on CVD might be mitigated by consuming a diet rich in fresh fruits and vegetables. The accuracy of these findings may be affected by recall bias in the INTERHEART study (that is, some people may not have remembered their diet accurately) and by the small number of CVD cases in the FINRISK study. Nevertheless, these findings suggest that gene–environment interactions are important drivers of CVD, and they raise the possibility that a sound diet can mediate the effects of 9p21 SNPs.
Additional Information
Please access these websites via the online version of this summary at
The American Heart Association provides information about many types of cardiovascular disease for patients, caregivers, and professionals and tips on keeping the heart healthy
The UK National Health Service Choices website provides information about cardiovascular disease and stroke
Information is available from the British Heart Foundation on heart disease and keeping the heart healthy
The US National Heart Lung and Blood Institute provides information on a wide range of cardiovascular diseases
MedlinePlus provides links to many other sources of information on heart diseases, vascular diseases, and stroke (in English and Spanish)
The US Centers for Disease Control and Prevention has a simple fact sheet on gene-environment interactions; the US National Institute of Environmental Health Sciences provides links to other information on gene-environment interactions
More information is available on the INTERHEART study and on the FINRISK study
PMCID: PMC3191151  PMID: 22022235
6.  Integrating EMR-Linked and In Vivo Functional Genetic Data to Identify New Genotype-Phenotype Associations 
PLoS ONE  2014;9(6):e100322.
The coupling of electronic medical records (EMR) with genetic data has created the potential for implementing reverse genetic approaches in humans, whereby the function of a gene is inferred from the shared pattern of morbidity among homozygotes of a genetic variant. We explored the feasibility of this approach to identify phenotypes associated with low frequency variants using Vanderbilt's EMR-based BioVU resource. We analyzed 1,658 low frequency non-synonymous SNPs (nsSNPs) with a minor allele frequency (MAF)<10% collected on 8,546 subjects. For each nsSNP, we identified diagnoses shared by at least 2 minor allele homozygotes and with an association p<0.05. The diagnoses were reviewed by a clinician to ascertain whether they may share a common mechanistic basis. While a number of biologically compelling clinical patterns of association were observed, the frequency of these associations was identical to that observed using genotype-permuted data sets, indicating that the associations were likely due to chance. To refine our analysis associations, we then restricted the analysis to 711 nsSNPs in genes with phenotypes in the On-line Mendelian Inheritance in Man (OMIM) or knock-out mouse phenotype databases. An initial comparison of the EMR diagnoses to the known in vivo functions of the gene identified 25 candidate nsSNPs, 19 of which had significant genotype-phenotype associations when tested using matched controls. Twleve of the 19 nsSNPs associations were confirmed by a detailed record review. Four of 12 nsSNP-phenotype associations were successfully replicated in an independent data set: thrombosis (F5,rs6031), seizures/convulsions (GPR98,rs13157270), macular degeneration (CNGB3,rs3735972), and GI bleeding (HGFAC,rs16844401). These analyses demonstrate the feasibility and challenges of using reverse genetics approaches to identify novel gene-phenotype associations in human subjects using low frequency variants. As increasing amounts of rare variant data are generated from modern genotyping and sequence platforms, model organism data may be an important tool to enable discovery.
PMCID: PMC4065041  PMID: 24949630
7.  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.
PMCID: PMC3775722  PMID: 24068893
8.  A comprehensive analysis of common IGF1, IGFBP1 and IGFBP3 genetic variation with prospective IGF-I and IGFBP-3 blood levels and prostate cancer risk among Caucasians† 
Human Molecular Genetics  2010;19(15):3089-3101.
The insulin-like growth factor (IGF) pathway has been implicated in prostate development and carcinogenesis. We conducted a comprehensive analysis, utilizing a resequencing and tagging single-nucleotide polymorphism (SNP) approach, between common genetic variation in the IGF1, IGF binding protein (BP) 1, and IGFBP3 genes with IGF-I and IGFBP-3 blood levels, and prostate cancer (PCa) risk, among Caucasians in the NCI Breast and Prostate Cancer Cohort Consortium. We genotyped 14 IGF1 SNPs and 16 IGFBP1/IGFBP3 SNPs to capture common [minor allele frequency (MAF) ≥ 5%] variation among Caucasians. For each SNP, we assessed the geometric mean difference in IGF blood levels (N = 5684) across genotypes and the association with PCa risk (6012 PCa cases/6641 controls). We present two-sided statistical tests and correct for multiple comparisons. A non-synonymous IGFBP3 SNP in exon 1, rs2854746 (Gly32Ala), was associated with IGFBP-3 blood levels (Padj = 8.8 × 10−43) after adjusting for the previously established IGFBP3 promoter polymorphism A-202C (rs2854744); IGFBP-3 blood levels were 6.3% higher for each minor allele. For IGF1 SNP rs4764695, the risk estimates among heterozygotes was 1.01 (99% CI: 0.90–1.14) and 1.20 (99% CI: 1.06–1.37) for variant homozygotes with overall PCa risk. The corrected allelic P-value was 8.7 × 10−3. IGF-I levels were significantly associated with PCa risk (Ptrend = 0.02) with a 21% increase of PCa risk when compared with the highest quartile to the lowest quartile. We have identified SNPs significantly associated with IGFBP-3 blood levels, but none of these alter PCa risk; however, a novel IGF1 SNP, not associated with IGF-I blood levels, shows preliminary evidence for association with PCa risk among Caucasians.
PMCID: PMC2901143  PMID: 20484221
9.  Association Analysis of the FTO Gene with Obesity in Children of Caucasian and African Ancestry Reveals a Common Tagging SNP 
PLoS ONE  2008;3(3):e1746.
Recently an association was demonstrated between the single nucleotide polymorphism (SNP), rs9939609, within the FTO locus and obesity as a consequence of a genome wide association (GWA) study of type 2 diabetes in adults. We examined the effects of two perfect surrogates for this SNP plus 11 other SNPs at this locus with respect to our childhood obesity cohort, consisting of both Caucasians and African Americans (AA). Utilizing data from our ongoing GWA study in our cohort of 418 Caucasian obese children (BMI≥95th percentile), 2,270 Caucasian controls (BMI<95th percentile), 578 AA obese children and 1,424 AA controls, we investigated the association of the previously reported variation at the FTO locus with the childhood form of this disease in both ethnicities. The minor allele frequencies (MAF) of rs8050136 and rs3751812 (perfect surrogates for rs9939609 i.e. both r2 = 1) in the Caucasian cases were 0.448 and 0.443 respectively while they were 0.391 and 0.386 in Caucasian controls respectively, yielding for both an odds ratio (OR) of 1.27 (95% CI 1.08–1.47; P = 0.0022). Furthermore, the MAFs of rs8050136 and rs3751812 in the AA cases were 0.449 and 0.115 respectively while they were 0.436 and 0.090 in AA controls respectively, yielding an OR of 1.05 (95% CI 0.91–1.21; P = 0.49) and of 1.31 (95% CI 1.050–1.643; P = 0.017) respectively. Investigating all 13 SNPs present on the Illumina HumanHap550 BeadChip in this region of linkage disequilibrium, rs3751812 was the only SNP conferring significant risk in AA. We have therefore replicated and refined the association in an AA cohort and distilled a tag-SNP, rs3751812, which captures the ancestral origin of the actual mutation. As such, variants in the FTO gene confer a similar magnitude of risk of obesity to children as to their adult counterparts and appear to have a global impact.
PMCID: PMC2262153  PMID: 18335027
10.  Genetic association analysis of LARS2 with type 2 diabetes 
Diabetologia  2009;53(1):103-110.
LARS2 has been previously identified as a potential type 2 diabetes susceptibility gene through the low-frequency H324Q (rs71645922) variant (minor allele frequency [MAF] 3.0%). However, this association did not achieve genome-wide levels of significance. The aim of this study was to establish the true contribution of this variant and common variants in LARS2 (MAF > 5%) to type 2 diabetes risk.
We combined genome-wide association data (n = 10,128) from the DIAGRAM consortium with independent data derived from a tagging single nucleotide polymorphism (SNP) approach in Dutch individuals (n = 999) and took forward two SNPs of interest to replication in up to 11,163 Dutch participants (rs17637703 and rs952621). In addition, because inspection of genome-wide association study data identified a cluster of low-frequency variants with evidence of type 2 diabetes association, we attempted replication of rs9825041 (a proxy for this group) and the previously identified H324Q variant in up to 35,715 participants of European descent.
No association between the common SNPs in LARS2 and type 2 diabetes was found. Our replication studies for the two low-frequency variants, rs9825041 and H324Q, failed to confirm an association with type 2 diabetes in Dutch, Scandinavian and UK samples (OR 1.03 [95% CI 0.95–1.12], p = 0.45, n = 31,962 and OR 0.99 [0.90–1.08], p = 0.78, n = 35,715 respectively).
In this study, the largest study examining the role of sequence variants in LARS2 in type 2 diabetes susceptibility, we found no evidence to support previous data indicating a role in type 2 diabetes susceptibility.
Electronic supplementary material
The online version of this article (doi:10.1007/s00125-009-1557-7) contains supplementary material, which is available to authorised users.
PMCID: PMC2789927  PMID: 19847392
Genetics; LARS2; Mitochondria; SNP; Type 2 diabetes
11.  A role for coding functional variants in HNF4A in Type 2 Diabetes susceptibility 
Diabetologia  2010;54(1):111-119.
Rare mutations in the gene (HNF4A) encoding the transcription factor HNF-4A account for ~5% of cases of maturity-onset diabetes of the young (MODY) and more frequent variants in this gene may be involved in multifactorial forms of diabetes. Two low frequency, non-synonymous variants in HNF4A (V255M, minor allele frequency [MAF] ~0.1%, T130I, MAF ~3.0%), known to influence downstream HNF-4A target gene expression, are of interest but previous type 2 diabetes association reports were inconclusive. We aimed to evaluate the contribution of these variants to type 2 diabetes susceptibility through large-scale association analysis.
We genotyped both variants in at least 5745 cases and 14756 population controls from the UK and Denmark. We also undertook an expanded association-analysis including previously reported and novel genotype data obtained in Danish, Finnish, Canadian and Swedish samples. A meta-analysis incorporating all published association studies of the T130I variant was subsequently carried out in a maximum sample size of 14279 cases and 26835 controls.
We found no association between V255M and type 2 diabetes in either the initial (p=0.28) or expanded analysis (p=0.44). However, T130I demonstrated a modest association with type 2 diabetes in the UK and Danish samples (additive per allele OR 1.17 [1.08-1.28]; p=1.5×10−4), which was strengthened in the meta-analysis (OR 1.20 [1.10-1.30]; p=2.1×10−5).
Our data are consistent with T130I as a low frequency variant influencing type 2 diabetes risk, but are not conclusive when judged against stringent standards for genome-wide significance. This study exemplifies the difficulties encountered in association testing of low frequency variants.
PMCID: PMC3119815  PMID: 20878384
Type 2 Diabetes; HNF4A; Low frequency variants; T130I; V255M
12.  A genome-wide scan of 10 000 gene-centric variants and colorectal cancer risk 
European Journal of Human Genetics  2009;17(11):1507-1514.
Genome scans based on gene-centric single nucleotide polymorphisms (SNPs) have been proposed as an efficient approach to identify disease-causing variants that is complementary to scans based on tagging SNPs. Adopting this approach to identify low-penetrance susceptibility alleles for colorectal cancer (CRC) we analysed genotype data from 9109 gene-centric SNPs, 7014 of which were non-synonymous (nsSNPs), in 2873 cases and 2871 controls using Illumina iselect arrays. Overall the distribution of associations was not significantly different from the null. No SNP achieved globally significant association after correction for multiple testing (lowest P value 1.7 × 10−4, rs727299). We then analysed the dataset incorporating information on the functional consequences of nsSNPs. We used results from the in silico algorithm PolyPhen as prior information to weight the association statistics, with weights estimated from the observed test statistics within predefined groups of SNPs. Incorporating this information did not, however, yield any further evidence of a specific association (lowest P value 2.2 × 10−4, rs1133950). There was a strong relationship between effect size and SNPs predicted to be damaging (P=1.63 × 10−5), however, these variants which are most likely to impact on risk are rare (MAF<5%). Hence although the rationale for searching for low-penetrance cancer susceptibly alleles by conducting genome-wide scans of coding changes is strong, in practice it is likely that natural selection has rendered such alleles to be too rare to be detected by association studies of the size employed.
PMCID: PMC2986682  PMID: 19471308
polymorphism; cancer; risk
13.  Low-frequency intermediate penetrance variants in the ROCK1 gene predispose to Tetralogy of Fallot 
BMC Genetics  2013;14:57.
Epidemiological studies indicate a substantial excess familial recurrence of non-syndromic Tetralogy of Fallot (TOF), implicating genetic factors that remain largely unknown. The Rho induced kinase 1 gene (ROCK1) is a key component of the planar cell polarity signalling pathway, which plays an important role in normal cardiac development. The aim of this study was to investigate the role of genetic variation in ROCK1 on the risk of TOF.
ROCK1 was sequenced in a discovery cohort of 93 non-syndromic TOF probands to identify rare variants. TagSNPs were selected to capture commoner variation in ROCK1. Novel variants and TagSNPs were genotyped in a discovery cohort of 458 TOF cases and 1331 healthy controls, and positive findings were replicated in a further 209 TOF cases and 1290 healthy controls. Association between genotypes and TOF was assessed using LAMP.
A rare SNP (c.807C > T; rs56085230) discovered by sequencing was associated with TOF risk (p = 0.006) in the discovery cohort. The variant was also significantly associated with the risk of TOF in the replication cohort (p = 0.018). In the combined cohorts the odds ratio for TOF was 2.61 (95% CI 1.58-4.30); p < 0.0001. The minor allele frequency of rs56085230 in the cases was 0.02, and in the controls it was 0.007. The variant accounted for 1% of the population attributable risk (PAR) of TOF. We also found significant association with TOF for an uncommon TagSNP in ROCK1, rs288979 (OR 1.64 [95% CI 1.15-2.30]; p = 1.5x10-5). The minor allele frequency of rs288979 in the controls was 0.043, and the variant accounted for 11% of the PAR of TOF. These association signals were independent of each other, providing additional internal validation of our result.
Low frequency intermediate penetrance (LFIP) variants in the ROCK1 gene predispose to the risk of TOF.
PMCID: PMC3734041  PMID: 23782575
Congenital heart disease; Tetralogy of fallot; Genetics; Planar cell polarity pathway
14.  Predicting Disease Risk Using Bootstrap Ranking and Classification Algorithms 
PLoS Computational Biology  2013;9(8):e1003200.
Genome-wide association studies (GWAS) are widely used to search for genetic loci that underlie human disease. Another goal is to predict disease risk for different individuals given their genetic sequence. Such predictions could either be used as a “black box” in order to promote changes in life-style and screening for early diagnosis, or as a model that can be studied to better understand the mechanism of the disease. Current methods for risk prediction typically rank single nucleotide polymorphisms (SNPs) by the p-value of their association with the disease, and use the top-associated SNPs as input to a classification algorithm. However, the predictive power of such methods is relatively poor. To improve the predictive power, we devised BootRank, which uses bootstrapping in order to obtain a robust prioritization of SNPs for use in predictive models. We show that BootRank improves the ability to predict disease risk of unseen individuals in the Wellcome Trust Case Control Consortium (WTCCC) data and results in a more robust set of SNPs and a larger number of enriched pathways being associated with the different diseases. Finally, we show that combining BootRank with seven different classification algorithms improves performance compared to previous studies that used the WTCCC data. Notably, diseases for which BootRank results in the largest improvements were recently shown to have more heritability than previously thought, likely due to contributions from variants with low minimum allele frequency (MAF), suggesting that BootRank can be beneficial in cases where SNPs affecting the disease are poorly tagged or have low MAF. Overall, our results show that improving disease risk prediction from genotypic information may be a tangible goal, with potential implications for personalized disease screening and treatment.
Author Summary
Genome-wide association studies are widely used to search for genetic loci that underlie human disease. Another goal is to predict disease risk for different individuals given their genetic sequence. Such predictions could either be used as a “black box” in order to promote changes in life-style and screening for early diagnosis, or as a model that can be studied to better understand the mechanism of the disease. Current methods for risk prediction have relatively poor performance, with one possible explanation being the fact they rely on a noisy ranking of genetic variants given to them as input. To improve the predictive power, we devised BootRank, a ranking method less sensitive to noise. We show that BootRank improves the ability to predict disease risk of unseen individuals in the Wellcome Trust Case Control Consortium (WTCCC) data, and that combining BootRank with different classification algorithms improves performance compared to previous studies that used these data. Overall, our results show that improving disease risk prediction from genotypic information may be a tangible goal, with potential implications for personalized disease screening and treatment.
PMCID: PMC3749941  PMID: 23990773
15.  Implication of next-generation sequencing on association studies 
BMC Genomics  2011;12:322.
Next-generation sequencing technologies can effectively detect the entire spectrum of genomic variation and provide a powerful tool for systematic exploration of the universe of common, low frequency and rare variants in the entire genome. However, the current paradigm for genome-wide association studies (GWAS) is to catalogue and genotype common variants (5% < MAF). The methods and study design for testing the association of low frequency (0.5% < MAF ≤ 5%) and rare variation (MAF ≤ 0.5%) have not been thoroughly investigated. The 1000 Genomes Project represents one such endeavour to characterize the human genetic variation pattern at the MAF = 1% level as a foundation for association studies. In this report, we explore different strategies and study designs for the near future GWAS in the post-era, based on both low coverage pilot data and exon pilot data in 1000 Genomes Project.
We investigated the linkage disequilibrium (LD) pattern among common and low frequency SNPs and its implication for association studies. We found that the LD between low frequency alleles and low frequency alleles, and low frequency alleles and common alleles are much weaker than the LD between common and common alleles. We examined various tagging designs with and without statistical imputation approaches and compare their power against de novo resequencing in mapping causal variants under various disease models. We used the low coverage pilot data which contain ~14 M SNPs as a hypothetical genotype-array platform (Pilot 14 M) to interrogate its impact on the selection of tag SNPs, mapping coverage and power of association tests. We found that even after imputation we still observed 45.4% of low frequency SNPs which were untaggable and only 67.7% of the low frequency variation was covered by the Pilot 14 M array.
This suggested GWAS based on SNP arrays would be ill-suited for association studies of low frequency variation.
PMCID: PMC3148210  PMID: 21682891
16.  Gene-Gene Interactions Lead to Higher Risk for Development of Type 2 Diabetes in an Ashkenazi Jewish Population 
PLoS ONE  2010;5(3):e9903.
Evidence has accumulated that multiple genetic and environmental factors play important roles in determining susceptibility to type 2 diabetes (T2D). Although variants from candidate genes have become prime targets for genetic analysis, few studies have considered their interplay. Our goal was to evaluate interactions among SNPs within genes frequently identified as associated with T2D.
Methods/Principal Findings
Logistic regression was used to study interactions among 4 SNPs, one each from HNF4A[rs1884613], TCF7L2[rs12255372], WFS1[rs10010131], and KCNJ11[rs5219] in a case-control Ashkenazi sample of 974 diabetic subjects and 896 controls. Nonparametric multifactor dimensionality reduction (MDR) and generalized MDR (GMDR) were used to confirm findings from the logistic regression analysis. HNF4A and WFS1 SNPs were associated with T2D in logistic regression analyses [P<0.0001, P<0.0002, respectively]. Interaction between these SNPs were also strong using parametric or nonparametric methods: the unadjusted odds of being affected with T2D was 3 times greater in subjects with the HNF4A and WFS1 risk alleles than those without either (95% CI = [1.7–5.3]; P≤0.0001). Although the univariate association between the TCF7L2 SNP and T2D was relatively modest [P = 0.02], when paired with the HNF4A SNP, the OR for subjects with risk alleles in both SNPs was 2.4 [95% CI = 1.7–3.4; P≤0.0001]. The KCNJ11 variant reached significance only when paired with either the HNF4A or WFSI SNPs: unadjusted ORs were 2.0 [95% CI = 1.4–2.8; P≤0.0001] and 2.3 [95% CI = 1.2-4.4; P≤0.0001], respectively. MDR and GMDR results were consistent with the parametric findings.
These results provide evidence of strong independent associations between T2D and SNPs in HNF4A and WFS1 and their interaction in our Ashkenazi sample. We also observed an interaction in the nonparametric analysis between the HNF4A and KCNJ11 SNPs (P≤0.001), demonstrating that an independently non-significant variant may interact with another variant resulting in an increased disease risk.
PMCID: PMC2845632  PMID: 20361036
17.  Relevance of the ACTN4 gene in African Americans with non-diabetic ESRD 
American journal of nephrology  2012;36(3):252-260.
African Americans (AAs) are predisposed to non-diabetic (non-DM) end-stage renal disease (ESRD) and studies have shown a genetic component to this risk. Rare mutations in ACTN4 (α-actinin-4) an actin binding protein expressed in podocytes cause familial focal segmental glomerulosclerosis.
We assessed the contribution of coding variants in ACTN4 to non-DM ESRD risk in AAs. Nineteen exons, 2800 bases of the promoter and 392 bases of the 3’ untranslated region of ACTN4 were sequenced in 96 AA non-DM ESRD cases and 96 non-nephropathy controls (384 chromosomes). Sixty-seven single nucleotide polymorphisms (SNPs) including 51 novel SNPs were identified. The SNPs comprised 33 intronic, 21 promoter, 12 exonic, and 1 3’ variant. Sixty-two of the SNPs were genotyped in 296 AA non-DM ESRD cases and 358 non-nephropathy controls.
One SNP, rs10404257, was associated with non-DM ESRD (p<1.0E-4, odds ratio (OR)=0.76, confidence interval (CI)=0.59–0.98; additive model). Forty-seven SNPs had minor allele frequencies less than 5%. These SNPs were segregated into risk and protective SNPs and each category was collapsed into a single marker, designated by the presence or absence of any rare allele. The presence of any rare allele at a risk SNP was significantly associated with non-DM ESRD (p = 0.001, dominant model). The SNPs with the strongest evidence for association (n = 20) were genotyped in an independent set of 467 non-DM ESRD cases and 279 controls. Although, rs10404257 was not associated in this replication sample, when the samples were combined rs10404257 was modestly associated (p=0.032, OR=0.78, CI=0.63–0.98; dominant model). SNPs were tested for interaction with markers in the APOL1 gene, previously associated with non-DM ESRD in AAs and rs10404257 was modestly associated (p = 0.0261, additive model).
This detailed evaluation of ACTN4 variation revealed limited evidence of association with non-DM ESRD in AAs.
PMCID: PMC3510331  PMID: 22965004
ACTN4; non-diabetic ESRD; FSGS; kidney; hypertensive nephrosclerosis; African Americans
18.  CYP2D6 gene variants: association with breast cancer specific survival in a cohort of breast cancer patients from the United Kingdom treated with adjuvant tamoxifen 
Tamoxifen is one of the most effective adjuvant breast cancer therapies available. Its metabolism involves the phase I enzyme, cytochrome P4502D6 (CYP2D6), encoded by the highly polymorphic CYP2D6 gene. CYP2D6 variants resulting in poor metabolism of tamoxifen are hypothesised to reduce its efficacy. An FDA-approved pre-treatment CYP2D6 gene testing assay is available. However, evidence from published studies evaluating CYP2D6 variants as predictive factors of tamoxifen efficacy and clinical outcome are conflicting, querying the clinical utility of CYP2D6 testing. We investigated the association of CYP2D6 variants with breast cancer specific survival (BCSS) in breast cancer patients receiving tamoxifen.
This was a population based case-cohort study. We genotyped known functional variants (n = 7; minor allele frequency (MAF) > 0.01) and single nucleotide polymorphisms (SNPs) (n = 5; MAF > 0.05) tagging all known common variants (tagSNPs), in CYP2D6 in 6640 DNA samples from patients with invasive breast cancer from SEARCH (Studies of Epidemiology and Risk factors in Cancer Heredity); 3155 cases had received tamoxifen therapy. There were 312 deaths from breast cancer, in the tamoxifen treated patients, with over 18000 years of cumulative follow-up. The association between genotype and BCSS was evaluated using Cox proportional hazards regression analysis.
In tamoxifen treated patients, there was weak evidence that the poor-metaboliser variant, CYP2D6*6 (MAF = 0.01), was associated with decreased BCSS (P = 0.02; HR = 1.95; 95% CI = 1.12-3.40). No other variants, including CYP2D6*4 (MAF = 0.20), previously reported to be associated with poorer clinical outcomes, were associated with differences in BCSS, in either the tamoxifen or non-tamoxifen groups.
CYP2D6*6 may affect BCSS in tamoxifen-treated patients. However, the absence of an association with survival in more frequent variants, including CYP2D6*4, questions the validity of the reported association between CYP2D6 genotype and treatment response in breast cancer. Until larger, prospective studies confirming any associations are available, routine CYP2D6 genetic testing should not be used in the clinical setting.
PMCID: PMC2949659  PMID: 20731819
19.  The CHRNA5-CHRNA3-CHRNB4 nicotinic receptor subunit gene cluster affects risk for nicotine dependence in African-Americans and in European-Americans 
Cancer research  2009;69(17):6848-6856.
Genetic association studies have demonstrated the importance of variants in the CHRNA5-CHRNA3-CHRNB4 cholinergic nicotinic receptor subunit gene cluster on chromosome 15q24-25.1 in risk for nicotine dependence, smoking, and lung cancer in populations of European descent. We have now carried out a detailed study of this region using dense genotyping in both European- and African-Americans.
We genotyped 75 known single-nucleotide-polymorphisms (SNPs) and one sequencing-discovered SNP in an African-American (AA) sample (N = 710) and European-American (EA) sample (N = 2062). Cases were nicotine-dependent and controls were non-dependent smokers.
The non-synonymous CHRNA5 SNP rs16969968 is the most significant SNP associated with nicotine dependence in the full sample of 2772 subjects (p = 4.49×10−8, OR 1.42 (1.25–1.61)) as well as in AAs only (p = 0.015, OR = 2.04 (1.15–3.62)) and EAs only (p = 4.14×10−7, OR = 1.40 (1.23–1.59)). Other SNPs that have been shown to affect mRNA levels of CHRNA5 in EAs are associated with nicotine dependence in AAs but not in EAs. The CHRNA3 SNP rs578776, which has low correlation with rs16969968, is associated with nicotine dependence in EAs but not in AAs. Less common SNPs (frequency ≤ 5%) also are associated with nicotine dependence.
In summary, multiple variants in this gene cluster contribute to nicotine dependence risk, and some are also associated with functional effects on CHRNA5. The non-synonymous SNP rs16969968, a known risk variant in European-descent populations, is also significantly associated with risk in African-Americans. Additional SNPs contribute in distinct ways to risk in these two populations.
PMCID: PMC2874321  PMID: 19706762
genetic association; smoking; cholinergic nicotinic receptors; nicotinic acetylcholine receptors
20.  A Comprehensive Examination of CYP19 Variation and Risk of Breast Cancer Using Two Haplotype Tagging Approaches 
Numerous studies point to a positive relationship between elevated levels of estrogens and increased risk of breast. Androgens are converted to estrogens by the aromatase enzyme, which is encoded by the CYP19 gene. We recently published resequencing data on 88 polymorphisms identified in that gene. The hypothesis tested in this study was that polymorphisms, or haplotypes, in CYP19 are related to risk of breast cancer.
Incident cases of breast cancer were identified through the Division of Medical Oncology at the Mayo Clinic in Rochester, MN. Controls were patients visiting Mayo for an annual medical examination. Controls were frequency matched to cases based on age and region of residence. Tag-polymorphisms were selected using 2 methods: 1) 12 variants using the tag-selection method of Carlson et al [1]; and 2) 12 variants using the haplotype method of Stram [2]. Six SNPs were selected by both methods. Genotyping was conducted using SNPStream, TaqMan and RFLP analyses. Logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CI). Analyses were conducted among all cases and controls, or stratified by estrogen receptor alpha (ER) status and/or menopausal status.
A total of 750 cases (60% postmenopausal) and 732 controls (75% postmenopausal) were included. No association with breast cancer risk was detected for individual variants, selected tagSNPs or hap-tag SNPs despite 80% power to detect odds ratios as low as 1.49 for MAF of 0.10. Similarly, stratified analyses based on ER status or menopausal status failed to detect any association with breast cancer risk.
These analyses suggest that variants of CYP19 are not associated with risk of breast cancer.
PMCID: PMC2868324  PMID: 17004113
Aromatase; Breast; Breast Cancer; CYP19; CYP19A1; Epidemiology; Etiology; Molecular Biology
21.  Inflammation, Insulin Resistance, and Diabetes—Mendelian Randomization Using CRP Haplotypes Points Upstream 
PLoS Medicine  2008;5(8):e155.
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.
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
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
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)
PMCID: PMC2504484  PMID: 18700811
22.  Re-sequencing Expands Our Understanding of the Phenotypic Impact of Variants at GWAS Loci 
PLoS Genetics  2014;10(1):e1004147.
Genome-wide association studies (GWAS) have identified >500 common variants associated with quantitative metabolic traits, but in aggregate such variants explain at most 20–30% of the heritable component of population variation in these traits. To further investigate the impact of genotypic variation on metabolic traits, we conducted re-sequencing studies in >6,000 members of a Finnish population cohort (The Northern Finland Birth Cohort of 1966 [NFBC]) and a type 2 diabetes case-control sample (The Finland-United States Investigation of NIDDM Genetics [FUSION] study). By sequencing the coding sequence and 5′ and 3′ untranslated regions of 78 genes at 17 GWAS loci associated with one or more of six metabolic traits (serum levels of fasting HDL-C, LDL-C, total cholesterol, triglycerides, plasma glucose, and insulin), and conducting both single-variant and gene-level association tests, we obtained a more complete understanding of phenotype-genotype associations at eight of these loci. At all eight of these loci, the identification of new associations provides significant evidence for multiple genetic signals to one or more phenotypes, and at two loci, in the genes ABCA1 and CETP, we found significant gene-level evidence of association to non-synonymous variants with MAF<1%. Additionally, two potentially deleterious variants that demonstrated significant associations (rs138726309, a missense variant in G6PC2, and rs28933094, a missense variant in LIPC) were considerably more common in these Finnish samples than in European reference populations, supporting our prior hypothesis that deleterious variants could attain high frequencies in this isolated population, likely due to the effects of population bottlenecks. Our results highlight the value of large, well-phenotyped samples for rare-variant association analysis, and the challenge of evaluating the phenotypic impact of such variants.
Author Summary
Abnormal serum levels of various metabolites, including measures relevant to cholesterol, other fats, and sugars, are known to be risk factors for cardiovascular disease and type 2 diabetes. Identification of the genes that play a role in generating such abnormalities could advance the development of new treatment and prevention strategies for these disorders. Investigations of common genetic variants carried out in large sets of research subjects have successfully pinpointed such genes within many regions of the human genome. However, these studies often have not led to the identification of the specific genetic variations affecting metabolic traits. To attempt to detect such causal variations, we sequenced genes in 17 genomic regions implicated in metabolic traits in >6,000 people from Finland. By conducting statistical analyses relating specific variations (individually and grouped by gene) to the measures for these metabolic traits observed in the study subjects, we added to our understanding of how genotypes affect these traits. Our findings support a long-held hypothesis that the unique history of the Finnish population provides important advantages for analyzing the relationship between genetic variations and biomedically important traits.
PMCID: PMC3907339  PMID: 24497850
23.  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.
PMCID: PMC2000969  PMID: 17922574
24.  A comprehensive resequence analysis of the KLK15–KLK3–KLK2 locus on chromosome 19q13.33 
Human Genetics  2009;127(1):91-99.
Single nucleotide polymorphisms (SNPs) in the KLK3 gene on chromosome 19q13.33 are associated with serum prostate-specific antigen (PSA) levels. Recent genome wide association studies of prostate cancer have yielded conflicting results for association of the same SNPs with prostate cancer risk. Since the KLK3 gene encodes the PSA protein that forms the basis for a widely used screening test for prostate cancer, it is critical to fully characterize genetic variation in this region and assess its relationship with the risk of prostate cancer. We have conducted a next-generation sequence analysis in 78 individuals of European ancestry to characterize common (minor allele frequency, MAF >1%) genetic variation in a 56 kb region on chromosome 19q13.33 centered on the KLK3 gene (chr19:56,019,829–56,076,043 bps). We identified 555 polymorphic loci in the process including 116 novel SNPs and 182 novel insertion/deletion polymorphisms (indels). Based on tagging analysis, 144 loci are necessary to tag the region at an r2 threshold of 0.8 and MAF of 1% or higher, while 86 loci are required to tag the region at an r2 threshold of 0.8 and MAF >5%. Our sequence data augments coverage by 35 and 78% as compared to variants in dbSNP and HapMap, respectively. We observed six non-synonymous amino acid or frame shift changes in the KLK3 gene and three changes in each of the neighboring genes, KLK15 and KLK2. Our study has generated a detailed map of common genetic variation in the genomic region surrounding the KLK3 gene, which should be useful for fine-mapping the association signal as well as determining the contribution of this locus to prostate cancer risk and/or regulation of PSA expression.
Electronic supplementary material
The online version of this article (doi:10.1007/s00439-009-0751-5) contains supplementary material, which is available to authorized users.
PMCID: PMC2793378  PMID: 19823874
25.  Multiple Common Susceptibility Variants near BMP Pathway Loci GREM1, BMP4, and BMP2 Explain Part of the Missing Heritability of Colorectal Cancer 
PLoS Genetics  2011;7(6):e1002105.
Genome-wide association studies (GWAS) have identified 14 tagging single nucleotide polymorphisms (tagSNPs) that are associated with the risk of colorectal cancer (CRC), and several of these tagSNPs are near bone morphogenetic protein (BMP) pathway loci. The penalty of multiple testing implicit in GWAS increases the attraction of complementary approaches for disease gene discovery, including candidate gene- or pathway-based analyses. The strongest candidate loci for additional predisposition SNPs are arguably those already known both to have functional relevance and to be involved in disease risk. To investigate this proposition, we searched for novel CRC susceptibility variants close to the BMP pathway genes GREM1 (15q13.3), BMP4 (14q22.2), and BMP2 (20p12.3) using sample sets totalling 24,910 CRC cases and 26,275 controls. We identified new, independent CRC predisposition SNPs close to BMP4 (rs1957636, P = 3.93×10−10) and BMP2 (rs4813802, P = 4.65×10−11). Near GREM1, we found using fine-mapping that the previously-identified association between tagSNP rs4779584 and CRC actually resulted from two independent signals represented by rs16969681 (P = 5.33×10−8) and rs11632715 (P = 2.30×10−10). As low-penetrance predisposition variants become harder to identify—owing to small effect sizes and/or low risk allele frequencies—approaches based on informed candidate gene selection may become increasingly attractive. Our data emphasise that genetic fine-mapping studies can deconvolute associations that have arisen owing to independent correlation of a tagSNP with more than one functional SNP, thus explaining some of the apparently missing heritability of common diseases.
Author Summary
Genome-wide association studies (GWAS) have identified several colorectal cancer (CRC) susceptibility polymorphisms near genes that encode proteins in the bone morphogenetic protein (BMP) pathway. However, most of the inherited susceptibility to CRC remains unexplained. We investigated three of the best candidate BMP genes (GREM1, BMP4, and BMP2) for additional polymorphisms associated with CRC. By extensive validation of polymorphisms with only modest evidence of association in the initial phases of the GWAS, we identified new, independent CRC predisposition polymorphisms close to BMP4 (rs1957636) and BMP2 (rs4813802). Near GREM1, we used additional genotyping around the GWAS-identified polymorphism rs4779584 to demonstrate two independent signals represented by rs16969681 and rs11632715. Common genes with modest effects on disease risk are becoming harder to identify, and approaches based on informed candidate gene selection may become increasingly attractive. In addition, genetic fine mapping around polymorphisms identified in GWAS can deconvolute associations which have arisen owing to two independent functional variants. These types of study can identify some of the apparently missing heritability of common disease.
PMCID: PMC3107194  PMID: 21655089

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