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author:("hvem, kristin")
1.  Loss-of-function mutations in SLC30A8 protect against type 2 diabetes 
Flannick, Jason | Thorleifsson, Gudmar | Beer, Nicola L. | Jacobs, Suzanne B. R. | Grarup, Niels | Burtt, Noël P. | Mahajan, Anubha | Fuchsberger, Christian | Atzmon, Gil | Benediktsson, Rafn | Blangero, John | Bowden, Don W. | Brandslund, Ivan | Brosnan, Julia | Burslem, Frank | Chambers, John | Cho, Yoon Shin | Christensen, Cramer | Douglas, Desirée A. | Duggirala, Ravindranath | Dymek, Zachary | Farjoun, Yossi | Fennell, Timothy | Fontanillas, Pierre | Forsén, Tom | Gabriel, Stacey | Glaser, Benjamin | Gudbjartsson, Daniel F. | Hanis, Craig | Hansen, Torben | Hreidarsson, Astradur B. | Hveem, Kristian | Ingelsson, Erik | Isomaa, Bo | Johansson, Stefan | Jørgensen, Torben | Jørgensen, Marit Eika | Kathiresan, Sekar | Kong, Augustine | Kooner, Jaspal | Kravic, Jasmina | Laakso, Markku | Lee, Jong-Young | Lind, Lars | Lindgren, Cecilia M | Linneberg, Allan | Masson, Gisli | Meitinger, Thomas | Mohlke, Karen L | Molven, Anders | Morris, Andrew P. | Potluri, Shobha | Rauramaa, Rainer | Ribel-Madsen, Rasmus | Richard, Ann-Marie | Rolph, Tim | Salomaa, Veikko | Segrè, Ayellet V. | Skärstrand, Hanna | Steinthorsdottir, Valgerdur | Stringham, Heather M. | Sulem, Patrick | Tai, E Shyong | Teo, Yik Ying | Teslovich, Tanya | Thorsteinsdottir, Unnur | Trimmer, Jeff K. | Tuomi, Tiinamaija | Tuomilehto, Jaakko | Vaziri-Sani, Fariba | Voight, Benjamin F. | Wilson, James G. | Boehnke, Michael | McCarthy, Mark I. | Njølstad, Pål R. | Pedersen, Oluf | Groop, Leif | Cox, David R. | Stefansson, Kari | Altshuler, David
Nature genetics  2014;46(4):357-363.
Loss-of-function mutations protective against human disease provide in vivo validation of therapeutic targets1,2,3, yet none are described for type 2 diabetes (T2D). Through sequencing or genotyping ~150,000 individuals across five ethnicities, we identified 12 rare protein-truncating variants in SLC30A8, which encodes an islet zinc transporter (ZnT8)4 and harbors a common variant (p.Trp325Arg) associated with T2D risk, glucose, and proinsulin levels5–7. Collectively, protein-truncating variant carriers had 65% reduced T2D risk (p=1.7×10−6), and non-diabetic Icelandic carriers of a frameshift variant (p.Lys34SerfsX50) demonstrated reduced glucose levels (−0.17 s.d., p=4.6×10−4). The two most common protein-truncating variants (p.Arg138X and p.Lys34SerfsX50) individually associate with T2D protection and encode unstable ZnT8 proteins. Previous functional study of SLC30A8 suggested reduced zinc transport increases T2D risk8,9, yet phenotypic heterogeneity was observed in rodent Slc30a8 knockouts10–15. Contrastingly, loss-of-function mutations in humans provide strong evidence that SLC30A8 haploinsufficiency protects against T2D, proposing ZnT8 inhibition as a therapeutic strategy in T2D prevention.
PMCID: PMC4051628  PMID: 24584071
3.  Large-scale fine mapping of the HNF1B locus and prostate cancer risk 
Human Molecular Genetics  2011;20(16):3322-3329.
Previous genome-wide association studies have identified two independent variants in HNF1B as susceptibility loci for prostate cancer risk. To fine-map common genetic variation in this region, we genotyped 79 single nucleotide polymorphisms (SNPs) in the 17q12 region harboring HNF1B in 10 272 prostate cancer cases and 9123 controls of European ancestry from 10 case–control studies as part of the Cancer Genetic Markers of Susceptibility (CGEMS) initiative. Ten SNPs were significantly related to prostate cancer risk at a genome-wide significance level of P < 5 × 10−8 with the most significant association with rs4430796 (P = 1.62 × 10−24). However, risk within this first locus was not entirely explained by rs4430796. Although modestly correlated (r2= 0.64), rs7405696 was also associated with risk (P = 9.35 × 10−23) even after adjustment for rs4430769 (P = 0.007). As expected, rs11649743 was related to prostate cancer risk (P = 3.54 × 10−8); however, the association within this second locus was stronger for rs4794758 (P = 4.95 × 10−10), which explained all of the risk observed with rs11649743 when both SNPs were included in the same model (P = 0.32 for rs11649743; P = 0.002 for rs4794758). Sequential conditional analyses indicated that five SNPs (rs4430796, rs7405696, rs4794758, rs1016990 and rs3094509) together comprise the best model for risk in this region. This study demonstrates a complex relationship between variants in the HNF1B region and prostate cancer risk. Further studies are needed to investigate the biological basis of the association of variants in 17q12 with prostate cancer.
PMCID: PMC3140817  PMID: 21576123
4.  Fine mapping of a region of chromosome 11q13 reveals multiple independent loci associated with risk of prostate cancer 
Human Molecular Genetics  2011;20(14):2869-2878.
Genome-wide association studies have identified prostate cancer susceptibility alleles on chromosome 11q13. As part of the Cancer Genetic Markers of Susceptibility (CGEMS) Initiative, the region flanking the most significant marker, rs10896449, was fine mapped in 10 272 cases and 9123 controls of European origin (10 studies) using 120 common single nucleotide polymorphisms (SNPs) selected by a two-staged tagging strategy using HapMap SNPs. Single-locus analysis identified 18 SNPs below genome-wide significance (P< 10−8) with rs10896449 the most significant (P= 7.94 × 10−19). Multi-locus models that included significant SNPs sequentially identified a second association at rs12793759 [odds ratio (OR) = 1.14, P= 4.76 × 10−5, adjusted P= 0.004] that is independent of rs10896449 and remained significant after adjustment for multiple testing within the region. rs10896438, a proxy of previously reported rs12418451 (r2= 0.96), independent of both rs10896449 and rs12793759 was detected (OR = 1.07, P= 5.92 × 10−3, adjusted P= 0.054). Our observation of a recombination hotspot that separates rs10896438 from rs10896449 and rs12793759, and low linkage disequilibrium (rs10896449–rs12793759, r2= 0.17; rs10896449–rs10896438, r2= 0.10; rs12793759–rs10896438, r2= 0.12) corroborate our finding of three independent signals. By analysis of tagged SNPs across ∼123 kb using next generation sequencing of 63 controls of European origin, 1000 Genome and HapMap data, we observed multiple surrogates for the three independent signals marked by rs10896449 (n= 31), rs10896438 (n= 24) and rs12793759 (n= 8). Our results indicate that a complex architecture underlying the common variants contributing to prostate cancer risk at 11q13. We estimate that at least 63 common variants should be considered in future studies designed to investigate the biological basis of the multiple association signals.
PMCID: PMC3118760  PMID: 21531787
5.  FTO, Type 2 Diabetes, and Weight Gain Throughout Adult Life 
Diabetes  2011;60(5):1637-1644.
FTO is the most important polygene identified for obesity. We aimed to investigate whether a variant in FTO affects type 2 diabetes risk entirely through its effect on BMI and how FTO influences BMI across adult life span.
Through regression models, we assessed the relationship between the FTO single nucleotide polymorphisms rs9939609, type 2 diabetes, and BMI across life span in subjects from the Norwegian population-based HUNT study using cross-sectional and longitudinal perspectives. For replication and meta-analysis, we used data from the Malmö Diet and Cancer (MDC) and Malmö Preventive Project (MPP) cohorts, comprising a total sample of 41,504 Scandinavians.
The meta-analysis revealed a highly significant association for rs9939609 with both type 2 diabetes (OR 1.13; P = 4.5 × 10−8) and the risk to develop incident type 2 diabetes (OR 1.16; P = 3.2 × 10−8). The associations remained also after correction for BMI and other anthropometric measures. Furthermore, we confirmed the strong effect on BMI (0.28 kg/m2 per risk allele; P = 2.0 × 10−26), with no heterogeneity between different age-groups. We found no differences in change of BMI over time according to rs9939609 risk alleles, neither overall (∆BMI = 0.0 [−0.05, 0.05]) nor in any individual age stratum, indicating no further weight gain attributable to FTO genotype in adults.
We have identified that a variant in FTO alters type 2 diabetes risk partly independent of its observed effect on BMI. The additional weight gain as a result of the FTO risk variant seems to occur before adulthood, and the BMI difference remains stable thereafter.
PMCID: PMC3292341  PMID: 21398525
6.  MDM2 Promoter SNP344T>A (rs1196333) Status Does Not Affect Cancer Risk 
PLoS ONE  2012;7(4):e36263.
The MDM2 proto-oncogene plays a key role in central cellular processes like growth control and apoptosis, and the gene locus is frequently amplified in sarcomas. Two polymorphisms located in the MDM2 promoter P2 have been shown to affect cancer risk. One of these polymorphisms (SNP309T>G; rs2279744) facilitates Sp1 transcription factor binding to the promoter and is associated with increased cancer risk. In contrast, SNP285G>C (rs117039649), located 24 bp upstream of rs2279744, and in complete linkage disequilibrium with the SNP309G allele, reduces Sp1 recruitment and lowers cancer risk. Thus, fine tuning of MDM2 expression has proven to be of significant importance with respect to tumorigenesis. We assessed the potential functional effects of a third MDM2 promoter P2 polymorphism (SNP344T>A; rs1196333) located on the SNP309T allele. While in silico analyses indicated SNP344A to modulate TFAP2A, SPIB and AP1 transcription factor binding, we found no effect of SNP344 status on MDM2 expression levels. Assessing the frequency of SNP344A in healthy Caucasians (n = 2,954) and patients suffering from ovarian (n = 1,927), breast (n = 1,271), endometrial (n = 895) or prostatic cancer (n = 641), we detected no significant difference in the distribution of this polymorphism between any of these cancer forms and healthy controls (6.1% in healthy controls, and 4.9%, 5.0%, 5.4% and 7.2% in the cancer groups, respectively). In conclusion, our findings provide no evidence indicating that SNP344A may affect MDM2 transcription or cancer risk.
PMCID: PMC3340411  PMID: 22558411
7.  Common Variants Show Predicted Polygenic Effects on Height in the Tails of the Distribution, Except in Extremely Short Individuals 
PLoS Genetics  2011;7(12):e1002439.
Common genetic variants have been shown to explain a fraction of the inherited variation for many common diseases and quantitative traits, including height, a classic polygenic trait. The extent to which common variation determines the phenotype of highly heritable traits such as height is uncertain, as is the extent to which common variation is relevant to individuals with more extreme phenotypes. To address these questions, we studied 1,214 individuals from the top and bottom extremes of the height distribution (tallest and shortest ∼1.5%), drawn from ∼78,000 individuals from the HUNT and FINRISK cohorts. We found that common variants still influence height at the extremes of the distribution: common variants (49/141) were nominally associated with height in the expected direction more often than is expected by chance (p<5×10−28), and the odds ratios in the extreme samples were consistent with the effects estimated previously in population-based data. To examine more closely whether the common variants have the expected effects, we calculated a weighted allele score (WAS), which is a weighted prediction of height for each individual based on the previously estimated effect sizes of the common variants in the overall population. The average WAS is consistent with expectation in the tall individuals, but was not as extreme as expected in the shortest individuals (p<0.006), indicating that some of the short stature is explained by factors other than common genetic variation. The discrepancy was more pronounced (p<10−6) in the most extreme individuals (height<0.25 percentile). The results at the extreme short tails are consistent with a large number of models incorporating either rare genetic non-additive or rare non-genetic factors that decrease height. We conclude that common genetic variants are associated with height at the extremes as well as across the population, but that additional factors become more prominent at the shorter extreme.
Author Summary
Although there are many loci in the human genome that have been discovered to be significantly associated with height, it is unclear if these loci have similar effects in extremely tall and short individuals. Here, we examine hundreds of extremely tall and short individuals in two population-based cohorts to see if these known height determining loci are as predictive as expected in these individuals. We found that these loci are generally as predictive of height as expected in these individuals but that they begin to be less predictive in the most extremely short individuals. We showed that this result is consistent with models that not only include the common variants but also multiple low frequency genetic variants that substantially decrease height. However, this result is also consistent with non-additive genetic effects or rare non-genetic factors that substantially decrease height. This finding suggests the possibility of a major role of low frequency variants, particularly in individuals with extreme phenotypes, and has implications on whole-genome or whole-exome sequencing efforts to discover rare genetic variation associated with complex traits.
PMCID: PMC3248463  PMID: 22242009
8.  Fine Mapping of Five Loci Associated with Low-Density Lipoprotein Cholesterol Detects Variants That Double the Explained Heritability 
PLoS Genetics  2011;7(7):e1002198.
Complex trait genome-wide association studies (GWAS) provide an efficient strategy for evaluating large numbers of common variants in large numbers of individuals and for identifying trait-associated variants. Nevertheless, GWAS often leave much of the trait heritability unexplained. We hypothesized that some of this unexplained heritability might be due to common and rare variants that reside in GWAS identified loci but lack appropriate proxies in modern genotyping arrays. To assess this hypothesis, we re-examined 7 genes (APOE, APOC1, APOC2, SORT1, LDLR, APOB, and PCSK9) in 5 loci associated with low-density lipoprotein cholesterol (LDL-C) in multiple GWAS. For each gene, we first catalogued genetic variation by re-sequencing 256 Sardinian individuals with extreme LDL-C values. Next, we genotyped variants identified by us and by the 1000 Genomes Project (totaling 3,277 SNPs) in 5,524 volunteers. We found that in one locus (PCSK9) the GWAS signal could be explained by a previously described low-frequency variant and that in three loci (PCSK9, APOE, and LDLR) there were additional variants independently associated with LDL-C, including a novel and rare LDLR variant that seems specific to Sardinians. Overall, this more detailed assessment of SNP variation in these loci increased estimates of the heritability of LDL-C accounted for by these genes from 3.1% to 6.5%. All association signals and the heritability estimates were successfully confirmed in a sample of ∼10,000 Finnish and Norwegian individuals. Our results thus suggest that focusing on variants accessible via GWAS can lead to clear underestimates of the trait heritability explained by a set of loci. Further, our results suggest that, as prelude to large-scale sequencing efforts, targeted re-sequencing efforts paired with large-scale genotyping will increase estimates of complex trait heritability explained by known loci.
Author Summary
Despite the striking success of genome-wide association studies in identifying genetic loci associated with common complex traits and diseases, much of the heritable risk for these traits and diseases remains unexplained. A higher resolution investigation of the genome through sequencing studies is expected to clarify the sources of this missing heritability. As a preview of what we might learn in these more detailed assessments of genetic variation, we used sequencing to identify potentially interesting variants in seven genes associated with low-density lipoprotein cholesterol (LDL-C) in 256 Sardinian individuals with extreme LDL-C levels, followed by large scale genotyping in 5,524 individuals, to examine newly discovered and previously described variants. We found that a combination of common and rare variants in these loci contributes to variation in LDL-C levels, and also that the initial estimate of the heritability explained by these loci doubled. Importantly, our results include a Sardinian-specific rare variant, highlighting the need for sequencing studies in isolated populations. Our results provide insights about what extensive whole-genome sequencing efforts are likely to reveal for the understanding of the genetic architecture of complex traits.
PMCID: PMC3145627  PMID: 21829380
9.  Refining the prostate cancer genetic association within the JAZF1 gene on chromosome 7p15.2 
Genome-wide association studies (GWAS) have identified multiple genetic variants associated with susceptibility to prostate cancer (PrCa). In the two-stage Cancer Genetic Markers of Susceptibility (CGEMS) prostate cancer scan, a single-nucleotide polymorphism (SNP) rs10486567 located within intron 2 of JAZF1 gene on chromosome 7p15.2 showed a promising association with PrCa overall (p = 2.14×10−6) with a suggestion of stronger association with aggressive disease (p = 1.2×10−7).
In the third stage of GWAS, we genotyped 106 JAZF1 SNPs in 10,286 PrCa cases and 9,135 controls of European ancestry.
The strongest association was observed with the initial marker, rs10486567, which now achieves genome-wide significance (p = 7.79×10−11, ORHET 1.19; 95%CI = 1.12 – 1.27 and ORHOM 1.37; 95%CI = 1.20 – 1.56). We did not confirm a previous suggestion of a stronger association of rs10486567 with aggressive disease (p = 1.60×10−4 for aggressive cancer, n=4,597; p = 3.25×10−8 for non-aggressive cancer, n=4,514). Based on a multi-locus model with adjustment for rs10486567, no additional independent signals were observed at chromosome 7p15.2. There was no association between PrCa risk and SNPs in JAZF1 previously associated with height (rs849140, p = 0.587), body stature (rs849141, tagged by rs849136, p = 0.171), risk of type 2 diabetes and systemic lupus erythematosus (rs864745, tagged by rs849142, p = 0.657).
rs10486567 remains the most significant marker for PrCa risk within JAZF1 in individuals of European ancestry.
Future studies should identify all variants in high LD with rs10486567 and evaluate their functional significance for PrCa.
PMCID: PMC2866032  PMID: 20406958
10.  Association between a 15q25 gene variant, smoking quantity and tobacco-related cancers among 17 000 individuals 
Background Genetic variants in 15q25 have been identified as potential risk markers for lung cancer (LC), but controversy exists as to whether this is a direct association, or whether the 15q variant is simply a proxy for increased exposure to tobacco carcinogens.
Methods We performed a detailed analysis of one 15q single nucleotide polymorphism (SNP) (rs16969968) with smoking behaviour and cancer risk in a total of 17 300 subjects from five LC studies and four upper aerodigestive tract (UADT) cancer studies.
Results Subjects with one minor allele smoked on average 0.3 cigarettes per day (CPD) more, whereas subjects with the homozygous minor AA genotype smoked on average 1.2 CPD more than subjects with a GG genotype (P < 0.001). The variant was associated with heavy smoking (>20 CPD) [odds ratio (OR) = 1.13, 95% confidence interval (CI) 0.96–1.34, P = 0.13 for heterozygotes and 1.81, 95% CI 1.39–2.35 for homozygotes, P < 0.0001]. The strong association between the variant and LC risk (OR = 1.30, 95% CI 1.23–1.38, P = 1 × 10–18), was virtually unchanged after adjusting for this smoking association (smoking adjusted OR = 1.27, 95% CI 1.19–1.35, P = 5 × 10–13). Furthermore, we found an association between the variant allele and an earlier age of LC onset (P = 0.02). The association was also noted in UADT cancers (OR = 1.08, 95% CI 1.01–1.15, P = 0.02). Genome wide association (GWA) analysis of over 300 000 SNPs on 11 219 subjects did not identify any additional variants related to smoking behaviour.
Conclusions This study confirms the strong association between 15q gene variants and LC and shows an independent association with smoking quantity, as well as an association with UADT cancers.
PMCID: PMC2913450  PMID: 19776245
Lung cancer; nicotine dependence; smoking quantity; UADT cancer
11.  Fine mapping the KLK3 locus on chromosome 19q13.33 associated with prostate cancer susceptibility and PSA levels 
Human Genetics  2011;129(6):675-685.
Measurements of serum prostate-specific antigen (PSA) protein levels form the basis for a widely used test to screen men for prostate cancer. Germline variants in the gene that encodes the PSA protein (KLK3) have been shown to be associated with both serum PSA levels and prostate cancer. Based on a resequencing analysis of a 56 kb region on chromosome 19q13.33, centered on the KLK3 gene, we fine mapped this locus by genotyping tag SNPs in 3,522 prostate cancer cases and 3,338 controls from five case–control studies. We did not observe a strong association with the KLK3 variant, reported in previous studies to confer risk for prostate cancer (rs2735839; P = 0.20) but did observe three highly correlated SNPs (rs17632542, rs62113212 and rs62113214) associated with prostate cancer [P = 3.41 × 10−4, per-allele trend odds ratio (OR) = 0.77, 95% CI = 0.67–0.89]. The signal was apparent only for nonaggressive prostate cancer cases with Gleason score <7 and disease stage 8 or stage ≥III (P = 0.31, per-allele trend OR = 1.12, 95% CI = 0.90–1.40). One of the three highly correlated SNPs, rs17632542, introduces a non-synonymous amino acid change in the KLK3 protein with a predicted benign or neutral functional impact. Baseline PSA levels were 43.7% higher in control subjects with no minor alleles (1.61 ng/ml, 95% CI = 1.49–1.72) than in those with one or more minor alleles at any one of the three SNPs (1.12 ng/ml, 95% CI = 0.96–1.28) (P = 9.70 × 10−5). Together our results suggest that germline KLK3 variants could influence the diagnosis of nonaggressive prostate cancer by influencing the likelihood of biopsy.
Electronic supplementary material
The online version of this article (doi:10.1007/s00439-011-0953-5) contains supplementary material, which is available to authorized users.
PMCID: PMC3092924  PMID: 21318478
12.  Evaluation of four novel genetic variants affecting hemoglobin A1c levels in a population-based type 2 diabetes cohort (the HUNT2 study) 
BMC Medical Genetics  2011;12:20.
Chronic hyperglycemia confers increased risk for long-term diabetes-associated complications and repeated hemoglobin A1c (HbA1c) measures are a widely used marker for glycemic control in diabetes treatment and follow-up. A recent genome-wide association study revealed four genetic loci, which were associated with HbA1c levels in adults with type 1 diabetes. We aimed to evaluate the effect of these loci on glycemic control in type 2 diabetes.
We genotyped 1,486 subjects with type 2 diabetes from a Norwegian population-based cohort (HUNT2) for single-nucleotide polymorphisms (SNPs) located near the BNC2, SORCS1, GSC and WDR72 loci. Through regression models, we examined their effects on HbA1c and non-fasting glucose levels individually and in a combined genetic score model.
No significant associations with HbA1c or glucose levels were found for the SORCS1, BNC2, GSC or WDR72 variants (all P-values > 0.05). Although the observed effects were non-significant and of much smaller magnitude than previously reported in type 1 diabetes, the SORCS1 risk variant showed a direction consistent with increased HbA1c and glucose levels, with an observed effect of 0.11% (P = 0.13) and 0.13 mmol/l (P = 0.43) increase per risk allele for HbA1c and glucose, respectively. In contrast, the WDR72 risk variant showed a borderline association with reduced HbA1c levels (β = -0.21, P = 0.06), and direction consistent with decreased glucose levels (β = -0.29, P = 0.29). The allele count model gave no evidence for a relationship between increasing number of risk alleles and increasing HbA1c levels (β = 0.04, P = 0.38).
The four recently reported SNPs affecting glycemic control in type 1 diabetes had no apparent effect on HbA1c in type 2 diabetes individually or by using a combined genetic score model. However, for the SORCS1 SNP, our findings do not rule out a possible relationship with HbA1c levels. Hence, further studies in other populations are needed to elucidate whether these novel sequence variants, especially rs1358030 near the SORCS1 locus, affect glycemic control in type 2 diabetes.
PMCID: PMC3044669  PMID: 21294870
13.  Quality, quantity and harmony: the DataSHaPER approach to integrating data across bioclinical studies 
Background Vast sample sizes are often essential in the quest to disentangle the complex interplay of the genetic, lifestyle, environmental and social factors that determine the aetiology and progression of chronic diseases. The pooling of information between studies is therefore of central importance to contemporary bioscience. However, there are many technical, ethico-legal and scientific challenges to be overcome if an effective, valid, pooled analysis is to be achieved. Perhaps most critically, any data that are to be analysed in this way must be adequately ‘harmonized’. This implies that the collection and recording of information and data must be done in a manner that is sufficiently similar in the different studies to allow valid synthesis to take place.
Methods This conceptual article describes the origins, purpose and scientific foundations of the DataSHaPER (DataSchema and Harmonization Platform for Epidemiological Research;, which has been created by a multidisciplinary consortium of experts that was pulled together and coordinated by three international organizations: P3G (Public Population Project in Genomics), PHOEBE (Promoting Harmonization of Epidemiological Biobanks in Europe) and CPT (Canadian Partnership for Tomorrow Project).
Results The DataSHaPER provides a flexible, structured approach to the harmonization and pooling of information between studies. Its two primary components, the ‘DataSchema’ and ‘Harmonization Platforms’, together support the preparation of effective data-collection protocols and provide a central reference to facilitate harmonization. The DataSHaPER supports both ‘prospective’ and ‘retrospective’ harmonization.
Conclusion It is hoped that this article will encourage readers to investigate the project further: the more the research groups and studies are actively involved, the more effective the DataSHaPER programme will ultimately be.
PMCID: PMC2972444  PMID: 20813861
Data synthesis; data quality; data pooling; harmonization; meta-analysis; DataSHaPER; prospective harmonization; retrospective harmonization
14.  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 single nucleotide polymorphisms (SNPs) with common diseases, the genetic model of inheritance is unknown. The authors extended and applied a Bayesian meta-analysis approach to data from 19 studies on 17 replicated associations with type 2 diabetes. For 13 SNPs, the data fitted very well to an additive model of inheritance for the diabetes risk allele; for 4 SNPs, the data were consistent with either an additive model or a dominant model; and for 2 SNPs, the data were consistent with an additive or recessive model. Results were robust to the use of different priors and after exclusion of data for which index SNPs had been examined indirectly through proxy markers. The Bayesian meta-analysis model yielded point estimates for the genetic effects that were very similar to those previously reported based on fixed- or random-effects models, but uncertainty about several of the effects was substantially larger. The authors also examined the extent of between-study heterogeneity in the genetic model and found generally small between-study deviation values for the genetic model parameter. Heterosis could not be excluded for 4 SNPs. Information on the genetic model of robustly replicated association signals derived from genome-wide association studies may be useful for predictive modeling and for designing biologic and functional experiments.
PMCID: PMC2732984  PMID: 19602701
Bayes theorem; diabetes mellitus, type 2; meta-analysis; models, genetic; polymorphism, genetic; population characteristics
15.  A multi-stage genome-wide association in breast cancer identifies two novel risk alleles at 1p11.2 and 14q24.1 (RAD51L1) 
Nature genetics  2009;41(5):579-584.
The Cancer Genetic Markers of Susceptibility (CGEMS) initiative has conducted a three-stage genome-wide association study (GWAS) of breast cancer in 9,770 cases and 10,799 controls. In Stage 1, we genotyped 528,173 single nucleotide polymorphisms (SNPs) in 1,145 cases of invasive breast cancer among postmenopausal white women, and 1,142 controls; in Stage 2, 24,909 SNPs with low p values observed in Stage 1 were analyzed in 4,547 cases and 4,434 controls. In Stage 3 we investigated 21 loci in 4,078 cases and 5,223 controls with low p values from Stage 1 and 2 combined. Two novel loci achieved genome-wide significance. A pericentromeric SNP on chromosome 1p11.2, rs11249433, (p=6.74 × 10-10 adjusted genotype test with 2 degrees of freedom) resides in a large block of linkage disequilibrium neighboring NOTCH2 and FCGR1B and is predominantly associated with estrogen receptor-positive breast cancer. A second SNP, rs999737 on chromosome 14q24.1 (p=1.74 × 10−7), localizes to RAD51L1, a gene in the homologous recombination DNA repair pathway, a prior candidate pathway for breast cancer susceptibility. We confirmed previously reported markers on chromosome 2q35, 5q11.2, 5p12, 8q24, 10q26, and 16q12.1. Our results underscore the importance of large-scale replication in the identification of low penetrance breast cancer alleles.
PMCID: PMC2928646  PMID: 19330030
16.  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.
PMCID: PMC2732984  PMID: 19602701
17.  A sequence variant in ZFHX3 on 16q22 associates with atrial fibrillation and ischemic stroke 
Nature genetics  2009;41(8):876-878.
We performed a genome-wide scan for sequence variants associating with atrial fibrillation in Iceland and followed up the most significant associations in samples from Iceland, Norway and USA. A sequence variant, rs7193343-T, in the ZFHX3 gene on chromosome 16q22 associated significantly with atrial fibrillation (combined OR=1.21, P=1.4·10-10). This variant also associates with ischemic stroke (OR=1.11, P=0.00054) and cardioembolic stroke (OR=1.22, P=0.00021) in a combined analysis of five stroke sample sets.
PMCID: PMC2740741  PMID: 19597491

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