To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.

To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.

It has recently been suggested that the low-frequency c.136–14_136–13insC variant in high-mobility group A1 (HMGA1) may strongly contribute to insulin resistance and type 2 diabetes risk. In our study, we attempted to confirm that HMGA1 is a novel type 2 diabetes locus in French Caucasians. The gene was sequenced in 368 type 2 diabetic case subjects with a family history of type 2 diabetes and 372 normoglycemic control subjects without a family history of type 2 diabetes. None of the 41 genetic variations identified were associated with type 2 diabetes. The lack of association between the c.136–14_136–13insC variant and type 2 diabetes was confirmed in an independent French group of 4,538 case subjects and 4,015 control subjects and in a large meta-analysis of 16,605 case subjects and 46,179 control subjects. Finally, this variant had no effects on metabolic traits and was not involved in variations of HMGA1 and insulin receptor (INSR) expressions. The c.136–14_136–13insC variant was not associated with type 2 diabetes in individuals of European descent. Our study emphasizes the need to analyze a large number of subjects to reliably assess the association of low-frequency variants with the disease.

We conducted a genome-wide association (GWA) meta-analysis of 4,604 endometriosis cases and 9,393 controls of Japanese1 and European2 ancestry. We show that rs12700667 on chromosome 7p15.2, previously found in Europeans, replicates in Japanese (P = 3.6 × 10−3), and confirm association of rs7521902 on 1p36.12 near WNT4. In addition, we establish association of rs13394619 in GREB1 on 2p25.1 and identify a novel locus on 12q22 near VEZT (rs10859871). Excluding European cases with minimal or unknown severity, we identified additional novel loci on 2p14 (rs4141819), 6p22.3 (rs7739264) and 9p21.3 (rs1537377). All seven SNP effects were replicated in an independent cohort and produced P < 5 × 10−8 in a combined analysis. Finally, we found a significant overlap in polygenic risk for endometriosis between the European and Japanese GWA cohorts (P = 8.8 × 10−11), indicating that many weakly associated SNPs represent true endometriosis risk loci and risk prediction and future targeted disease therapy may be transferred across these populations.

Objectives

There is increasing evidence that rare variants play a role in some complex traits, but their analysis is not straightforward. Locus-based tests become necessary due to low power in rare variant single-point association analyses. In addition, variant quality scores are available for sequencing data, but are rarely taken into account. Here, we propose two locus-based methods that incorporate variant quality scores: a regression-based collapsing approach and an allele-matching method.

Methods

Using simulated sequencing data we compare 4 locus-based tests of trait association under different scenarios of data quality. We test two collapsing-based approaches and two allele-matching-based approaches, taking into account variant quality scores and ignoring variant quality scores. We implement the collapsing and allele-matching approaches accounting for variant quality in the freely available ARIEL and AMELIA software.

Results

The incorporation of variant quality scores in locus-based association tests has power advantages over weighting each variant equally. The allele-matching methods are robust to the presence of both protective and risk variants in a locus, while collapsing methods exhibit a dramatic loss of power in this scenario.

Conclusions

The incorporation of variant quality scores should be a standard protocol when performing locus-based association analysis on sequencing data. The ARIEL and AMELIA software implement collapsing and allele-matching locus association analysis methods, respectively, that allow the incorporation of variant quality scores.

Summary

In the presence of epistasis multilocus association tests of human complex traits can provide powerful methods to detect susceptibility variants. We undertook multilocus analyses in 1924 type 2 diabetes cases and 2938 controls from the Wellcome Trust Case Control Consortium (WTCCC). We performed a two-dimensional genome-wide association (GWA) scan using joint two-locus tests of association including main and epistatic effects in 70,236 markers tagging common variants. We found two-locus association at 79 SNP-pairs at a Bonferroni-corrected P-value = 0.05 (uncorrected P-value = 2.14 × 10−11). The 79 pair-wise results always contained rs11196205 in TCF7L2 paired with 79 variants including confirmed variants in FTO, TSPAN8, and CDKAL1, which are associated in the absence of epistasis. However, the majority (82%) of the 79 variants did not have compelling single-locus association signals (P-value = 5 × 10−4). Analyses conditional on the single-locus effects at TCF7L2 established that the joint two-locus results could be attributed to single-locus association at TCF7L2 alone. Interaction analyses among the peak 80 regions and among 23 previously established diabetes candidate genes identified five SNP-pairs with case-control and case-only epistatic signals. Our results demonstrate the feasibility of systematic scans in GWA data, but confirm that single-locus association can underlie and obscure multilocus findings.

Human genome resequencing technologies are becoming ever more affordable and provide a valuable source of data about rare genetic variants in the human genome. Such rare variation may play an important role in explaining the missing heritability of complex human traits. We implement an existing method for analyzing rare variants by testing for association with the mutational load across genes. In this study, we make use of simulated data from the Genetic Analysis Workshop 17 to assess the power of this approach to detect association with simulated quantitative and dichotomous phenotypes and to evaluate the impact of missing genotypes on the power of the analysis. According to our results, the mutational load based rare variant analysis method is relatively robust to call-rate and is adequately powered for genome-wide association analysis.

This protocol describes how to perform basic statistical analysis in a population-based genetic association case-control study. The steps described involve the (i) appropriate selection of measures of association and relevance of disease models; (ii) appropriate selection of tests of association; (iii) visualization and interpretation of results; (iv) consideration of appropriate methods to control for multiple testing; and (v) replication strategies. Assuming no previous experience with software such as PLINK, R or Haploview, we describe how to use these popular tools for handling single-nucleotide polymorphism data in order to carry out tests of association and visualize and interpret results. This protocol assumes that data quality assessment and control has been performed, as described in a previous protocol, so that samples and markers deemed to have the potential to introduce bias to the study have been identified and removed. Study design, marker selection and quality control of case-control studies have also been discussed in earlier protocols. The protocol should take ~1 h to complete.

Background

Several susceptibility genes have been proposed for dyslexia (reading disability; RD) and specific language impairment (SLI). RD and SLI show comorbidity, but it is unclear whether a common genetic component is shared.

Methods

We have investigated whether candidate genes for RD and SLI affect specific cognitive traits or have broad effect on cognition. We have analyzed common risk variants within RD (MRPL19/C2ORF3, KIAA0319, and DCDC2) and language impairment (CMIP and ATP2C2) candidate loci in the Avon Longitudinal Study of Parents and Children cohort (n = 3725), representing children born in southwest England in the early 1990s.

Results

We detected associations between reading skills and KIAA0319, DCDC2, and CMIP. We show that DCDC2 is specifically associated with RD, whereas variants in CMIP and KIAA0319 are associated with reading skills across the ability range. The strongest associations were restricted to single-word reading and spelling measures, suggesting that these genes do not extend their effect to other reading and language-related skills. Inclusion of individuals with comorbidity tends to strengthen these associations. Our data do not support MRPL19/C2ORF3 as a locus involved in reading abilities nor CMIP/ATP2C2 as genes regulating language skills.

Conclusions

We provide further support for the role of KIAA0319 and DCDC2 in contributing to reading abilities and novel evidence that the language-disorder candidate gene CMIP is also implicated in reading processes. Additionally, we present novel data to evaluate the prevalence and comorbidity of RD and SLI, and we recommend not excluding individuals with comorbid RD and SLI when designing genetic association studies for RD.

Endometriosis is a common gynaecological disease associated with pelvic pain and sub-fertility. We conducted a genome-wide association (GWA) study in 3,194 surgically confirmed endometriosis cases and 7,060 controls from Australia and the UK. Polygenic predictive modelling showed significantly increased genetic loading among 1,364 cases with moderate-severe endometriosis. The strongest association signal was on 7p15.2 (rs12700667) for ‘all’ endometriosis (P = 2.6 × 10−7, OR = 1.22 (1.13-1.32)) and for moderate-severe disease (P = 1.5 × 10−9 (OR = 1.38 (1.24-1.53)). We replicated rs12700667 in an independent US cohort of 2,392 self-reported surgically confirmed endometriosis cases and 2,271 controls (P = 1.2 × 10−3, OR = 1.17 (1.06-1.28)), resulting in a genome-wide significant P-value of 1.4 × 10−9 (OR = 1.20 (1.13-1.27)) for ‘all’ endometriosis in our combined datasets of 5,586 cases and 9,331 controls. SNP rs12700667 is located in an inter-genic region upstream of plausible candidate genes NFE2L3 and HOXA10.

By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combinedP < 5 × 10−8. These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.

Interpretation of dense single nucleotide polymorphism (SNP) follow-up of genome-wide association or linkage scan signals can be facilitated by establishing expectation for the behaviour of primary mapping signals upon fine-mapping, under both null and alternative hypotheses. We examined the inferences that can be made regarding the posterior probability of a real genetic effect and considered different disease-mapping strategies and prior probabilities of association. We investigated the impact of the extent of linkage disequilibrium between the disease SNP and the primary analysis signal and the extent to which the disease gene can be physically localised under these scenarios. We found that large increases in significance (>2 orders of magnitude) appear in the exclusive domain of genuine genetic effects, especially in the follow-up of genome-wide association scans or consensus regions from multiple linkage scans. Fine-mapping significant association signals that reside directly under linkage peaks yield little improvement in an already high posterior probability of a real effect. Following fine-mapping, those signals that increase in significance also demonstrate improved localisation. We found local linkage disequiliptium patterns around the primary analysis signal(s) and tagging efficacy of typed markers to play an important role in determining a suitable interval for fine-mapping. Our findings help inform the interpretation and design of dense SNP-mapping follow-up studies, thus facilitating discrimination between a genuine genetic effect and chance fluctuation (false positive).

OBJECTIVE

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.

RESEARCH DESIGN AND METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

This protocol details the data quality assessment and control steps that are typically carried out during case-control association studies. The steps described involve the identification and removal of DNA samples and markers that introduce bias to the study. These critical steps are paramount to the success of a case-control study and are necessary before statistically testing for association. We describe how to use PLINK, a tool for handling SNP data, to carry out assessments of failure rate per-individual and per-SNP and to assess the degree of relatedness between individuals. We also detail other quality control procedures, including the use of SMARTPCA for the identification of ancestral outliers. These platforms were selected because they are user-friendly, widely used, and computationally efficient. Steps needed to detect and establish a disease association using case-control data are not discussed, as these are provided in a further protocol in the series. Issues concerning the study design and marker selection in case-control studies have been discussed in our earlier protocols. The protocol should take approximately 8 hours to complete.

OBJECTIVE

WFS1 (Wolfram Syndrome 1) SNPs are associated with risk of type 2 diabetes (T2D). Here, we aimed to refine this association and investigate the role of low frequency WFS1 variants in T2D risk.

RESEARCH DESIGN AND METHODS

For fine-mapping, we sequenced WFS1 exons, splice junctions and conserved non-coding sequences in 24 T2D cases and 68 controls, selected tagging SNPs, and genotyped these in 959 UK T2D cases and 1386 controls. The same genomic regions were sequenced in 1235 T2D cases and 1668 controls to compare the frequency of rarer variants between cases and controls.

RESULTS

Of 31 tagging SNPs, the strongest associated was the previously untested 3′ UTR rs1046320 (P=0.008); OR=0.84, P=6.59 × 10−7 on further replication in 3753 cases and 4198 controls. 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 (MAF<0.01) non-synonymous variants between T2D cases and controls (P=0.79). Two intermediate frequency (MAF 0.01-0.05) non-synonymous changes also showed no statistical association with T2D.

CONCLUSION

We identified six highly correlated SNPs that show strong and comparable associations with risk of T2D association 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 T2D risk in white UK populations, highlighting the complexities of undertaking association studies with low frequency variants identified by re-sequencing.

Association studies can focus on candidate gene(s), a particular genomic region, or adopt a genome wide association approach, each of which has implications for marker selection. The strategy for marker selection will affect the statistical power of the study to detect a disease association and is a crucial element of study design. The abundant single nucleotide polymorphisms (SNPs) are the markers of choice in genetic case-control association studies. The genotypes of neighbouring SNPs are often highly correlated (‘in linkage disequilibrium’ – LD) within a population which is utilised for selecting specific ‘tagSNPs’ to serve as proxies for other nearby SNPs in high LD. General guidelines for SNP selection in candidate genes/regions and genome-wide studies are provided in this protocol, along with illustrative examples. Publicly available web-based resources are utilised to browse and retrieve data and software such as Haploview and Goldsurfer2, are applied to investigate LD and to select tagSNPs.

Circulating glucose levels are tightly regulated. To identify novel glycemic loci, we performed meta-analyses of 21 genome-wide associations studies informative for fasting glucose (FG), fasting insulin (FI) and indices of β-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 non-diabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with FG/HOMA-B and two associated with FI/HOMA-IR. These include nine new FG loci (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and FAM148B) and one influencing FI/HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB/TMEM195 with type 2 diabetes (T2D). Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify T2D risk loci, as well as loci that elevate FG modestly, but do not cause overt diabetes.

The association between common variants in the FTO gene with weight, adiposity and body mass index (BMI) has now been widely replicated. Although the causal variant has yet to be identified, it most likely maps within a 47 kb region of intron 1 of FTO. We performed a genome-wide association study in the Sorbian population and evaluated the relationships between FTO variants and BMI and fat mass in this isolate of Slavonic origin resident in Germany. In a sample of 948 Sorbs, we could replicate the earlier reported associations of intron 1 SNPs with BMI (eg, P-value=0.003, β=0.02 for rs8050136). However, using genome-wide association data, we also detected a second independent signal mapping to a region in intron 2/3 about 40–60 kb away from the originally reported SNPs (eg, for rs17818902 association with BMI P-value=0.0006, β=−0.03 and with fat mass P-value=0.0018, β=−0.079). Both signals remain independently associated in the conditioned analyses. In conclusion, we extend the evidence that FTO variants are associated with BMI by putatively identifying a second susceptibility allele independent of that described earlier. Although further statistical analysis of these findings is hampered by the finite size of the Sorbian isolate, these findings should encourage other groups to seek alternative susceptibility variants within FTO (and other established susceptibility loci) using the opportunities afforded by analyses in populations with divergent mutational and/or demographic histories.

The tumour necrosis factor (TNF) gene and other genes flanking it in the major histocompatibility complex (MHC) class III region are potentially important mediators of both immunity and pathogenesis of malaria. We investigated the association of severe malaria with eleven haplotype tagging-polymorphisms for eleven MHC class III candidate genes, including TNF, lymphotoxin alpha (LTA), allograft inflammatory factor 1 (AIF1), and HLA-B associated transcript 2 (BAT2). An analysis of 2162 case-controls demonstrated the first evidence of association between a BAT2 polymorphism (rs1046089) and severe malaria.

Approximately 90% of humans are right-handed. Handedness is a heritable trait, yet the genetic basis is not well understood. Here we report a genome-wide association study for a quantitative measure of relative hand skill in individuals with dyslexia [reading disability (RD)]. The most highly associated marker, rs11855415 (P = 4.7 × 10−7), is located within PCSK6. Two independent cohorts with RD show the same trend, with the minor allele conferring greater relative right-hand skill. Meta-analysis of all three RD samples is genome-wide significant (n = 744, P = 2.0 × 10−8). Conversely, in the general population (n = 2666), we observe a trend towards reduced laterality of hand skill for the minor allele (P = 0.0020). These results provide molecular evidence that cerebral asymmetry and dyslexia are linked. Furthermore, PCSK6 is a protease that cleaves the left–right axis determining protein NODAL. Functional studies of PCSK6 promise insights into mechanisms underlying cerebral lateralization and dyslexia.

Background

Genome-wide association studies have been successful in finding common variants influencing common traits. However, these associations only account for a fraction of trait heritability. There has been a shift in the field towards studying low frequency and rare variants, which are now widely recognised as putative complex trait determinants. Despite this increasing focus on examining the role of low frequency and rare variants in complex disease susceptibility, there is a lack of user-friendly analytical packages implementing powerful association tests for the analysis of rare variants.

Results

We have developed two software tools, CCRaVAT (Case-Control Rare Variant Analysis Tool) and QuTie (Quantitative Trait), which enable efficient large-scale analysis of low frequency and rare variants. Both programs implement a collapsing method examining the accumulation of low frequency and rare variants across a locus of interest that has more power than single variant analysis. CCRaVAT carries out case-control analyses whereas QuTie has been developed for continuous trait analysis.

Conclusions

CCRaVAT and QuTie are easy to use software tools that allow users to perform genome-wide association analysis on low frequency and rare variants for both binary and quantitative traits. The software is freely available and provides the genetics community with a resource to perform association analysis on rarer genetic variants.

Genome-wide association studies (GWAS) conducted using commercial single nucleotide polymorphisms (SNP) arrays have proven to be a powerful tool for the detection of common disease susceptibility variants. However, their utility for the detection of lower frequency variants is yet to be practically investigated. Here we describe the application of a rare variant collapsing method to a large genome-wide SNP dataset, the Wellcome Trust Case Control Consortium rheumatoid arthritis (RA) GWAS. We partitioned the data into gene-centric bins and collapsed genotypes of low frequency variants (defined here as MAF ≤0.05) into a single count coupled with univariate analysis. We then prioritised gene regions for further investigation in an independent cohort of 3,355 cases and 2,427 controls based on rare variant signal p value and prior evidence to support involvement in RA. A total of 14,536 gene bins were investigated in the primary analysis and signals mapping to the TNFAIP3 and chr17q24 loci were selected for further investigation. We detected replicating association to low frequency variants in the TNFAIP3 gene (combined p = 6.6 × 10−6). Even though rare variants are not well-represented and can be difficult to genotype in GWAS, our study supports the application of low frequency variant collapsing methods to genome-wide SNP datasets as a means of exploiting data that are routinely ignored.

Electronic supplementary material

The online version of this article (doi:10.1007/s00439-010-0889-1) contains supplementary material, which is available to authorized users.

Genome-wide association (GWA) studies have proved to be extremely successful in identifying novel common polymorphisms contributing effects to the genetic component underlying complex traits. Nevertheless, one source of, as yet, undiscovered genetic determinants of complex traits are those mediated through the effects of rare variants. With the increasing availability of large-scale re-sequencing data for rare variant discovery, we have developed a novel statistical method for the detection of complex trait associations with these loci, based on searching for accumulations of minor alleles within the same functional unit. We have undertaken simulations to evaluate strategies for the identification of rare variant associations in population-based genetic studies when data are available from re-sequencing discovery efforts or from commercially available GWA chips. Our results demonstrate that methods based on accumulations of rare variants discovered through re-sequencing offer substantially greater power than conventional analysis of GWA data, and thus provide an exciting opportunity for future discovery of genetic determinants of complex traits. Genet. Epidemiol. 34: 188–193, 2010. © 2009 Wiley-Liss, Inc.

Despite the success of genome-wide association studies, much of the genetic contribution to complex human traits is still unexplained. One potential source of genetic variation that may contribute to this “missing heritability” is that which differs in magnitude and/or direction between males and females, which could result from sexual dimorphism in gene expression. Such sex-differentiated effects are common in model organisms, and are becoming increasingly evident in human complex traits through large-scale male- and female-specific meta-analyses. In this article, we review the methodology for meta-analysis of sex-specific genome-wide association studies, and propose a sex-differentiated test of association with quantitative or dichotomous traits, which allows for heterogeneity of allelic effects between males and females. We perform detailed simulations to compare the power of the proposed sex-differentiated meta-analysis with the more traditional “sex-combined” approach, which is ambivalent to gender. The results of this study highlight only a small loss in power for the sex-differentiated meta-analysis when the allelic effects of the causal variant are the same in males and females. However, over a range of models of heterogeneity in allelic effects between genders, our sex-differentiated meta-analysis strategy offers substantial gains in power, and thus has the potential to discover novel loci contributing effects to complex human traits with existing genome-wide association data. Genet. Epidemiol. 34:846–853, 2010. © 2010 Wiley-Liss, Inc.

The detection of loci contributing effects to complex human traits, and their subsequent fine-mapping for the location of causal variants, remains a considerable challenge for the genetics research community. Meta-analyses of genomewide association studies, primarily ascertained from European-descent populations, have made considerable advances in our understanding of complex trait genetics, although much of their heritability is still unexplained. With the increasing availability of genomewide association data from diverse populations, transethnic meta-analysis may offer an exciting opportunity to increase the power to detect novel complex trait loci and to improve the resolution of fine-mapping of causal variants by leveraging differences in local linkage disequilibrium structure between ethnic groups. However, we might also expect there to be substantial genetic heterogeneity between diverse populations, both in terms of the spectrum of causal variants and their allelic effects, which cannot easily be accommodated through traditional approaches to meta-analysis. In order to address this challenge, I propose novel transethnic meta-analysis methodology that takes account of the expected similarity in allelic effects between the most closely related populations, while allowing for heterogeneity between more diverse ethnic groups. This approach yields substantial improvements in performance, compared to fixed-effects meta-analysis, both in terms of power to detect association, and localization of the causal variant, over a range of models of heterogeneity between ethnic groups. Furthermore, when the similarity in allelic effects between populations is well captured by their relatedness, this approach has increased power and mapping resolution over random-effects meta-analysis. Genet. Epidemiol. 2011. © 2011 Wiley Periodicals, Inc.35: 809–;822, 2011.