Generalization and Dilution of Association Results from European GWAS in Populations of Non-European Ancestry: The PAGE Study

Carlson, Christopher S.; Matise, Tara C.; North, Kari E.; Haiman, Christopher A.; Fesinmeyer, Megan D.; Buyske, Steven; Schumacher, Fredrick; Peters, Ulrike; Franceschini, Nora; Ritchie, Marylyn D.; Duggan, David; Spencer, Kylee L.; Dumitrescu, Logan; Eaton, Charles B.; Thomas, Fridtjof; Young, Alicia; Carty, Cara L.; Heiss, Gerardo; Marchand, Loı̈c Le; Crawford, Dana C.; Hindorff, Lucia A.; Kooperberg, Charles

doi:10.1371/journal.pbio.1001661

Cited by 233 publications

(210 citation statements)

References 31 publications

(38 reference statements)

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“…However, rare variants are largely population-private, and their effects are unlikely to transfer to new populations. Because linkage disequilibrium and allele frequencies vary across ancestries, effect size estimates from diverse cohorts are typically more precise than from single-ancestry cohorts (and often tempered), 5 and the resolution of causal variant fine-mapping is considerably improved. 87 Across a range of genetic architectures, diverse cohorts provide the opportunity to reduce false positives.…”

Section: Discussionmentioning

confidence: 99%

“…GWASs have yielded tens of thousands of common genetic variants significantly associated with human medical and evolutionary phenotypes, most of which have replicated in other ethnic groups. [5][6][7] However, GWASs are optimally powered to discover common variant associations, and the European bias in GWASs results in associated SNPs with higher minor allele frequencies on average compared to other populations. The predictive power of GWAS findings and genetic diagnostic accuracy in non-Europeans are therefore limited by population differences in allele frequencies and linkage disequilibrium structure.…”

Section: Introductionmentioning

confidence: 99%

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Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

Gignoux

Walters

et al. 2017

The American Journal of Human Genetics

1,176

1,050

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The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

Gignoux

Walters

et al. 2017

The American Journal of Human Genetics

1,176

1,050

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“…In contrast, for a variety of complex traits studied to date, accumulating evidence suggests considerable numbers of overlap loci between ethnicities. [30][31][32][33] For these traits, a large proportion of SNPs selected based on a large European GWAS are expected to be relevant in non-European populations. Therefore, a set selected based on a large GWAS in a non-matching population is expected to harbor higher fraction of variants for the target population than a set selected on a small dataset in matching population.…”

Section: Discussionmentioning

confidence: 99%

Leveraging Multi-ethnic Evidence for Risk Assessment of Quantitative Traits in Minority Populations

Coram

Fang

Candille

et al. 2017

The American Journal of Human Genetics

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An essential component of precision medicine is the ability to predict an individual's risk of disease based on genetic and non-genetic factors. For complex traits and diseases, assessing the risk due to genetic factors is challenging because it requires knowledge of both the identity of variants that influence the trait and their corresponding allelic effects. Although the set of risk variants and their allelic effects may vary between populations, a large proportion of these variants were identified based on studies in populations of European descent. Heterogeneity in genetic architecture underlying complex traits and diseases, while broadly acknowledged, remains poorly characterized. Ignoring such heterogeneity likely reduces predictive accuracy for minority individuals. In this study, we propose an approach, called XP-BLUP, which ameliorates this ethnic disparity by combining trans-ethnic and ethnic-specific information. We build a polygenic model for complex traits that distinguishes candidate trait-relevant variants from the rest of the genome. The set of candidate variants are selected based on studies in any human population, yet the allelic effects are evaluated in a population-specific fashion. Simulation studies and real data analyses demonstrate that XP-BLUP adaptively utilizes trans-ethnic information and can substantially improve predictive accuracy in minority populations. At the same time, our study highlights the importance of the continued expansion of minority cohorts.

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“…In the Population Architecture Using Genomics and Epidemiology (PAGE) study, a consortium of multiancestry, population-based studies, 25% of tag SNPs identified in European GWAS had significantly different effect sizes in black cohorts. 38 This might imply limited sharing of causal variants between Europeans and Africans when compared with other ethnicities. Given the lower level of LD in African populations, the index SNP identified in European studies may simply fail to tag potentially shared casual variants.…”

Section: Studies In Non-european Populationsmentioning

confidence: 99%

Genetics of Coronary Artery Disease

McPherson

Tybjærg‐Hansen

2016

Circulation Research

302

187

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Genetic factors contribute importantly to the risk of coronary artery disease (CAD), and in the past decade, there has been major progress in this area. The tools applied include genome-wide association studies encompassing >200 000 individuals complemented by bioinformatic approaches, including 1000 Genomes imputation, expression quantitative trait locus analyses, and interrogation of Encyclopedia of DNA Elements, Roadmap, and other data sets. close to 60 common SNPs (minor allele frequency>0.05) associated with CAD risk and reaching genomewide significance (P<5×10 −8 ) have been identified. Furthermore, a total of 202 independent signals in 109 loci have achieved a false discovery rate (q<0.05) and together explain 28% of the estimated heritability of CAD. These data have been used successfully to create genetic risk scores that can improve risk prediction beyond conventional risk factors and identify those individuals who will benefit most from statin therapy. Such information also has important applications in clinical medicine and drug discovery by using a Mendelian randomization approach to interrogate the causal nature of many factors found to associate with CAD risk in epidemiological studies. In contrast to genome-wide association studies, whole-exome sequencing has provided valuable information directly relevant to genes with known roles in plasma lipoprotein metabolism but has, thus far, failed to identify other rare coding variants linked to CAD. Overall, recent studies have led to a broader understanding of the genetic architecture of CAD and demonstrate that it largely derives from the cumulative effect of multiple common risk alleles individually of small effect size rather than rare variants with large effects on CAD risk. Despite this success, there has been limited progress in understanding the function of the novel loci; the majority of which are in noncoding regions of the genome. (Circ Res. 2016;118:564-578.

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Generalization and Dilution of Association Results from European GWAS in Populations of Non-European Ancestry: The PAGE Study

Cited by 233 publications

References 31 publications

Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

Leveraging Multi-ethnic Evidence for Risk Assessment of Quantitative Traits in Minority Populations

Genetics of Coronary Artery Disease

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