Causal effects on complex traits are similar across segments of different continental ancestries within admixed individuals

Hou, Kangcheng; Ding, Yi; Xu, Ziqi; Wu, Yue; Bhattacharya, Arjun; Mester, Rachel; Belbin, Gillian M.; Conti, David V.; Darst, Burcu F.; Fornage, Myriam; Gignoux, Chris; Guo, Xiuqing; Haiman, Christopher; Kim, Michelle; Kooperberg, Charles; Lange, Leslie A.; Manichaikul, Ani; North, Kari E.; Nudelman, Natalie; Peters, Ulrike; Rasmussen‐Torvik, Laura J.; Rich, Stephen S.; Rotter, Jerome I.; Wheeler, Heather E.; Zhou, Ying; Sankararaman, Sriram; Paşaniuc, Bogdan

doi:10.1101/2022.08.16.22278868

Cited by 15 publications

(22 citation statements)

References 67 publications

(118 reference statements)

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“…While this is an estimate, we predict the true difference between estimated marginal effect sizes might be smaller than indicated by these empirical values because Tractor is more powerful in identifying SNPs with heterogenous effect sizes. This result reflects previous findings that causal effects are similar across ancestries within admixed populations 22 . Due to this similarity in effect size, most of the significant SNPs sit in the center of the heatmap.…”

Section: Resultssupporting

confidence: 90%

“…HetLanc occurs when a SNP exhibits different estimated allelic effect sizes depending on its local ancestry background. HetLanc can manifest itself at causal SNPs due to genetic interactions between multiple causal variants or differential environments, although recent work suggests that the magnitude and frequency of these types of epistatic effects between causal variants is limited 22 . A more common form of HetLanc is observed at non-causal SNPs that tag the causal effect in a differential manner across ancestries.…”

Section: Resultsmentioning

confidence: 99%

“…Differential linkage disequilibrium by local ancestry at these non-causal SNPs (tagged SNPs) can cause HetLanc even when allele frequencies and causal effect sizes are the same across ancestries. The extent to which HetLanc exists and the magnitude of these differences in effect sizes are yet uncertain [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] . However, the existence of HetLanc plays an important role in the power of GWAS methods to detect associations.…”

Section: Populationsmentioning

confidence: 99%

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Impact of cross-ancestry genetic architecture on GWAS in admixed populations

Mester

Hou

Ding

et al. 2023

Preprint

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Genome-wide association studies (GWAS) have identified thousands of variants for disease risk. These studies have predominantly been conducted in individuals of European ancestries, which raises questions about their transferability to individuals of other ancestries. Of particular interest are admixed populations, usually defined as populations with recent ancestry from two or more continental sources. Admixed genomes contain segments of distinct ancestries that vary in composition across individuals in the population, allowing for the same allele to induce risk for disease on different ancestral backgrounds. This mosaicism raises unique challenges for GWAS in admixed populations, such as the need to correctly adjust for population stratification to balance type I error with statistical power. In this work we quantify the impact of differences in estimated allelic effect sizes for risk variants between ancestry backgrounds on association statistics. Specifically, while the possibility of estimated allelic effect-size heterogeneity by ancestry (HetLanc) can be modeled when performing GWAS in admixed populations, the extent of HetLanc needed to overcome the penalty from an additional degree of freedom in the association statistic has not been thoroughly quantified. Using extensive simulations of admixed genotypes and phenotypes we find that modeling HetLanc in its absence reduces statistical power by up to 72%. This finding is especially pronounced in the presence of allele frequency differentiation. We replicate simulation results using 4,327 African-European admixed genomes from the UK Biobank for 12 traits to find that for most significant SNPs HetLanc is not large enough for GWAS to benefit from modeling heterogeneity.

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Section: Resultssupporting

confidence: 90%

Section: Resultsmentioning

confidence: 99%

Section: Populationsmentioning

confidence: 99%

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Impact of cross-ancestry genetic architecture on GWAS in admixed populations

Mester

Hou

Ding

et al. 2023

Preprint

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“…By carefully analyzing gene expression levels in LCLs from European and African individuals, we provided concrete answers to all three questions for the first time. We found that cis -genetic effects are highly similar across ancestries, which agrees with a recent study in organism-level complex traits 15 and is not expected to affect portability. Interestingly, we found that when causal variants differ in allele frequency between ancestries, PRS portability is substantially diminished independently of LD.…”

Section: Introductionsupporting

confidence: 92%

Allele frequency differences of causal variants have a major impact on low cross-ancestry portability of PRS

Saitou

Dahl

Wang

et al. 2022

Preprint

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Genome-wide association studies (GWAS) are overwhelmingly biased toward European ancestries. Nearly all existing studies agree that transferring genetic predictions from European ancestries to other populations results in a substantial loss of accuracy. This is commonly referred to as low portability of polygenic risk scores (PRS) and is one of the most important barriers to the ethical clinical deployment of PRS. Yet, it remains unclear how much various genetic factors, such as linkage disequilibrium (LD) differences, allele frequency differences or causal effect differences, contribute to low PRS portability. In this study, we used gene expression levels in lymphoblastoid cell lines (LCLs) as a simplified model of complex traits with minimal environmental variation, in order to understand how much each genetic factor contributes to PRS portability from European to African populations. We found thatcis-genetic effects on gene expression are highly similar between European and African individuals (). This stands in stark contrast to the very low estimates ofcis-genetic correlation between Europeans and Africans in previous studies, which we demonstrate are artifacts of statistical bias. We showed that portability decreases with increasing LD differences in thecis-regions. We also found that allele frequency differences of causal variants have a striking impact on PRS portability. For example, PRS portability is reduced by more than 32% when the causalcis-variant is common (minor allele frequency, MAF > 5%) in European samples (training population) but is rarer (MAF < 5%) in African samples (prediction population). While large allele frequency differences can decrease PRS portability through increasing LD differences, we also show that causal allele frequency can significantly impact portability independently of LD. This observation suggests that improving statistical fine-mapping alone does not overcome the loss of portability caused by causal allele frequency differences. Lastly, we also found that causal allele frequency is the main genetic factor underlying differential gene expression levels across ancestries. We conclude that causal genetic effects are highly similar in Europeans and Africans, and low PRS portability is primarily due to allele frequency differences.

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“…There is increasing evidence showing that most common variants are shared between-ancestries, thus supporting high cross-ancestry 𝑟 𝑔 for most traits 7,16 . However, estimates of 𝑟 𝑔 can be affected by phenotypic and environmental heterogeneity between different populations 15,40 . A consideration when constructing PRS based on multi-ancestry GWAS using summary-level based methods, such as P+T and PRS-CS, is which LD reference panel best approximates the LD structure between SNPs while being most readily available to researchers.…”

Section: Discussionmentioning

confidence: 99%

Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology

Wang

Kanai

Tan

et al. 2022

Preprint

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Polygenic risk scores built from multi-ancestry genome-wide association studies (GWAS, PRSmulti) have the potential to improve PRS accuracy and generalizability across populations. To provide the best practice to leverage the increasing diversity of genomic studies, we used large-scale simulated and empirical data to investigate how ancestry composition, trait-specific genetic architecture, and PRS methodology affect the performance of PRSmulti as compared to PRS constructed from single-ancestry GWAS (PRSsingle). In both simulations on 6 various scenarios and empirical analyses on 17 anthropometric and blood panel traits, we showed that the accuracy of PRSmulti overall outperformed PRSsingle in the understudied target populations, except for a few comparisons where the understudied population only accounted for a very small proportion of the multi-ancestry GWAS. Further, using substantially fewer samples for traits such as height and mean corpuscular volume from Biobank Japan (BBJ) may achieve comparable accuracies to using 320,000 European (EUR) individuals from UK Biobank (UKBB). Finally, we find that incorporating PRS based on local ancestry-informed GWAS and large-scale EUR-based PRS improved predictive performance than using EUR-based PRS alone in understudied African (AFR) population, especially for less polygenic traits when there are variants with large ancestry-specific effects. Overall, our study provides insights into how ancestry composition and genetic architecture impact polygenic prediction across populations, particularly across imbalanced sample sizes. Our work also highlights the need for increasing diversity in genetic studies to achieve equitable PRS performance across ancestral populations and provides practical guidance on developing PRS from multiple resources.

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Causal effects on complex traits are similar across segments of different continental ancestries within admixed individuals

Cited by 15 publications

References 67 publications

Impact of cross-ancestry genetic architecture on GWAS in admixed populations

Impact of cross-ancestry genetic architecture on GWAS in admixed populations

Allele frequency differences of causal variants have a major impact on low cross-ancestry portability of PRS

Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology

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