Multi-ancestry genome-wide gene–smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids

Li, Yize

doi:10.1038/s41588-019-0378-y

Cited by 113 publications

(99 citation statements)

References 66 publications

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“…Instead, trans-ethnic studies have been increasingly popular. They incorporate genotype data from different ethnicities to boost statistic power with increasing sample sizes, which have the benefit to discover disease/trait associated loci and fine-mapping causal variants associated with complex traits or diseases 13,[35][36][37][38] .…”

Section: Discussionmentioning

confidence: 99%

Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations

Wang

Guo

et al. 2020

Preprint

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Polygenic scores (PGS) have been widely used to predict complex traits and risk of diseases using variants identified from genome-wide association studies (GWASs). To date, most GWASs have been conducted in populations of European ancestry, which limits the use of GWAS-derived PGS in non-European populations. Here, we develop a new theory to predict the relative accuracy (RA, relative to the accuracy in populations of the same ancestry as the discovery population) of PGS across ancestries. We used simulations and real data from the UK Biobank to evaluate our results. We found across various simulation scenarios that the RA of PGS based on trait-associated SNPs can be predicted accurately from modelling linkage disequilibrium (LD), minor allele frequencies (MAF), cross-population correlations of SNP effect sizes and heritability. Altogether, we find that LD and MAF differences between ancestries explain alone up to ~70% of the loss of RA using European-based PGS in African ancestry for traits like body mass index and height. Our results suggest that causal variants underlying common genetic variation identified in European ancestry GWASs are mostly shared across continents.Polygenic scores (PGS, also known as PRS when applied to diseases) are now routinely utilised to predict complex traits and risk of diseases from findings of genome-wide association studies (GWASs). Over recent years, the predictive performances of PGS have steadily increased with GWASs sample sizes, as predicted by theory 1 . However, the over-representation of European ancestry in the majority of GWASs has been shown to yield an unbalanced improvement of PGS prediction accuracy, in particular in non-European ancestry populations 2,3 . For example, Duncan et al. 2 report the average accuracy of PGS across multiple traits to be ~64% lower in individuals of African ancestry as compared to individuals of European ancestry. Similarly, Martin et al. 3 report, across multiple traits, reductions of PGS accuracy relative to European ancestry of ~37%, ~50% and ~78% in individuals of South-Asian, East-Asian and African ancestries respectively. Although increasingly emphasised in the recent GWAS literature, it is worth noting that the "loss of accuracy" problem is not utterly new. Indeed, a number of studies in the animal breeding literature have previously reported lower accuracy of genomic selection across genetically distant breeds 4,5 , consistent with the observation of limited transferability of GWAS findings across diverse human populations 6,7 . These studies also highlight major factors influencing that loss such as differences between populations in causal variants effect sizes, in alleles frequencies and in linkage disequilibrium (LD) between causal variants and SNPs assayed in GWAS. 6,8,9 To illustrate the latter point, consider a SNP which LD r 2 with a causal variant equals 0.8 in the discovery population and 0.6 in the target population. Such a SNP would therefore explain 25%=(1-0.6/0.8) less trait variation and thus be less predictive in t...

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Section: Discussionmentioning

confidence: 99%

Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations

Wang

Guo

et al. 2020

Preprint

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“…Compared to a fixed-effects model approach (e.g. 25 ), a mixed-model approach like ours could account for genetic covariance among individuals. Compared to StructLMM 8 , which is a linear mixedmodel approach that examines G-C interaction for one SNP at a time, our approach estimates the G-C interaction aggregated over SNPs for the entire genome, thereby providing genome-wide estimates of of G-C interaction.…”

Section: Discussionmentioning

confidence: 99%

A Whole-Genome Approach Discovers Novel Genetic and Non-Genetic Variance Components Modulated by Lifestyle for Cardiovascular Health

Zhou

Werf

Carson‐Chahhoud

et al. 2019

Preprint

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Both genetic and non-genetic factors can predispose individuals to cardiovascular risk. Finding ways to alter these predispositions is important for cardiovascular disease (CVD) prevention. Here, we use a novel whole-genome framework to estimate genetic and non-genetic effects on-hence their predispositions to-cardiovascular risk and determine whether they vary with respect to lifestyle factors. We performed analyses on the Atherosclerosis Risk in Communities Study (ARIC, N=6,180) and validated findings using the UK Biobank (UKBB, N=14,076-34,538). Cardiovascular risk was measured using 23 traits in the ARIC and eight traits in the UKBB, such as body mass index (BMI), resting heart rate, white blood cell count and blood pressure; and lifestyle factors included information on physical activity, smoking, alcohol consumption and dietary intake. Physical activity altered both genetic and non-genetic effects on heart rate and BMI, genetic effects on HDL cholesterol level, and nongenetic effects on waist-to-hip ratio. Alcohol consumption altered both genetic and non-genetic effects on BMI, while smoking altered non-genetic effects on heart rate, pulse pressure, and white blood cell count. In addition, saturated fat intake modified genetic effects on BMI, and total daily energy intake modified non-genetic effects on waist-to-hip ratio. These results highlight the relevance of lifestyle changes for CVD prevention. We also stratified individuals according to their genetic predispositions and showed notable differences in the effects of lifestyle on cardiovascular risk across stratified groups, implying the need for individualizing lifestyle changes for CVD prevention. Finally, we showed that neglecting lifestyle modulation of genetic and nongenetic effects will on average reduce SNP heritability estimates of cardiovascular traits by a small yet significant amount, primarily owing to overestimation of residual variance. Thus, current SNP heritability estimates for cardiovascular traits, which commonly do not consider modulating effects of lifestyle covariates, are likely underestimated.

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“…Fifth, APOB encodes for apolipoprotein B, the essential apolipoprotein in chylomicrons, LDLs, and very-low density lipoproteins. Although its relationship with HDL biology may seem distant, gene variants located in this gene have been surprisingly associated with HDL cholesterol levels in four large genome-wide association studies 32,[38][39][40] .…”

Section: Discussionmentioning

confidence: 99%

High-density lipoprotein characteristics and coronary heart disease: a Mendelian randomization study

Prats‐Uribe¹,

Sayols-Baixeras²,

Fernández‐Sanlés³

et al. 2019

Preprint

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2Background. The causal role of high-density lipoproteins (HDL) in coronary artery disease 3 (CAD) has been questioned. Our aim was to analyze whether genetically determined 4 quantitative and qualitative HDL characteristics were independently associated with CAD. 5Methods. We designed a two-sample multivariate Mendelian randomization study with 6 available genome-wide association summary data. We identified genetic variants associated 7 with HDL cholesterol and apolipoprotein A-I quantity, HDL size, particle levels, and lipid 8

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Multi-ancestry genome-wide gene–smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids

Cited by 113 publications

References 66 publications

Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations

Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations

A Whole-Genome Approach Discovers Novel Genetic and Non-Genetic Variance Components Modulated by Lifestyle for Cardiovascular Health

High-density lipoprotein characteristics and coronary heart disease: a Mendelian randomization study

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