Variable prediction accuracy of polygenic scores within an ancestry group

Mostafavi, Hakhamanesh; Harpak, Arbel; Conley, Dalton; Pritchard, Jonathan K.; Przeworski, Molly

doi:10.1101/629949

Cited by 129 publications

(191 citation statements)

References 68 publications

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“…Educational Attainment (EA) shows an especially large within-family attenuation in performance relative to the other predictors. This has been noticed in other studies [26]. The results suggest that at least some of the observed power in polygenic prediction of EA among non-sibling individuals comes from effects such as subtle population stratification (perhaps correlated to environmental conditions or family socio-economic status) [8], genetic nurture [9], or other environmental-genetic correlations [5][6][7].…”

Section: Performance Difference: Siblings Vs Non-sibling Pairssupporting

confidence: 76%

Within-Family Validation of Polygenic Risk Scores and Complex Trait Prediction

Lello

Raben

Hsu

2020

Preprint

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We test a variety of polygenic predictors using tens of thousands of genetic siblings for whom we have SNP genotypes, health status, and phenotype information in late adulthood. Siblings have typically experienced similar environments during childhood, and exhibit negligible population stratification relative to each other. Therefore, the ability to predict differences in disease risk or complex trait values between siblings is a strong test of genomic prediction in humans. We compare validation results obtained using non-sibling subjects to those obtained among siblings and find that typically most of the predictive power persists in within-family designs. In the case of disease risk we test the extent to which higher polygenic risk score (PRS) identifies the affected sibling, and also compute Relative Risk Reduction as a function of risk score threshold. For quantitative traits we examine between-sibling differences in trait values as a function of predicted differences, and compare to performance in non-sibling pairs. Example results: Given 1 sibling with normal-range PRS score (<84 percentile) and 1 sibling with high PRS score (top few percentiles), the predictors identify the affected sibling about 70-90% of the time across a variety of disease conditions, including Breast Cancer, Heart Attack, Diabetes, etc. For height, the predictor correctly identifies the taller sibling roughly 80 percent of the time when the (male) height difference is 2 inches or more.

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Section: Performance Difference: Siblings Vs Non-sibling Pairssupporting

confidence: 76%

Within-Family Validation of Polygenic Risk Scores and Complex Trait Prediction

Lello

Raben

Hsu

2020

Preprint

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“…However, these tests are largely built using European cohorts and have proven difficult to translate to non-European populations 57–59 . Even within homogeneous populations, environmental factors can have marked effects on gene expression measurements, and on the interpretability of genomic-based tests of disease risk 60 , highlighting a secondary risk of such biased European sampling: limiting not only the genomic diversity under study, but the environmental diversity as well, to general detriment. This study provides a valuable first step in the characterization of the processes shaping gene expression changes in Island Southeast Asia.…”

Section: Discussionmentioning

confidence: 99%

Genome-wide DNA methylation and gene expression patterns reflect genetic ancestry and environmental differences across the Indonesian archipelago

Natri

Bobowik

Kusuma

et al. 2019

Preprint

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34Indonesia is the world's fourth most populous country, host to striking levels of human diversity, regional 35 patterns of admixture, and varying degrees of introgression from both Neanderthals and Denisovans. 36 However, it has been largely excluded from the human genomics sequencing boom of the last decade. 37 To serve as a benchmark dataset of molecular phenotypes across the region, we generated genome-wide 38 CpG methylation and gene expression measurements in over 100 individuals from three locations that 39 capture the major genomic and geographical axes of diversity across the Indonesian archipelago. 40Investigating between-and within-island differences, we find up to 10% of tested genes are differentially 41 expressed between the islands of Mentawai (Sumatra) and New Guinea. Variation in gene expression is 42 closely associated with DNA methylation, with expression levels of 9.7% of genes strongly correlating 43 with nearby CpG methylation, and many of these genes being differentially expressed between islands. 44Genes identified in our differential expression and methylation analyses are enriched in pathways 45 involved in immunity, highlighting Indonesia tropical role as a source of infectious disease diversity and 46 the strong selective pressures these diseases have exerted on humans. Finally, we identify robust within-47 island variation in DNA methylation and gene expression, likely driven by very local environmental 48 differences across sampling sites. Together, these results strongly suggest complex relationships between 49 DNA methylation, transcription, archaic hominin introgression and immunity, all jointly shaped by the 50 environment. This has implications for the application of genomic medicine, both in critically 51 understudied Indonesia and globally, and will allow a better understanding of the interacting roles of 52 genomic and environmental factors shaping molecular and complex phenotypes. 53 54 55 56 57 58 59 60 61 Modern human genomics does not equitably represent the full breadth of humanity. While genome 62 sequences for people of European descent now number a million or more, most of the world is deeply 63 understudied 1 . This is particularly true of Indonesia 2 , a country geographically as large as continental 64 Europe and the world's fourth largest by population. Genomic diversity in Indonesia is strikingly 65 different to other well-characterized East Asian populations, such as Han Chinese and Japanese, but this 66 diversity is not captured in large global datasets like the 1000 Genomes Project 3 or the Simons Genome 67 Diversity Project 4 . The first Indonesian genome sequences were only reported in 2016 5 with the first 68 representative survey of diversity across the archipelago only appearing in 2019 6 . This extreme lack of 69 representation extends to molecular phenotypes. To our knowledge, only one genome-wide gene 70 expression study has been published 7 from the region, focused exclusively on host-pathogen interactions 71 with P. falciparum. There are...

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“…In addition to confirming our results, this analysis also suggests that CI can be reliably tested using internally-constructed PRS in the future. This can be essential for applications to less-studied traits, populations, or environmental contexts (Liu et al, 2018;Martin et al, 2019Martin et al, , 2017Mefford et al, 2019;Mostafavi et al, 2019).…”

Section: Replicating Coordinated Interaction With External Prs In Uk mentioning

confidence: 99%

Coordinated Interaction: A model and test for globally signed epistasis in complex traits

Sheppard

Rappoport

et al. 2020

Preprint

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Interactions between genetic variants -epistasis -is pervasive in model systems and can profoundly impact evolutionary adaption, population disease dynamics, genetic mapping, and precision medicine efforts. In this work we develop a model for structured polygenic epistasis, called Coordinated Interaction (CI), and prove that several recent theories of genetic architecture fall under the formal umbrella of CI. Unlike standard polygenic epistasis models that assume interaction and main effects are independent, in the CI model, sets of SNPs broadly interact positively or negatively, on balance skewing the penetrance of main genetic effects. To test for the existence of CI we propose the even-odd (EO) test and prove it is calibrated in a range of realistic biological models. Applying the EO test in the UK Biobank, we find evidence of CI in 14 of 26 traits spanning disease, anthropometric, and blood categories. Finally, we extend the EO test to tissue-specific enrichment and identify several plausible tissue-trait pairs. Overall, CI is a new dimension of genetic architecture that can capture structured, systemic interactions in complex human traits.

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Variable prediction accuracy of polygenic scores within an ancestry group

Cited by 129 publications

References 68 publications

Within-Family Validation of Polygenic Risk Scores and Complex Trait Prediction

Within-Family Validation of Polygenic Risk Scores and Complex Trait Prediction

Genome-wide DNA methylation and gene expression patterns reflect genetic ancestry and environmental differences across the Indonesian archipelago

Coordinated Interaction: A model and test for globally signed epistasis in complex traits

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