Comparing Within- and Between-Family Polygenic Score Prediction

Selzam, Saskia; Ritchie, Stuart J.; Pingault, Jean‐Baptiste; Reynolds, Chandra A.; O’Reilly, Paul; Plomin, Robert

doi:10.1016/j.ajhg.2019.06.006

Cited by 222 publications

(267 citation statements)

References 79 publications

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“…These effects survive within-family validity testing, and attenuation of predictive power tends to be modest (most of the power remains in the sibling tests). Our results for height, body mass index (BMI), EA, and Fluid Intelligence are similar recent results found in [12], utilizing data from the Twins Early Development Study (TEDS). As far as we know, this paper is the first to analyze a variety of disease risks using within-family designs.…”

supporting

confidence: 86%

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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supporting

confidence: 86%

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

Lello

Raben

Hsu

2020

Preprint

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“…showing that the effects of passive gene-environment correlation reduced the variance explained by polygenic scores by 30-50% (Selzam et al 2019;Kong et al 2018). Notably, Cheesman et al Comparison of adopted and non-adopted individuals reveals gene-environment interplay for education in the UK Biobank 24 although we have controlled for the passive form by using adoptees, there are other geneenvironment correlation mechanisms that are essential in how genes influence traits in everyone, including adoptees.…”

Section: Discussionmentioning

confidence: 89%

“…Cheesman et al Comparison of adopted and non-adopted individuals reveals gene-environment interplay for education in the UK Biobank 5 More recently, researchers have applied genomic tools to family data to estimate direct and indirect effects on educational attainment (Bates et al 2018;Kong et al 2018;Domingue et al 2015;Wertz et al 2018;Selzam et al 2019;Young et al 2018). These designs can be thought of as conceptually related to adoption designs, since they account for shared genes between parents and offspring.…”

Section: Introductionmentioning

confidence: 99%

“…To shed further light on the moderation of polygenic prediction by adoption status, we also tested for gene-environment interaction using a model with polygenic scores for education as the predictor, adoption as the exposure and education as the outcome. As a negative control, we tested the polygenic prediction comparison between adoptees and non-adopted individuals for height, which has not shown evidence of passive geneenvironment correlation in previous studies (Kong et al 2018;Selzam et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of adopted and non-adopted individuals reveals gene-environment interplay for education in the UK Biobank

Cheesman

Hunjan

Coleman

et al. 2019

Preprint

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Individual-level polygenic scores can now explain ~10% of the variation in number of years of completed education. However, associations between polygenic scores and education capture not only genetic propensity but information about the environment that individuals are exposed to. This is because individuals passively inherit effects of parental genotypes, since their parents typically also provide the rearing environment. In other words, the strong correlation between offspring and parent genotypes results in an association between the offspring genotypes and the rearing environment. This is termed passive geneenvironment correlation. We present an approach to test for the extent of passive geneenvironment correlation for education without requiring intergenerational data. Specifically, we use information from 6311 individuals in the UK Biobank who were adopted in childhood to compare genetic influence on education between adoptees and non-adopted individuals.Adoptees' rearing environments are less correlated with their genotypes, because they do not share genes with their adoptive parents. We find that polygenic scores are twice as predictive of years of education in non-adopted individuals compared to adoptees (R 2 = 0.074 vs 0.037, difference test p= 8.23 x 10 -24 ). We provide another kind of evidence for the influence of parental behaviour on offspring education: individuals in the lowest decile of education polygenic score attain significantly more education if they are adopted, possibly due to educationally supportive adoptive environments. Overall, these results suggest that genetic influences on education are mediated via the home environment. As such, polygenic prediction of educational attainment represents gene-environment correlations just as much as it represents direct genetic effects.Cheesman et al.

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“…Second, it is well known that PSs lose substantial power, or may even be invalid, when applied across different populations [55][56][57]. Moreover, even within a single population, subtle remaining ethnic and geographic stratification effects may result in inflated estimates of ps 2 [58][59][60], limiting applicability to individual prediction. Third, SNP effects may be environmentally sensitive, and may not be consistent across time and place [61].…”

Section: Discussionmentioning

confidence: 99%

Screening human embryos for polygenic traits has limited utility

Karavani

Zuk²,

Zeevi³

et al. 2019

Preprint

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Genome-wide association studies have led to the development of polygenic score (PS) predictors that explain increasing proportions of the variance in human complex traits. In parallel, progress in preimplantation genetic testing now allows genome-wide genotyping of embryos generated via in vitro fertilization (IVF). Jointly, these developments suggest the possibility of screening embryos for polygenic traits such as height or cognitive function. There are clear ethical, legal, and societal concerns regarding such a procedure, but these cannot be properly discussed in the absence of data on the expected outcomes of screening. Here, we use theory, simulations, and real data to evaluate the potential gain of PS-based embryo selection, defined as the expected difference in trait value between the top-scoring embryo and an average, unselected embryo. We observe that the gain increases very slowly with the number of embryos, but more rapidly with increased variance explained by the PS. Given currently available polygenic predictors and typical IVF yields, the average gain due to selection would be ≈2.5cm if selecting for height, and ≈2.5 IQ (intelligence quotient) points if selecting for cognitive function. These mean values are accompanied by wide confidence intervals; in real data drawn from nuclear families with up to 20 offspring each, we observe that the offspring with the highest PS for height was the tallest only in 25% of the families. We discuss prospects and limitations of PS-based embryo selection for the foreseeable future.Polygenic scores (PS) are derived from large-scale genome-wide association studies (GWAS) of complex traits, which can be quantitative (such as intelligence or height) or categorical (such as disease status, in which case they are often referred to as 'polygenic risk scores') [8]. A PS is the count of effect alleles in an individual's genome, weighted by each allele's strength of association with the trait of interest in an independent GWAS [9]. The predictive power of a PS is usually represented by ps 2 , or the proportion of variance of the quantitative trait explained by the PS. To date, the largest GWAS of intelligence [10,11] has demonstrated a relatively modest out-of-sample ps 2 (≈5%), despite large sample sizes (n≈300,000 individuals). By contrast, recent large-scale GWASs of height have attained ps 2 of approximately 25%, while demonstrating a highly polygenic genetic architecture similar to intelligence [12]. Consequently, in the present report, we analyze height in addition to cognitive function, which also allows us to exploit several datasets in which height data, but not intelligence data, are available.PSs are typically evaluated on a cohort basis, and are not used to differentiate one individual from another (although a recent report has demonstrated that, for an extraordinarily tall NBA player, the PS for height was >4 standard deviations above the population mean [13]). In order for polygenic embryo selection to hold potential utility (independent of ethical considera...

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Comparing Within- and Between-Family Polygenic Score Prediction

Cited by 222 publications

References 79 publications

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

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

Comparison of adopted and non-adopted individuals reveals gene-environment interplay for education in the UK Biobank

Screening human embryos for polygenic traits has limited utility

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