Alexander I. Young

We introduce a novel approach, Genomic-Relatedness-Matched Association (GRMA) studies, as an alternative to genome-wide association studies (GWAS). GWAS are typically restricted to samples of mostly unrelated individuals with a single, shared continental ancestry and nevertheless can still be biased by gene-environment correlation and assortative mating. In contrast, GRMA can be implemented in ancestrally diverse samples—retaining individuals of mixed or underrepresented ancestries and eliminating the need to assign labels to ancestry groups—and can reduce bias relative to standard GWAS. GRMA matches each individual to a group of controls whose pairwise relatedness with the individual exceeds a user-specified threshold. It generates SNP-level summary statistics based on within-group associations. In applications using the UK Biobank and All of Us data, we find that GRMA compares favorably to GWAS methods in terms of bias, precision, and population coverage. GRMA enables several novel findings; for example, we find that “genetic nurture” is unlikely to be an important source of genome-wide bias in population GWAS of body mass index, height, and educational attainment. The method is computationally efficient and supported by open-source software, facilitating its application in large-scale scientific and health-related studies.

Preimplantation genetic testing for polygenic risk (PGT-P) holds great promise for reducing lifetime disease burden, but has been held back by the difficulty of genotyping embryos. Preimplantation genetic testing for aneuploidy (PGT-A) is a standard-of-care technology used in over half of in vitro fertilization (IVF) cycles in the United States. PGT-A is used to detect chromosomal abnormalities using ultra-low-pass (ULP) sequencing data (typically 0.002x to 0.006x) or, less commonly, genotyping array-based data. Here we describe ImputePGTA, a Hidden Markov Model-based algorithm that enables accurate reconstruction of embryo genomes from array or ULP sequencing data from embryos and parental genome data. A key innovation of our algorithm is its ability to provide accurate embryo genotypes and polygenic scores (PGSs) along with posterior distributions given limited embryo data and imperfectly phased parental haplotypes, as encountered in real-world applications. The accuracy of the embryo genome reconstruction increases with that of the phasing quality of parental haplotypes. We describe a method, phaseGrafter, that improves parental phasing by combining statistical phasing from short-reads with read-backed phasing from long-reads, which further enable phasing of rare pathogenic variants. We validate our results through simulations, downsampled gold standard data, and comparison of six reconstructed embryo genomes from real PGT-A data to high-coverage, post-birth whole genome sequencing data. Our imputed embryo genotypes have a dosage correlation of 0.961 with high-quality post-birth genotypes (0.998 when using embryo array data). The imputed embryo polygenic scores for 17 diseases have a mean absolute difference of 0.16 standard deviations (0.023 when using embryo array data) with PGSs calculated from high-quality post-birth genotypes, lower than from imputation of array data from reference panels. We show that the attenuation in expected gains from embryo selection due to posterior uncertainty is only ∼5-10% for typical PGT-A data. Our approach removes an important technological barrier to using PGT-P and will facilitate more widespread adoption.

Polygenic indexes (PGIs) — DNA-based predictors of individual phenotypes — have become essential tools across biomedical and social sciences. We introduce Version 2 of the Polygenic Index Repository, which expands phenotype coverage from 47 to 61, increases the number of participating datasets from 11 to 20, and adopts a more consistent and improved methodology for PGI construction. For 16 phenotypes, we leverage summary statistics from an updated GWAS meta-analysis with greater statistical power compared to the original release, thereby improving the PGI’s predictive power. To improve power for family-based analyses, we provide imputed parental PGIs in all datasets with first-degree relatives and offer a framework for interpreting results from analyses that control for parental PGIs. We illustrate the utility of parental PGIs using two applications: (1) comparing PGI associations with and without parental PGI controls for all phenotypes in two Repository datasets with family data, and (2) for BMI and diastolic blood pressure, exploring the contribution of causal versus non-causal components of PGI associations to the imperfect portability of PGIs across subgroups within a genetic ancestry. Collectively, the updates enhance predictive performance, broaden the Repository’s scope, and introduce novel resources that reduce confounding bias and improve interpretability.

Polygenic indexes (PGIs) trained on samples of European genetic ancestries often lose substantial predictive power when applied to non-European ancestries. While this portability problem is well recognized, its manifestation in behavioral and social traits remains understudied, and the factors driving this accuracy loss warrant more comprehensive analysis. Using data from the UK Biobank and Health and Retirement Study, we conduct a systematic analysis of PGI portability for 52 health-related, behavioral, and social phenotypes. We advance prior literature by using genome-wide PGIs, assessing cross-ancestry heritability differences, and comparing the performance of PGIs based on standard versus family-based GWAS. Our findings confirm systematic reductions in PGI predictive power for non-European ancestries—lowest in African (24%), followed by East Asian (37%) and South Asian (51%) genetic ancestries—with biologically proximal traits exhibiting greater portability than behavioral and social traits. We show that the relative importance of factors underlying reduced portability varies across traits and ancestries: in African ancestries, linkage disequilibrium and allele frequency differences explain most of the loss (82%), compared with smaller contributions in East (34%) and South Asian (25%) ancestries. Finally, we find that family-based GWAS PGIs can modestly improve portability for select traits, such as BMI in African ancestry, suggesting that part of the portability gap may reflect population-specific confounds in standard PGIs.

Polygenic indexes (PGIs) trained on samples of European genetic ancestries often lose substantial predictive power when applied to non-European ancestries. While this portability problem is well recognized, its manifestation in behavioral and social traits remains understudied, and the factors driving this accuracy loss warrant more comprehensive analysis. Using data from the UK Biobank and Health and Retirement Study, we conduct a systematic analysis of PGI portability for 52 health-related, behavioral, and social phenotypes. We advance prior literature by using genome-wide PGIs, assessing cross-ancestry heritability differences, and comparing the performance of PGIs based on standard versus family-based GWAS. Our findings confirm systematic reductions in PGI predictive power for non-European ancestries—with relative accuracy being lowest in African (24%), followed by East Asian (37%) and South Asian (51%) genetic ancestries. We also find that biologically proximal traits exhibit greater portability than behavioral and social traits. We show that the relative importance of factors underlying reduced portability varies across traits and ancestries: in African ancestries, linkage disequilibrium and allele frequency differences explain most of the loss (82%), compared with smaller contributions in East (34%) and South Asian (25%) ancestries. Finally, we find that family-based GWAS PGIs can modestly improve portability for select traits, such as BMI in African ancestry, suggesting that part of the portability gap may reflect population-specific confounds in standard PGIs.

The clinical implementation of polygenic scores (PGSs) for disease risk prediction, particularly in reproductive health applications, requires rigorous validation. Here, we develop seventeen disease PGSs by conducting large-scale GWAS meta-analyses, and we validate our scores in out-of-sample prediction analyses. We achieve state-of-the-art predictive performance, consistently matching or outperforming academic and commercial benchmarks, with liability R ² reaching up to 0.21 (type 2 diabetes). The performance of a PGS for embryo screening depends on its predictive ability within-family, which can be lower than its prediction ability among unrelated individuals. However, very few disease PGSs have been tested within-family. We perform systematic within-family validation of our disease PGSs, finding no decrease in predictive performance within-family for 16 of 17 scores. PGS performance typically declines with genetic distance from training data, an effect that needs to be accounted for to give properly calibrated predictions across ancestries. We perform extensive calibration of our scores’ performance across different ancestries, finding improved cross-ancestry performance compared to previous approaches, especially in African and East Asian populations. This is likely due to the fact our scores are constructed using a method that incorporates functional genomic annotations on more than 7 million variants, enabling a degree of fine-mapping of causal variants shared across ancestries. We illustrate clinical utility through examining the risk reduction that could be achieved through embryo screening for type 2 diabetes: selecting among 10 embryos is expected to reduce absolute disease risk by 12-20% in families where both parents are affected, with similar relative risk reductions across ancestries. These findings establish a framework for implementing PGS in reproductive medicine while demonstrating both the technology’s potential for disease prevention and the methodological standards required for responsible clinical translation.

Polygenic indexes (PGIs) — DNA-based predictors of individual phenotypes — have become essential tools across biomedical and social sciences. We introduce Version 2 of the Polygenic Index Repository, which expands phenotype coverage from 47 to 61, increases the number of participating datasets from 11 to 20, and adopts a more consistent and improved methodology for PGI construction. For 16 phenotypes, we leverage summary statistics from an updated GWAS meta-analysis with greater statistical power compared to the original release, thereby improving the PGI’s predictive power. To improve power for family-based analyses, we provide imputed parental PGIs in all datasets with first-degree relatives and offer a framework for interpreting results from analyses that control for parental PGIs. We illustrate the utility of parental PGIs using two applications: (1) comparing PGI associations with and without parental PGI controls for all phenotypes in two Repository datasets with family data, and (2) for BMI and diastolic blood pressure, exploring the contribution of causal versus non-causal components of PGI associations to the imperfect portability of PGIs across subgroups within a genetic ancestry. Collectively, the updates enhance predictive performance, broaden the Repository’s scope, and introduce novel resources that reduce confounding bias and improve interpretability.

Family-based genome-wide association studies (FGWASs) use random, within-family genetic variation to remove confounding from estimates of direct genetic effects (DGEs). Here we introduce a ‘unified estimator’ that includes individuals without genotyped relatives, unifying standard and FGWAS while increasing power for DGE estimation. We also introduce a ‘robust estimator’ that is not biased in structured and/or admixed populations. In an analysis of 19 phenotypes in the UK Biobank, the unified estimator in the White British subsample and the robust estimator (applied without ancestry restrictions) increased the effective sample size for DGEs by 46.9% to 106.5% and 10.3% to 21.0%, respectively, compared to using genetic differences between siblings. Polygenic predictors derived from the unified estimator demonstrated superior out-of-sample prediction ability compared to other family-based methods. We implemented the methods in the software package snipar in an efficient linear mixed model that accounts for sample relatedness and sibling shared environment.

Although rare neurodevelopmental conditions have a large Mendelian component¹, common genetic variants also contribute to risk^2,3. However, little is known about how this polygenic risk is distributed among patients with these conditions and their parents nor its interplay with rare variants. It is also unclear whether polygenic background affects risk directly through alleles transmitted from parents to children, or whether indirect genetic effects mediated through the family environment⁴ also play a role. Here we addressed these questions using genetic data from 11,573 patients with rare neurodevelopmental conditions, 9,128 of their parents and 26,869 controls. Common variants explained around 10% of variance in risk. Patients with a monogenic diagnosis had significantly less polygenic risk than those without, supporting a liability threshold model⁵. A polygenic score for neurodevelopmental conditions showed only a direct genetic effect. By contrast, polygenic scores for educational attainment and cognitive performance showed no direct genetic effect, but the non-transmitted alleles in the parents were correlated with the child’s risk, potentially due to indirect genetic effects and/or parental assortment for these traits⁴. Indeed, as expected under parental assortment, we show that common variant predisposition for neurodevelopmental conditions is correlated with the rare variant component of risk. These findings indicate that future studies should investigate the possible role and nature of indirect genetic effects on rare neurodevelopmental conditions, and consider the contribution of common and rare variants simultaneously when studying cognition-related phenotypes.

There is recognition among human complex-trait geneticists that not only are many common assumptions made for the sake of statistical tractability (e.g., random mating, independence of parent/offspring environments) unlikely to apply in many contexts, but that methods reliant on such assumptions can yield misleading results, even in large samples. Investigations of the consequences of violating these assumptions so far have focused on individual perturbations operating in isolation. Here, we analyze widely used estimators of genetic architectural parameters, including LD-score regression and both population-based and within-family GWAS, across a broad array of perturbations to classical assumptions, such as multivariate assortative mating and vertical transmission (parental effects on offspring phenotypes not mediated by genetic inheritance). We find that widely-used statistical approaches are unreliable across a broad range of perturbations, and that structural sources of confounding often operate synergistically to distort conclusions. For example, mild multivariate assortative mating and vertical transmission together can dramatically inflate heritability estimates and GWAS false positive rates. Further, GWAS will become progressively more polluted by off-target associations as sample sizes increase. Given these challenges, we introduce xftsim, a forward time simulation library capable of modeling a wide range of genetic architectures, mating regimes, and transmission dynamics, to facilitate the systematic comparison of existing approaches and the development of robust methods. Together, our findings illustrate the importance of comprehensive sensitivity analysis and present a valuable tool for future research.