# Alexander Strudwick Young

I’m an assistant professor in the Human Genetics Department at [UCLA](https://medschool.ucla.edu/people/alexander-young-dphil).

I am interested in how genetic variation gives rise to the diversity of humans in all their facets: physical traits, behaviours, social outcomes. A central theme of my research is how we can use the randomisation of genetic material within-families to disentangle nature and nurture. This then enables us to understand how genetic and cultural transmission from parents to offspring, combined with mating patterns, shape the distribution of traits in the population.

I develop theory, statistical methods, and [software](https://alextisyoung.com/software/) for analyzing genetic data.

Please get in touch if you’re interested in working with me as a pre-doctoral research assistant, graduate student, or postdoc. You can apply for a postdoc position [here](https://recruit.apo.ucla.edu/JPF10842).

In previous years, I obtained degrees in mathematics and statistics, computational biology, and a doctorate in genomic medicine and statistics from the University of Oxford. I worked at deCODE Genetics in Reykjavik.

I used to write a blog, archived [here](https://geneticvariance.wordpress.com/blog/).

I am advisor to [Herasight](https://www.herasight.com/), a company building advanced genetic tests for IVF, where I’ve worked on algorithms for genotyping embryos from ultra-low-pass sequencing data used to detect aneuploidies (PGT-A).

I am a faculty member of the UCLA Jonsson Comprehensive Cancer Center, and I am currently battling metastatic rectal cancer. See [youngcrc.com](https://www.youngcrc.com/), where I have open-sourced all the data from my case.

## [selected publications](https://alextisyoung.com/publications/)

![Graphical overview of the Mexico City ancestry study comparing unrelated people with families](https://alextisyoung.com/assets/img/publication_preview/mexican_ancestry.png)

### Within-family effect of ancestry on complex traits in a Mexican population

Siqi Wang, Jaime Berumen, Alejandra Vergara-Lope, and Paulina Baca, Elizabeth Barrera, Fernando Rivas, Diego Aguilar-Ramirez, Rory Collins, Jonathan R. Emberson, Michael Hill, Michael E. Goddard, Loic Yengo, Alexander Strudwick Young, Jesus Alegre-Díaz, Pablo Kuri-Morales, Roberto Tapia-Conyer, Jason M. Torres, Peter M. Visscher

*Nature*, 2026

Abs [HTML](https://www.nature.com/articles/s41586-026-11039-9) [PDF](https://www.nature.com/articles/s41586-026-11039-9.pdf) [Website](https://www.medrxiv.org/content/10.1101/2025.09.09.25335237)

Human populations differ in disease prevalence and phenotypes, but the extent to which differences are caused by genetic factors is unknown for most complex traits. Comparing phenotypic means across populations is confounded by environmental differences and using polygenic predictors can lead to biased inference. Family-based analyses of people of genetically admixed ancestry enable estimation of ancestry effects unconfounded by ancestry–environment correlations. Here we leverage genetic data from admixed adults in the Mexico City Prospective Study to estimate within-family ancestry effects. We assessed genetic ancestry and 15 complex traits in 52,583 unrelated people and 39,714 relatives from 17,627 families. At the population level, relative to European ancestry, the effect of Indigenous American ancestry was −1.98 s.d. (P < 2 × 10⁻¹⁶) for height and a natural log odds ratio of 1.73 (95% confidence interval, 1.54–1.92) for type 2 diabetes. Within families, the effect of Indigenous American ancestry was −1.51 s.d. (P = 10⁻⁸) for height and natural log odds ratio of 5.13 (95% confidence interval, 2.48–7.78) for type 2 diabetes. These effects are supported by between-ancestry differences in trait-increasing allele counts and evidence of selection at trait-associated loci. We found no within-family ancestry effect on educational attainment or other traits despite significant associations at the population level, implying environmental causes or confounding. Overall, this study provides an experimental design to study between-ancestry genetic effects and identifies significant ancestry differences for height, type 2 diabetes and metabolic traits in a genetically diverse population from Mexico City.

![Educational attainment polygenic prediction accuracy increases with GWAS sample size in Add Health and HRS](https://alextisyoung.com/assets/img/publication_preview/social_science_genomics.png)

### Social-Science Genomics: Progress, Challenges, and Future Directions

Daniel J. Benjamin, David Cesarini, Patrick Turley, and Alexander Strudwick Young

*Journal of Economic Literature*, 2026

Forthcoming

Abs [HTML](https://www.aeaweb.org/articles?id=10.1257/jel.20261604) [PDF](https://www.chapman.edu/research/institutes-and-centers/economic-science-institute/_files/ifree-papers-and-photos/dan-benjamin-2026.pdf) [Website](https://www.nber.org/papers/w32404)

Rapid progress has been made in identifying links between human genetic variation and social and behavioral phenotypes. Applications in mainstream economics are beginning to emerge. This review aims to provide the background needed to bring the interested economist to the frontier of social-science genomics. Our review is structured around a statistical framework that nests many of the key methods, concepts and tools found in the literature. We clarify key assumptions and appropriate interpretations. After critically reviewing several significant applications, we conclude by outlining future advances in genetics that will enable more and improved applications, and we discuss the ethical and communication challenges that arise in this area of research.

![ImputePGTA: accurate embryo genotyping and polygenic scoring from ultra-low-pass sequencing](https://alextisyoung.com/assets/img/publication_preview/imputepgta.png)

### ImputePGTA: accurate embryo genotyping and polygenic scoring from ultra-low-pass sequencing

Jeremiah H Li, Tobias Wolfram, Ivan Davidson, and Justin Schleede, Jennifer Swift, Spencer Moore, David Stern, Michael Christensen, Alexander Strudwick Young

*medRxiv*, 2025

Preprint; revised February 2026

Abs [HTML](https://www.medrxiv.org/content/10.1101/2025.11.07.25339763v2) [PDF](https://www.medrxiv.org/content/medrxiv/early/2026/02/14/2025.11.07.25339763.full.pdf)

Preimplantation genetic testing (PGT) for polygenic risk (PGT-P) holds great promise for reducing lifetime disease burden, but genotyping embryos remains difficult. PGT for aneuploidy (PGT-A) is a routine test used in over half of in vitro fertilization cycles in the United States, typically via ultra-low-pass (ULP) sequencing (∼0.004x) or, less commonly, genotyping arrays. Here we describe an approach that enables accurate embryo genotyping from PGT-A data when combined with estimated parental haplotypes. We develop a Coupled Hidden Markov Model, ImputePGTA, which jointly infers inheritance patterns from parents to offspring as well as phasing errors in parental haplotypes, along with an inference algorithm that scales linearly with the number of embryos. The performance of our approach depends on the phasing of parental haplotypes, which we improve through a method, phaseGrafter, that combines evidence from short and long reads, further enabling imputation of rare variants. We validate our approach through simulations and comparison of embryo genomes reconstructed from real PGT-A data to post-birth whole genome sequencing data. When using long reads for parental phasing, we achieve a dosage correlation of 0.98 with high-quality post-birth genotypes, and a mean absolute difference of 0.11 standard deviations across 17 disease polygenic scores, lower than from imputation of genotyping array data from reference panels. Uncertainty from imputation from ULP PGT-A data with accurate parental phasing results in only a ∼2% attenuation in expected gains from embryo selection for typical embryo cohort sizes. Our approach removes an important technological barrier to using PGT-P and is already facilitating more widespread adoption.

![Family-based genome-wide association study designs for increased power and robustness](https://alextisyoung.com/assets/img/publication_preview/41588_2025_2118_Fig5_HTML.webp)

### Family-based genome-wide association study designs for increased power and robustness

Junming Guan, Tammy Tan, Seyed Moeen Nehzati, and Michael Bennett, Patrick Turley, Daniel J Benjamin, Alexander Strudwick Young

*Nature Genetics*, 2025

Abs [HTML](https://www.nature.com/articles/s41588-025-02118-0) [PDF](https://www.nature.com/articles/s41588-025-02118-0.pdf) [Code](https://github.com/AlexTISYoung/snipar)

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.

![Family-GWAS reveals effects of environment and mating on genetic associations](https://alextisyoung.com/assets/img/publication_preview/direct_pop_rg_density_sig_edited.png)

### Family-GWAS reveals effects of environment and mating on genetic associations

Tammy Tan, Hariharan Jayashankar, Junming Guan, and Seyed Moeen Nehzati, Mahdi Mir, Michael Bennett, Esben Agerbo, Rafael Ahlskog, Ville Andrade Anapaz, Bjørn Olav Åsvold, Stefania Benonisdottir, Laxmi Bhatta, Dorret I. Boomsma, Ben Brumpton, Archie Campbell, Christopher F. Chabris, Rosa Cheesman, Zhengming Chen, China Kadoorie Biobank Collaborative Group, Eco Geus, Erik A. Ehli, Abdelrahman G. Elnahas, Estonian Biobank Research Team, Finngen, Andrea Ganna, Alexandros Giannelis, Liisa Hakaste, Ailin Falkmo Hansen, Alexandra Havdahl, Caroline Hayward, Jouke-Jan Hottenga, Mikkel Aagaard Houmark, Kristian Hveem, Jaakko Kaprio, Arnulf Langhammer, Antti Latvala, James J. Lee, Mikko Lehtovirta, Liming Li, LifeLines Cohort Study, Kuang Lin, Richard Karlsson Linnér, Stefano Lombardi, Nicholas G. Martin, Matt McGue, Sarah E. Medland, Andres Metspalu, Brittany L. Mitchell, Guiyan Ni, Ilja M. Nolte, Matthew T. Oetjens, Sven Oskarsson, Teemu Palviainen, Rashmi B. Prasad, Anu Reigo, Kadri Reis, Julia Sidorenko, Karri Silventoinen, Harold Snieder, Tiinamaija Tuomi, Bjarni J. Vilhjálmsson, Robin G. Walters, Emily A. Willoughby, Bendik S. Winsvold, Eivind Ystrom, Jonathan Flint, Loic Yengo, Peter M. Visscher, Augustine Kong, Elliot M. Tucker-Drob, Richard Border, David Cesarini, Patrick Turley, Aysu Okbay, Daniel J. Benjamin, Alexander Strudwick Young

*medRxiv*, 2026

Preprint; revised January 22, 2026

Abs [HTML](https://www.medrxiv.org/content/10.1101/2024.10.01.24314703v3) [PDF](https://www.medrxiv.org/content/medrxiv/early/2026/01/22/2024.10.01.24314703.full.pdf) [Blog](https://twitter.com/AlexTISYoung/status/1843288303325593923)

Genome-wide association studies (GWAS) have discovered thousands of replicable genetic associations, guiding drug target discovery and powering genetic prediction of human phenotypes and diseases. However, genetic associations can be affected by gene-environment correlations and non-random mating, which can lead to biased inferences in downstream analyses. Family-based GWAS (FGWAS) uses the natural experiment of random assignment of genotype within families to separate out the contribution of direct genetic effects (DGEs) — causal effects of alleles in an individual on an individual — from other factors contributing to genetic associations. Here, we report results from an FGWAS meta-analysis of 34 phenotypes from 17 cohorts. We found evidence that factors uncorrelated with DGEs make substantial contributions to genetic associations for 27 phenotypes, with population stratification confounding — a form of gene-environment correlation — likely the major cause. By estimating SNP heritability and genetic correlations using DGEs, we found evidence that assortative mating has led to overestimation of SNP heritability for 5 phenotypes and overestimation of the degree of shared genetic effects (pleiotropy) between 22 pairs of phenotypes. Polygenic predictors constructed from DGEs are particularly useful for studying natural selection, assortative mating, and indirect genetic effects (effects of relatives’ genes mediated through the family environment). We validate our meta-analysis results by predicting phenotypes in hold-out samples using polygenic predictors constructed from DGEs, achieving statistically significant out-of-sample prediction for 24 phenotypes with little attenuation of predictive power within-families. We provide FGWAS summary statistics for 34 phenotypes that can be used for downstream analyses. Our study provides both a template for performing FGWAS and an argument for its value for debiasing inferences and understanding the impact of environment and mating patterns.

![Estimation of indirect genetic effects and heritability under assortative mating](https://alextisyoung.com/assets/img/publication_preview/two_gen_estimation.png)

### Estimation of indirect genetic effects and heritability under assortative mating

Alexander Strudwick Young

*bioRxiv*, 2023

Abs [HTML](https://www.biorxiv.org/content/10.1101/2023.07.10.548458v1) [PDF](https://alextisyoung.com/assets/pdf/intergenerational.pdf) [Supp](https://alextisyoung.com/assets/pdf/IGE_AM_supplement.pdf) [Blog](https://x.com/AlexTISYoung/status/1679003623483912198?s=20) [Code](https://github.com/AlexTISYoung/snipar) [Website](https://snipar.readthedocs.io/en/latest/simulation.html#adjusting-for-assortative-mating)

Both direct genetic effects (effects of alleles in an individual on that individual) and indirect genetic effects — effects of alleles in an individual (e.g. parents) on another individual (e.g. offspring) — can contribute to phenotypic variation and genotype-phenotype associations. Here, we consider a phenotype affected by direct and parental indirect genetic effects under assortative mating at equilibrium. We generalize classical theory to derive a decomposition of the equilibrium phenotypic variance in terms of direct and indirect genetic effect components. We extend this theory to show that popular methods for estimating indirect genetic effects or ‘genetic nurture’ through analysis of parental and offspring polygenic predictors (called polygenic indices or scores — PGIs or PGSs) are substantially biased by assortative mating. We propose an improved method for estimating indirect genetic effects while accounting for assortative mating that can also correct heritability estimates for bias due to assortative mating. We validate our method in simulations and apply it to PGIs for height and educational attainment (EA), estimating that the equilibrium heritability of height is 0.699 (S.E. = 0.075) and finding no evidence for indirect genetic effects on height. We estimate a very high correlation between parents’ underlying genetic components for EA, 0.755 (S.E. = 0.035), which is inconsistent with twin based estimates of the heritability of EA, possibly due to confounding in the EA PGI and/or in twin studies. We implement our method in the software package snipar, enabling researchers to apply the method to data including observed and/or imputed parental genotypes. We provide a theoretical framework for understanding the results of PGI analyses and a practical methodology for estimating heritability and indirect genetic effects while accounting for assortative mating.

![Mendelian imputation of parental genotypes improves estimates of direct genetic effects](https://alextisyoung.com/assets/img/publication_preview/snipar_flowchart.png)

### Mendelian imputation of parental genotypes improves estimates of direct genetic effects

Alexander Strudwick Young, Seyed Moeen Nehzati, Stefania Benonisdottir, and Aysu Okbay, Hariharan Jayashankar, Chanwook Lee, David Cesarini, Daniel J Benjamin, Patrick Turley, Augustine Kong

*Nature genetics*, 2022

Abs [HTML](https://www.nature.com/articles/s41588-022-01085-0) [PDF](https://alextisyoung.com/assets/pdf/snipar.pdf) [Supp](https://alextisyoung.com/assets/pdf/snipar_supp.pdf) [Blog](https://x.com/AlexTISYoung/status/1534916164861562880?s=20) [Code](https://github.com/AlexTISYoung/snipar) [Website](https://snipar.readthedocs.io/en/latest/guide.html)

Effects estimated by genome-wide association studies (GWASs) include effects of alleles in an individual on that individual (direct genetic effects), indirect genetic effects (for example, effects of alleles in parents on offspring through the environment) and bias from confounding. Within-family genetic variation is random, enabling unbiased estimation of direct genetic effects when parents are genotyped. However, parental genotypes are often missing. We introduce a method that imputes missing parental genotypes and estimates direct genetic effects. Our method, implemented in the software package snipar (single-nucleotide imputation of parents), gives more precise estimates of direct genetic effects than existing approaches. Using 39,614 individuals from the UK Biobank with at least one genotyped sibling/parent, we estimate the correlation between direct genetic effects and effects from standard GWASs for nine phenotypes, including educational attainment (r = 0.739, standard error (s.e.) = 0.086) and cognitive ability (r = 0.490, s.e. = 0.086). Our results demonstrate substantial confounding bias in standard GWASs for some phenotypes.

![Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals](https://alextisyoung.com/assets/img/publication_preview/ea4_preview.png)

### Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

Aysu Okbay, Yeda Wu, Nancy Wang, and Hariharan Jayashankar, Michael Bennett, Seyed Moeen Nehzati, Julia Sidorenko, Hyeokmoon Kweon, Grant Goldman, Tamara Gjorgjieva, others

*Nature genetics*, 2022

Abs [HTML](https://www.nature.com/articles/s41588-022-01016-z) [PDF](https://alextisyoung.com/assets/pdf/ea4.pdf) [Supp](https://alextisyoung.com/assets/pdf/ea4_supp.pdf) [Blog](https://x.com/AlexTISYoung/status/1509557326935576577?s=20) [Code](https://github.com/AlexTISYoung/SNIPar/tree/EA4)

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ∼3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12–16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI’s magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.

![Deconstructing the sources of genotype-phenotype associations in humans](https://alextisyoung.com/assets/img/publication_preview/deconstructing.png)

### Deconstructing the sources of genotype-phenotype associations in humans

Alexander Strudwick Young, Stefania Benonisdottir, Molly Przeworski, and Augustine Kong

*Science*, 2019

Abs [HTML](https://www.science.org/doi/full/10.1126/science.aax3710) [PDF](https://alextisyoung.com/assets/pdf/deconstructing.pdf)

Efforts to link variation in the human genome to phenotypes have progressed at a tremendous pace in recent decades. Most human traits have been shown to be affected by a large number of genetic variants across the genome. To interpret these associations and to use them reliably—in particular for phenotypic prediction—a better understanding of the many sources of genotype-phenotype associations is necessary. We summarize the progress that has been made in this direction in humans, notably in decomposing direct and indirect genetic effects as well as population structure confounding. We discuss the natural next steps in data collection and methodology development, with a focus on what can be gained by analyzing genotype and phenotype data from close relatives.

![Solving the missing heritability problem](https://alextisyoung.com/assets/img/publication_preview/solving.png)

### Solving the missing heritability problem

Alexander Strudwick Young

*PLoS genetics*, 2019

[HTML](https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1008222) [PDF](https://alextisyoung.com/assets/pdf/solving.pdf)

![Relatedness disequilibrium regression estimates heritability without environmental bias](https://alextisyoung.com/assets/img/publication_preview/RDR.png)

### Relatedness disequilibrium regression estimates heritability without environmental bias

Alexander Strudwick Young, Michael L Frigge, Daniel F Gudbjartsson, and Gudmar Thorleifsson, Gyda Bjornsdottir, Patrick Sulem, Gisli Masson, Unnur Thorsteinsdottir, Kari Stefansson, Augustine Kong

*Nature genetics*, 2018

Abs [HTML](https://www.nature.com/articles/s41588-018-0178-9) [PDF](https://alextisyoung.com/assets/pdf/RDR.pdf) [Supp](https://alextisyoung.com/assets/pdf/RDR_supp.pdf) [Blog](https://geneticvariance.wordpress.com/2018/08/13/relatedness-disequilibrium-regression-explained/) [Code](https://github.com/AlexTISYoung/RDR)

Heritability measures the proportion of trait variation that is due to genetic inheritance. Measurement of heritability is important in the nature-versus-nurture debate. However, existing estimates of heritability may be biased by environmental effects. Here, we introduce relatedness disequilibrium regression (RDR), a novel method for estimating heritability. RDR avoids most sources of environmental bias by exploiting variation in relatedness due to random Mendelian segregation. We used a sample of 54,888 Icelanders who had both parents genotyped to estimate the heritability of 14 traits, including height (55.4%, s.e. 4.4%) and educational attainment (17.0%, s.e. 9.4%). Our results suggest that some other estimates of heritability may be inflated by environmental effects.

![Identifying loci affecting trait variability and detecting interactions in genome-wide association studies](https://alextisyoung.com/assets/img/publication_preview/41588_2018_225_Fig2_HTML.webp)

### Identifying loci affecting trait variability and detecting interactions in genome-wide association studies

Alexander Strudwick Young, Fabian L Wauthier, and Peter Donnelly

*Nature genetics*, 2018

Abs [HTML](https://www.nature.com/articles/s41588-018-0225-6) [PDF](https://alextisyoung.com/assets/pdf/hlmm.pdf) [Supp](https://alextisyoung.com/assets/pdf/hlmm_supp.pdf) [Blog](https://geneticvariance.wordpress.com/2018/10/15/finding-genetic-effects-on-phenotypic-variability/) [Code](https://github.com/AlexTISYoung/hlmm)

Identification of genetic variants with effects on trait variability can provide insights into the biological mechanisms that control variation and can identify potential interactions. We propose a two-degree-of-freedom test for jointly testing mean and variance effects to identify such variants. We implement the test in a linear mixed model, for which we provide an efficient algorithm and software. To focus on biologically interesting settings, we develop a test for dispersion effects, that is, variance effects not driven solely by mean effects when the trait distribution is non-normal. We apply our approach to body mass index in the subsample of the UK Biobank population with British ancestry (n ∼408,000) and show that our approach can increase the power to detect associated loci. We identify and replicate novel associations with significant variance effects that cannot be explained by the non-normality of body mass index, and we provide suggestive evidence for a connection between leptin levels and body mass index variability.

Source: [Alexander Strudwick Young](https://alextisyoung.com/)