Quantification of frequency-dependent genetic architectures in 25 UK Biobank traits reveals action of negative selection. - Schoech, Armin P; Jordan, Daniel M; Loh, Po-Ru; Gazal, Steven; O'Connor, Luke J; Balick, Daniel J; Palamara, Pier F; Finucane, Hilary K; Sunyaev, Shamil R; Price, Alkes L (Nat Commun, 2019)
Understanding the role of rare variants is important in elucidating the genetic basis of human disease. Negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1 - p)], where p is the MAF and negative values of α imply larger effect sizes for rare variants. We estimate α for 25 UK Biobank diseases and complex traits. All traits produce negative α estimates, with best-fit mean of -0.38 (s.e. 0.02) across traits. Despite larger rare variant effect sizes, rare variants (MAF < 1%) explain less than 10% of total SNP-heritability for most traits analyzed. Using evolutionary modeling and forward simulations, we validate the α model of MAF-dependent trait effects and assess plausible values of relevant evolutionary parameters.
Keywords: Algorithms; Alleles; Biological Specimen Banks; Gene Frequency; Genome-Wide Association Study; Genotype; Humans; Models, Genetic; Polymorphism, Single Nucleotide; Quantitative Trait, Heritable; Selection, Genetic; United Kingdom

Quantifying the Polygenic Contribution to Cutaneous Squamous Cell Carcinoma Risk. - Sordillo, Joanne E; Kraft, Peter; Wu, Ann Chen; Asgari, Maryam M (J Invest Dermatol, 2018)
Genetic factors play an important role in cutaneous squamous cell carcinoma risk. Genome-wide association studies have identified 21 single nucleotide polymorphisms associated with cutaneous squamous cell carcinoma risk. Yet no studies have attempted to quantify the contribution of heritability to cutaneous squamous cell carcinoma risk by calculating the population attributable risk using a combination of all discovered genetic variants. Using an additive multi-locus linear logistic model, we determined the cumulative association of these 21 genetic regions to cutaneous squamous cell carcinoma population attributable risk. We computed a multi-locus population attributable risk of 62%, suggesting that if the effects of all the risk alleles were removed from a population, the cutaneous squamous cell carcinoma risk would drop by 62%. Using stratified analysis, we also examined the impact of sex on polygenic risk score, and found that men have an increased relative risk throughout the spectrum of the polygenic risk score. Quantifying the impact of genetic predisposition on the proportion of cancer cases can guide future research decisions and public health policy planning.
Keywords: Carcinoma, Squamous Cell; Female; Genetic Loci; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Linear Models; Logistic Models; Male; Models, Biological; Polymorphism, Single Nucleotide; Risk Assessment; Risk Factors; Sex Factors; Skin Neoplasms