@article {67,
	title = {Extreme Polygenicity of Complex Traits Is Explained by Negative Selection.},
	journal = {Am J Hum Genet},
	volume = {105},
	year = {2019},
	month = {2019 Sep 05},
	pages = {456-476},
	abstract = {<p>Complex traits and common diseases are extremely polygenic, their heritability spread across thousands of loci. One possible explanation is that thousands of genes and loci have similarly important biological effects when mutated. However, we hypothesize that for most complex traits, relatively few genes and loci are critical, and negative selection-purging large-effect mutations in these regions-leaves behind common-variant associations in thousands of less critical regions instead. We refer to this phenomenon as flattening. To quantify its effects, we introduce a mathematical definition of polygenicity, the effective number of independently associated SNPs (M), which describes how evenly the heritability of a trait is spread across the genome. We developed a method, stratified LD fourth moments regression (S-LD4M), to estimate M, validating that it produces robust estimates in simulations. Analyzing 33 complex traits (average N = 361k), we determined that heritability is spread \~{}4{\texttimes} more evenly among common SNPs than among low-frequency SNPs. This difference, together with evolutionary modeling of new mutations, suggests that complex traits would be orders of magnitude less polygenic if not for the influence of negative selection. We also determined that heritability is spread more evenly within functionally important regions in proportion to their heritability enrichment; functionally important regions do not harbor common SNPs with greatly increased causal effect sizes, due to selective constraint. Our results suggest that for most complex traits, the genes and loci with the most critical biological effects often differ from those with the strongest common-variant associations.</p>},
	issn = {1537-6605},
	doi = {10.1016/j.ajhg.2019.07.003},
	author = {O{\textquoteright}Connor, Luke J and Schoech, Armin P and Hormozdiari, Farhad and Gazal, Steven and Patterson, Nick and Price, Alkes L}
}
@article {48,
	title = {Quantification of frequency-dependent genetic architectures in 25 UK Biobank traits reveals action of negative selection.},
	journal = {Nat Commun},
	volume = {10},
	year = {2019},
	month = {2019 Feb 15},
	pages = {790},
	abstract = {<p>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.</p>},
	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},
	issn = {2041-1723},
	doi = {10.1038/s41467-019-08424-6},
	author = {Schoech, Armin P and Jordan, Daniel M and Loh, Po-Ru and Gazal, Steven and O{\textquoteright}Connor, Luke J and Balick, Daniel J and Palamara, Pier F and Finucane, Hilary K and Sunyaev, Shamil R and Price, Alkes L}
}
