@article {132, title = {Disentangling selection on genetically correlated polygenic traits via whole-genome genealogies.}, journal = {Am J Hum Genet}, volume = {108}, year = {2021}, month = {2021 02 04}, pages = {219-239}, abstract = {

We present a full-likelihood method to infer polygenic adaptation from DNA sequence variation and GWAS summary statistics to quantify recent transient directional selection acting on a complex trait. Through simulations of polygenic trait architecture evolution and GWASs, we show the method substantially improves power over current methods. We examine the robustness of the method under stratification, uncertainty and bias in marginal effects, uncertainty in the causal SNPs, allelic heterogeneity, negative selection, and low GWAS sample size. The method can quantify selection acting on correlated traits, controlling for pleiotropy even among traits with strong genetic correlation (|r|=80\%) while retaining high power to attribute selection to the causal trait. When the causal trait is excluded from analysis, selection is attributed to its closest proxy. We discuss limitations of the method, cautioning against strongly causal interpretations of the results, and the possibility of undetectable gene-by-environment (GxE) interactions. We apply the method to 56 human polygenic traits, revealing signals of directional selection on pigmentation, life history, glycated hemoglobin (HbA1c), and other traits. We also conduct joint testing of 137 pairs of genetically correlated traits, revealing widespread correlated response acting on these traits (2.6-fold enrichment, p = 1.5~{\texttimes} 10). Signs of selection on some traits previously reported as adaptive (e.g., educational attainment and hair color) are largely attributable to correlated response (p = 2.9~{\texttimes} 10 and 1.7~{\texttimes} 10, respectively). Lastly, our joint test shows antagonistic selection has increased type 2 diabetes risk and decrease HbA1c (p = 1.5~{\texttimes} 10).

}, keywords = {Computer Simulation, Diabetes Mellitus, Type 2, Evolution, Molecular, Gene-Environment Interaction, Genetic Heterogeneity, Genetic Pleiotropy, Genome, Human, Genome-Wide Association Study, Glycated Hemoglobin A, Humans, Models, Genetic, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Sample Size, Selection, Genetic}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2020.12.005}, author = {Stern, Aaron J and Speidel, Leo and Zaitlen, Noah A and Nielsen, Rasmus} } @article {88, title = {Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex.}, journal = {Cell Rep}, volume = {31}, year = {2020}, month = {2020 04 07}, pages = {107489}, abstract = {

Gene expression levels vary across developmental stage, cell type, and region in the brain. Genomic variants also contribute to the variation in expression, and some neuropsychiatric disorder loci may exert their effects through this mechanism. To investigate these relationships, we present BrainVar, a unique resource of paired whole-genome and bulk tissue RNA sequencing from the dorsolateral prefrontal cortex of 176 individuals across prenatal and postnatal development. Here we identify common variants that alter gene expression (expression quantitative trait loci [eQTLs]) constantly across development or predominantly during prenatal or postnatal stages. Both "constant" and "temporal-predominant" eQTLs are enriched for loci associated with neuropsychiatric traits and disorders and colocalize with specific variants. Expression levels of more than 12,000 genes rise or fall in a concerted late-fetal transition, with the transitional genes enriched for cell-type-specific genes and neuropsychiatric risk loci, underscoring the importance of cataloging developmental trajectories in understanding cortical physiology and pathology.

}, keywords = {Base Sequence, Brain, Computational Biology, Databases, Genetic, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Genomics, Humans, Phenotype, Polymorphism, Single Nucleotide, Prefrontal Cortex, Quantitative Trait Loci, Sequence Analysis, RNA, Transcriptome, Whole Exome Sequencing, Whole Genome Sequencing}, issn = {2211-1247}, doi = {10.1016/j.celrep.2020.03.053}, author = {Werling, Donna M and Pochareddy, Sirisha and Choi, Jinmyung and An, Joon-Yong and Sheppard, Brooke and Peng, Minshi and Li, Zhen and Dastmalchi, Claudia and Santpere, Gabriel and Sousa, Andr{\'e} M M and Tebbenkamp, Andrew T N and Kaur, Navjot and Gulden, Forrest O and Breen, Michael S and Liang, Lindsay and Gilson, Michael C and Zhao, Xuefang and Dong, Shan and Klei, Lambertus and Cicek, A Ercument and Buxbaum, Joseph D and Adle-Biassette, Homa and Thomas, Jean-Leon and Aldinger, Kimberly A and O{\textquoteright}Day, Diana R and Glass, Ian A and Zaitlen, Noah A and Talkowski, Michael E and Roeder, Kathryn and State, Matthew W and Devlin, Bernie and Sanders, Stephan J and Sestan, Nenad} } @article {49, title = {An evolutionary compass for detecting signals of polygenic selection and mutational bias.}, journal = {Evol Lett}, volume = {3}, year = {2019}, month = {2019 Feb}, pages = {69-79}, abstract = {

Selection and mutation shape the genetic variation underlying~human traits, but the specific evolutionary mechanisms driving~complex trait variation are largely unknown. We developed a statistical method that uses polarized genome-wide association study (GWAS) summary statistics from a single population to detect signals of mutational bias and selection. We found evidence for nonneutral signals on variation underlying several traits (body mass index [BMI], schizophrenia, Crohn{\textquoteright}s disease, educational attainment, and height). We then used simulations that incorporate simultaneous negative and positive selection to show that these signals are consistent with mutational bias and shifts in the fitness-phenotype relationship, but not stabilizing selection or mutational bias alone. We additionally replicate two of our top three signals (BMI and educational attainment) in an external cohort, and show that population stratification may have confounded GWAS summary statistics for height in the GIANT cohort. Our results provide a flexible and powerful framework for evolutionary analysis of complex phenotypes in humans and other species, and offer insights into the evolutionary mechanisms driving variation in human polygenic~traits.

}, issn = {2056-3744}, doi = {10.1002/evl3.97}, author = {Uricchio, Lawrence H and Kitano, Hugo C and Gusev, Alexander and Zaitlen, Noah A} }