@article {53, title = {Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies.}, journal = {Elife}, volume = {8}, year = {2019}, month = {2019 Mar 21}, abstract = {

Genetic predictions of height differ among human populations and these differences have been interpreted as evidence of polygenic adaptation. These differences were first detected using SNPs genome-wide significantly associated with height, and shown to grow stronger when large numbers of sub-significant SNPs were included, leading to excitement about the prospect of analyzing large fractions of the genome to detect polygenic adaptation for multiple traits. Previous studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the analyses in the UK Biobank, a much more homogeneously designed study. We show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population stratification. More generally, our results imply that typical constructions of polygenic scores are sensitive to population stratification and that population-level differences should be interpreted with caution.

Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor{\textquoteright}s assessment is that all the issues have been addressed (see decision letter).

}, issn = {2050-084X}, doi = {10.7554/eLife.39702}, author = {Sohail, Mashaal and Maier, Robert M and Ganna, Andrea and Bloemendal, Alex and Martin, Alicia R and Turchin, Michael C and Chiang, Charleston Wk and Hirschhorn, Joel and Daly, Mark J and Patterson, Nick and Neale, Benjamin and Mathieson, Iain and Reich, David and Sunyaev, Shamil R} } @article {36, title = {Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases.}, journal = {Nat Genet}, volume = {50}, year = {2018}, month = {2018 May}, pages = {693-698}, abstract = {

Horizontal pleiotropy occurs when the variant has an effect on disease outside of its effect on the exposure in Mendelian randomization (MR). Violation of the {\textquoteright}no horizontal pleiotropy{\textquoteright} assumption can cause severe bias in MR. We developed the Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test to identify horizontal pleiotropic outliers in multi-instrument summary-level MR testing. We showed using simulations that the MR-PRESSO test is best suited when horizontal pleiotropy occurs in <50\% of instruments. Next we applied the MR-PRESSO test, along with several other MR tests, to complex traits and diseases and found that horizontal pleiotropy (i) was detectable in over 48\% of significant causal relationships in MR; (ii) introduced distortions in the causal estimates in MR that ranged on average from -131\% to 201\%; (iii) induced false-positive causal relationships in up to 10\% of relationships; and (iv) could be corrected in some but not all instances.

}, keywords = {Disease, Genetic Pleiotropy, Genetic Predisposition to Disease, Genetic Variation, Humans}, issn = {1546-1718}, doi = {10.1038/s41588-018-0099-7}, author = {Verbanck, Marie and Chen, Chia-Yen and Neale, Benjamin and Do, Ron} } @article {34, title = {A synthetic-diploid benchmark for accurate variant-calling evaluation.}, journal = {Nat Methods}, volume = {15}, year = {2018}, month = {2018 Aug}, pages = {595-597}, abstract = {

Existing benchmark datasets for use in evaluating variant-calling accuracy are constructed from a consensus of known short-variant callers, and they are thus biased toward easy regions that are accessible by these algorithms. We derived a new benchmark dataset from the de novo PacBio assemblies of two fully homozygous human cell lines, which provides a relatively more accurate and less biased estimate of small-variant-calling error rates in a realistic context.

}, keywords = {Algorithms, Benchmarking, Cell Line, Tumor, Databases, Genetic, Diploidy, Female, Genetic Variation, Genome, Human, Homozygote, Humans, Hydatidiform Mole, Pregnancy, Synthetic Biology, Uterine Neoplasms, Whole Genome Sequencing}, issn = {1548-7105}, doi = {10.1038/s41592-018-0054-7}, author = {Li, Heng and Bloom, Jonathan M and Farjoun, Yossi and Fleharty, Mark and Gauthier, Laura and Neale, Benjamin and MacArthur, Daniel} }