%0 Journal Article %1 sohail2018signals %A Sohail, Mashaal %A Maier, Robert M. %A Ganna, Andrea %A Bloemendal, Alexander %A Martin, Alicia R. %A Turchin, Michael C. %A Chiang, Charleston W. K. %A Hirschhorn, Joel N. %A Daly, Mark J. %A Patterson, Nick %A Neale, Benjamin M. %A Mathieson, Iain %A Reich, David %A Sunyaev, Shamil R. %D 2018 %I Cold Spring Harbor Laboratory %J bioRxiv %K GWAS human_genome human_height methods polygenic_variation quantitative_genetics %R 10.1101/355057 %T Signals of polygenic adaptation on height have been overestimated due to uncorrected population structure in genome-wide association studies %U https://www.biorxiv.org/content/early/2018/06/28/355057 %X Genetic predictions of height differ among human populations and these differences are too large to be explained by genetic drift. This observation has been interpreted as evidence of polygenic adaptation. Differences across populations were detected using SNPs genome-wide significantly associated with height, and many studies also found that the signals grew stronger when large numbers of sub-significant SNPs were analyzed. This has led to excitement about the prospect of analyzing large fractions of the genome to detect subtle signals of selection and claims of polygenic adaptation for multiple traits. Polygenic adaptation studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the height analyses in the UK Biobank, a much more homogeneously designed study. Our results show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population structure.

@article{sohail2018signals, abstract = {Genetic predictions of height differ among human populations and these differences are too large to be explained by genetic drift. This observation has been interpreted as evidence of polygenic adaptation. Differences across populations were detected using SNPs genome-wide significantly associated with height, and many studies also found that the signals grew stronger when large numbers of sub-significant SNPs were analyzed. This has led to excitement about the prospect of analyzing large fractions of the genome to detect subtle signals of selection and claims of polygenic adaptation for multiple traits. Polygenic adaptation studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the height analyses in the UK Biobank, a much more homogeneously designed study. Our results show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population structure.}, added-at = {2018-07-04T19:09:00.000+0200}, author = {Sohail, Mashaal and Maier, Robert M. and Ganna, Andrea and Bloemendal, Alexander and Martin, Alicia R. and Turchin, Michael C. and Chiang, Charleston W. K. and Hirschhorn, Joel N. and Daly, Mark J. and Patterson, Nick and Neale, Benjamin M. and Mathieson, Iain and Reich, David and Sunyaev, Shamil R.}, biburl = {https://www.bibsonomy.org/bibtex/2bed4733cd4d39d0b2d6b0036e480e85a/peter.ralph}, doi = {10.1101/355057}, eprint = {https://www.biorxiv.org/content/early/2018/06/28/355057.full.pdf}, interhash = {7aa562f5f43efe26ef70d5efba4f6424}, intrahash = {bed4733cd4d39d0b2d6b0036e480e85a}, journal = {bioRxiv}, keywords = {GWAS human_genome human_height methods polygenic_variation quantitative_genetics}, publisher = {Cold Spring Harbor Laboratory}, timestamp = {2018-07-04T19:09:00.000+0200}, title = {Signals of polygenic adaptation on height have been overestimated due to uncorrected population structure in genome-wide association studies}, url = {https://www.biorxiv.org/content/early/2018/06/28/355057}, year = 2018 }