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UKBB_noPCsA GWAS for human height in the UK Biobank. Linear regression without any structure correction---with only genotype, age, sex and sequencing array as covariates (unrelated British ancestry individuals only). See the paper for the plink command and more details.UKBB_sib_gwasA GWAS for human height in the UK Biobank sibs. Family-based sib-pair analysis. See the paper for the plink command and more details.IRL-GBR allele frequency differencesLogistic regression using self-identified as "White British" or "White Irish" in the UK Biobank were compared with distinct phenotype labels. See paper for plink command line and more details.BvI.nocovar.Irish.glm.logistic.gzGBR-TSI allele frequency differencesIndividuals from the GBR and TSI populations from 1000G Phase 3 were assigned binary phenotype labels and a chi square test was performed for allele frequency differences. See paper for the plink command line and more details.gwas.hwe1e6.geno05.nocovar.chisq.British.assoc.gzUK Biobank Acknowledgement Several recent papers have reported strong signals of selection on European polygenic height scores. These analyses used height effect estimates from the GIANT consortium and replication studies. Here, we describe a new analysis based on the the UK Biobank (UKB), a large, independent dataset. We find that the signals of selection using UKB effect estimates are strongly attenuated or absent. We also provide evidence that previous analyses were confounded by population stratification. Therefore, the conclusion of strong polygenic adaptation now lacks support. Moreover, these discrepancies highlight (1) that methods for correcting for population stratification in GWAS may not always be sufficient for polygenic trait analyses, and (2) that claims of differences in polygenic scores between populations should be treated with caution until these issues are better understood.

Objectives: Traditionally, summarization of research themes and trends within a given discipline was accomplished by manual review of scientific works in the field. However, with the ushering in of the age of “big data”, new methods for discovery of such information become necessary as traditional techniques become increasingly difficult to apply due to the exponential growth of document repositories. Our objectives are to develop a pipeline for unsupervised theme extraction and summarization of thematic trends in document repositories, and to test it by applying it to a specific domain. Methods: To that end, we detail a pipeline, which utilizes machine learning and natural language processing for unsupervised theme extraction, and a novel method for summarization of thematic trends, and network mapping for visualization of thematic relations. We then apply this pipeline to a collection of anesthesiology abstracts. Results: We demonstrate how this pipeline enables discovery of major themes and temporal trends in anesthesiology research and facilitates document classification and corpus exploration. Discussion: The relation of prevalent topics and extracted trends to recent events in both anesthesiology, and healthcare in general, demonstrates the pipeline’s utility. Furthermore, the agreement between the unsupervised thematic grouping and human-assigned classification validates the pipeline’s accuracy and demonstrates another potential use. Conclusion: The described pipeline enables summarization and exploration of large document repositories, facilitates classification, aids in trend identification. A more robust and user-friendly interface will facilitate the expansion of this methodology to other domains. This will be the focus of future work for our group. Rusanov et alData from "Trends in anesthesiology research: A machine learning approach to theme discovery and summarization" by Rusanov, Alexander, Miotto, Riccardo, Weng, Chunhua

UK Biobank custom height association statistics on ~700k genotyped SNPsThe zip file contains six files: (1) ukb_cal_v2_height_allancestry_10pcs_assoc_linear.tsv (2) ukb_cal_v2_height_allancestry_nopcs_assoc_linear.tsv (3) ukb_cal_v2_height_britishancestry_10pcs_assoc_linear.tsv (4) ukb_cal_v2_height_britishancestry_nopcs_assoc_linear.tsv (5) ukb_cal_v2_height_sibs_perm_qfam.tsv (6) ukb_cal_v2_height_wbsibs_perm_qfam.tsv (1) - (4) are height GWAS estimates on all samples / white British samples using 10 PCs as covariates or no PCs as covariates. Sex was included as covariate in all analyses. (3) is equivalent to the UK Biobank height GWAS from the Neale lab. The remaining small differences can be explained by genotype differences in the UK Biobank imputed data and genotyped data. (5) and (6) are family based estimates from 20166 sibling pairs of any ancestry (5) and 17358 sibling pairs where both siblings are of white British ancestry (6) in the UK Biobank. Pairs of samples with IBS0 > 0.0018 and Kinship coefficient > 0.185 were identified as sibling pairs. For the analyses in Sohail, Maier et al., only the subset of ~300,000 SNPs with SDS scores was used. For a description of the columns in files (1)-(4) please see the PLINK documentation for the ‘--linear’ command. Column “A2” has been added and denotes the non-effect allele. For a description of the columns in files (5) and (6) please see the PLINK documentation for the ‘--qfam’ command. Column “A2” has been added and denotes the non-effect allele. “EMP1” and “NP” refer to permutation p-value and number of permutations, respectively. Please note: These data are derived from the UK Biobank Resource under Application Number 18597.sohail_maier_2018.zip 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 structure. More generally, our results imply that typical constructions of polygenic scores are sensitive to population structure and that population-level differences should be interpreted with caution.