SummaryMany common variants have been associated with hematological traits, but identification of causal genes and pathways has proven challenging. We performed a genome-wide association analysis in the UK Biobank and INTERVAL studies, testing 29.5 million genetic variants for association with 36 red cell, white cell, and platelet properties in 173,480 European-ancestry participants. This effort yielded hundreds of low frequency (<5%) and rare (<1%) variants with a strong impact on blood cell phenotypes. Our data highlight general properties of the allelic architecture of complex traits, including the proportion of the heritable component of each blood trait explained by the polygenic signal across different genome regulatory domains. Finally, through Mendelian randomization, we provide evidence of shared genetic pathways linking blood cell indices with complex pathologies, including autoimmune diseases, schizophrenia, and coronary heart disease and evidence suggesting previously reported population associations between blood cell indices and cardiovascular disease may be non-causal.

SummaryCharacterizing the multifaceted contribution of genetic and epigenetic factors to disease phenotypes is a major challenge in human genetics and medicine. We carried out high-resolution genetic, epigenetic, and transcriptomic profiling in three major human immune cell types (CD14+ monocytes, CD16+ neutrophils, and naive CD4+ T cells) from up to 197 individuals. We assess, quantitatively, the relative contribution of cis-genetic and epigenetic factors to transcription and evaluate their impact as potential sources of confounding in epigenome-wide association studies. Further, we characterize highly coordinated genetic effects on gene expression, methylation, and histone variation through quantitative trait locus (QTL) mapping and allele-specific (AS) analyses. Finally, we demonstrate colocalization of molecular trait QTLs at 345 unique immune disease loci. This expansive, high-resolution atlas of multi-omics changes yields insights into cell-type-specific correlation between diverse genomic inputs, more generalizable correlations between these inputs, and defines molecular events that may underpin complex disease risk.

Highlights d The Uganda Genome Resource comprises genetic and phenotypic data on 6,400 individuals d Ugandans show geographically correlated genetic substructure and complex admixture d The Uganda sequence panel substantially improves imputation in African populations d The Uganda Genome Resource enables novel discovery of loci associated with traits

Eosinophilic granulomatosis with polyangiitis (EGPA) is a rare inflammatory disease of unknown cause. 30% of patients have anti-neutrophil cytoplasmic antibodies (ANCA) specific for myeloperoxidase (MPO). Here, we describe a genome-wide association study in 676 EGPA cases and 6809 controls, that identifies 4 EGPA-associated loci through conventional case-control analysis, and 4 additional associations through a conditional false discovery rate approach. Many variants are also associated with asthma and six are associated with eosinophil count in the general population. Through Mendelian randomisation, we show that a primary tendency to eosinophilia contributes to EGPA susceptibility. Stratification by ANCA reveals that EGPA comprises two genetically and clinically distinct syndromes. MPO+ ANCA EGPA is an eosinophilic autoimmune disease sharing certain clinical features and an HLA-DQ association with MPO+ ANCA-associated vasculitis, while ANCA-negative EGPA may instead have a mucosal/barrier dysfunction origin. Four candidate genes are targets of therapies in development, supporting their exploration in EGPA.

Large-scale whole genome sequence datasets offer novel opportunities to identify genetic variation underlying human traits. Here we apply genotype imputation based on whole genome sequence data from the UK10K and the 1000 Genomes Projects into 35,981 study participants of European ancestry, followed by association analysis with twenty quantitative cardiometabolic and hematologic traits. We describe 17 novel associations, including six rare (minor allele frequency [MAF]<1%) or low frequency variants (1%

Linking non-coding genetic variants associated with the risk of diseases or disease-relevant traits to target genes is a crucial step to realize GWAS potential in the introduction of precision medicine. Here we set out to determine the mechanisms underpinning variant association with platelet quantitative traits using cell type-matched epigenomic data and promoter long-range interactions. We identify potential regulatory functions for 423 of 565 (75%) non-coding variants associated with platelet traits and we demonstrate, through ex vivo and proof of principle genome editing validation, that variants in super enhancers play an important role in controlling archetypical platelet functions.

33 Background 34Very low depth sequencing is a cost-effective approach to capture low-frequency and rare 35 variation in complex trait association studies. Here, we perform cohort-wide whole genome 36 sequencing (WGS) at 1x depth coupled to genome-wide association analysis in 2,347 37 individuals from two isolated populations. 38 Results 39We establish a robust pipeline for calling 1x WGS data, achieving an average minor allele 40 concordance of 97% when compared to genotyping chip data. 9.5% of variants called using 41 1x WGS are variants with a high predicted quality not captured by genome-wide association 42 study (GWAS) data in the same individuals imputed to a dense haplotype reference panel. 43Of the 54 association signals arising from genome-wide association analysis of 1x WGS 44 variants with 25 haematological traits (at p<5x10 -7 ), only 57% are recapitulated by the 45 imputed GWAS results in the same samples. Differences in strength of evidence for 46 association are smaller for common than for low-frequency and rare variant signals. We 47 further exemplify power gains by establishing robust evidence for a novel association 48 between rs6489858, an intronic variant in RPH3A and increased lymphocyte count 49 (beta=0.13, SE=0.11, p=8x10 -12 ), which replicates in an independent dataset comprising 50 173,480 samples. 51 Conclusions 52We show that 1x WGS is an efficient alternative to imputed GWAS chip designs for 53 empowering next-generation association studies in complex traits. We demonstrate that 54 population-scale 1x WGS allows the interrogation of a large number of low-frequency and 55All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/169789 doi: bioRxiv preprint first posted online alternative, cost-efficient approach to capture low-frequency variation in large studies. 75

Family studies for Crohn disease (CD) report extensive linkage on chromosome 16q and pinpoint NOD2 as a possible causative locus. However, linkage is also observed in families that do not bear the most frequent NOD2 causative mutations, but no other signals on 16q have been found so far in published genome-wide association studies. Our aim is to identify this missing genetic contribution. We apply a powerful genetic mapping approach to the Wellcome Trust Case-Control Consortium and the National Institute of Diabetes and Digestive and Kidney Diseases genome-wide association data on CD. This method takes into account the underlying structure of linkage disequilibrium (LD) by using genetic distances from LD maps and provides a location for the causal agent. We find genetic heterogeneity within the NOD2 locus and also show an independent and unsuspected involvement of the neighboring gene, CYLD. We find associations with the IRF8 region and the region containing CDH1 and CDH3, as well as substantial phenotypic and genetic heterogeneity for CD itself. The genes are known to be involved in inflammation and immune dysregulation. These findings provide insight into the genetics of CD and suggest promising directions for understanding disease heterogeneity. The application of this method thus paves the way for understanding complex inheritance in general, leading to the dissection of different pathways and ultimately, personalized treatment.