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The number of variants that have a non-zero effect on a trait ( i.e . polygenicity) is a fundamental parameter in the study of the genetic architecture of a complex trait. Although many previous studies have investigated polygenicity at a genome-wide scale, a detailed understanding of how polygenicity varies across genomic regions is currently lacking. In this work, we propose an accurate and scalable statistical framework to estimate regional polygenicity for a complex trait. We show that our approach yields approximately unbiased estimates of regional polygenicity in simulations across a wide-range of various genetic architectures. We then partition the polygenicity of anthropometric and blood pressure traits across 6-Mb genomic regions ( N = 290K, UK Biobank) and observe that all analyzed traits are highly polygenic: over one-third of regions harbor at least one causal variant for each of the traits analyzed. Additionally, we observe wide variation in regional polygenicity: on average across all traits, 48.9% of regions contain at least 5 causal SNPs, 5.44% of regions contain at least 50 causal SNPs. Finally, we find that heritability is proportional to polygenicity at the regional level, which is consistent with the hypothesis that heritability enrichments are largely driven by the variation in the number of causal SNPs. 

Abstract Tissue function is dependent on correct cellular organization and behavior. As a result, the identification and study of genes that contribute to tissue morphogenesis is of paramount importance to the fields of cell and developmental biology. Many of the genes required for tissue patterning and organization are highly conserved between phyla. This has led to the emergence of several model organisms and developmental systems that are used to study tissue morphogenesis. One such model is the Drosophila melanogaster pupal eye that has a highly stereotyped arrangement of cells. In addition, the pupal eye is postmitotic that allows for the study of tissue morphogenesis independent from any effects of proliferation. While the changes in cell morphology and organization that occur throughout pupal eye development are well documented, less is known about the corresponding transcriptional changes that choreograph these processes. To identify these transcriptional changes, we dissected wild-type Canton S pupal eyes and performed RNA-sequencing. Our analyses identified differential expression of many loci that are documented regulators of pupal eye morphogenesis and contribute to multiple biological processes including signaling, axon projection, adhesion, and cell survival. We also identified differential expression of genes not previously implicated in pupal eye morphogenesis such as components of the Toll pathway, several non-classical cadherins, and components of the muscle sarcomere, which could suggest these loci function as novel patterning factors. 

Abstract Background Large medical centers in urban areas, like Los Angeles, care for a diverse patient population and offer the potential to study the interplay between genetic ancestry and social determinants of health. Here, we explore the implications of genetic ancestry within the University of California, Los Angeles (UCLA) ATLAS Community Health Initiative—an ancestrally diverse biobank of genomic data linked with de-identified electronic health records (EHRs) of UCLA Health patients ( N =36,736). Methods We quantify the extensive continental and subcontinental genetic diversity within the ATLAS data through principal component analysis, identity-by-descent, and genetic admixture. We assess the relationship between genetically inferred ancestry (GIA) and >1500 EHR-derived phenotypes (phecodes). Finally, we demonstrate the utility of genetic data linked with EHR to perform ancestry-specific and multi-ancestry genome and phenome-wide scans across a broad set of disease phenotypes. Results We identify 5 continental-scale GIA clusters including European American (EA), African American (AA), Hispanic Latino American (HL), South Asian American (SAA) and East Asian American (EAA) individuals and 7 subcontinental GIA clusters within the EAA GIA corresponding to Chinese American, Vietnamese American, and Japanese American individuals. Although we broadly find that self-identified race/ethnicity (SIRE) is highly correlated with GIA, we still observe marked differences between the two, emphasizing that the populations defined by these two criteria are not analogous. We find a total of 259 significant associations between continental GIA and phecodes even after accounting for individuals’ SIRE, demonstrating that for some phenotypes, GIA provides information not already captured by SIRE. GWAS identifies significant associations for liver disease in the 22q13.31 locus across the HL and EAA GIA groups (HL p -value=2.32×10 −16 , EAA p -value=6.73×10 −11 ). A subsequent PheWAS at the top SNP reveals significant associations with neurologic and neoplastic phenotypes specifically within the HL GIA group. Conclusions Overall, our results explore the interplay between SIRE and GIA within a disease context and underscore the utility of studying the genomes of diverse individuals through biobank-scale genotyping linked with EHR-based phenotyping. 

The Drosophila eye is an outstanding model system for exploring fundamental mechanisms of growth and development. The adult eye is composed of a perfect hexagonal lattice of ∼750 unit eyes, or ommatidia, each containing precisely 20 well-characterized cells. The eye develops from the eye/antennal imaginal disc, a flattened epithelial sac. During larval and pupal development, cells in the disc grow and undergo compartmentalisation, cell cycle arrest, differentiation, directed movement, and apoptosis, all utilising gene networks and signalling pathways similar to those in vertebrates. The genetic accessibility of Drosophila, together with the precision of eye development, makes the fly retina an extremely useful system with which to investigate the roles of genes and signalling pathways in development. 

Abstract MotivationA large proportion of risk regions identified by genome-wide association studies (GWAS) are shared across multiple diseases and traits. Understanding whether this clustering is due to sharing of causal variants or chance colocalization can provide insights into shared etiology of complex traits and diseases. ResultsIn this work, we propose a flexible, unifying framework to quantify the overlap between a pair of traits called UNITY (Unifying Non-Infinitesimal Trait analYsis). We formulate a Bayesian generative model that relates the overlap between pairs of traits to GWAS summary statistic data under a non-infinitesimal genetic architecture underlying each trait. We propose a Metropolis–Hastings sampler to compute the posterior density of the genetic overlap parameters in this model. We validate our method through comprehensive simulations and analyze summary statistics from height and body mass index GWAS to show that it produces estimates consistent with the known genetic makeup of both traits. Availability and implementationThe UNITY software is made freely available to the research community at: https://github.com/bogdanlab/UNITY. Supplementary informationSupplementary data are available at Bioinformatics online.