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Souaiaia, T.

Publications and source records attributed to Souaiaia, T..

5 recordsLinked to original sources

Striking Departures from Polygenic Architecture in the Tails of Complex Traits

Understanding the genetic architecture of human traits is of key biological, medical and evolutionary importance[1]. Despite much progress, little is known about how genetic architecture varies across the trait continuum and, in particular, if it differs in the tails of complex traits, where disease often occurs. Here, applying a novel approach based on polygenic scores, we reveal striking departures from polygenic architecture across 148 quantitative trait tails, consistent with distinct concentrations of high-impact rare alleles in one or both tails of most of the traits. We demonstrate replication of these results across ancestries, cohorts, repeat measures, and using an orthogonal family-based approach[2]. Furthermore, trait tails with inferred enrichment of rare alleles are associated with more exome study hits, reduced fecundity, advanced paternal age, and lower predictive accuracy of polygenic scores. Finally, we find evidence of ongoing selection consistent with the observed departures in polygenicity and demonstrate, via simulation, that traits under stabilising selection are expected to have tails enriched for rare, large-effect alleles. Overall, our findings suggest that while common variants of small effect likely account for most of the heritability in complex traits[3], rare variants of large effect are often more important in the trait tails, particularly among individuals at highest risk of disease. Our study has implications for rare variant discovery, the utility of polygenic scores, the study of selection in humans, and for the relative importance of common and rare variants to complex traits and diseases.

genetics↗

BridgePRS: A powerful trans-ancestry Polygenic Risk Score method

Polygenic Risk Scores (PRS) have huge potential to contribute to biomedical research and to a future of precision medicine, but to date their calculation relies largely on Europeanancestry GWAS data. This global bias makes most PRS substantially less accurate in individuals of non-European ancestry. Here we present BridgePRS, a novel Bayesian PRS method that leverages shared genetic effects across ancestries to increase the accuracy of PRS in non-European populations. The performance of BridgePRS is evaluated in simulated data and real UK Biobank (UKB) data across 19 traits in African, South Asian and East Asian ancestry individuals, using both UKB and Biobank Japan GWAS summary statistics. BridgePRS is compared to the leading alternative, PRS-CSx, and two single-ancestry PRS methods adapted for trans-ancestry prediction. PRS trained in the UK Biobank are then validated out-of-cohort in the independent Mount Sinai (New York) BioMe Biobank. Simulations reveal that BridgePRS performance, relative to PRS-CSx, increases as uncertainty increases: with lower heritability, higher polygenicity, greater between-population genetic diversity, and when causal variants are not present in the data. Our simulation results are consistent with real data analyses in which BridgePRS has better predictive accuracy in African ancestry samples, especially in out-of-cohort prediction (into BioMe), which shows a 60% boost in mean R2 compared to PRS-CSx (P = 2 x 10-6). BridgePRS performs the full PRS analysis pipeline, is computationally efficient, and is a powerful method for deriving PRS in diverse and under-represented ancestry populations.

genetics↗

Sibling Similarity Can Reveal Key Insights Into Genetic Architecture

The use of siblings to infer the factors influencing complex traits has been a cornerstone of quantitative genetics. Here we utilise siblings for a novel application: the inference of genetic architecture, specifically that relating to individuals with extreme trait values (e.g. in the top 1%). Inferring the genetic architecture most relevant to this group of individuals is important because they are at greatest risk of disease and may be more likely to harbour rare variants of large effect due to natural selection. We develop a theoretical framework that derives expected distributions of sibling trait values based on an index siblings trait value, estimated trait heritability, and null assumptions that include infinitesimal genetic effects and environmental factors that are either controlled for or have combined Gaussian effects. This framework is then used to develop statistical tests powered to distinguish between trait tails characterised by common polygenic architecture from those that include substantial enrichments of de novo or rare variant (Mendelian) architecture. We apply our tests to UK Biobank data here, although we note that they can be used to infer genetic architecture in any cohort or health registry that includes siblings and their trait values, since these tests do not use genetic data. We describe how our approach has the potential to help disentangle the genetic and environmental causes of extreme trait values, and to improve the design and power of future sequencing studies to detect rare variants.

genetics↗

Transcriptional Profiling of Primate Central Nucleus of the Amygdala Neurons to Understand the Molecular Underpinnings of Early Life Anxious Temperament

Children exhibiting extreme anxious temperament (AT) are at an increased risk to develop anxiety and depression. Work in young rhesus monkeys mechanistically links the central nucleus of the amygdala (Ce) to AT. Here, we used laser capture microscopy and RNA sequencing in 47 young rhesus monkeys to investigate AT s molecular underpinnings by focusing on lateral Ce (CeL) neurons. We found 528 AT-related transcripts, including protein kinase C type-delta (PKC{delta}), a CeL microcircuit cell marker implicated in rodent threat processing. We characterized PKC{delta} neurons in the rhesus CeL, compared their distribution to the mouse, and demonstrated that a subset of these neurons project to the laterodorsal bed nucleus of the stria terminalis (BSTLd). These findings present evidence in the primate of a CeL to BSTLd circuit that maybe relevant to understanding human anxiety and points to specific molecules within this circuit that could serve as potential treatment targets for anxiety disorders.

neuroscience↗

Gene expression in patient-derived neural progenitors provide insights into neurodevelopmental aspects of schizophrenia

BACKGROUNDGWAS of schizophrenia demonstrated that variations in the non-coding regions are responsible for most of common variation heritability of the disease. It is hypothesized that these risk variants alter gene expression. Thus, studying alterations in gene expression in schizophrenia may provide a direct approach to understanding the etiology of the disease. In this study we use Cultured Neural progenitor cells derived from Olfactory Neuroepithelium (CNON) as a genetically unaltered cellular model to elucidate the neurodevelopmental aspects of schizophrenia.\n\nMETHODSWe performed a gene expression study using RNA-Seq of CNON from 111 controls and 144 individuals with schizophrenia. Differentially expressed (DEX) genes were identified with DESeq2, using covariates to correct for sex, age, library batches and one surrogate variable component.\n\nRESULTS80 genes were DEX (FDR<10%), showing enrichment in cell migration, cell adhesion, developmental process, synapse assembly, cell proliferation and related gene ontology categories. Cadherin and Wnt signaling pathways were positive in overrepresentation test, and, in addition, many genes are specifically involved in Wnt5A signaling. The DEX genes were significantly, enriched in the genes overlapping SNPs with genome-wide significant association from the PGC GWAS of schizophrenia (PGC SCZ2). We also found substantial overlap with genes associated with other psychiatric disorders or brain development, enrichment in the same GO categories as genes with mutations de novo in schizophrenia, and studies of iPSC-derived neural progenitor cells.\n\nCONCLUSIONSCNON cells are a good model of the neurodevelopmental aspects of schizophrenia and can be used to elucidate the etiology of the disorder.

genetics↗