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Perdry, H.

Publications and source records attributed to Perdry, H..

6 recordsLinked to original sources
bioRxiv2023

A model for co-occurrent assortative mating and vertical cultural transmission and its impact on measures of genetic associations

Assortative mating for a given phenotype is the phenomenon by which mates select each other based on their phenotypic similarity. Other phenomena can create positive correlation between the parents and the offsprings environment: vertical cultural transmission, or dynastic effects. When these phenomena occur together, they induce a gene-environment correlation at the population scale. It will impact genetic measures of associations such as SNP effect size and SNP-heritability. In this paper, we provide a complete mathematical modelling of both assortative mating and vertical cultural transmission in the classical framework of the polygenic additive model. We establish for the first time the theoretical evolution and equilibrium values of all quantities of interest involved. We then derive its consequences on typical genetic epidemiology analyses; including both population and family-based study designs. We show the consequences on heritability estimation, Genome-Wide Association Studies (GWAS), and the variance explained by polygenic scores. We validate our calculations through simple forward-time simulations.

genetics↗
bioRxiv2023

SURFBAT: a surrogate family-based association test building on large imputation reference panels

Genotype-phenotype association tests are typically adjusted for population stratification using principal components that are estimated genome-wide. This lacks resolution when analysing populations with fine structure and/or individuals with fine levels of admixture. This can affect power and precision, and is a particularly relevant consideration when control individuals are recruited using geographic selection criteria. Such is the case in France where we have recently created reference panels of individuals anchored to different geographic regions. To make correct comparisons against case groups, who would likely be gathered from large urban areas, new methods are needed. We present SURFBAT (a SURrogate Family Based Association Test) which performs an approximation of the transmission-disequilibrium test. Our method hinges on the application of genotype imputation algorithms to match similar haplotypes between the case and control groups. This permits us to approximate local ancestry informed posterior probabilities of un-transmitted parental alleles of each case individual. SURFBAT provides an association test that is inherently robust to fine-scale population stratification and opens up the possibility of efficiently using large imputation reference panels as control groups for association testing. The method is suitable when the control panel spans the local ancestry spectrum of the case-group population and each control has similar paternal and maternal ancestries. This is the case for our reference panels where individuals have their four grand-parents born in the same geographic area. In contrast to other methods for association testing that incorporate local-ancestry inference, SURFBAT does not require a set of ancestry groups to be defined, nor for local ancestry to be explicitly estimated. We demonstrate the interest of our tool on simulated datasets created from the 1000 Genomes project and the FranceGenRef project, as well as on a real-data example for a group of case individuals affected by Brugada syndrome.

genetics↗
bioRxiv2021

Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score

Rare variant association tests (RVAT) have been developed to study the contribution of rare variants widely accessible through high-throughput sequencing technologies. RVAT require to aggregate rare variants in testing units and to filter variants to retain only the most likely causal ones. In the exome, genes are natural testing units and variants are usually filtered based on their functional consequences. However, when dealing with whole-genome sequence (WGS) data, both steps are challenging. No natural biological unit is available for aggregating rare variants. Sliding windows procedures have been proposed to circumvent this difficulty, however they are blind to biological information and result in a large number of tests. We propose a new strategy to perform RVAT on WGS data: "RAVA-FIRST" (RAre Variant Association using Functionally-InfoRmed STeps) comprising three steps. (1) New testing units are defined genome-wide based on functionally-adjusted Combined Annotation Dependent Depletion (CADD) scores of variants observed in the GnomAD populations, which are referred to as "CADD regions". (2) A region-dependent filtering of rare variants is applied in each CADD region. (3) A functionally-informed burden test is performed with sub-scores computed for each genomic category within each CADD region. Both on simulations and real data, RAVA-FIRST was found to outperform other WGS-based RVAT. Applied to a WGS dataset of venous thromboembolism patients, we identified an intergenic region on chromosome 18 that is enriched for rare variants in early-onset patients and that was that was missed by standard sliding windows procedures. RAVA-FIRST enables new investigations of rare non-coding variants in complex diseases, facilitated by its implementation in the R package Ravages. Author SummaryTechnological progresses have made possible whole genome sequencing at an unprecedented scale, opening up the possibility to explore the role of genetic variants of low frequency in common diseases. The challenge is now methodological and requires the development of novel methods and strategies to analyse sequencing data that are not limited to assessing the role of coding variants. With RAVA-FIRST, we propose a novel strategy to investigate the role of rare variants in the whole-genome that takes benefit from biological information. Especially, RAVA-FIRST relies on testing units that go beyond genes to gather rare variants in the association tests. In this work, we show that this new strategy presents several advantages compared to existing methods. RAVA-FIRST offers an easy and straightforward analysis of genome-wide rare variants, especially the intergenic ones which are frequently left behind, making it a promising tool to get a better understanding of the biology of complex diseases.

genetics↗
bioRxiv2020

Mixed Logistic Regression in Genome-WideAssociation Studies

MotivationMixed linear models (MLM) have been widely used to account for population structure in case-control genome-wide association studies, the status being analyzed as a quantitative phenotype. Chen et al. proved that this method is inappropriate and proposed a score test for the mixed logistic regression (MLR). However this test does not allow an estimation of the variants effects. ResultsWe propose two computationally efficient methods to estimate the variants effects. Their properties are evaluated on two simulations sets, and compared with other methods (MLM, logistic regression). MLR performs the best in all circumstances. The variants effects are well evaluated by our methods, with a moderate bias when the effect sizes are large. Additionally, we propose a stratified QQ-plot, enhancing the diagnosis of p-values inflation or deflation, when population strata are not clearly identified in the sample. AvailabilityAll methods are implemented in the R package milorGWAS available at https://github.com/genostats/milorGWAS. Contactherve.perdry@u-psud.fr Supplementary informationSupplementary data are available at Bioinformatics online.

genomics↗
bioRxiv2018

First genome-wide association study of non-severe malaria in two birth cohorts in Benin: hints towards the involvement of the STAT3 pathway.

Recent research efforts to identify genes involved in the susceptibility to P. falciparum malaria have focused on severe forms of malaria, with several genome-wide association studies (GWAS) and multi-center analyses published this past decade. Here we present the first GWAS performed on mild malaria susceptibility in young children, designed to identify genetic variants involved in innate immunity or innate resistance mechanisms. Two cohorts of infants from southern Benin (525 and 250 individuals respectively), used as discovery and replication cohorts, were closely followed from birth to 18-24 months of age, with an assessment of a space-and time-dependent risk of exposure to vector bites. GWAS was performed on 15.5 million genotyped and imputed variants, with the susceptibility of infants to mild malaria attacks and to malaria infections as a whole (both symptomatic and asymptomatic). Infant susceptibility to malaria was assessed by considering all malaria events occurring during the follow-up using a Cox-model for recurrent events. We found strong statistical support for a role of PTPRT, a tyrosine phosphatase receptors involved in STAT3 pathway, with the protection against both mild malaria attacks and malaria infections (p=9.70x10-8 and p=1.78x10-7 respectively in the discovery cohort, and both p < 0.05 in the replication cohort). Furthermore, our study highlights several other genes, among them, UROC1, ACER3 and the PLAG2 cluster, whose biological functions are relevant in malaria infection. Results show that despite the difficulty of setting up such longitudinal field studies, GWAS on non-severe malaria can successfully identify new candidate genes and inform physiological mechanisms underlying natural protection against malaria.\n\nAUTHOR SUMMARYMalaria remains a major worldwide public health problem with circa 219 million cases and 435,000 deaths per year. As for many infectious diseases, the genetics of the host plays a role in the disease course. So far, most studies on such genetic factors focused on severe malaria forms. Our study aimed to find factors associated with simple forms (asymptomatic forms and uncomplicated clinical forms). We used dense genome-wide data of two cohorts of infants closely followed during two years in Benin, and we incorporated an environmental risk of exposure estimated at individual level from entomological, climatic, and environmental data. This allowed us to unravel several genes that appear to play a role in the susceptibility to malaria infection, thus demonstrating the feasibility of a genome-wide approach on non-severe forms. Functional studies are needed to confirm the role of the genetic factors highlighted here. Some of these genes represent interesting targets for future prevention strategies and drug development.

genetics↗