Biology subjects

Feusier, J.

Publications and source records attributed to Feusier, J..

6 recordsLinked to original sources

bioRxiv2020

De novo structural mutation rates and gamete-of-origin biases revealed through genome sequencing of 2,396 families

Each human genome includes de novo mutations that arose during gametogenesis. While these germline mutations represent a fundamental source of new genetic diversity, they can also create deleterious alleles that impact fitness. The germline mutation rate for single nucleotide variants and factors that significantly influence this rate, such as parental age, are now well established. However, far less is known about the frequency, distribution, and features that impact de novo structural mutations. We report a large, family-based study of germline mutations, excluding aneuploidy, that affect genome structure among 572 genomes from 33 families in a multigenerational CEPH-Utah cohort and 2,363 cases of non-familial autism spectrum disorder (ASD), 1,938 unaffected siblings, and both parents (9,599 genomes in total). We find that de novo structural mutations detected by alignment-based, short-read WGS occurred at an overall rate of at least 0.160 events per genome in unaffected individuals and was significantly higher (0.206 per genome) in ASD cases. In both probands and unaffected samples, nearly 73% of de novo structural mutations arose in paternal gametes, and predict most de novo structural mutations to be caused by mutational mechanisms that do not require sequence homology. After multiple testing correction we did not observe a statistically significant correlation between parental age and the rate of de novo structural variation in offspring. These results highlight that a spectrum of mutational mechanisms contribute to germline structural mutations, and that these mechanisms likely have markedly different rates and selective pressures than those leading to point mutations.

genomics↗

bioRxiv2019

TypeTE: a tool to genotype mobile element insertions from whole genome resequencing data

Alu retrotransposons account for more than 10% of the human genome, and insertions of these elements create structural variants segregating in human populations. Such polymorphic Alu are powerful markers to understand population structure, and they represent variants that can greatly impact genome function, including gene expression. Accurate genotyping of Alu and other mobile elements has been challenging. Indeed, we found that Alu genotypes previously called for the 1000 Genomes Project are sometimes erroneous, which poses significant problems for phasing these insertions with other variants that comprise the haplotype. To ameliorate this issue, we introduce a new pipeline -- TypeTE -- which genotypes Alu insertions from whole-genome sequencing data. Starting from a list of polymorphic Alus, TypeTE identifies the hallmarks (poly-A tail and target site duplication) and orientation of Alu insertions using local re-assembly to reconstruct presence and absence alleles. Genotype likelihoods are then computed after re-mapping sequencing reads to the reconstructed alleles. Using a gold standard set of PCR-based genotyping of >200 loci, we show that TypeTE improves genotype accuracy from 83% to 92% in the 1000 Genomes dataset. TypeTE can be readily adapted to other retrotransposon families and brings a valuable toolbox addition for population genomics.

bioinformatics↗

bioRxiv2018

Pedigree-based estimation of human mobile element retrotransposition rates

Germline mutation rates in humans have been estimated for a variety of mutation types, including single nucleotide and large structural variants. Here we directly measure the germline retrotransposition rate for the three active retrotransposon elements: L1, Alu, and SVA. We utilized three tools for calling Mobile Element Insertions (MEIs) (MELT, RUFUS, and TranSurVeyor) on blood-derived whole genome sequence (WGS) data from 603 CEPH individuals, comprising 33 three-generation pedigrees. We identified 27 de novo MEIs in 440 births. The retrotransposition rate estimates for Alu elements, one in 40, is roughly half the rate estimated using phylogenetic analyses, a difference in magnitude similar to that observed for single nucleotide variants. The L1 retrotransposition rate is one in 62 births and is within range of previous estimates (1:20-1:200 births). The SVA retrotransposition rate, one in 55 births, is much higher than the previous estimate of one in 900 births. Our large, three-generation pedigrees allowed us to assess parent-of-origin effects and the timing of insertion events in either gametogenesis or early embryonic development. We find a statistically significant paternal bias in Alu retrotransposition. Our study represents the first in-depth analysis of the rate and dynamics of human retrotransposition from WGS data in three-generation human pedigrees.

genetics↗

bioRxiv2018

Resequencing and association mapping of the generalist pathogen Botrytis cinerea

We performed whole genome resequencing of 84 field isolates of Botrytis cinerea, largely collected from a local set of plant species. Combined with 13 previously resequenced isolates sampled from diverse locations, this gave a collection of 97 isolates for studies of natural variation. Alignment to the reference sequence T4 and SNP detection provided further data for population genetics analysis including a mapping population for association studies. Although much of the genomic diversity was captured in the original 13 isolates, the additional genomes increased total diversity in the population by a third. Surprisingly, the same additional genomes increase mitochondrial diversity 2-fold. Across the population, LD was limited and decayed rapidly, reflecting frequent outcrossings. Effectively, this sampling strategy increased the level of genetic diversity available, whilst limiting the problem of population stratification and enabling GWAS of several phenotypes on common Arabidopsis plants affected in disease pathways. Overlap of results using all GWAS methods revealed numerous candidate genes / pathways that potentially contribute to its broad host range and offer conceivable pathogen decrease targets.

genomics↗

bioRxiv2018

Plant-Necrotroph Co-transcriptome Networks Illuminate a Metabolic Battlefield

A central goal of studying host-pathogen interaction research is to understand how the host and pathogen manipulate each other to promote their own fitness in a pathosystem. Co-transcriptomic approaches can simultaneously analyze dual transcriptomes during infection and provide a systematic map of the cross-kingdom communication between two species. Here we used the Arabidopsis-B. cinerea pathosystem to test how plant host and fungal pathogen interaction at the transcriptomic level during infection. We assessed the impact of natural genetic diversity in the pathogen and plant host by utilization of a collection of 96 isolates of B. cinerea infection on Arabidopsis wild-type and two mutants with jasmonate or salicylic acid compromised immunities. We identified ten B. cinerea gene co-expression networks (GCNs) that encode known or novel virulence mechanisms. We constructed a dual interaction network by combining four host-and ten pathogen-GCNs into a single network, which revealed potential connections between the fungal and plant GCNs involving both novel and conserved mechanisms. These co-transcriptome data shed lights on the potential mechanisms underlying host-pathogen interaction and illustrate the continued need for advancements of in planta analysis of dual-species dynamics.

systems biology↗

bioRxiv2018

Combining Digital Imaging and Genome Wide Association Mapping to Dissect Uncharacterized Traits in Plant/Pathogen Interactions

Plant resistance to generalist pathogens with broad host ranges, such as Botrytis cinerea, is typically quantitative and highly polygenic. Recent studies have begun to elucidate the molecular genetic basis underpinning plant-pathogen interactions using commonly measured traits including lesion size and/or pathogen biomass. Yet with the advent of digital imaging and phenomics, there are a large number of additional resistance traits available to study quantitative resistance. In this study, we used high-throughput digital imaging analysis to investigate previously uncharacterized visual traits of plant-pathogen interactions related disease resistance using the Arabidopsis thaliana/Botrytis cinerea pathosystem. Using a large collection of 75 visual traits collected from every lesion, we focused on lesion color, lesion shape, and lesion size, to test how these aspects of the interaction are genetically related. Using genome wide association (GWA) mapping in A. thaliana, we show that lesion color and shape are genetically separable traits associated with plant-disease resistance. Using defined mutants in 23 candidate genes from the GWA mapping, we could identify and show that novel loci associated with each different plant-pathogen interaction trait, which expands our understanding of the functional mechanisms driving plant disease resistance.\n\nSummaryDigital imaging allows the identification of genes controlling novel lesion traits.

plant biology↗