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Lorieux, M.

Publications and source records attributed to Lorieux, M..

10 recordsLinked to original sources
bioRxiv2024

Fast and accurate imputation of genotypes from noisy low-coverage sequencing data in bi-parental populations

MotivationGenotyping of bi-parental populations can be performed with low-coverage next-generation sequencing (LC-NGS). This allows the creation of highly saturated genetic maps at reasonable cost, precisely localized recombination breakpoints (i.e., the crossovers), and minimized mapping intervals for quantitative-trait locus analysis. The main issues with these low-coverage genotyping methods are (1) poor performance at heterozygous loci, (2) high percentage of missing data, (3) local errors due to erroneous mapping of sequencing reads and reference genome mistakes, and (4) global, technical errors inherent to NGS itself. Recent methods like Tassel-FSFHap or LB-Impute are excellent at addressing issues 1 and 2, but nonetheless perform poorly when issues 3 and 4 are persistent in a dataset (i.e., "noisy" data). Here, we present a new algorithm for imputation of LC-NGS data that eliminates the need of complex pre-filtering of noisy data, accurately types heterozygous chromosomal regions, precisely estimates crossover positions, corrects erroneous data, and imputes missing data. The imputation of genotypes and recombination breakpoints is based on maximum-likelihood estimation. We compare its performance with Tassel-FSFHap and LB-Impute using simulated data and two real datasets. NOISYmputer is consistently more efficient than the two other software tested and reaches average breakpoint precision of 99.9% and average recall of 99.6% on illumina simulated dataset. NOISYmputer consistently provides precise map size estimations when applied to real datasets while alternative tools may exhibit errors ranging from 3 to 1845 times the real size of the chromosomes in centimorgans. Furthermore, the algorithm is not only highly effective in terms of precision and recall but is also particularly economical in its use of RAM and computation time, being much faster than Hidden Markov Model methods. AvailabilityNOISYmputer and its source code are available as a multiplatform (Linux, macOS, Windows) Java executable at the URL https://gitlab.cirad.fr/noisymputer/noisymputerstandalone/-/tree/1.0.0-RELEASE?reftype=tags.

bioinformatics↗
bioRxiv2023

African rice (Oryza glaberrima) genomic introgressions impacting upon panicle architecture in Asian rice (O. sativa) lead to the identification of key QTLs

BackgroundDeveloping high yielding varieties is a major challenge for breeders tackling the challenges of climate change in agriculture. The panicle (inflorescence) architecture of rice is one of the key components of yield potential and displays high inter- and intra-specific variability. The genus Oryza features two different crop species: Asian rice (Oryza sativa L.) and the African rice (O. glaberrima Steud). One of the main morphological differences between the two independently domesticated species is the structure (or complexity) of the panicle, with O. sativa displaying a highly branched panicle, which in turn produces a larger number of grains than that of O. glaberrima. The genetic interactions that govern the diversity of panicle complexity within and between the two species are still poorly understood. ResultsTo identify genetic factors linked to panicle architecture diversity in the two species, we used a set of 60 Chromosome Segment Substitution Lines (CSSLs) issued from third generation backcross (BC3DH) and carrying genomic segments from O. glaberrima cv. MG12 in the genetic background of O. sativa Tropical Japonica cv. Caiapo. Phenotypic data were collected for rachis and primary branch length, primary, secondary and tertiary branch number and spikelet number. A total of 15 QTLs were localized on chromosomes 1, 2, 3, 7, 11 and 12 and QTLs associated with enhanced secondary and tertiary branch numbers were detected in two CSSLs. Furthermore, BC4F3:5 lines carrying different combinations of substituted segments were produced to decipher the effects of the identified QTL regions on variations in panicle architecture. A detailed analysis of phenotypes versus genotypes was carried out between the two parental genomes within these regions in order to understand how O. glaberrima introgression events may lead to alterations in panicle traits. ConclusionOur analysis led to the detection of genomic variations between O. sativa cv. Caiapo and O. glaberrima cv. MG12 in regions associated with enhanced panicle traits in specific CSSLs. These regions contain a number of key genes that regulate panicle development in O. sativa and their interspecific genomic variations may explain the phenotypic effects observed.

plant biology↗
bioRxiv2022

A more complete story of the genetic bases of resistance to the rice hoja blanca disease

Rice hoja blanca (RHB) is one of the most serious diseases in rice growing areas in tropical Americas. Its causal agent is Rice hoja blanca virus (RHBV), transmitted by the planthopper Tagosodes orizicolus Muir. Genetic resistance is the most effective and environment-friendly way of controlling the disease. So far, only one major quantitative trait locus (QTL) of Oryza sativa ssp. japonica origin, qHBV4.1, that alters incidence of the virus symptoms in two Colombian cultivars has been reported. This resistance has already started to be broken, stressing the urgent need for diversifying the resistance sources. In the present study we performed a search for new QTLs of O. sativa indica origin associated with RHB resistance. We used four F2:3 segregating populations derived from indica resistant varieties crossed with a highly susceptible japonica pivot parent. Beside the standard method for measuring disease incidence, we developed a new method based on computer-assisted image processing to determine the affected leaf area (ALA) as a measure of symptom severity. Based on the disease severity and incidence scores in the F3 families under greenhouse conditions, and SNP genotyping of the F2 individuals, we identified four new indica QTLs for RHB resistance on rice chromosomes 4, 6 and 11, namely qHBV4.2WAS208, qHBV6.1PTB25, qHBV11.1 and qHBV11.2. We also confirmed the wide-range action of qHBV4.1. Among the five QTLs, qHBV4.1 and qHBV11.1 had the largest effects on incidence and severity, respectively. These results provide a more complete understanding of the genetic bases of RHBV resistance in the cultivated rice gene pool, and can be used to develop marker-aided breeding strategies to improve RHB resistance. The power of joint-and meta-analyses allowed precise mapping and candidate gene identification, providing the basis for positional cloning of the two major QTLs qHBV4.1 and qHBV11.1.

genetics↗
bioRxiv2022

Pan-genome inversion index reveals evolutionary insights into the subpopulation structure of Asian rice (Oryza sativa)

Understanding and exploiting genetic diversity is a key factor for the productive and stable production of rice. Utilizing 16 high-quality genomes that represent the subpopulation structure of Asian rice (O. sativa), plus the genomes of two close relatives (O. rufipogon and O. punctata), we built a pan-genome inversion index of 1,054 non-redundant inversions that span an average of [~] 14% of the O. sativa cv. Nipponbare reference genome sequence. Using this index we estimated an inversion rate of 1,100 inversions per million years in Asian rice, which is 37 to 73 times higher than previously estimated for plants. Detailed analyses of these inversions showed evidence of their effects on gene regulation, recombination rate, linkage disequilibrium and agronomic trait performance. Our study uncovers the prevalence and scale of large inversions ([≥] 100 kb) across the pan-genome of Asian rice, and hints at their largely unexplored role in functional biology and crop performance.

genomics↗
bioRxiv2021

Direct fast estimator for recombination frequency in the F2 cross

In this short note, a new unbiased maximum-likelihood estimator is proposed for the recombination frequency in the F2 cross. The estimator is much faster to calculate than its EM algorithm equivalent, yet as efficient. Simulation studies are carried to illustrate the gain over another simple estimate proposed by Benito & Gallego (2004).

bioinformatics↗
bioRxiv2020

Multiple streams of genetic diversity in Japonica rice

In-depth studies on the genetic diversity of crops indicate that domestication is likely a drawn-out process that differs from the traditional representation of a simple rapid bottleneck. Asian cultivated rice provides a clear picture of multiple foundations of crop diversity. Among them, Japonica rice is likely the group derived from the first human manipulations of this species. We make use of the 3,000 Rice Genomes (3K RG) data set, first described in 2018, to explore the genetic diversity of traditional Japonica rice. After delineating introgressions from the Indica and cAus cultivar groups, we mask these traces to analyse Japonica diversity in more depth. We find differentiation between the established "temperate", "subtropical" and "tropical" subgroups, and identify stream-like traces of highly divergent sources from broad geographic ranges and subgroups. We characterize five such streams, most visible respectively in: 1) Indonesia, 2) continental Southeast Asia, 3) China, 4) uplands of Japan, and 5) Bhutan. These streams likely consist of ancient alien introgressions propagated through geneflow to different degrees. They currently appear as long genome segments conserved among specific germplasm groups, as well as shorter segments more broadly distributed across diverse germplasm along what could be adaptive corridors. They are all represented in the Japonica component of cBasmati varieties, thought to have emerged over two millennia ago. We thus provide strong evidence that Japonica, the group posited as being the most direct product of a simple domestication process in China, is an aggregate derived from multiple waves of admixture and represents a composite gene pool with ancient Asia-wide population dynamics.

genomics↗
bioRxiv2019

Twelve Platinum-Standard Reference Genomes Sequences (PSRefSeq) that complete the full range of genetic diversity of Asian rice

As the human population grows from 7.8 billion to 10 billion over the next 30 years, breeders must do everything possible to create crops that are highly productive and nutritious, while simultaneously having less of an environmental footprint. Rice will play a critical role in meeting this demand and thus, knowledge of the full repertoire of genetic diversity that exists in germplasm banks across the globe is required. To meet this demand, we describe the generation, validation and preliminary analyses of transposable element and long-range structural variation content of 12 near-gap-free reference genome sequences (RefSeqs) from representatives of 12 of 15 subpopulations of cultivated rice. When combined with 4 existing RefSeqs, that represent the 3 remaining rice subpopulations and the largest admixed population, this collection of 16 Platinum Standard RefSeqs (PSRefSeq) can be used as a pan-genome template to map resequencing data to detect virtually all standing natural variation that exists in the pan-cultivated rice genome.

genomics↗
bioRxiv2019

NOISYmputer: genotype imputation in bi-parental populationsfor noisy low-coverage next-generation sequencing data

MotivationLow-coverage next-generation sequencing (LC-NGS) methods can be used to genotype bi-parental populations. This approach allows the creation of highly saturated genetic maps at reasonable cost, precisely localized recombination breakpoints, and minimize mapping intervals for quantitative-trait locus analysis.\n\nThe main issues with these genotyping methods are (1) poor performance at heterozygous loci, (2) a high percentage of missing data, (3) local errors due to erroneous mapping of sequencing reads and reference genome mistakes, and (4) global, technical errors inherent to NGS itself.\n\nRecent methods like Tassel-FSFHap or LB-Impute are excellent at addressing issues 1 and 2, but nonetheless perform poorly when issues 3 and 4 are persistent in a dataset (i.e. \"noisy\" data). Here, we present an algorithm for imputation of LC-NGS data that eliminates the need of complex pre-filtering of noisy data, accurately types heterozygous chromosomic regions, corrects erroneous data, and imputes missing data. We compare its performance with Tassel-FSFHap, LB-Impute, and Genotype-Corrector using simulated data and three real datasets: a rice single seed descent (SSD) population genotyped by genotyping by sequencing (GBS) by whole genome sequencing (WGS), and a sorghum SSD population genotyped by GBS.\n\nAvailabilityNOISYmputer, a Microsoft Excel-Visual Basic for Applications program that implements the algorithm, is available at mapdisto.free.fr. It runs in Apple macOS and Microsoft Windows operating systems.\n\nSupplementary files: Download link

bioinformatics↗
bioRxiv2016

Constructing high-density linkage maps with MapDisto 2.0

We present the second major release of MapDisto, a multi-platform, user-friendly computer program for constructing genetic linkage maps of experimental populations. This version includes several new major features: (i) handling of very large genotyping datasets like the ones generated by genotyping-by-sequencing (GBS); (ii) direct importation and conversion of Variant Call Format (VCF) files; (iii) detection of linkage, i.e. construction of linkage groups in case of segregation distortion; (iv) data imputation on VCF files using a new approach, called LB-Impute. These features operate through inclusion of new Java modules that are installed and used transparently by MapDisto; (v) QTL detection via a new R/qtl graphical interface.\n\nThe program is available free of charge at mapdisto.free.fr.

bioinformatics↗