Pariya Behrouzi1, Ernst C Wit2. 1. Department of Plant Sciences, Wageningen University and Research, Wageningen, The Netherlands. 2. Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland.
Abstract
MOTIVATION: Linkage maps are used to identify the location of genes responsible for traits and diseases. New sequencing techniques have created opportunities to substantially increase the density of genetic markers. Such revolutionary advances in technology have given rise to new challenges, such as creating high-density linkage maps. Current multiple testing approaches based on pairwise recombination fractions are underpowered in the high-dimensional setting and do not extend easily to polyploid species. To remedy these issues, we propose to construct linkage maps using graphical models either via a sparse Gaussian copula or a non-paranormal skeptic approach. RESULTS: We determine linkage groups, typically chromosomes, and the order of markers in each linkage group by inferring the conditional independence relationships among large numbers of markers in the genome. Through simulations, we illustrate the utility of our map construction method and compare its performance with other available methods, both when the data are clean and contain no missing observations and when data contain genotyping errors. Our comprehensive map construction method makes full use of the dosage SNP data to reconstruct linkage map for any bi-parental diploid and polyploid species. We apply the proposed method to three genotype datasets: barley, peanut and potato from diploid and polyploid populations. AVAILABILITY AND IMPLEMENTATION: The method is implemented in the R package netgwas which is freely available at https://cran.r-project.org/web/packages/netgwas. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Linkage maps are used to identify the location of genes responsible for traits and diseases. New sequencing techniques have created opportunities to substantially increase the density of genetic markers. Such revolutionary advances in technology have given rise to new challenges, such as creating high-density linkage maps. Current multiple testing approaches based on pairwise recombination fractions are underpowered in the high-dimensional setting and do not extend easily to polyploid species. To remedy these issues, we propose to construct linkage maps using graphical models either via a sparse Gaussian copula or a non-paranormal skeptic approach. RESULTS: We determine linkage groups, typically chromosomes, and the order of markers in each linkage group by inferring the conditional independence relationships among large numbers of markers in the genome. Through simulations, we illustrate the utility of our map construction method and compare its performance with other available methods, both when the data are clean and contain no missing observations and when data contain genotyping errors. Our comprehensive map construction method makes full use of the dosage SNP data to reconstruct linkage map for any bi-parental diploid and polyploid species. We apply the proposed method to three genotype datasets: barley, peanut and potato from diploid and polyploid populations. AVAILABILITY AND IMPLEMENTATION: The method is implemented in the R package netgwas which is freely available at https://cran.r-project.org/web/packages/netgwas. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Pariya Behrouzi; Pol Grootswagers; Paul L C Keizer; Ellen T H C Smeets; Edith J M Feskens; Lisette C P G M de Groot; Fred A van Eeuwijk Journal: J Nutr Date: 2020-03-01 Impact factor: 4.798
Authors: Sara Montanari; Susan Thomson; Sarah Cordiner; Catrin S Günther; Poppy Miller; Cecilia H Deng; Tony McGhie; Mareike Knäbel; Toshi Foster; Janice Turner; David Chagné; Richard Espley Journal: Front Plant Sci Date: 2022-09-23 Impact factor: 6.627