DOI: 10.5061/dryad.36rm1
title: Data from: Estimating parent-specific QTL effects through cumulating linked identity-by-state SNP effects in multiparental populations

description: The emergence of multiparental mapping populations enabled plant
 geneticists to gain deeper insights into the genetic architecture of major
 agronomic traits and to map quantitative trait loci (QTLs) controlling the
 expression of these traits. Although the investigated mapping populations
 are similar, one open question is whether genotype data should be modelled
 as identical by state (IBS) or identical by descent (IBD). Whereas IBS
 simply makes use of raw genotype scores to distinguish alleles, IBD data
 are derived from parental offspring information. We report on comparing
 IBS and IBD by applying two multiple regression models on four traits
 studied in the barley nested association mapping (NAM) population HEB-25.
 We observed that modelling parent-specific IBD genotypes produced a lower
 number of significant QTLs with increased prediction abilities compared
 with modelling IBS genotypes. However, at lower trait heritabilities the
 IBS model produced higher prediction abilities. We developed a method to
 estimate multiallelic QTL effects in multiparental populations from simple
 biallelic IBS data. This method is based on cumulating IBS-derived
 single-nucleotide polymorphism (SNP) effect estimates in a defined genetic
 region surrounding a QTL. Comparing the resulting parent-specific QTL
 effects with those obtained from IBD approaches revealed high accordance
 that could be confirmed through simulations. The method turned out to be
 also applicable to a barley multiparent advanced generation inter-cross
 (MAGIC) population. The ‘cumulation method’ represents a universal
 approach to differentiate parent-specific QTL effects in multiparental
 populations, even if no IBD information is available. In future, the
 method could further benefit from the availability of much denser SNP
 maps.
description: Additional File 1 - IBS
 and IBD matricesThis file contains all
 genotype scores of the identity-by-state (IBS) and identity-by-descent
 (IBD) genotype matrices. Polymorphic SNP alleles originating from Barke or
 the wild barley donors of the NAM population HEB-25 are easily
 distinguishable by state. Based on the Barke reference genotype, the wild
 barley allele can be specified in each segregating family. To setup the
 IBS matrix the state of the homozygous Barke allele was coded as 0, while
 HEB lines that showed a homozygous wild barley genotype were assigned a
 value of 2. Consequently, heterozygous HEB lines were assigned a value of
 1. If a SNP was monomorphic in one HEB family but polymorphic in a second
 family, lines of the first HEB family were assigned a genotype value of 0,
 since their state is not different from the Barke allele. Gaps resulting
 from missing genotypes (0.6% of all data points) were filled with the mean
 of polymorphic flanking markers, based on the map of Maurer et al. (2015).
 This way a complete genotype data set (IBS) was retained, which is
 required to carry out the following multiple regression GWAS. To convert
 the IBS matrix into an IBD matrix we first replaced each marker value that
 was monomorphic in a HEB family by an empty value. Then, the resulting
 gaps (44.9% of all data points) were filled with the mean of the next
 polymorphic flanking markers of this gap. This way we can distinguish
 whether the allele is inherited from the recurrent parent Barke or a wild
 donor across the whole NAM population. The newly assigned IBD value
 reflects the marker’s probability of being inherited from the wild barley
 donor.Additional data File 1 - IBS
 and IBD matrices_revised.xlsxAdditional File 2 - All
 phenotype dataThis file contains all
 phenotype data that were used for GWAS. Trait values are shown as
 BLUEs.Additional data File 2 - All
 phenotype data.xlsxAdditional File 3 - All
 significant markers and effect estimatesThis file contains all
 significant markers and their effect estimates obtained by two different
 GWAS models (IBS-M and IBD-MxF).Additional data File 3 - All
 significant markers and effect
 estimates.xlsxAdditional File 4 -
 Markers in complete LDThis file contains a
 list of all 305 SNP markers that are in complete LD to another marker and,
 thus, have been excluded from our study.Additional data File 4 -
 Markers in complete LD.xlsxAdditional File 5 -
 iSelect marker namesFile containing a
 translation for the marker names used in Maurer et al. and the official
 iSelect marker names.Additional data File 5 -
 iSelect marker names.xlsx