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QTL mapping in autotetraploids using SNP dosage information.

ABSTRACT: Dense linkage maps derived by analysing SNP dosage in autotetraploids provide detailed information about the location of, and genetic model at, quantitative trait loci. Recent developments in sequencing and genotyping technologies enable researchers to generate high-density single nucleotide polymorphism (SNP) genotype data for mapping studies. For polyploid species, the SNP genotypes are informative about allele dosage, and Hackett et al. (PLoS ONE 8:e63939, 2013) presented theory about how dosage information can be used in linkage map construction and quantitative trait locus (QTL) mapping for an F1 population in an autotetraploid species. Here, QTL mapping using dosage information is explored for simulated phenotypic traits of moderate heritability and possibly non-additive effects. Different mapping strategies are compared, looking at additive and more complicated models, and model fitting as a single step or by iteratively re-weighted modelling. We recommend fitting an additive model without iterative re-weighting, and then exploring non-additive models for the genotype means estimated at the most likely position. We apply this strategy to re-analyse traits of high heritability from a potato population of 190 F1 individuals: flower colour, maturity, height and resistance to late blight (Phytophthora infestans (Mont.) de Bary) and potato cyst nematode (Globodera pallida), using a map of 3839 SNPs. The approximate confidence intervals for QTL locations have been improved by the detailed linkage map, and more information about the genetic model at each QTL has been revealed. For several of the reported QTLs, candidate SNPs can be identified, and used to propose candidate trait genes. We conclude that the high marker density is informative about the genetic model at loci of large effects, but that larger populations are needed to detect smaller QTLs.

SUBMITTER: Hackett CA

PROVIDER: S-EPMC4145212 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundFemale fertility is important for the maintenance of the production in a dairy cattle herd. Two QTL regions on BTA04 and on BTA13 previously detected in Nordic Holstein (NH) and validated in the Danish Jersey (DJ) and Nordic Red (NR) were investigated further in the present study to further refine the QTL locations. Refined QTL regions were imputed to the full sequence data. The genes in the regions were then studied to ascertain their possible effect on fertility traits.ResultsBTA04 was screened for number of inseminations (AIS), 56-day non-return rate (NRR), days from first to last insemination (IFL), and the interval from calving to first insemination (ICF) in the range of 38,257,758 to 40,890,784 bp, whereas BTA13 was screened for ICF only in the range from 21,236,959 to 46,150,079 with the HD bovine SNP array for NH, DJ and NR. No markers in the DJ and NR breeds reached significance. By analyzing imputed sequence data the QTL position on BTA04 was narrowed down to two regions in the NH. In these two regions a total of 9 genes were identified. BTA13 was analyzed using sequence data for the NH breed. The highest -log10(P-value) was 19.41 at 33,903,159 bp. Two regions were identified: Region 1: 33,900,143-33,908,994 bp and Region 2: 34,051,815-34,056,728 bp. SNPs within and between these two regions were annotated as intergenic.ConclusionScreening BTA04 and BTA13 for female fertility traits in NH, NR and DJ suggested that the QTL for female fertility were specific for NH. A missense mutation in CD36 showed the strongest association with fertility traits on BTA04. The annotated SNPs on BTA13 were all intergenic variants. It is possible that BTA13 at this stage is poorly annotated such that the associated polymorphisms are located in as-yet undiscovered genes. Fertility traits are complex traits as many different biological and physiological factors determine whether a cow is fertile. Therefore it is not expected that there is a simple explanation with an obvious candidate gene but it is more likely a network of genes and intragenic variants that explain the variation of these traits.

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QTL mapping in autotetraploids using SNP dosage information.

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