Dataset Information

Building Ultra-High-Density Linkage Maps Based on Efficient Filtering of Trustable Markers.

ABSTRACT: The study is focused on addressing the problem of building genetic maps in the presence of ?103-104 of markers per chromosome. We consider a spectrum of situations with intrachromosomal heterogeneity of recombination rate, different level of genotyping errors, and missing data. In the ideal scenario of the absence of errors and missing data, the majority of markers should appear as groups of cosegregating markers ("twins") representing no challenge for map construction. The central aspect of the proposed approach is to take into account the structure of the marker space, where each twin group (TG) and singleton markers are represented as points of this space. The confounding effect of genotyping errors and missing data leads to reduction of TG size, but upon a low level of these effects surviving TGs can still be used as a source of reliable skeletal markers. Increase in the level of confounding effects results in a considerable decrease in the number or even disappearance of usable TGs and, correspondingly, of skeletal markers. Here, we show that the paucity of informative markers can be compensated by detecting kernels of markers in the marker space using a clustering procedure, and demonstrate the utility of this approach for high-density genetic map construction on simulated and experimentally obtained genotyping datasets.

SUBMITTER: Ronin YI

PROVIDER: S-EPMC5500130 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Building Ultra-High-Density Linkage Maps Based on Efficient Filtering of Trustable Markers.

Ronin Yefim I YI Mester David I DI Minkov Dina G DG Akhunov Eduard E Korol Abraham B AB

Genetics 20170516 3

The study is focused on addressing the problem of building genetic maps in the presence of ∼10<sup>3</sup>-10<sup>4</sup> of markers per chromosome. We consider a spectrum of situations with intrachromosomal heterogeneity of recombination rate, different level of genotyping errors, and missing data. In the ideal scenario of the absence of errors and missing data, the majority of markers should appear as groups of cosegregating markers ("twins") representing no challenge for map construction. The ...[more]

PMID: 28512186

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Project description:BACKGROUND: Linkage disequilibrium (LD) maps can provide a wealth of information on specific marker-phenotype relationships, especially in areas of the genome where positional candidate genes with similar functions are located. A recently published high resolution radiation hybrid map of bovine chromosome 14 (BTA14) together with the bovine physical map have enabled the creation of more accurate LD maps for BTA14 in both dairy and beef cattle. RESULTS: Over 500 Single Nucleotide Polymorphism (SNP) markers from both Angus and Holstein animals had their phased haplotypes estimated using GENOPROB and their pairwise r2 values compared. For both breeds, results showed that average LD extends at moderate levels up to 100 kilo base pairs (kbp) and falls to background levels after 500 kbp. Haplotype block structure analysis using HAPLOVIEW under the four gamete rule identified 122 haplotype blocks for both Angus and Holstein. In addition, SNP tagging analysis identified 410 SNPs and 420 SNPs in Holstein and Angus, respectively, for future whole genome association studies on BTA14. Correlation analysis for marker pairs common to these two breeds confirmed that there are no substantial correlations between r-values at distances over 10 kbp. Comparison of extended haplotype homozygosity (EHH), which calculates the LD decay away from a core haplotype, shows that in Holstein there is long range LD decay away from the DGAT1 region consistent with the selection for milk fat % in this population. Comparison of EHH values for Angus in the same region shows very little long range LD. CONCLUSION: Overall, the results presented here can be applied in future single or haplotype association analysis for both populations, aiding in confirming or excluding potential polymorphisms as causative mutations, especially around Quantitative Trait Loci regions. In addition, knowledge of specific LD information among markers will aid the research community in selecting appropriate markers for whole genome association studies.

Dataset Information

Building Ultra-High-Density Linkage Maps Based on Efficient Filtering of Trustable Markers.

Publications

Building Ultra-High-Density Linkage Maps Based on Efficient Filtering of Trustable Markers.

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