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Abstract

Based on pairwise identity-by-state (IBS) distances and whole-genome SNP data, kinship was investigated in the Israeli Holstein population. A total of 789 bulls, including most of the artificial insemination sires in service since 1987, were genotyped by the BovineSNP50 BeadChip. This sample included up to five generations. For each bull-by-bull combination, three states are possible for each marker: no match, a single match and both alleles match. Summing over all markers, the 932 598 IBS scores (three match frequencies*310 866 bull-by-bull combinations) were visualized using three-dimensional coordinates that corresponded to the frequencies of the three possible states. Results were reduced to two dimensions using the transformations x' = 0.7071(1 + freq1-freq2) and y' = 1.2247freq0. Bull-by-bull pairs were grouped according to their level of kinship, and canonical scores were calculated using discriminant analysis and the x' and y' features. Of the 474 pairs of recorded maternal grandsire-grandson with both individuals genotyped, the probability for 28 pairs to belong to this level of kinship was low (P < 0.05), suggesting an error rate of around 3% per generation in pedigree determination.

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... Similar recommendations can be given for the foreseeable exclusive use of SNPs in the near future. The evaluation and applicability of SNPs in parentage control has been shown in several studies [3,30,[44][45][46]. However, it is also clear that the currently recommended minimal number of SNPs might not be sufficient to eliminate false-negative results [28,47]. ...
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Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
Identifi-cation of Mendelian inconsistencies between SNP and pedigree information of sibs Effect of misidentification on genetic gain and estimation of breeding value in dairy cattle populations
  • Calus M P L Mulder
  • H A Bastiaansen
Calus M.P.L., Mulder H.A. & Bastiaansen J.W.M. (2011) Identifi-cation of Mendelian inconsistencies between SNP and pedigree information of sibs. Genetics, Selection, Evolution 43, 34. Israel C. & Weller J.I. (2000) Effect of misidentification on genetic gain and estimation of breeding value in dairy cattle populations. Journal of Dairy Science 83, 181–7.