NATIONL CENTER OF PLANT GENE RESEARCH(WUHAN)
Parent-independent genotyping for constructing an ultrahigh-density linkage map based on population sequencing
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Parent-independent genotyping for constructing an ultrahigh-density linkage map based on population sequencing


Xie W, Feng Q, Yu H, Huang X, Zhao Q, Xing Y, Yu S, Han B, Zhang Q.


National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China.

Abstract


Bar-coded multiplexed sequencing approaches based on new-generation sequencing technologies provide capacity to sequence a mapping population in a single sequencing run. However, such approaches usually generate low-coverage and error-prone sequences for each line in a population. Thus, it is a significant challenge to genotype individual lines in a population for linkage map construction based on low-coverage sequences without the availability of high-quality genotype data of the parental lines. In this paper, we report a method for constructing ultrahigh-density linkage maps composed of high-quality single-nucleotide polymorphisms (SNPs) based on low-coverage sequences of recombinant inbred lines. First, all potential SNPs were identified to obtain drafts of parental genotypes using a maximum parsimonious inference of recombination, making maximum use of SNP information found in the entire population. Second, high-quality SNPs were identified by filtering out low-quality ones by permutations involving resampling of windows of SNPs followed by Bayesian inference. Third, lines in the mapping population were genotyped using the high-quality SNPs assisted by a hidden Markov model. With 0.05x genome sequence per line, an ultrahigh-density linkage map composed of bins of high-quality SNPs using 238 recombinant inbred lines derived from a cross between two rice varieties was constructed. Using this map, a quantitative trait locus for grain width (GW5) was localized to its presumed genomic region in a bin of 200 kb, confirming the accuracy and quality of the map. This method is generally applicable in genetic map construction with low-coverage sequence data.