Parent-independent genotyping for constructing an ultrahigh-density linkage map based on population sequencing

Abstract

Bar-coded multiplexed sequencing approaches based on newgeneration 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 0.05× 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.genomics | maximum parsimony of recombination | Bayesian inference | hidden Markov model | rice G enetic maps provide the bases for a wide range of genetic and genomic studies and are pivotal for mapping and identifying genes associated with phenotypic performance, referred to as traits. The resolution of a genetic linkage map depends on the number of recombination events in the mapping population and the density of molecular markers. The number of recombination events depends on how the population is created, whereas the density of the markers can be improved continually with advances in molecular techniques. Traditional molecular markers that have been widely used in genotyping assays of populations, although laborious and time-consuming, have limitations in throughput and can provide information only for low-density maps, and thus are of low efficiency.Oligonucleotide microarrays, composed of millions of probes based on genome sequences, can capture large numbers of sequence variations between different samples by comparative genomic hybridization, which can be used for high-throughput marker discovery and genotyping (1-3). However, restrictions in microarray design and the number of probes on the microarrays limit the applications of this technology. In addition, it is cost-prohibitive for genotyping especially if the mapping population is large.New sequencing t...

Multiplexed shotgun genotyping for rapid and efficient genetic mapping

“…Second, because our method does not depend on manual shearing, small amounts of DNA isolated from single individuals can be processed, which makes our approach ideal for application to small and nonclonal species, such as insects. Third, like Xie et al (2010), our method implements an HMM algorithm to assign ancestry states to chromosomal segments. This has the advantage of providing a formal statistical framework for estimating ancestry, thus avoiding window-based approaches (e.g., Huang et al 2009), and allows imputation of ancestry at genomic positions with missing data.…”

“…We also developed a statistical framework to assign ancestry to chromosomal segments and detect recombination breakpoints based on a Hidden Markov Model. As an alternative to making hard assignments of chromosomal segments to one parent or the other (e.g., Xie et al 2010), we use ''soft'' ancestry assignments in the form of a probability distribution on the ancestry, as proposed and implemented in a variety of similar contexts (e.g., Falush et al 2003). This approach is highly suited to data in which the coverage, and thus the precision with which breakpoints can be localized, varies among individuals in a multiplexed genotyping experiment.…”

Genetic composition of yield heterosis in an elite rice hybrid

“…The entire population was field tested for yield traits, resulting in data that were particularly useful for studying the genetics of heterosis by mapping heterotic effects and identifying genetic components contributing to heterosis. More recently we resequenced the RILs and developed an algorithm for constructing an ultrahigh-density bin map, which provided more precise information for genetic mapping (32,33).In this study, we analyzed the genetic effects of all of the bins in the IMF2 population, including additive, dominance, and epistasis, and assessed their relative contributions to heterosis in the F 1 hybrid. The analyses displayed a picture that accumulation of small advantages genome-wide could explain the genetic basis of heterosis in this elite rice hybrid.…”

“…The entire population was field tested for yield traits, resulting in data that were particularly useful for studying the genetics of heterosis by mapping heterotic effects and identifying genetic components contributing to heterosis. More recently we resequenced the RILs and developed an algorithm for constructing an ultrahigh-density bin map, which provided more precise information for genetic mapping (32,33).…”

SEG-Map: A Novel Software for Genotype Calling and Genetic Map Construction from Next-generation Sequencing

“…And newly available sequencing platforms with up to 10 Gb output per lane and techniques for higher multiplex levels will make this method even faster and more cost-effective. Through this approach, a high-resolution map can be constructed and nearly all recombination events in one population can be identified (Huang et al 2009;Xie et al 2010). Then traits are able to be associated with the individuals of the population efficiently (Rounsley and Last 2010).…”