Next-generation sequencing (NGS) approaches are widely used in genome-wide genetic marker discovery and genotyping. However, current NGS approaches are not easy to apply to general outbred populations (human and some major farm animals) for SNP identification because of the high level of heterogeneity and phase ambiguity in the haplotype. Here, we reported a new method for SNP genotyping, called genotyping by genome reducing and sequencing (GGRS) to genotype outbred species. Through an improved procedure for library preparation and a marker discovery and genotyping pipeline, the GGRS approach can genotype outbred species cost-effectively and high-reproducibly. We also evaluated the efficiency and accuracy of our approach for high-density SNP discovery and genotyping in a large genome pig species (2.8 Gb), for which more than 70,000 single nucleotide polymorphisms (SNPs) can be identified for an expenditure of only $80 (USD)/sample.
SummaryThe Chinese indigenous pig breeds in the Taihu Lake region are the most prolific pig breeds in the world. In this study, we investigated the genetic diversity and population structure of six breeds, including Meishan, Erhualian, Mi, Fengjing, Shawutou and Jiaxing Black, in this region using whole‐genome SNP data. A high SNP with proportions of polymorphic markers ranging from 0.925 to 0.995 was exhibited by the Chinese indigenous pigs in the Taihu Lake region. The allelic richness and expected heterozygosity also were calculated and indicated that the genetic diversity of the Meishan breed was the greatest, whereas that of the Fengjing breed was the lowest. The genetic differentiation, as indicated by the fixation index, exhibited an overall mean of 0.149. Both neighbor‐joining tree and principal components analysis were able to distinguish the breeds from each other, but structure analysis indicated that the Mi and Erhualian breeds exhibited similar major signals of admixture. With this genome‐wide comprehensive survey of the genetic diversity and population structure of the indigenous Chinese pigs in the Taihu Lake region, we confirmed the rationality of the current breed classification of the pigs in this region.
We report a novel algorithm, iBLUP, to impute missing genotypes by simultaneously and comprehensively using identity by descent and linkage disequilibrium information. The simulation studies showed that the algorithm exhibited drastically tolerance to high missing rate, especially for rare variants than other common imputation methods, e.g. BEAGLE and fastPHASE. At a missing rate of 70%, the accuracy of BEAGLE and fastPHASE dropped to 0.82 and 0.74 respectively while iBLUP retained an accuracy of 0.95. For minor allele, the accuracy of BEAGLE and fastPHASE decreased to −0.1 and 0.03, while iBLUP still had an accuracy of 0.61.We implemented the algorithm in a publicly available software package also named iBLUP. The application of iBLUP for processing real sequencing data in an outbred pig population was demonstrated.
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