coPLINK: A complementary tool to PLINK

haploMAGIC: accurate phasing and detection of recombination in multiparental populations despite genotyping errors

Recombination is a key mechanism in breeding for promoting genetic variability. Multiparental populations (MPPs) constitute an excellent platform for precise genotype phasing, identification of genome-wide crossovers (COs), estimation of recombination frequencies, and construction of recombination maps. Here, we introduce haploMAGIC, a pipeline to detect COs in MPPs with single-nucleotide polymorphism (SNP) data by exploiting the pedigree relationships for accurate genotype phasing and inference of grandparental haplotypes. haploMAGIC applies filtering to prevent false-positive COs due to genotyping errors (GEs), a common problem in high-throughput SNP analysis of complex plant genomes. Hence, it discards haploblocks not reaching a specified minimum number of informative alleles. A performance analysis using populations simulated with AlphaSimR revealed that haploMAGIC improves upon existing methods of CO detection in terms of recall and precision, most notably when GE rates are high. Furthermore, we constructed recombination maps using haploMAGIC with high-resolution genotype data from 2 large multiparental populations of winter rapeseed (Brassica napus). The results demonstrate the applicability of the pipeline in real-world scenarios and showed good correlations in recombination frequency compared with alternative software. Therefore, we propose haploMAGIC as an accurate tool at CO detection with MPPs that shows robustness against GEs.

Recessive/dominant model: Alternative choice in case-control-based genome-wide association studies

An additive genetic model is usually employed in case-control-based genome-wide association studies. The model usually encodes "AA", "Aa" and "aa" ("a" represents the minor allele) as three different numbers, implying the contribution of genotype "Aa" to the phenotype is different from "AA" and "aa". From the perspective of biological phenomena, the coding is reasonable since the phenotypes of lives are not "black and white". A case-control based study, however, has only two phenotypes, case and control, which means that the phenotypes are "black and white". It suggests that a recessive/dominant model may be an alternative to the additive model. In order to investigate whether the alternative is feasible, we conducted comparative experiments on several models used in those studies through chi-square test and logistic regression. Our simulation experiments demonstrate that a recessive model is better than the additive model. The area under the curve of the former has increased by 5% compared with the latter, the discrimination of identifying risk single nucleotide polymorphisms has been improved by 61%, and the precision has also reached 1.10 times that of the latter. Furthermore, the real data experiments show that the precision and area under the curve of the former are 16% and 20% higher than the latter respectively, and the area under the curve of dominant model of the former is 13% higher than the latter. The results indicate a recessive/dominant model may be an alternative to the additive model and suggest a new route for case-control-based studies.