Effect of genotype imputation on genome-enabled prediction of complex traits: an empirical study with mice data

Evaluation of MC1R high-throughput nucleotide sequencing data generated by the 1000 Genomes Project

The advent of next-generation sequencing allows simultaneous processing of several genomic regions/individuals, increasing the availability and accuracy of whole-genome data. However, these new approaches may present some errors and bias due to alignment, genotype calling, and imputation methods. Despite these flaws, data obtained by next-generation sequencing can be valuable for population and evolutionary studies of specific genes, such as genes related to how pigmentation evolved among populations, one of the main topics in human evolutionary biology. Melanocortin-1 receptor (MC1R) is one of the most studied genes involved in pigmentation variation. As MC1R has already been suggested to affect melanogenesis and increase risk of developing melanoma, it constitutes one of the best models to understand how natural selection acts on pigmentation. Here we employed a locally developed pipeline to obtain genotype and haplotype data for MC1R from the raw sequencing data provided by the 1000 Genomes FTP site. We also compared such genotype data to Phase 3 VCF to evaluate its quality and discover any polymorphic sites that may have been overlooked. In conclusion, either the VCF file or one of the presently described pipelines could be used to obtain reliable and accurate genotype calling from the 1000 Genomes Phase 3 data.