Investigative Genetics 2011, 2:24 doi:10.1186/2041-2223-2-24
Contrasting signals of positive selection in genes involved in human skin color variation from tests based on SNP scans and resequencing
Johanna Maria de Gruijter et al.
Numerous genome-wide scans conducted by genotyping previously-ascertained single nucleotide polymorphisms (SNPs) have provided candidate signatures of positive selection in various regions of the human genome, including in genes involved in pigmentation traits. However, it is unclear how well the signatures discovered by such haplotype-based test statistics can be reproduced in tests based on full resequence data. Four genes, OCA2, TYRP1, DCT and KITLG, implicated in human skin color variation, have shown evidence for positive selection in Europeans and East Asians in previous SNP-scan data. In the current study, we resequenced 4.7-6.7 kb of DNA from each of these genes in Africans, Europeans, East Asians and South Asians.
Applying all commonly-used allele frequency distribution neutrality test statistics to the newly generated sequence data provided conflicting results in respect of evidence for positive selection. Previous haplotype-based findings could not be clearly confirmed. The application of Markov Chain Monte Carlo Approximate Bayesian Computation to these sequence data using a simple forward simulator revealed broad posterior distributions of the selective parameters for all four genes providing no support for positive selection. However, when we applied this approach to published sequence data on SLC45A2, another human pigmentation candidate gene, we could readily confirm evidence for positive selection as previously detected with sequence-based and some haplotype-based tests.
Overall, our data indicate that even genes that are strong biological candidates for positive selection and show reproducible signatures of positive selection in SNP scans do not always show the same replicability of selection signals in other tests, which should be considered in future studies on detecting positive selection in genetic data.