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MC1R diversity in Northern Island Melanesia has not been constrained by strong purifying selection and cannot explain pigmentation phenotype variation in the region.

BMC genetics | Oct 19, 2015

BACKGROUND: Variation in human skin pigmentation evolved in response to the selective pressure of ultra-violet radiation (UVR). Selection to maintain darker skin in high UVR environments is expected to constrain pigmentation phenotype and variation in pigmentation loci. Consistent with this hypothesis, the gene MC1R exhibits reduced diversity in African populations from high UVR regions compared to low-UVR non-African populations. However, MC1R diversity in non-African populations that have evolved under high-UVR conditions is not well characterized. METHODS: In order to test the hypothesis that MC1R variation has been constrained in Melanesians the coding region of the MC1R gene was sequenced in 188 individuals from Northern Island Melanesia. The role of purifying selection was assessed using a modified McDonald Kreitman's test. Pairwise FST was calculated between Melanesian populations and populations from the 1000 Genomes Project. The SNP rs2228479 was genotyped in a larger sample (n = 635) of Melanesians and tested for associations with skin and hair pigmentation. RESULTS: We observe three nonsynonymous and two synonymous mutations. A modified McDonald Kreitman's test failed to detect a significant signal of purifying selection. Pairwise FST values calculated between the four islands sampled here indicate little regional substructure in MC1R. When compared to African, European, East and South Asian populations, Melanesians do not exhibit reduced population divergence (measured as FST) or a high proportion of haplotype sharing with Africans, as one might expect if ancestral haplotypes were conserved across high UVR populations in and out of Africa. The only common nonsynonymous polymorphism observed, rs2228479, is not significantly associated with skin or hair pigmentation in a larger sample of Melanesians. CONCLUSIONS: The pattern of sequence diversity here does not support a model of strong selective constraint on MC1R in Northern Island Melanesia This absence of strong constraint, as well as the recent population history of the region, may explain the observed frequencies of the derived rs2228479 allele. These results emphasize the complex genetic architecture of pigmentation phenotypes, which are controlled by multiple, possibly interacting loci. They also highlight the role that population history can play in influencing phenotypic diversity in the absence of strong natural selection.

Pubmed ID: 26482799 RIS Download

Mesh terms: Adult | Base Sequence | Gene Frequency | Genetic Association Studies | Genetic Variation | Genetics, Population | Genome, Human | Geography | Haplotypes | Humans | Melanesia | Phenotype | Polymorphism, Single Nucleotide | Receptor, Melanocortin, Type 1 | Selection, Genetic | Skin Pigmentation

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International collaboration producing an extensive public catalog of human genetic variation, including SNPs and structural variants, and their haplotype contexts, in an effort to provide a foundation for investigating the relationship between genotype and phenotype. The genomes of about 2500 unidentified people from about 25 populations around the world were sequenced using next-generation sequencing technologies. Redundant sequencing on various platforms and by different groups of scientists of the same samples can be compared. The results of the study are freely and publicly accessible to researchers worldwide. The consortium identified the following populations whose DNA will be sequenced: Yoruba in Ibadan, Nigeria; Japanese in Tokyo; Chinese in Beijing; Utah residents with ancestry from northern and western Europe; Luhya in Webuye, Kenya; Maasai in Kinyawa, Kenya; Toscani in Italy; Gujarati Indians in Houston; Chinese in metropolitan Denver; people of Mexican ancestry in Los Angeles; and people of African ancestry in the southwestern United States. The goal Project is to find most genetic variants that have frequencies of at least 1% in the populations studied. Sequencing is still too expensive to deeply sequence the many samples being studied for this project. However, any particular region of the genome generally contains a limited number of haplotypes. Data can be combined across many samples to allow efficient detection of most of the variants in a region. The Project currently plans to sequence each sample to about 4X coverage; at this depth sequencing cannot provide the complete genotype of each sample, but should allow the detection of most variants with frequencies as low as 1%. Combining the data from 2500 samples should allow highly accurate estimation (imputation) of the variants and genotypes for each sample that were not seen directly by the light sequencing. All samples from the 1000 genomes are available as lymphoblastoid cell lines (LCLs) and LCL derived DNA from the Coriell Cell Repository as part of the NHGRI Catalog. The sequence and alignment data generated by the 1000genomes project is made available as quickly as possible via their mirrored ftp sites. ftp://ftp.1000genomes.ebi.ac.uk ftp://ftp-trace.ncbi.nlm.nih.gov/1000genomes

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1000 Genomes Project and AWS

A dataset containing the full genomic sequence of 1,700 individuals, freely available for research use. The 1000 Genomes Project is an international research effort coordinated by a consortium of 75 companies and organizations to establish the most detailed catalogue of human genetic variation. The project has grown to 200 terabytes of genomic data including DNA sequenced from more than 1,700 individuals that researchers can now access on AWS for use in disease research free of charge. The dataset containing the full genomic sequence of 1,700 individuals is now available to all via Amazon S3. The data can be found at: http://s3.amazonaws.com/1000genomes The 1000 Genomes Project aims to include the genomes of more than 2,662 individuals from 26 populations around the world, and the NIH will continue to add the remaining genome samples to the data collection this year. Public Data Sets on AWS provide a centralized repository of public data hosted on Amazon Simple Storage Service (Amazon S3). The data can be seamlessly accessed from AWS services such Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic MapReduce (Amazon EMR), which provide organizations with the highly scalable compute resources needed to take advantage of these large data collections. AWS is storing the public data sets at no charge to the community. Researchers pay only for the additional AWS resources they need for further processing or analysis of the data. All 200 TB of the latest 1000 Genomes Project data is available in a publicly available Amazon S3 bucket. You can access the data via simple HTTP requests, or take advantage of the AWS SDKs in languages such as Ruby, Java, Python, .NET and PHP. Researchers can use the Amazon EC2 utility computing service to dive into this data without the usual capital investment required to work with data at this scale. AWS also provides a number of orchestration and automation services to help teams make their research available to others to remix and reuse. Making the data available via a bucket in Amazon S3 also means that customers can crunch the information using Hadoop via Amazon Elastic MapReduce, and take advantage of the growing collection of tools for running bioinformatics job flows, such as CloudBurst and Crossbow.

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PLINK

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Software for beautiful sequence alignment, assembly and analysis.

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