"With increasing availability of polymorphic molecular markers across genomes, examining population structure using a large number of loci has become a common practice in evolutionary biology and human genetics . In assigning individual membership and inferences, investigators have found that some markers (or variants) are more informative than others . "
[Show abstract][Hide abstract] ABSTRACT: Next-generation sequencing technologies now make it possible to genotype and measure hundreds of thousands of rare genetic variations in individuals across the genome. Characterization of high-density genetic variation facilitates control of population genetic structure on a finer scale before large-scale genotyping in disease genetics studies. Population structure is a well-known, prevalent, and important factor in common variant genetic studies, but its relevance in rare variants is unclear. We perform an extensive population structure analysis using common and rare functional variants from the Genetic Analysis Workshop 17 mini-exome sequence. The analysis based on common functional variants required 388 principal components to account for 90% of the variation in population structure. However, an analysis based on rare variants required 532 significant principal components to account for similar levels of variation. Using rare variants, we detected fine-scale substructure beyond the population structure identified using common functional variants. Our results show that the level of population structure embedded in rare variant data is different from the level embedded in common variant data and that correcting for population structure is only as good as the level one wishes to correct.
"The comprehensive identification and control of population genetic structure and dissection of polymorphism are important steps in genomic studies aimed at gene mapping through (either directly or indirectly) linkage disequilibrium (LD) [1-4]. Previous estimates of population structure have provided tremendous insight into population genetics and human evolution, and have increased our knowledge of the distribution of genetic variation and relationships among human populations [5-8]. "
[Show abstract][Hide abstract] ABSTRACT: Emerging technologies now make it possible to genotype hundreds of thousands of genetic variations in individuals, across the genome. The study of loci at finer scales will facilitate the understanding of genetic variation at genomic and geographic levels. We examined global and chromosomal variations across HapMap populations using 3.7 million single nucleotide polymorphisms to search for the most stratified genomic regions of human populations and linked these regions to ontological annotation and functional network analysis. To achieve this, we used five complementary statistical and genetic network procedures: principal component (PC), cluster, discriminant, fixation index (FST) and network/pathway analyses. At the global level, the first two PC scores were sufficient to account for major population structure; however, chromosomal level analysis detected subtle forms of population structure within continental populations, and as many as 31 PCs were required to classify individuals into homogeneous groups. Using recommended population ancestry differentiation measures, a total of 126 regions of the genome were catalogued. Gene ontology and networks analyses revealed that these regions included the genes encoding oculocutaneous albinism II (OCA2), hect domain and RLD 2 (HERC2), ectodysplasin A receptor (EDAR) and solute carrier family 45, member 2 (SLC45A2). These genes are associated with melanin production, which is involved in the development of skin and hair colour, skin cancer and eye pigmentation. We also identified the genes encoding interferon-γ (IFNG) and death-associated protein kinase 1 (DAPK1), which are associated with cell death, inflammatory and immunological diseases. An in-depth understanding of these genomic regions may help to explain variations in adaptation to different environments. Our approach offers a comprehensive strategy for analysing chromosome-based population structure and differentiation, and demonstrates the application of complementary statistical and functional network analysis in human genetic variation studies.
Human genomics 05/2011; 5(4):220-40. DOI:10.1186/1479-7364-5-4-220 · 2.15 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Mapping genes by chromosome walking is a widely used technique applicable to cloning virtually any gene that is identifiable by mutagenesis. We isolated the gene responsible for the recessive mutation rsf1 (for reduced sensitivity to far-red light) in the Arabidopsis Columbia accession by using classical genetic analysis and two recently developed technologies: genotyping high-density oligonucleotide DNA array and denaturing high-performance liquid chromatography (DHPLC). The Arabidopsis AT412 genotyping array and 32 F(2) plants were used to map the rsf1 mutation close to the top of chromosome 1 to an interval of approximately 500 kb. Using DHPLC, we found and genotyped additional markers for fine mapping, shortening the interval to approximately 50 kb. The mutant gene was directly identified by DHPLC by comparing amplicons generated separately from the rsf1 mutant and the parent strain Columbia. DHPLC analysis yielded polymorphic profiles in two overlapping polymorphic amplicons attributable to a 13-bp deletion in the third of five exons of a gene encoding a 292-amino acid protein with a basic helix-loop-helix (bHLH) domain. The mutation in rsf1 results in a truncated protein consisting of the first 129 amino acids but lacking the bHLH domain. Cloning the RSF1 gene strongly suggests that numerous phytochrome A-mediated responses require a bHLH class transcription factor.
The Plant Cell 01/2001; 12(12):2485-2498. · 9.34 Impact Factor
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