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Evaluation of the SNP tagging approach in an independent population sample: Array-based SNP discovery in Sami
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
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2007 (English)In: Human Genetics, ISSN 0340-6717, E-ISSN 1432-1203, Vol. 122, no 2, 141-150 p.Article in journal (Refereed) Published
Abstract [en]

Significant efforts have been made to determine the correlation structure of common SNPs in the human genome. One method has been to identify the sets of tagSNPs that capture most of the genetic variation. Here, we evaluate the transferability of tagSNPs between populations using a population sample of Sami, the indigenous people of Scandinavia. Array-based SNP discovery in a 4.4 Mb region of 28 phased copies of chromosome 21 uncovered 5,132 segregating sites, 3,188 of which had a minimum minor allele frequency (mMAF) of 0.1. Due to the population structure and consequently high LD, the number of tagSNPs needed to capture all SNP variation in Sami is much lower than that for the HapMap populations. TagSNPs identified from the HapMap data perform only slightly better in the Sami than choosing tagSNPs at random from the same set of common SNPs. Surprisingly, tagSNPs defined from the HapMap data did not perform better than selecting the same number of SNPs at random from all SNPs discovered in Sami. Nearly half (46%) of the Sami SNPs with a mMAF of 0.1 are not present in the HapMap dataset. Among sites overlapping between Sami and HapMap populations, 18% are not tagged by the European American (CEU) HapMap tagSNPs, while 43% of the SNPs that are unique to Sami are not tagged by the CEU tagSNPs. These results point to serious limitations in the transferability of common tagSNPs to capture random sequence variation, even between closely related populations, such as CEU and Sami.

Place, publisher, year, edition, pages
2007. Vol. 122, no 2, 141-150 p.
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:uu:diva-95152DOI: 10.1007/s00439-007-0379-2ISI: 000250236600003PubMedID: 17554563OAI: oai:DiVA.org:uu-95152DiVA: diva2:169253
Available from: 2006-11-17 Created: 2006-11-17 Last updated: 2017-12-14Bibliographically approved
In thesis
1. Genome Variation in Human Populations: Exploring the Effects of Demographic History and the Potential for Mapping of Complex Traits
Open this publication in new window or tab >>Genome Variation in Human Populations: Exploring the Effects of Demographic History and the Potential for Mapping of Complex Traits
2006 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A major challenge in human genetics is to understand the genetic variation underlying common diseases. In this thesis, I focus on forces creating differences between individuals and genomic regions, methods for characterizing genomic variation, and the association between genomic and phenotypic variation. Genetic markers are widely used to locate genes associated with different phenotypes. In my first paper, I describe novel algorithms for automatic genotype determination of microsatellite markers, a procedure which is currently both time-consuming and error prone.

The co-segregation of genetic markers in a population leads to non-random association of alleles at different loci - linkage disequilibrium (LD). LD varies throughout the genome and differs between populations due to factors such as their demographic history. In my second paper, I discuss the increased power, for mapping of human traits, that results from studying a population with appreciable levels of LD such as is found in the Swedish Sami population.

Lately, large-scale analyses of single nucleotide polymorphisms (SNPs) have become available and efforts have been made to identify a set of SNPs, which captures most of the genome variation in a population (tagSNPs). In my third paper, I describe the limitations of this approach when applied to data from an independent population sample of randomly ascertained SNPs. The transferability of tagSNPs between populations is poor, presumably due to variation in allele frequencies and the bias towards common SNPs used in most studies.

The level of genomic variation is influenced by population structure, recombination and mutation rate, as well as natural selection. During the exodus from Africa, humans have adapted to new environmental conditions. In my fourth paper, I describe a new method for identifying genomic regions carrying signatures of recent positive selection and apply this to an available dataset of millions of SNPs.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2006. 42 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 201
Keyword
Genetics, genetics, evolution, microsatellie, SNP, selection, linkage disequilibrium, haplotype, Genetik
Identifiers
urn:nbn:se:uu:diva-7293 (URN)91-554-6722-9 (ISBN)
Public defence
2006-12-08, Rudbecksalen, Rudbeck Laboratory, Dag Hammarskjölds väg 20, Uppsala, 13:15
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Available from: 2006-11-17 Created: 2006-11-17 Last updated: 2011-01-20Bibliographically approved

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