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A novel method for automatic genotyping of microsatellite markers based on parametric pattern recognition
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.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
2003 (English)In: Human Genetics, ISSN 0340-6717, E-ISSN 1432-1203, Vol. 113, no 4, 316-324 p.Article in journal (Refereed) Published
Abstract [en]

Genetic mapping of loci affecting complex phenotypes in human and other organisms is presently being conducted on a very large scale, using either microsatellite or single nucleotide polymorphism (SNP) markers and by partly automated methods. A critical step in this process is the conversion of the instrument output into genotypes, both a time-consuming and error prone procedure. Errors made during this calling of genotypes will dramatically reduce the ability to map the location of loci underlying a phenotype. Accurate methods for automatic genotype calling are therefore important. Here, we describe novel algorithms for automatic calling of microsatellite genotypes using parametric pattern recognition. The analysis of microsatellite data is complicated both by the occurrence of stutter bands, which arise from Taq polymerase misreading the number of repeats, and additional bands derived form the non-template dependent addition of a nucleotide to the 3' end of the PCR products. These problems, together with the fact that the lengths of two alleles in a heterozygous individual may differ by only two nucleotides, complicate the development of an automated process. The novel algorithms markedly reduce the need for manual editing and the frequency of miscalls, and compares very favourably with commercially available software for automatic microsatellite genotyping.

Place, publisher, year, edition, pages
2003. Vol. 113, no 4, 316-324 p.
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-95150DOI: 10.1007/s00439-003-0973-xPubMedID: 12883999OAI: oai:DiVA.org:uu-95150DiVA: diva2:169251
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
Opponent
Supervisors
Available from: 2006-11-17 Created: 2006-11-17 Last updated: 2011-01-20Bibliographically approved

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Johansson, Åsa

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