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Development of Variance Component Methods for Genetic Dissection of Complex Traits
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis presents several developments on Variance component (VC) approach for Quantitative Trait Locus (QTL) mapping.

The first part consists of methodological improvements: a new fast and efficient method for estimating IBD matrices, have been developed. The new method makes a better use of the computer resources in terms of computational power and storage memory, facilitating further improvements by resolving methodological bottlenecks in algorithms to scan multiple QTL. A new VC model have also been developed in order to consider and evaluate the correlation of the allelic effects within parental lines origin in experimental outbred crosses. The method was tested on simulated and experimental data and revealed a higher or similar power to detect QTL than linear regression based QTL mapping.

The second part focused on the prospect to analyze multi-generational pedigrees by VC approach. The IBD estimation algorithm was extended to include haplotype information in addition to genotype and pedigree to improve the accuracy of the IBD estimates, and a new haplotyping algorithm was developed for limiting the risk of haplotyping errors in multigenerational pedigrees. Those newly developed methods where subsequently applied for the analysis of a nine generations AIL pedigree obtained after crossing two chicken lines divergently selected for body weight. Nine QTL described in a F2 population were replicated in the AIL pedigree, and our strategy to use both genotype and phenotype information from all individuals in the entire pedigree clearly made efficient use of the available genotype information provided in AIL.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis , 2009. , p. 32
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 646
National Category
Bioinformatics and Systems Biology
Research subject
Genetics
Identifiers
URN: urn:nbn:se:uu:diva-101399ISBN: 978-91-554-7534-5 (print)OAI: oai:DiVA.org:uu-101399DiVA, id: diva2:212911
Public defence
2009-06-12, C8:305, BMC, Husargatan 3, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2009-05-20 Created: 2009-04-24 Last updated: 2011-03-08Bibliographically approved
List of papers
1. An Improved Method for Quantitative Trait Loci Detection and Identification of Within-Line Segregation in F2 Intercross Designs
Open this publication in new window or tab >>An Improved Method for Quantitative Trait Loci Detection and Identification of Within-Line Segregation in F2 Intercross Designs
2008 (English)In: Genetics, ISSN 0016-6731, E-ISSN 1943-2631, Vol. 178, no 4, p. 2315-2326Article in journal (Refereed) Published
Abstract [en]

We present a new flexible, simple, and power ful genome-scan method (flexible intercross analysis, FIA) for detecting quantitative trait loci (QTL) in experimental line crosses. The method is based on a pure random-effects model that simultaneously models between- and within-line QTL variation for single as well as epistatic QTL. It utilizes the score statistic and thereby facilitates computationally efficient significance testing based on empirical significance thresholds obtained by means of permutations. The properties of the method are explored using simulations and analyses of experimental data. The simulations showed that the power of FIA was as good as, or better than, Haley–Knott regression and that FIA was rather insensitive to the level of allelic fixation in the founders, especially for pedigrees with few founders. A chromosome scan was conducted for a meat quality trait in an F2 intercross in pigs where a mutation in the halothane (Ryanodine receptor, RYR1) gene with a large effect on meat quality was known to segregate in one founder line. FIA obtained significant support for the halothane-associated QTL and identified the base generation allele with the mutated allele. A genome scan was also performed in a previously analyzed chicken F2 intercross. In the chicken intercross analysis, four previously detected QTL were confirmed at a 5% genomewide significance level, and FIA gave strong evidence (P , 0.01) for two of these QTL to be segregating within the founder lines. FIA was also extended to account for epistasis and using simulations we show that the method provides good estimates of epistatic QTL variance even for segregating QTL. Extensions of FIA and its applications on other intercross populations including backcrosses, advanced intercross lines, and heterogeneous stocks are also discussed.

National Category
Genetics
Research subject
Genetics
Identifiers
urn:nbn:se:uu:diva-101358 (URN)10.1534/genetics.107.083162 (DOI)000255239600039 ()18430952 (PubMedID)
Available from: 2009-05-06 Created: 2009-04-23 Last updated: 2017-12-13Bibliographically approved
2. Fine mapping and replication of QTL in outbred chicken advanced intercross lines
Open this publication in new window or tab >>Fine mapping and replication of QTL in outbred chicken advanced intercross lines
Show others...
2011 (English)In: Genetics Selection Evolution, ISSN 0999-193X, E-ISSN 1297-9686, Vol. 43, p. 3-Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Linkage mapping is used to identify genomic regions affecting the expression of complex traits. However, when experimental crosses such as F2 populations or backcrosses are used to map regions containing a Quantitative Trait Locus (QTL), the size of the regions identified remains quite large, i.e. 10 or more Mb. Thus, other experimental strategies are needed to refine the QTL locations. Advanced Intercross Lines (AIL) are produced by repeated intercrossing of F2 animals and successive generations, which decrease linkage disequilibrium in a controlled manner. Although this approach is seen as promising, both to replicate QTL analyses and fine-map QTL, only a few AIL datasets, all originating from inbred founders, have been reported in the literature.

METHODS: We have produced a nine-generation AIL pedigree (n = 1529) from two outbred chicken lines divergently selected for body weight at eight weeks of age. All animals were weighed at eight weeks of age and genotyped for SNP located in nine genomic regions where significant or suggestive QTL had previously been detected in the F2 population. In parallel, we have developed a novel strategy to analyse the data that uses both genotype and pedigree information of all AIL individuals to replicate the detection of and fine-map QTL affecting juvenile body weight.

RESULTS: Five of the nine QTL detected with the original F2 population were confirmed and fine-mapped with the AIL, while for the remaining four, only suggestive evidence of their existence was obtained. All original QTL were confirmed as a single locus, except for one, which split into two linked QTL.

CONCLUSIONS: Our results indicate that many of the QTL, which are genome-wide significant or suggestive in the analyses of large intercross populations, are true effects that can be replicated and fine-mapped using AIL. Key factors for success are the use of large populations and powerful statistical tools. Moreover, we believe that the statistical methods we have developed to efficiently study outbred AIL populations will increase the number of organisms for which in-depth complex traits can be analyzed.

 

National Category
Genetics
Research subject
Genetics
Identifiers
urn:nbn:se:uu:diva-101398 (URN)10.1186/1297-9686-43-3 (DOI)000287133300001 ()21241486 (PubMedID)
Available from: 2009-04-24 Created: 2009-04-24 Last updated: 2017-12-13
3.
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4. A genetic algorithm based method for stringent haplotyping of family data
Open this publication in new window or tab >>A genetic algorithm based method for stringent haplotyping of family data
2009 (English)In: BMC Genetics, ISSN 1471-2156, E-ISSN 1471-2156, Vol. 10, article id 57Article in journal (Refereed) Published
Abstract [en]

Background: The linkage phase, or haplotype, is an extra level of information that in addition to genotype and pedigree can be useful for reconstructing the inheritance pattern of the alleles in a pedigree, and computing for example Identity By Descent probabilities. If a haplotype is provided, the precision of estimated IBD probabilities increases, as long as the haplotype is estimated without errors. It is therefore important to only use haplotypes that are strongly supported by the available data for IBD estimation, to avoid introducing new errors due to erroneous linkage phases.

Results: We propose a genetic algorithm based method for haplotype estimation in family data that includes a stringency parameter. This allows the user to decide the error tolerance level when inferring parental origin of the alleles. This is a novel feature compared to existing methods for haplotype estimation. We show that using a high stringency produces haplotype data with few errors, whereas a low stringency provides haplotype estimates in most situations, but with an increased number of errors.

Conclusion: By including a stringency criterion in our haplotyping method, the user is able to maintain the error rate at a suitable level for the particular study; one can select anything from haplotyped data with very small proportion of errors and a higher proportion of non-inferred haplotypes, to data with phase estimates for every marker, when haplotype errors are tolerable. Giving this choice makes the method more flexible and useful in a wide range of applications as it is able to fulfil different requirements regarding the tolerance for haplotype errors, or uncertain marker-phases.

National Category
Bioinformatics and Systems Biology
Research subject
Genetics
Identifiers
urn:nbn:se:uu:diva-101397 (URN)10.1186/1471-2156-10-57 (DOI)000270360900001 ()19761594 (PubMedID)
Note

Manuscripttitle in list of papers in thesis: A genetic algorithm based haplotyping method provides better control on haplotype error rate

Available from: 2009-04-24 Created: 2009-04-24 Last updated: 2017-12-13Bibliographically approved

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