Literature DB >> 15367908

A Monte Carlo algorithm for computing the IBD matrices using incomplete marker information.

Y Mao1, S Xu.   

Abstract

Identity-By-Descent (IBD) is a general measurement of the relationship between two groups of genes. If the two groups consist of two homologous genes, one from each individual, the IBD is called the coancestry between the two individuals. Coancestry is an important concept in both population and quantitative genetics. It is the probability that both genes are copies of the same gene in the genealogy. The average coancestry value at a random locus in a population reflects the level of population diversity, effective population size, the level of inbreeding and other attributes. Coancestry is also the building block for the covariance structure used to estimate the additive genetic variance component for a quantitative trait. There are many other types of IBD matrices, depending on the natures of the genes included in each group, and these IBD matrices vary from locus to locus. Molecular markers distributed along the genome provide information that can be used to infer these locus-specific IBD matrices. As a result, we can estimate and test the variance components of a quantitative trait contributed by these loci using the inferred IBD matrices. In this study, we develop the concept of locus-specific epistatic IBD matrices and a Monte Carlo method to infer these IBD matrices. The method is suitable for large pedigrees with arbitrary complexity and various levels of missing marker information. With these locus-specific IBD matrices, we are ready to search for quantitative trait loci along the genome in complicated pedigrees.

Mesh:

Substances:

Year:  2005        PMID: 15367908     DOI: 10.1038/sj.hdy.6800564

Source DB:  PubMed          Journal:  Heredity (Edinb)        ISSN: 0018-067X            Impact factor:   3.821


  5 in total

1.  Bayesian mapping of quantitative trait loci for multiple complex traits with the use of variance components.

Authors:  Jianfeng Liu; Yongjun Liu; Xiaogang Liu; Hong-Wen Deng
Journal:  Am J Hum Genet       Date:  2007-07-03       Impact factor: 11.025

2.  Whole population, genome-wide mapping of hidden relatedness.

Authors:  Alexander Gusev; Jennifer K Lowe; Markus Stoffel; Mark J Daly; David Altshuler; Jan L Breslow; Jeffrey M Friedman; Itsik Pe'er
Journal:  Genome Res       Date:  2008-10-29       Impact factor: 9.043

3.  Combined linkage disequilibrium and linkage mapping: Bayesian multilocus approach.

Authors:  P Pikkuhookana; M J Sillanpää
Journal:  Heredity (Edinb)       Date:  2013-11-20       Impact factor: 3.821

4.  Efficient Markov chain Monte Carlo implementation of Bayesian analysis of additive and dominance genetic variances in noninbred pedigrees.

Authors:  Patrik Waldmann; Jon Hallander; Fabian Hoti; Mikko J Sillanpää
Journal:  Genetics       Date:  2008-06       Impact factor: 4.562

5.  Estimating genealogies from linked marker data: a Bayesian approach.

Authors:  Dario Gasbarra; Matti Pirinen; Mikko J Sillanpää; Elja Arjas
Journal:  BMC Bioinformatics       Date:  2007-10-25       Impact factor: 3.169

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.