Literature DB >> 21565108

A simplified estimator of two and four gene relationship coefficients.

Stuart C Thomas1.   

Abstract

Molecular marker data provide a means of circumventing the problem of not knowing the population structure of a natural population, as observed similarities between a pair's genotypes provide information on their genetic relationship. Numerous method-of-moment (MOM) estimators have been developed for estimating relationship coefficients using this information. Here, I present a simplified form of Wang's 2002 relationship estimator that is not dependent upon a previously required weighting scheme, thus improving the efficiency of the estimator when used with genuinely related pairs. The new estimator is compared against other estimators under a range of conditions, including situations where the parameter estimates are truncated to lie within the legitimate parameter space. The advantages of the new estimator are most notable for the two-gene coefficient of relatedness. Truncating the MOM estimators results in parameter estimates whose properties are similar to maximum likelihood estimates, with them having generally lower sampling variances, but being biased.
© 2010 Blackwell Publishing Ltd.

Year:  2010        PMID: 21565108     DOI: 10.1111/j.1755-0998.2010.02840.x

Source DB:  PubMed          Journal:  Mol Ecol Resour        ISSN: 1755-098X            Impact factor:   7.090


  3 in total

1.  Estimating Relatedness in the Presence of Null Alleles.

Authors:  Kang Huang; Kermit Ritland; Derek W Dunn; Xiaoguang Qi; Songtao Guo; Baoguo Li
Journal:  Genetics       Date:  2015-10-23       Impact factor: 4.562

Review 2.  A maximum-likelihood estimation of pairwise relatedness for autopolyploids.

Authors:  K Huang; S T Guo; M R Shattuck; S T Chen; X G Qi; P Zhang; B G Li
Journal:  Heredity (Edinb)       Date:  2014-11-05       Impact factor: 3.821

3.  Estimating pairwise relatedness in a small sample of individuals.

Authors:  J Wang
Journal:  Heredity (Edinb)       Date:  2017-08-30       Impact factor: 3.821

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.