Joint Estimation of Pedigrees and Effective Population Size Using Markov Chain Monte Carlo. Issue 3 (22nd May 2019)

• Record Type: Journal Article
• Authors: Ko, Amy; Nielsen, Rasmus
• Is Part Of: Genetics. Volume 212:Issue 3(2019)
• Page Start: 855
• Page End: 868
• Publication Date: 2019-05-22
• Subjects: pedigree inference -- effective population size -- Markov Chain Monte Carlo; Genetics -- Periodicals; 576.5
• DOI: 10.1534/genetics.119.302280
• Languages: English
• ISSNs: 0016-6731

Abstract

Abstract: Pedigrees provide the genealogical relationships among individuals at a fine resolution and serve an important function in many areas of genetic studies. One such use of pedigree information is in the estimation of the short-term effective population size ( N e ), which is of great relevance in fields such as conservation genetics. Despite the usefulness of pedigrees, however, they are often an unknown parameter and must be inferred from genetic data. In this study, we present a Bayesian method to jointly estimate pedigrees and N e from genetic markers using Markov Chain Monte Carlo. Our method supports analysis of a large number of markers and individuals within a single generation with the use of a composite likelihood, which significantly increases computational efficiency. We show, on simulated data, that our method is able to jointly estimate relationships up to first cousins and N e with high accuracy. We also apply the method on a real dataset of house sparrows to reconstruct their previously unreported pedigree.