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Edge, M.

Publications and source records attributed to Edge, M..

2 recordsLinked to original sources

How lucky was the genetic investigation in the Golden State Killer case?

Long-range forensic familial searching is a new method in forensic genetics. In long-range search, a sample of interest is genotyped at single-nucleotide polymorphism (SNP) markers, and the genotype is compared with a large database in order to find relatives. Here, we perform some simple calculations that explore the basic phenomena that govern long-range searching. Two opposing phenomena--one genealogical and one genetic--govern the success of the search in a database of a given size. As one considers more distant genealogical relationships, any target sample is likely to have more relatives--on average, one has more second cousins than first cousins, and so on. But more distant relatives are also harder to detect genetically. Starting with third cousins, there is an appreciable chance that a given genealogical relationship will not be detectable genetically. Given the balance of these genealogical and genetic phenomena and the size of databases currently queryable by law enforcement, it is likely that most people with substantial recent ancestry in the United States are accessible via long-range search. NoteThis material was originally posted on the Coop lab site on May 7th, 2018, soon after the reporting of the arrest of Joseph DeAngelo in the Golden State Killer case, one of the first high-profile uses of long-range familial search. Subsequently, Erlich et al. (2018) published a detailed analysis in a large empirical dataset along with a theoretical analysis of a model similar to the one we use here, obtaining results broadly consistent with the ones presented here. Because Erlich and colleagues kindly cited this work when describing their model, we thought it would be appropriate to post this material in a venue where it is more easily cited.

genomics↗

Reconstructing the history of polygenic scores using coalescent trees

1Genome-wide association studies (GWAS) have revealed that many traits are highly polygenic, in that their within-population variance is governed in part by small-effect variants at many genetic loci. Standard population-genetic methods for inferring evolutionary history are ill-suited for polygenic traits--when there are many variants of small effect, signatures of natural selection are spread across the genome and subtle at any one locus. In the last several years, several methods have emerged for detecting the action of natural selection on polygenic scores, sums of genotypes weighted by GWAS effect sizes. However, most existing methods do not reveal the timing or strength of selection. Here, we present a set of methods for estimating the historical time course of a population-mean polygenic score using local coalescent trees at GWAS loci. These time courses are estimated by using coalescent theory to relate the branch lengths of trees to allele-frequency change. The resulting time course can be tested for evidence of natural selection. We present theory and simulations supporting our procedures, as well as estimated time courses of polygenic scores for human height. Because of its grounding in coalescent theory, the framework presented here can be extended to a variety of demographic scenarios, and its usefulness will increase as both GWAS and ancestral recombination graph (ARG) inference continue to progress.

evolutionary biology↗