Biomedical subjects

David J Balding

Publications and source records attributed to David J Balding.

4 recordsLinked to original sources

PubMed2003

Likelihood-based inference for genetic correlation coefficients.

We review Wright's original definitions of the genetic correlation coefficients F(ST), F(IT), and F(IS), pointing out ambiguities and the difficulties that these have generated. We also briefly survey some subsequent approaches to defining and estimating the coefficients. We then propose a general framework in which the coefficients are defined, their properties established, and likelihood-based inference implemented. Likelihood methods of inference are proposed both for bi-allelic and multi-allelic loci, within a hierarchical model which allows sharing of information both across subpopulations and across loci, but without assuming constancy in either case. This framework can be used, for example, to detect environment-related diversifying selection.

Alleles↗

PubMed2002

Implications for DNA identification arising from an analysis of Australian forensic databases.

Previous analyses of Australian samples have suggested that populations of the same broad racial group (Caucasian, Asian, Aboriginal) tend to be genetically similar across states. This suggests that a single national Australian database for each such group may be feasible, which would greatly facilitate casework. We have investigated samples drawn from each of these groups in different Australian states, and have quantified the genetic homogeneity across states within each racial group in terms of the "coancestry coefficient" F(ST). In accord with earlier results, we find that F(ST) values, as estimated from these data, are very small for Caucasians and Asians, usually <0.5%. We find that "declared" Aborigines (which includes many with partly Aboriginal genetic heritage) are also genetically similar across states, although they display some differentiation from a "pure" Aboriginal population (almost entirely of Aboriginal genetic heritage).

Australia↗

PubMed2002

Approximate Bayesian computation in population genetics.

We propose a new method for approximate Bayesian statistical inference on the basis of summary statistics. The method is suited to complex problems that arise in population genetics, extending ideas developed in this setting by earlier authors. Properties of the posterior distribution of a parameter, such as its mean or density curve, are approximated without explicit likelihood calculations. This is achieved by fitting a local-linear regression of simulated parameter values on simulated summary statistics, and then substituting the observed summary statistics into the regression equation. The method combines many of the advantages of Bayesian statistical inference with the computational efficiency of methods based on summary statistics. A key advantage of the method is that the nuisance parameters are automatically integrated out in the simulation step, so that the large numbers of nuisance parameters that arise in population genetics problems can be handled without difficulty. Simulation results indicate computational and statistical efficiency that compares favorably with those of alternative methods previously proposed in the literature. We also compare the relative efficiency of inferences obtained using methods based on summary statistics with those obtained directly from the data using MCMC.

Bayes Theorem↗

PubMed2002

The DNA database search controversy.

A recent article in Biometrics (Stockmarr, 1999, 55, 671-677) has generated correspondence (56, 1274-1277; 57, 976-980) reigniting a controversy started by a 1996 report on DNA profile evidence issued by the U.S. National Research Council (NRC). The issue concerns the evidential weight of a DNA profile match when the match results from a search through a profile database. The views of both Stockmarr and the NRC report conflict with those of many statisticians working in the area, and the differing viewpoints lead to dramatically different assessments of evidence. I outline reasons why Stockmarr and the NRC report are wrong. I also briefly discuss possible reasons why forensic applications tend to be problematic for statisticians.

DNA↗