Significant genetic correlations among Caucasians at forensic DNA loci

Abstract

Although the effect of population differentiation on the forensic use of DNA profiles has been the subject of controversy for some years now, the debate has largely failed to focus on the genetical questions directly relevant to the forensic context. We re-analyse two published data sets and find that they convey much the same message for forensic inference, in contrast with the dramatically differing conclusions of the original authors. The analysis is likelihood-based and combines information across loci and across populations without assuming constant genetic differentiation. Our results suggest that the relevant genetic correlation coefficients are too large to be ignored in forensic work: although DNA profile evidence is typically very strong, the effect of genetic correlations can be important in some cases. Such correlations can, however, be accommodated in an appropriate assessment of evidential strength so that population genetic issues should not present a barrier to the efficient and fair use of DNA profile evidence.

Inference under a Wright-Fisher model using an accurate beta approximation

“…We have developed a new approximation to the distribution of allele frequency (DAF) as a function of time, conditional on an initial frequency, under a Wright-Fisher model with linear evolutionary pressures. Our work provides an accurate extension of the beta approximation (Balding and Nichols 1995;Balding and Nichols 1997;Sirén et al 2011;Sirén 2012). As noted by Gautier and Vitalis (2013), the beta distribution ignores the possibility of loss or fixation of alleles.…”

“…Moment-based approximations are less ambitious in that they aim at fitting mathematical convenient distributions by equating the first moments of the true DAF. Such approximations typically use either the normal distribution (Nicholson et al 2002;Coop et al 2010;Gautier et al 2010;Pickrell and Pritchard 2012;Terhorst et al 2015) or the beta distribution (Balding and Nichols 1995;Balding and Nichols 1997;Sirén et al 2011;Sirén 2012). The rationale behind the use of these distributions is two-fold.…”

Inferring the Demographic History of Inbred Species From Genome-Wide SNP Frequency Data

“…Here we use an alternative model that captures the fact that genotypes within populations will be correlated due to inbreeding, pushing the distribution of genotypes towards homozygotes. To capture this correlation among genotypes, Balding and Nichols (1995, 1997) proposed a probability model to incorporate inbreeding using a beta-binomial distribution. Under this model, individual genotypes are a random variable, G i ∈ {0, 1, 2}, for the number of copies of the derived allele in individual i ( i = 1, …, n ) such that Pr ( G i = g ) at an individual locus with allele frequency p ∈ (0, 1) and population inbreeding coefficient F ∈ (0, 1) is beta-binomial with the following form: …”

Sample Planning Optimization Tool for conservation and population Genetics (SPOTG): a software for choosing the appropriate number of markers and samples

“…Assignment tests depend on polymorphism of the markers and shape of allele frequency distributions (Manel, Berthier & Luikart 2002), so the user provides global allele frequencies (or chooses from a distribution), and degree of divergence (FST) for possible source populations. In-silico population allele frequencies are 'built' using the sampling formula for FST (Balding & Nichols 1997;Gaggiotti et al 2004), which generates frequency distributions for each deme of a subdivided population with chosen genetic differentiation and marker characteristics. Once population allele frequencies are defined, a number of alleles (sample size, times two for diploids) is sampled probabilistically, and genotypes are 'created' by randomly combining alleles.…”