Accurate Inference of Subtle Population Structure (and Other Genetic Discontinuities) Using Principal Coordinates

Accurate inference of genetic discontinuities between populations is an essential component of intraspecific biodiversity and evolution studies, as well as associative genetics. The most widely-used methods to infer population structure are model-based, Bayesian MCMC procedures that minimize Hardy-Weinberg and linkage disequilibrium within subpopulations. These methods are useful, but suffer from large computational requirements and a dependence on modeling assumptions that may not be met in real data sets. Here we describe the development of a new approach, PCO-MC, which couples principal coordinate analysis to a clustering procedure for the inference of population structure from multilocus genotype data.PCO-MC uses data from all principal coordinate axes simultaneously to calculate a multidimensional "density landscape", from which the number of subpopulations, and the membership within subpopulations, is determined using a valley-seeking algorithm. Using extensive simulations, we show that this approach outperforms a Bayesian MCMC procedure when many loci (e.g. 100) are sampled, but that the Bayesian procedure is marginally superior with few loci (e.g. 10). When presented with sufficient data, PCO-MC accurately delineated subpopulations with population F(st) values as low as 0.03 (G'(st)>0.2), whereas the limit of resolution of the Bayesian approach was F(st) = 0.05 (G'(st)>0.35).We draw a distinction between population structure inference for describing biodiversity as opposed to Type I error control in associative genetics. We suggest that discrete assignments, like those produced by PCO-MC, are appropriate for circumscribing units of biodiversity whereas expression of population structure as a continuous variable is more useful for case-control correction in structured association studies

The Relationship between Mother’s Child Abuse Potential and Current Mental Health Symptoms: Implications for Screening and Referral

This analysis examined data from mothers at 2 of the 9 sites participating in Substance Abuse and Mental Health Services Administration\u27s (SAMHSA\u27s) national Women Co-occurring Disorders and Violence Study (WCDVS). According to previous literature, it was hypothesized that women in the WCDVS would be at high risk of perpetrating child abuse. This research examined mothers\u27 potential for physical child abuse and assessed the association between child abuse potential, current mental health symptoms, alcohol and drug use severity, and trauma. Results revealed that participants had significant potential for child abuse. Hierarchical regression analyses revealed that current mental health symptoms were the strongest predictor of mothers\u27 scores on the Child Abuse Potential (CAP) Inventory. This study highlights the important relationships between commonly used instruments across the mental health, substance, and child welfare fields and the potential dual use of these instruments. Implications for policy and practice are discussed