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“Rapidly developing sequencing technologies and declining costs have made it possible to collect genome-scale data from population-level samples in nonmodel systems. Inferential

tools for historical demography given these data sets are, at present, underdeveloped. In particular, approximate Bayesian computation (ABC) has yet to be widely embraced by researchers generating these data. Here, we demonstrate the promise of ABC for analysis of the large data sets that are now attainable from nonmodel taxa through current genomic sequencing technologies. We develop and test an ABC framework for model selection and parameter estimation, given histories of three-population divergence DAPT ic50 with admixture. We then CBL0137 inhibitor explore different sampling regimes to illustrate how sampling more loci, longer loci or more individuals affects the quality of model selection and parameter estimation in this ABC framework. Our results

show that inferences improved substantially with increases in the number and/or length of sequenced loci, while less benefit was gained by sampling large numbers of individuals. Optimal sampling strategies given our inferential models included at least 2000 loci, each approximately 2 kb in length, sampled from five diploid individuals per population, although specific strategies are model and question dependent. We tested our ABC approach through simulation-based cross-validations and illustrate its application using previously analysed data from the oak gall wasp, Biorhiza pallida.”
“Background: Emerging research suggests that young adult sexual minorities (identifying as lesbian, gay, or bisexual or engaging in same-sex attractions or behaviors) experience poorer health than their majority counterparts, but many measures of health inequity remain unexamined in population-based research. Purpose: To describe a wide range of health status and healthcare access characteristics of sexual minorities in comparison with those of the majority population in a click here national sample of U.S. young adults. Methods: Binary and multinomial logistic regression analyses

of Wave IV data (2008) from the National Longitudinal Study of Adolescent Health (participants aged 24-32 years, n = 13,088) were conducted. Health measures were self-rated health; diagnosis of any of several physical or mental illnesses or sexually transmitted infections; measured BMI; depression classified from self-reported symptoms; use of antidepressant and anxiolytic medication; uninsured; forgone care; and receipt of physical, dental, and psychological services. Analyses were conducted in 2012-2013. Results: Sexual minority women had elevated odds of most adverse health conditions and lower odds of receiving a physical or dental examination. Sexual minority men had elevated odds of fewer adverse health conditions. Conclusions: Young adult sexual minorities are at higher risk of poor physical and mental health.