Biology subjects

Williams, B.

Publications and source records attributed to Williams, B..

3 recordsLinked to original sources

bioRxiv2018

Genetics & the Geography of Health, Behavior, and Attainment

Peoples life chances can be predicted by their neighborhoods. This observation is driving efforts to improve lives by changing neighborhoods. Some neighborhood effects may be causal, supporting neighborhood-level interventions. Other neighborhood effects may reflect selection of families with different characteristics into different neighborhoods, supporting interventions that target families/individuals directly. To test how selection affects different neighborhood-linked problems, we linked neighborhood data with genetic, health, and social-outcome data for >7,000 European-descent UK and US young people in the E-Risk and Add Health Studies. We tested selection/concentration of genetic risks for obesity, schizophrenia, teen-pregnancy, and poor educational outcomes in high-risk neighborhoods, including genetic analysis of neighborhood mobility. Findings argue against genetic selection/concentration as an explanation for neighborhood gradients in obesity and mental-health problems, suggesting neighborhoods may be causal. In contrast, modest genetic selection/concentration was evident for teen-pregnancy and poor educational outcomes, suggesting neighborhood effects for these outcomes should be interpreted with care.

genetics↗

bioRxiv2018

A Polygenic Score for Higher Educational Attainment is Associated with Larger Brains

People who score higher on intelligence tests tend to have larger brains. Twin studies suggest the same genetic factors influence both brain size and intelligence. This has led to the hypothesis that genetics influence intelligence partly by contributing to development of larger brains. We tested this hypothesis with molecular genetic data using discoveries from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. We analyzed genetic, brain imaging, and cognitive test data from the UK Biobank, the Dunedin Study, the Brain Genomics Superstruct Project (GSP), and the Duke Neurogenetics Study (DNS) (combined N=8,271). We measured genetics using polygenic scores based on published GWAS. We conducted meta-analysis to test associations among participants genetics, total brain volume (i.e., brain size), and cognitive test performance. Consistent with previous findings, participants with higher polygenic scores achieved higher scores on cognitive tests, as did participants with larger brains. Participants with higher polygenic scores also had larger brains. We found some evidence that brain size partly mediated associations between participants education polygenic scores and their cognitive test performance. Effect-sizes were larger in the population-based UK Biobank and Dunedin samples than in the GSP and DNS samples. Sensitivity analysis suggested this effect-size difference partly reflected restricted range of cognitive performance in the GSP and DNS samples. Recruitment and retention of population-representative samples should be a priority for neuroscience research. Findings suggest promise for studies integrating GWAS discoveries with brain imaging data to understand neurobiology linking genetics with individual differences in cognitive performance.

neuroscience↗

bioRxiv2017

Antiretroviral treatment, prevention of transmission, and modeling the HIV epidemic: why the ART efficacy, coverage, and effectiveness parameters matter

IntroductionHIV remains a major public health threat with over 75 million deaths, 2 million annual infections and over 1 million HIV-associated TB cases a year. Population-based studies suggest a marked decline in incidence, prevalence and deaths, mostly likely due to treatment expansion, in countries in East and Southern Africa. This calls into question the ART efficacy, effectiveness and coverage parameters used by many modelers to project HIV incidence and prevalence.\n\nMethodsFor 2015 and 2016 we reviewed global and national mathematical modeling studies regarding ART impact (with or without other HIV prevention interventions) and/or 90-90-90 on either new HIV infections or investment or both. We reviewed these HIV epidemiologic and costing models for their structure and parameterization around ART; we directly compared two models to illustrate differences in outcome.\n\nResultsThe nine models published in 2015 or 2016 included parameters for ART effectiveness ranging from 20% to 86% for ART effectiveness. Model 1 limits eligibility for ART initiation to 80% coverage of people living with HIV and with a CD4+ cell count below 350 cells/L, 70% retention, and ART reduces transmission by 80%, with a derived ART effectiveness of 20%. Model 2 assumes 90-90-90 by 2020 (i.e., 73% viral suppression of estimated PLHIV), ART reduces transmission by 96% in those on ART and virally suppressed, and by 88% in those on ART but not virally suppressed with a derived effectiveness of 86% and consequent decline towards ending AIDS and HIV elimination. ART parameter selection and assumptions dominate and low ART effectiveness translates into lower impact.\n\nDiscussionUsing more realistic parameters for ART effectiveness suggests that through expanding access and supporting sustainable viral suppression it will be possible to significantly reduce transmission and eliminate HIV in many settings.

epidemiology↗