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This Week in Genome Biology: Nov 28, 2018

A University of Zurich-led team searches for signs of selection and adaptive evolution in orangutan species from Sumatra and Borneo. The researchers considered Pongo abelii, P. tapanuliensis, P. pygmaeus, and P. pygmaeus sub-species found in North Sumatra, Sumatra's South Tapanuli region, and Borneo, respectively. Using genome sequences for 35 of the P. abelii and P. pygmaeus orangutans, they saw signs of positive selection involving brain, learning, and glucose metabolism genes in orangutans at resource-rich Sumatran sites, while the Borneo orangutans appeared more apt to have positive selection peaks at genes behind lipid metabolism, cardiac, and muscle function. The authors say the work "provides a framework from which to develop and test more complex hypotheses about adaptive evolution in non-human great apes and to explore differences in adaptive evolution between our own species and our closest relatives."

Researchers from Cardiff University and elsewhere search for expression quantitative trait loci in the developing human brain using RNA sequence and genome-wide genotyping data. The team profiled 120 second trimester brain samples to find genome-wide fetal eQTLs influencing the expression of specific genes or transcripts, including variants at a polymorphic chromosome 17 locus and eQTLs that overlapped with variants previously implicated in conditions such as attention deficit hyperactivity disorder, schizophrenia, and bipolar disorder. Along with this apparent eQTL enrichment at neuropsychiatric risk variant sites, the authors report seeing some related gene expression shifts mediated by these genetic factors.

A Georgia Institute of Technology team describes potential problems in accurately estimating genetic disease risk between populations. With a combination of cross-population sequence data, genome-wide association study results, and computer simulations, the researchers highlighted population-related ancestral and derived allele frequency differences for variants at disease-related loci, particularly in populations from Africa. Based on their population-specific GWAS simulations and data analyses, the authors suggest that "polygenic risk scores can be grossly misestimated for individuals of African descent" and "imply that caution must be taken when extrapolating GWAS results from one population to predict disease risks in another population." GenomeWeb has more on this study, here