April 28 2016

5:00 pm 154 BSRB

EcoEvoPub Series

Graduate Student Presentations


Rachel Johnston
Department of Ecology and Evolutionary Biology, UCLA

"RNA-Seq Reveals Pervasive Effects of Aging on Gene Expression in Wild Wolves"

With the exception of humans, gene expression patterns have principally been studied under controlled conditions, overlooking the suite of developmental and environmental influences that organisms encounter under conditions in which natural selection operates. We used RNA-Seq of whole blood to assess the relative impacts of social status, age, disease, and sex on gene expression levels in a natural population of gray wolves (Canis lupus). Our findings suggest that age is broadly associated with gene expression levels, whereas other examined factors have minimal effects on gene expression. Further, our results reveal evolutionarily conserved signatures of senescence, such as immunosenescence and metabolic aging, between wolves and humans despite major differences in life history and environment. The more rapid expression differences observed in aging wolves is evolutionarily appropriate given the species' high level of extrinsic mortality due to intraspecific aggression. This work provides evolutionary insight into aging patterns observed in domestic dogs and demonstrates the applicability of studying natural populations to investigate the mechanisms of aging.

Bernard Kim
Department of Ecology and Evolutionary Biology, UCLA

"What do we learn about deleterious variation in humans by using large datasets?"

The distribution of fitness effects (DFE) describes the mutation rate of new variants of different selective effects and is a fundamental concept in population genetics. It quantifies the effect of selection on new mutations, thus predicting substitution rates, the number of segregating deleterious variants, and genetic load. However, the most widely used DFE in human population genetics, also used in other mammalian studies, was inferred from a sample of 12 African-Americans. Other estimates of the DFE from larger samples differ significantly, purportedly due to the additional information contained in low-frequency variants. Here I present a new method built using the Poisson Random Field framework to infer the DFE in a tractable and computationally efficient manner. We use our method to fit a DFE to a sample of 1298 Danish individuals and infer fewer strongly deleterious mutations but more weakly and moderately deleterious mutations than previous estimates. We show sample size explains some of these differences, but also that methodological issues explain some of the previous estimates. Furthermore, we find the relative proportions of the DFE to be robust to the assumed underlying distribution, suggesting parametric methods can be effective at capturing the functional form of the DFE.















































































































































































































































































































































































































































































































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