Ryan Hernandez, PhD
|School||UCSF School of Pharmacy|
|Department||Bioengineering and Therapeutic Sciences|
|Address||1700 4th Street|
San Francisco CA 94158
The complex interaction of evolutionary forces that affected our ancestors have shaped the patterns of genetic variation that we see across human populations today. Teasing apart the relative contributions of evolutionary forces such as natural selection and demographic effects from observed genomic patterns of variation is therefore a fundamental problem for understanding the history of our species, the process of adaptation, and the genetics of both normal physiology as well as human disease. The 1000 Genomes Project marks a turning point toward the age of personal genomics. As we enter a time when it becomes feasible to sequence the genomes of entire cohorts, we will finally be within reach of identifying the specific mutations and larger scale structural variants that underlie our susceptibility to disease. This advance will also bring us much closer to the broader inclusion of admixed minority populations such as Latinos and African-Americans into genomic research. However, as the size and complexity of genomic data sets grow by orders of magnitude, so too does the need for novel algorithms that can synthesize disparate sources of information into an amalgamated picture of human genomic variation that will be useful for understanding the genetic basis of complex phenotypes and diseases.
My research focuses on developing statistical and computational tools for analyzing large-scale resequencing data. I am currently playing a large role in the population genetic analysis of the 1000 Genomes Project. These data will provide an unprecedented opportunity to probe into the evolutionary history of our species, and to begin to address highly detailed questions about the demographic history of our world's people and the interactions of the selective forces that affected our ancestors.
Through both simulations and theoretical modeling, I will seek to quantify the effect of natural selection purging deleterious mutations from the human genome on linked neutral loci (i.e. background selection). There is growing evidence that there are many targets of recurrent deleterious mutations distributed throughout our genome (both in protein coding regions as well as conserved non-coding regions), yet the effect that such a process has on flanking DNA has not been well characterized. By simulating data under realistic models of human evolution, I will be able to uncover the circumstances under which background selection confounds our inference of adaptation and historical demographic events. Moreover, such simulations will allow me to identify novel characteristics of background selection, which could then be used to identify novel targets of deleterious mutations in the human genome. By constructing a population genetic model of background selection, more accurate inference of our species' demographic history can be obtained, and a more thorough understanding of adaptation can be achieved. This work will have significant implications for the identification of regions of the human genome that contribute to disease using genomic resequencing data.
However, I am humbled by the fact that analyzing genomic sequences alone will always provide an incomplete picture of disease, no matter how many samples and populations we are able to collect. If we are then to make further headway on understanding the causes of global health disparities, novel statistical techniques that can integrate vast sources of information will be key. Today, breakthroughs are being made by large scale GWAS, detailed epidemiological models, proteomic analysis, systems biology, and in a wide range of other fields. Future innovations, though, will stem from leveraging advances in these fields against each other into a single, coherent framework.
Population and evolutionary genetics/genomics, computational biology and bioinformatics, population structure and admixture, statistical genetics/genomics, bioengineering, human evolution, 1,000 Genomes Project
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