Ryan D. Hernandez
University of California-San Francisco, CA·Genetics
Why do disease risks differ among people with different ancestral backgrounds, and how much of that difference reflects genes rather than social categories like race and ethnicity? The answer matters for precision medicine, which tailors care to a person's biology but has been built largely on data from people of European descent.
- Fields
Genetics
Ecology & Evolution
Computer Science
- Topics
population genetics
human evolution
statistical genetics
- Grants
- 2 known grants, through May 2030
What they work on
- Measure how race, ethnicity and genetic ancestry each contribute to molecular traits and to heart, lung, blood and sleep disorders, using TOPMed whole genome sequencing, RNA-seq gene expression and metabolomic data from over 180,000 people.
- Determine how variants that differ in frequency across ancestry groups shape the genetic architecture of molecular and complex traits in TOPMed, a large and ancestrally diverse US cohort.
- Characterize the genetic signatures of recessive harmful variants and associative overdominance using the site frequency spectrum, ancestral recombination graphs and haplotype structure, including tests at the immune-related MHC locus.
- Define genetic relatedness groups at continental and sub-continental scales using ancestral recombination graphs, which trace DNA lineages through time, as an alternative to predefined racial and ethnic categories.
- Build flexible simulators of multivariate complex traits with genetic and non-genetic interactions, including power simulators for rare variant association tests in diverse populations.
More about the research
The work here pairs very large DNA datasets with evolutionary theory and computer simulation, asking how genetic variation shapes traits and disease across the full continuum of human populations in the US.
Why it matters
Genetic medicine has largely been built on a narrow slice of humanity. Clarifying how ancestry, race and ethnicity relate to heart, lung, blood and sleep disorders could help make genetic risk prediction work fairly for Black, Hispanic/Latino and other underrepresented groups. The relatedness groupings and simulation tools may also give researchers better ways to design and power genetic studies in diverse populations.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Mateus Gouveia
Morehouse School of Medicine, GA·Genetics·Early-career
Map subcontinental ancestries across Africa, the Americas, and Europe by assembling genome-wide data on roughly 75,000 ancestrally diverse individuals and applying current population genomics methods.
Andrew D Kern
University of Oregon, OR·Ecology & Evolution
Develop methods to infer dispersal, how far individuals move, directly from population genomic data, and assess how spatial structure affects GWAS, genome-wide studies linking variants to traits, with Anopheles gambiae data in mind.
Nicholette Allred
Wake Forest University, NC·Genetics
Identify rare genetic variants associated with liver fat in 23,156 ethnically diverse TOPMed participants, using whole genome sequencing with single-variant and gene-level rare-variant burden tests.
The school
University of California-San Francisco: 89 labs on PI Pages, $2.1B a year on research, 164 research doctorates a year.
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