Kirk Lohmueller
University of California-Los Angeles, CA·Genetics
Most new mutations that matter for health are harmful, yet how these deleterious mutations shape evolution and complex traits such as disease risk is still unclear. A key quantity is the distribution of fitness effects (DFE), a summary of how strongly natural selection acts against new mutations, from nearly harmless to severely damaging.
- Fields
Genetics
Ecology & Evolution
Computer Science
- Topics
population genetics
population genomics
deleterious mutations
- Grants
- 1 known grant, through May 2027
What they work on
- Infer the distribution of fitness effects for noncoding regulatory mutations by combining large-scale functional genomic data with polymorphism data, since most disease-associated variants fall outside protein-coding genes.
- Develop new computational approaches to infer fitness effects of complex mutations such as short tandem repeats and copy number variants, enabling direct comparison of how harmful different mutation types are.
- Test how population size, organismal complexity and lifespan influence the distribution of fitness effects, by comparing polymorphism data from multiple species.
- Test the lab's previously developed model for the existence of dominance, using human polymorphism data and yeast functional genomic data, where dominance describes whether one mutated copy of a gene is enough to matter.
- Use detailed forward simulations to establish whether population genetic models combining multiple evolutionary forces simultaneously can explain observed genetic variation across genomes.
More about the research
Knowing the DFE helps explain why genomes vary the way they do, and it helps researchers tell disease-causing variants apart from harmless ones.
Why it matters
A major hurdle in finding genes behind complex diseases is separating variants that disrupt biological function from harmless ones. Evolutionary information is routinely used to rank candidate variants or to model background genetic variation. By clarifying how different kinds of harmful mutations behave, including noncoding and structural ones, this work could make those evolutionary tools more useful for identifying biologically relevant and pathogenic variants.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Xinjun Zhang
University of Michigan, MI·Genetics
Develop machine learning methods to detect archaic introgression from unknown 'ghost' archaic humans in East Asian genomes.
Kerry Geiler-Samerotte
Arizona State University Campus Immersion, AZ·Ecology & Evolution·Early-career
Evolve thousands of DNA-barcoded yeast lineages across subtly different temperatures to measure how the genetic basis of adaptation and the distribution of fitness effects (DFE) shift along the gradient.
Lena Hileman
University of Kansas, KS·Ecology & Evolution
Confirm about twenty genomic regions that distinguish bee- from hummingbird-pollinated Penstemon species, using genome-wide association mapping to link them to floral syndrome traits.
The school
University of California-Los Angeles: 84 labs on PI Pages, $1.9B a year on research, 803 research doctorates a year.
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