Research
We seek to understand the genetic basis of common diseases and traits. Most of our research involves the development of new methods, drawing on a range of statistical and mathematical approaches.
Genetic architecture of rare and common variation
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Both common and rare genetic variants influence common diseases and complex traits. We have developed methods to quantify their contributions, to estimate their genetic architecture, and to understand how it is influenced by natural selection. Current areas of focus include integrating functional annotation data, characterizing the genetic architecture of autism, and making forecasts for what will be found in rare-variant association studies at larger sample size.
Weiner*, Nadig* et al. 2023 Nature
Weiner et al. 2022 AJHG
O’Connor 2021 Nat Genet
O’Connor et al. 2019 AJHG
Methods leveraging genealogical relatedness
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All humans are genealogical relatives, sharing haplotypes inherited from common ancestors, and haplotype sharing can be leveraged in statistical applications. We developed a method that derives an efficient representation of linkage disequilibrium (LD) from an inferred genealogy, greatly improving the runtime of statistical calculations involving the LD matrix. We are developing applications of these LD representations for heritability partitioning and polygenic risk prediction. We are also developing methods for genealogical inference.