Price Lab
Our research focuses on the development of statistical methods for uncovering the genetic basis of human disease, and on the population genetics underlying these methods. Areas of interest include functional components of heritability, common vs. rare variant architectures and the impact of negative selection, and disease mapping in structured populations.
Building 2, Room 211
Harvard T.H. School of Public Health
Our Focus
Genome-wide associations studies (GWAS) have identified tens of thousands of genetic variants robustly associated to common disease, but in most instances the functional role of those genetic variants is poorly understood. Integrating GWAS with functional data (gene expression, epigenomic data, etc.) in bulk tissue or single cells has immense potential to elucidate genome-wide genetic architectures, pinpoint biological mechanisms at specific GWAS loci, and improve polygenic prediction of disease risk. The development of computationally scalable statistical methods for each of these problem formulations is a pressing challenge.
What We Do
The genome-wide genetic architecture of a disease characterizes the distribution of causal disease variants and their effect sizes across different variant classes, defined by allele frequencies, functional annotations, and tissue/cell-type-specific features. Understanding genome-wide genetic architectures is a prerequisite to generating data and developing statistical methods to effectively pinpoint biological mechanisms and improve polygenic prediction of disease risk.
Pinpointing biological mechanisms at specific GWAS loci can inform our understanding of disease etiology and nominate potential drug targets. It is now widely accepted that GWAS loci with a small impact on disease risk can nominate drug targets with a large impact on disease treatment.
Polygenic prediction of disease risk can inform intervention and therapeutic strategies, and is approaching clinical utility for many common diseases. Non-genetic prediction of disease risk can also be highly informative, motivating strategies for combining genetic and non-genetic information.
Our Research
Identifying disease-critical tissues, cell types and cell subtypes
Integrating GWAS data with functional data from bulk tissues or single cells can identify disease-critical tissues, cell types, and cell subtypes, informing our understanding of disease etiology.
Leveraging molecular traits to pinpoint causal disease genes
Identifying disease causal genes is more actionable than identifying causal variants. Molecular traits (gene expression, protein levels, etc.) provide a route towards identifying causal genes.
Impact of negative selection on common disease architectures
Negative selection has led to MAF-dependent and LD-dependent architectures and extreme polygenicity. Many aspects of the impact of negative selection on common disease architectures are still emerging
Improving the fine-mapping and polygenic risk prediction in diverse populations
Human populations exhibit small differences in variant allele frequencies but large differences in LD, providing both opportunities and challenges for fine-mapping and polygenic risk prediction.
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