We use computational and statistical tools to address the question: what are the causes and consequences of genetic variation in human populations? We work to understand the evolutionary forces that act on genetic variation, as well as the molecular and phenotypic consequences of this variation. Our research involves the development of statistical models for integrating diverse sources of data, with application to many areas of human genetics, including genomic approaches to understanding gene regulation, mapping genetic variants that influence traits, and inference about natural selection and demography.

Examples of major research interests are below; however, we are generally interested in problems where large-scale genomic data can lead to fundamental biological insights.

Mechanisms by which genetic variation influences phenotypes

The field of human genetics has been transformed by genome-wide association studies (GWAS), which have identified thousands of genomic regions that contain polymorphisms that influence a wide variety of traits and diseases. In general, the loci identified in GWAS of multifactorial traits have small effect sizes and are located outside of protein-coding exons. This motivates methods for understanding the mechanisms by which polymorphisms influence gene expression, and in turn, how variation in gene expression influences traits.

We are actively developing methods for building causal networks linking specific genetic variants to disease risk through intermediate phenotypes like gene expression and histone modification. This work involves analysis of high-throughput genomic data like RNA-seq and DNAse-seq, as well as GWAS.

Representative publications:
Pickrell (2014) Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. [pubmed]

Pickrell et al. (2010) Understanding mechanisms underlying human gene expression variation with RNA sequencing. [pubmed]

Pickrell et al. (2010) Noisy splicing drives mRNA isoform diversity in human cells. [pubmed]

Learning about history and adaptation from genomic data

Genomic data contains a record of the history of a species, from its patterns of migration to the selective pressures it has encountered. Using modern sequencing technologies, it is now possible to access this record on an unprecedented scale. To a large extent, data generation is no longer a limiting factor in population genetics.

We are actively developing methods for using large-scale genomic data to learn about human history and adaptation. This works involves developing novel population genetics models, and collaborations with anthropologists and linguists.

Representative publications:

Pickrell and Reich (2014) Toward a new history and geography of human genes informed by ancient DNA. [pubmed]

Pickrell et al. (2014) Ancient west Eurasian ancestry in southern and eastern Africa. [pubmed]

Pickrell and Pritchard (2012) Inference of population splits and mixtures from genome-wide allele frequency data. [pubmed]

Pickrell et al. (2012) The genetic prehistory of southern Africa. [pubmed]

Pickrell et al. (2009) Signals of recent positive selection in a worldwide sample of human populations. [pubmed]