The Nielsen Group works on statistical and computational methods and their applications in population genetics, medical genetics, molecular ecology, and molecular evolution.

We are generously hosted by both the Department of Integrative Biology and the Department of Statistics at UC Berkeley – and by the Bioinformatics Centre at the Department of Biology, University of Copenhagen. We also very much enjoy our affiliation with Beijing Genomics Institute. You can find our contact information here.

Population Genetics – we work on theory, methods development, and applied issues in population genetics. Lately, we have been particularly focused on developing methods for analyzing New Generation Sequencing (NGS) data.  Many of these methods are implemented in ANGSD. We have also worked extensively on methods for detecting natural selection.  Our method for detecting ongoing selection from haplotypes is implemented in nSL.

Medical Statistical Genetics – we have developed methods for association mapping using NGS data in the presence of uncertainty regarding genotype calls. We have also developed methods for association mapping that can take gene-gene and gene-environment interactions into account and model the effects of multiple mutations. Recently, we have been very interested in the use of Identity-By-Descent (IBD) identification in apparently outbred samples as a tool for genetic mapping.

Molecular Ecology – we have developed a number of different methods for estimating demographic parameters from genetic data. Some of the methods are available in the programs IM and IMa. We have been particularly interested in developing methods for estimating divergence times and migration rates, and in applications to human data. Recently we have worked on methods that take advantage of  linkage information in whole genome sequencing data.

Molecular Evolution – our work in molecular evolution has mostly focused on methods for detecting natural selection. Many of these methods are implemented in the computer program PAML. Recently, we have also been interested in the development and application of methods for estimating evolutionary parameters from expression level data obtained from RNAseq studies.