Genetic variants that impact gene expression play a central role in the genetics of complex traits and in evolution. Yet the precise links between genetic variation and changes in gene regulation are poorly understood and it remains very difficult to predict which variants have regulatory effects in any given cell type. In this talk I will describe work we have done on identifying genetic variants that impact gene expression and understanding the primary mechanisms by which such variants act.

Approximating the coalescence process with recombination as a Markov model along sequences—an approach called the Sequential Markov Coalescent or SMC—greatly reduces the complexity of modelling sequences. Rather than modelling the full joint probability of all nucleotides it suffices to model the probability of pairs of neighbouring nucleotides. Combined with hidden Markov models, SMC has been used to develop a number of different inference models in recent years, capable of drawing inference from full genomic sequence alignments.

I will give a short overview of the different methods based on SMC, then talk about a number of models we have developed in Aarhus and how we have used these methods to analyse the genetics of ancestral species, especially the great apes.

We have recently developed whole-genome in-solution capture (WISC), a fast, flexible, and inexpensive whole genome capture approach which uses biotinylated RNA baits for capturing genomic DNA from genomes of interest (Carpenter et al., 2013, AJHG). Previously, we demonstrated the power of WISC on Illumina libraries created from four Iron Age and Bronze Age human teeth from Bulgaria, as well as bone samples from seven Peruvian mummies and a Bronze Age hair sample from Denmark. Prior to capture, shotgun sequencing of these libraries yielded an average of 1.2% of reads mapping to the human genome (including duplicates). After capture, this fraction increased dramatically, with up to 59% of reads mapped to human and folds enrichment ranging from 6X to 159X. In this talk, we will discuss our extension of WISC to three pressing problems: (1) extending WISC to non-human model systems, (2) improving metagenomic sequencing via human genome subtraction, and (3) development of “gold standards” for validation in ancient DNA (aDNA). In the first project, we capture the genomes of 5 ancient dogs and wolves, including one wolf from the 19th century, one wolf from the Late Pleistocene, two pre-Columbian dogs (one from Mexico and one from Peru), and a Mesolithic dog from Yugoslavia. We demonstrate the adjustments that must be made to adapt the protocol to different species. In the second project, we developed a “negative” capture or subtraction approach where we enrich for genomic DNA not bound to the RNA-baits. In the application to metagenomic sequencing, we saw a depletion of human DNA in saliva from 49% pre-capture (~4.8M Illumina MiSeq reads per sample) to 3% (~275K reads) without loss of complexity in the remaining library. Finally, we will discuss development of “gold standards” for aDNA sequencing via orthogonal technology validation. We have resequenced a subset of the original 12 samples used in the WISC study on the Ion Torrent Proton system and found that orthogonal validation reduces the false positive rate of PCR induced library variants.

We present a flexible and robust simulation-based framework to infer demographic parameters from the site frequency spectrum (SFS) computed on large genomic data sets. This composite-likelihood approach allows the study of arbitrarily complex evolutionary models. We compare our approach to dadi, the current reference in the field, and show that our approach has better convergence properties for complex models. We present an application of our methodology to non-coding genomic SNP data from four human populations and further show the versatility of our framework by extending it to the inference of demographic parameters from SNP chips with known ascertainment, such as that recently released by Affymetrix to study human origins. We discuss potential extensions of our approach, which appears generally well suited to study complex scenarios from large genomic data sets.

Joint work with Vitor C. Sousa.

Chromosomal segments that are identical by descent (IBD) were recently shown to convey information about population-level features such as demography, natural selection and heritability of common traits. In a recent work [1], we have developed analytical models for the relationship between haplotype sharing and demography, and shown that IBD sharing provides an effective way for reconstructing demographic events of the recent millennia, where classical methods are typically underpowered. We now extend the developed models to accommodate the simultaneous analysis of multiple demes, providing insight into recent migration rates as well as population size fluctuations. Using this approach we analyzed sequencing data for 498 unrelated individuals from 11 Dutch provinces (The Genome of Netherlands Project). Pairs of individuals from all the analyzed provinces are found to share several IBD segments of length greater than 1 centimorgan (cM), suggesting recent common ancestry of these groups. We observe a north-to-south gradient of declining IBD sharing frequency. While the chance of sharing long (>7 CM), extremely recent IBD segments correlates with modern-day geographic distance, shorter segments are more frequently shared with individuals currently residing in the north of the country, regardless of the individuals’ modern location. Using the developed analytical methods, we reconstruct coalescent distributions and migration rates across the analyzed provinces. In all cases we find evidence for recent exponential growth at different rates for different provinces, with substantial recent gene flow between these demes. Using the retrieved model, we estimate the average haploid pair of Dutch individuals in the studied dataset to find a common ancestor ~1600 years before present, with earlier common ancestors typically found in northern provinces and variation that depends on modern geographic location.

Variation in human DNA sequences account for a significant amount of genetic risk factors for common disease such as hypertension, diabetes, Alzheimer's disease, and cancer. Identifying the human sequence variation that makes up the genetic basis of common disease will have a tremendous impact on medicine in many ways. Recent efforts to identify these genetic factors through large scale association studies which compare information on variation between a set of healthy and diseased individuals have been remarkably successful. However, despite the success of these initial studies, many challenges and open questions remain on how to design and analyze the results of association studies. As several recent studies have demonstrated, confounding factors such as population structure and measurement errors can complicate genetics analysis by causing many spurious associations. Yet little is understood about how these confounding factors affect analyses and how to correct for these factors. In this talk I will discuss several recently developed methods based on linear mixed models for correcting for both known and unknown confounding factors in genetic studies.

Human populations have undergone dramatic changes in population size in the past 100,000 years, including a severe bottleneck of non-African populations and recent explosive population growth. There is currently great interest in how these demographic events may have affected the burden of deleterious mutations in individuals and the allele frequency spectrum of disease mutations in populations. Here we use population genetic models to show that—contrary to previous conjectures—recent human demography has likely had very little impact on the average burden of deleterious mutations carried by individuals. This prediction is supported by exome sequence data showing that African American and European American individuals carry very similar burdens of damaging mutations. We next consider whether recent population growth has increased the importance of very rare mutations in complex traits. Our analysis predicts that for most classes of disease variants, rare alleles are unlikely to contribute a large fraction of the total genetic variance, and that the impact of recent growth is likely to be modest. However, for diseases that have a direct impact on fitness, strongly deleterious rare mutations likely do play important roles, and the impact of very rare mutations will be far greater as a result of recent growth. In summary, demographic history has dramatically impacted patterns of variation in different human populations, but these changes have likely had little impact on either genetic load or on the importance of rare variants for most complex traits.