Heather A. Lawson

The genomic distribution of population substructure in four populations using 8,525 autosomal SNPs

Understanding the nature of evolutionary relationships among persons and populations is important for the efficient application of genome science to biomedical research. We have analysed 8,525 autosomal single nucleotide polymorphisms (SNPs) in 84 individuals from four populations: African-American, European-American, Chinese and Japanese. Individual relationships were reconstructed using the allele sharing distance and the neighbour-joining tree making method. Trees show clear clustering according to population, with the root branching from the African-American clade. The African-American cluster is much less star-like than European-American and East Asian clusters, primarily because of admixture. Furthermore, on the East Asian branch, all ten Chinese individuals cluster together and all ten Japanese individuals cluster together. Using positional information, we demonstrate strong correlations between inter-marker distance and both locus-specific FST (the proportion of total variation due to differentiation) levels and branch lengths. Chromosomal maps of the distribution of locus-specific branch lengths were constructed by combining these data with other published SNP markers (total of 33,704 SNPs). These maps clearly illustrate a non-uniform distribution of human genetic substructure, an instructional and useful paradigm for education and research.

Genomic imprinting and parent-of-origin effects on complex traits

Parent-of-origin effects occur when the phenotypic effect of an allele depends on whether it is inherited from the mother or the father. Several phenomena can cause parent-of-origin effects, but the best characterized is parent-of-origin-dependent gene expression associated with genomic imprinting. The development of new mapping approaches applied to the growing abundance of genomic data has demonstrated that imprinted genes can be important contributors to complex trait variation. Therefore, to understand the genetic architecture and evolution of complex traits, including complex diseases and traits of agricultural importance, it is crucial to account for these parent-of-origin effects. Here, we discuss patterns of phenotypic variation associated with imprinting, evidence supporting its role in complex trait variation and approaches for identifying its molecular signatures.

Exploring long-range genome interactions using the WashU Epigenome Browser

To the Editor : Eukar yotic chromosomes are a highly organized three-dimensional entity folded through a tightly regulated process1,2 with important functions that include bringing distal regulatory elements into the vicinity of their target gene promoters and arranging the chromosomes into distinct compartments3–6. Recent technological innovations, including chromosome conformation capture carbon copy (5C), Hi-C and chromatin interaction analysis by paired-end tag sequencing (ChIA-PET), have facilitated the discovery of chromosomal organization principles and folding architectures at unprecedented scales and resolution. Each technology also comes with corresponding computational tools7,8 to process and visualize its specific data type (Supplementary Note 1). However, visualizing and navigating long-range interaction data, as well as integrating these interactions with other epigenomics data, remains a much-desired capability and a daunting challenge9. We have extended the WashU Epigenome Browser10 (http:// epigenomegateway.wustl.edu/), which currently hosts thousands of epigenome and transcriptome data sets for multiple cell types, tissues, individuals and species, to support multiple types of long-range genome interaction data. This enables investigators to explore epigenomic data in the context of higher-order chromosomal domains and to generate multiple types of intuitive, publication-quality figures of interactions (see tutorial in Supplementary Note 2). In Figure 1 we display the histonemodification profile and long-range interaction data of two human cell lines (IMR90 and K562) side by side and note that regions can exhibit similar interaction patterns while showing different histone modifications (such as the boundary region between domains 1 and 2) (Supplementary Methods). These observations are consistent with the hypothesis that chromatin domains are stable across cell types but can have different epigenetic profiles in different cells. Genes within each domain are regulated epigenetically in a cell type– specific manner5. Integrating higherorder chromatin interaction data with other genomic data could potentially reveal novel insights about mechanisms underlying gene and genome regulation. Pairs of interacting regions can be joined by arcs (Fig. 1b)—a suitable representation for sparse interactions—as is common with ChIA-PET and sometimes found in 5C data sets, or they can be indicated by filled rectangles in a heat map (Fig. 1c) for dense interactions (as is typical for Hi-C and some regions in 5C data sets). Investigators can click on the arcs or heat-map cells to invoke a companion Browser panel (Supplementary Fig. 1), which displays epigenomic data over the distal interacting locus. This companion panel can be navigated independently, enabling comparison of data patterns of interacting loci in the same view. Thus, investigators can observe several loci that are distant in their genomic coordinates but are inferred to be spatially close to each other in the nucleus. Both the “arc” and “heat-map” modes display only interactions that are contained within the current browsing range while omitting interactions beyond the range. To visualize the complete set of interactions, investigators can invoke the “Circlet View” (Supplementary Fig. 2), in which the chromosomal axis curls to form a circle and interactions are displayed as arcs inside the circle. Investigators can choose to display a single chromosome or to include interacting chromosomes to achieve a wholegenome perspective of the interactions. Investigators can also toggle between “thin,” “full” or “density” mode for any data track (Supplementary Figs. 3–5). “Gene Set View” and genomic juxtaposition can be combined with the long-range interaction function to focus on the interaction events in a subregion of the genome (Fig. 1). An investigator’s own long-range interaction data can be displayed on the Browser via the custom track or Data Hub function. The Browser currently hosts over 100 genomewide chromatin interaction data sets for human, mouse and fly. We expect such data to become increasingly available, and the browser will be a helpful tool for exploring how eukaryotic genomes function as nonlinear systems.

Imputation of Single-Nucleotide Polymorphisms in Inbred Mice Using Local Phylogeny

We present full-genome genotype imputations for 100 classical laboratory mouse strains, using a novel method. Using genotypes at 549,683 SNP loci obtained with the Mouse Diversity Array, we partitioned the genome of 100 mouse strains into 40,647 intervals that exhibit no evidence of historical recombination. For each of these intervals we inferred a local phylogenetic tree. We combined these data with 12 million loci with sequence variations recently discovered by whole-genome sequencing in a common subset of 12 classical laboratory strains. For each phylogenetic tree we identified strains sharing a leaf node with one or more of the sequenced strains. We then imputed high- and medium-confidence genotypes for each of 88 nonsequenced genomes. Among inbred strains, we imputed 92% of SNPs genome-wide, with 71% in high-confidence regions. Our method produced 977 million new genotypes with an estimated per-SNP error rate of 0.083% in high-confidence regions and 0.37% genome-wide. Our analysis identified which of the 88 nonsequenced strains would be the most informative for improving full-genome imputation, as well as which additional strain sequences will reveal more new genetic variants. Imputed sequences and quality scores can be downloaded and visualized online.

Diet-Dependent Genetic and Genomic Imprinting Effects on Obesity in Mice

Although the current obesity epidemic is of environmental origin, there is substantial genetic variation in individual response to an obesogenic environment. In this study, we perform a genome‐wide scan for quantitative trait loci (QTLs) affecting obesity per se, or an obese response to a high‐fat diet in mice from the LG/J by SM/J Advanced Intercross (AI) Line (Wustl:LG, SM‐G16). A total of 1,002 animals from 78 F16 full sibships were weaned at 3 weeks of age and half of each litter placed on high‐ and low‐fat diets. Animals remained on the diet until 20 weeks of age when they were necropsied and the weights of the reproductive, kidney, mesenteric, and inguinal fat depots were recorded. Effects on these phenotypes, along with total fat depot weight and carcass weight at necropsy, were mapped across the genome using 1,402 autosomal single‐nucleotide polymorphism (SNP) markers. Haplotypes were reconstructed and additive, dominance, and imprinting genotype scores were derived every 1 cM along the F16 map. Analysis was performed using a mixed model with additive, dominance, and imprinting genotype scores, their interactions with sex, diet, and with sex‐by‐diet as fixed effects and with family and its interaction with sex, diet, and sex‐by‐diet as random effects. We discovered 95 trait‐specific QTLs mapping to 40 locations. Most QTLs had additive effects with dominance and imprinting effects occurring at two‐thirds of the loci. Nearly every locus interacted with sex and/or diet in important ways demonstrating that gene effects are primarily context dependent, changing depending on sex and/or diet.

Genetic Effects at Pleiotropic Loci Are Context-Dependent with Consequences for the Maintenance of Genetic Variation in Populations

Context-dependent genetic effects, including genotype-by-environment and genotype-by-sex interactions, are a potential mechanism by which genetic variation of complex traits is maintained in populations. Pleiotropic genetic effects are also thought to play an important role in evolution, reflecting functional and developmental relationships among traits. We examine context-dependent genetic effects at pleiotropic loci associated with normal variation in multiple metabolic syndrome (MetS) components (obesity, dyslipidemia, and diabetes-related traits). MetS prevalence is increasing in Western societies and, while environmental in origin, presents substantial variation in individual response. We identify 23 pleiotropic MetS quantitative trait loci (QTL) in an F16 advanced intercross between the LG/J and SM/J inbred mouse strains (Wustl:LG,SM-G16; n = 1002). Half of each family was fed a high-fat diet and half fed a low-fat diet; and additive, dominance, and parent-of-origin imprinting genotypic effects were examined in animals partitioned into sex, diet, and sex-by-diet cohorts. We examine the context-dependency of the underlying additive, dominance, and imprinting genetic effects of the traits associated with these pleiotropic QTL. Further, we examine sequence polymorphisms (SNPs) between LG/J and SM/J as well as differential expression of positional candidate genes in these regions. We show that genetic associations are different in different sex, diet, and sex-by-diet settings. We also show that over- or underdominance and ecological cross-over interactions for single phenotypes may not be common, however multidimensional synthetic phenotypes at loci with pleiotropic effects can produce situations that favor the maintenance of genetic variation in populations. Our findings have important implications for evolution and the notion of personalized medicine.

The importance of context to the genetic architecture of diabetes-related traits is revealed in a genome-wide scan of a LG/J × SM/J murine model.

Variations in diabetic phenotypes are caused by complex interactions of genetic effects, environmental factors, and the interplay between the two. We tease apart these complex interactions by examining genome-wide genetic and epigenetic effects on diabetes-related traits among different sex, diet, and sex-by-diet cohorts in a Mus musculus model. We conducted a genome-wide scan for quantitative trait loci that affect serum glucose and insulin levels and response to glucose stress in an F16 Advanced Intercross Line of the LG/J and SM/J intercross (Wustl:LG,SM-G16). Half of each sibship was fed a high-fat diet and half was fed a relatively low-fat diet. Context-dependent genetic (additive and dominance) and epigenetic (parent-of-origin imprinting) effects were characterized by partitioning animals into sex, diet, and sex-by-diet cohorts. We found that different cohorts often have unique genetic effects at the same loci, and that genetic signals can be masked or erroneously assigned to specific cohorts if they are not considered individually. Our data demonstrate that the effects of genes on complex trait variation are highly context-dependent and that the same genomic sequence can affect traits differently depending on an individual’s sex and/or dietary environment. Our results have important implications for studies of complex traits in humans.

Genetic, epigenetic, and gene-by-diet interaction effects underlie variation in serum lipids in a LG/JxSM/J murine model.

Variation in serum cholesterol, free-fatty acids, and triglycerides is associated with cardiovascular disease (CVD) risk factors. There is great interest in characterizing the underlying genetic architecture of these risk factors, because they vary greatly within and among human populations and between the sexes. We present results of a genome-wide scan for quantitative trait loci (QTL) affecting serum cholesterol, free-fatty acids, and triglycerides in an F16 advanced intercross line of LG/J and SM/J (Wustl:LG,SM-G16). Half of the population was fed a high-fat diet and half was fed a relatively low-fat diet. Context-dependent genetic (additive and dominance) and epigenetic (imprinting) effects were characterized by partitioning animals into sex, diet, and sex-by-diet cohorts. Here we examine genetic, environmental, and genetic-by-environmental interactions of QTL overlapping previously identified loci associated with CVD risk factors, and we add to the serum lipid QTL landscape by identifying new loci.

Healing quantitative trait loci in a combined cross analysis using related mouse strain crosses

Inbred mouse strains MRL and LG share the ability to fully heal ear hole punches with the full range of appropriate tissues without scarring. They also share a common ancestry, MRL being formed from a multi-strain cross with two final backcrosses to LG before being inbred by brother-sister mating. Many gene-mapping studies for healing ability have been performed using these two strains, resulting in the location of about 20 quantitative trait loci (QTLs). Here, we combine two of these crosses (N=638), MRL/lpr × C57BL/6NTac and LG/J × SM/J, in a single combined cross analysis to increase the mapping power, decrease QTL support intervals, separate multiple QTLs and establish allelic states at individual QTL. The combined cross analysis located 11 QTLs, 6 affecting only one cross (5 LG × SM and 1 MRL × B6) and 5 affecting both crosses, approximately the number of common QTLs expected given strain SNP similarity. Amongst the five QTLs mapped in both crosses, three had significantly different genetic effects, additive in one cross and over or underdominant in the other. It is possible that allelic states at these three loci are different in SM and B6 because they lead to differences in dominance interactions with the LG and MRL alleles. QTL support intervals are 40% smaller in the combined cross analysis than in either of the single crosses. Combined cross analysis was successful in enhancing the interpretation of earlier QTL results for these strains.

Metabolic Syndrome Components in Murine Models

Animal models have enriched understanding of the physiological basis of metabolic disorders and advanced identification of genetic risk factors underlying the metabolic syndrome (MetS). Murine models are especially appropriate for this type of research, and are an excellent resource not only for identifying candidate genomic regions, but also for illuminating the possible molecular mechanisms or pathways affected in individual components of MetS. In this review, we briefly discuss findings from mouse models of metabolic disorders, particularly in light of issues raised by the recent flood of human genome-wide association studies (GWAS) results. We describe how mouse models are revealing that genotype interacts with environment in important ways, indicating that the underlying genetics of MetS is highly context dependant. Further we show that epistasis, imprinting and maternal effects each contribute to the genetic architecture underlying variation in metabolic traits, and mouse models provide an opportunity to dissect these aspects of the genetic architecture that are difficult if not impossible to ascertain in humans. Finally we discuss how knowledge gained from mouse models can be used in conjunction with comparative genomic methods and bioinformatic resources to inform human MetS research.