Natural variation in cardiac metabolism and gene expression in Fundulus heteroclitus

Individual variation in gene expression is important for evolutionary adaptation and susceptibility to diseases and pathologies. In this study, we address the functional importance of this variation by comparing cardiac metabolism to patterns of mRNA expression using microarrays. There is extensive variation in both cardiac metabolism and the expression of metabolic genes among individuals of the teleost fish Fundulus heteroclitus from natural outbred populations raised in a common environment: metabolism differed among individuals by a factor of more than 2, and expression levels of 94% of genes were significantly different (P < 0.01) between individuals in a population. This unexpectedly high variation in metabolic gene expression explains much of the variation in metabolism, suggesting that it is biologically relevant. The patterns of gene expression that are most important in explaining cardiac metabolism differ between groups of individuals. Apparently, the variation in metabolism seems to be related to different patterns of gene expression in the different groups of individuals. The magnitude of differences in gene expression in these groups is not important; large changes in expression have no greater predictive value than small changes. These data suggest that variation in physiological performance is related to the subtle variation in gene expression and that this relationship differs among individuals.     

Detection of regulatory variation in mouse genes

Functional polymorphism in genes can be classified as coding variation, altering the amino-acid sequence of the encoded protein, or regulatory variation, affecting the level or pattern of expression of the gene. Coding variation can be recognized directly from DNA sequence, and consequently its frequency and characteristics have been extensively described. By contrast, virtually nothing is known about the extent to which gene regulation varies in populations. Yet it is likely that regulatory variants are important in modulating gene function: alterations in gene regulation have been proposed to influence disease susceptibility and to have been the primary substrate for the evolution of species. Here, we report a systematic study to assess the extent of cis-acting regulatory variation in 69 genes across four inbred mouse strains. We find that at least four of these genes show allelic differences in expression level of 1.5-fold or greater, and that some of these differences are tissue specific. The results show that the impact of regulatory variants can be detected at a significant frequency in a genomic survey and suggest that such variation may have important consequences for organismal phenotype and evolution. The results indicate that larger-scale surveys in both mouse and human could identify a substantial number of genes with common regulatory variation.     

Population genomics of human gene expression

Genetic variation influences gene expression, and this variation in gene expression can be efficiently mapped to specific genomic regions and variants. Here we have used gene expression profiling of Epstein-Barr virus-transformed lymphoblastoid cell lines of all 270 individuals genotyped in the HapMap Consortium to elucidate the detailed features of genetic variation underlying gene expression variation. We find that gene expression is heritable and that differentiation between populations is in agreement with earlier small-scale studies. A detailed association analysis of over 2.2 million common SNPs per population (5% frequency in HapMap) with gene expression identified at least 1,348 genes with association signals in cis and at least 180 in trans. Replication in at least one independent population was achieved for 37% of cis signals and 15% of trans signals, respectively. Our results strongly support an abundance of cis-regulatory variation in the human genome. Detection of trans effects is limited but suggests that regulatory variation may be the key primary effect contributing to phenotypic variation in humans. We also explore several methodologies that improve the current state of analysis of gene expression variation.     

Systematic documentation and analysis of human genetic variation in hemoglobinopathies using the microattribution approach

We developed a series of interrelated locus-specific databases to store all published and unpublished genetic variation related to hemoglobinopathies and thalassemia and implemented microattribution to encourage submission of unpublished observations of genetic variation to these public repositories. A total of 1,941 unique genetic variants in 37 genes, encoding globins and other erythroid proteins, are currently documented in these databases, with reciprocal attribution of microcitations to data contributors. Our project provides the first example of implementing microattribution to incentivise submission of all known genetic variation in a defined system. It has demonstrably increased the reporting of human variants, leading to a comprehensive online resource for systematically describing human genetic variation in the globin genes and other genes contributing to hemoglobinopathies and thalassemias. The principles established here will serve as a model for other systems and for the analysis of other common and/or complex human genetic diseases.     

A single-nucleotide polymorphism tagging set for human drug metabolism and transport

Interindividual variability in drug response, ranging from no therapeutic benefit to life-threatening adverse reactions, is influenced by variation in genes that control the absorption, distribution, metabolism and excretion of drugs. We genotyped 904 single-nucleotide polymorphisms (SNPs) from 55 such genes in two population samples (European and Japanese) and identified a set of tagging SNPs that represents the common variation in these genes, both known and unknown. Extensive empirical evaluations, including a direct assessment of association with candidate functional SNPs in a new, larger population sample, validated the performance of these tagging SNPs and confirmed their utility for linkage-disequilibrium mapping in pharmacogenetics. The analyses also suggest that rare variation is not amenable to tagging strategies.     

Dopa decarboxylase (Ddc) affects variation in Drosophila longevity

Mutational analyses in model organisms have shown that genes affecting metabolism and stress resistance regulate life span, but the genes responsible for variation in longevity in natural populations are largely unidentified. Previously, we mapped quantitative trait loci (QTLs) affecting variation in longevity between two Drosophila melanogaster strains. Here, we show that the longevity QTL in the 36E;38B cytogenetic interval on chromosome 2 contains multiple closely linked QTLs, including the Dopa decarboxylase (Ddc) locus. Complementation tests to mutations show that Ddc is a positional candidate gene for life span in these strains. Linkage disequilibrium (LD) mapping in a sample of 173 alleles from a single population shows that three common molecular polymorphisms in Ddc account for 15.5% of the genetic contribution to variance in life span from chromosome 2. The polymorphisms are in strong LD, and the effects of the haplotypes on longevity suggest that the polymorphisms are maintained by balancing selection. DDC catalyzes the final step in the synthesis of the neurotransmitters, dopamine and serotonin. Thus, these data implicate variation in the synthesis of bioamines as a factor contributing to natural variation in individual life span.     

Evidence for substantial fine-scale variation in recombination rates across the human genome

Characterizing fine-scale variation in human recombination rates is important, both to deepen understanding of the recombination process and to aid the design of disease association studies. Current genetic maps show that rates vary on a megabase scale, but studying finer-scale variation using pedigrees is difficult. Sperm-typing experiments have characterized regions where crossovers cluster into 1-2-kb hot spots, but technical difficulties limit the number of studies. An alternative is to use population variation to infer fine-scale characteristics of the recombination process. Several surveys reported 'block-like' patterns of diversity, which may reflect fine-scale recombination rate variation, but limitations of available methods made this impossible to assess. Here, we applied a new statistical method, which overcomes these limitations, to infer patterns of fine-scale recombination rate variation in 74 genes. We found extensive rate variation both within and among genes. In particular, recombination hot spots are a common feature of the human genome: 47% (35 of 74) of genes showed substantive evidence for a hot spot, and many more showed evidence for some rate variation. No primary sequence characteristics are consistently associated with precise hot-spot location, although G+C content and nucleotide diversity are correlated with local recombination rate.     

A high-resolution HLA and SNP haplotype map for disease association studies in the extended human MHC

The proteins encoded by the classical HLA class I and class II genes in the major histocompatibility complex (MHC) are highly polymorphic and are essential in self versus non-self immune recognition. HLA variation is a crucial determinant of transplant rejection and susceptibility to a large number of infectious and autoimmune diseases. Yet identification of causal variants is problematic owing to linkage disequilibrium that extends across multiple HLA and non-HLA genes in the MHC. We therefore set out to characterize the linkage disequilibrium patterns between the highly polymorphic HLA genes and background variation by typing the classical HLA genes and >7,500 common SNPs and deletion-insertion polymorphisms across four population samples. The analysis provides informative tag SNPs that capture much of the common variation in the MHC region and that could be used in disease association studies, and it provides new insight into the evolutionary dynamics and ancestral origins of the HLA loci and their haplotypes.     

Estimating coverage and power for genetic association studies using near-complete variation data

Although studies suggest that SNPs derived from HapMap provide promising coverage and power for association studies, the lack of alternative variation datasets limits independent analysis. Using near-complete variation data for 76 genes resequenced in HapMap samples, we find that coverage of common variation by commercial genotyping arrays is substantially lower compared to the HapMap-based estimates. We quantify the power offered by these arrays for a range of disease models.     

Genetic variation in laboratory mice

Characterizing the patterns of genetic variation in an organism provides fundamental insight into the evolutionary history of the organism and defines the scope and nature of studies that must be designed to correlate genotype to phenotype. Given the pre-eminent role of the inbred mouse in biomedical research, considerable effort has been undertaken in recent years to describe more fully the nature and amount of genetic variation among the numerous strains of mice that are in widest use. Here, we discuss recent studies that have contributed to an emerging understanding of the unique variation patterns found in inbred strains of mice and how they have arisen through a combination of natural evolution and human-directed breeding. These preliminary results have ramifications for genetic research into complex biomedical traits and are the basis for the development of future variation resources.     

A stress-sensitive reporter predicts longevity in isogenic populations of Caenorhabditis elegans

When both genotype and environment are held constant, 'chance' variation in the lifespan of individuals in a population is still quite large. Using isogenic populations of the nematode Caenorhabditis elegans, we show that, on the first day of adult life, chance variation in the level of induction of a green fluorescent protein (GFP) reporter coupled to a promoter from the gene hsp-16.2 predicts as much as a fourfold variation in subsequent survival. The same reporter is also a predictor of ability to withstand a subsequent lethal thermal stress. The level of induction of GFP is not heritable, and GFP expression levels in other reporter constructs are not associated with differences in longevity. HSP-16.2 itself is probably not responsible for the observed differences in survival but instead probably reflects a hidden, heterogeneous, but now quantifiable, physiological state that dictates the ability of an organism to deal with the rigors of living.     

Common genetic variants account for differences in gene expression among ethnic groups

Variation in DNA sequence contributes to individual differences in quantitative traits, but in humans the specific sequence variants are known for very few traits. We characterized variation in gene expression in cells from individuals belonging to three major population groups. This quantitative phenotype differs significantly between European-derived and Asian-derived populations for 1,097 of 4,197 genes tested. For the phenotypes with the strongest evidence of cis determinants, most of the variation is due to allele frequency differences at cis-linked regulators. The results show that specific genetic variation among populations contributes appreciably to differences in gene expression phenotypes. Populations differ in prevalence of many complex genetic diseases, such as diabetes and cardiovascular disease. As some of these are probably influenced by the level of gene expression, our results suggest that allele frequency differences at regulatory polymorphisms also account for some population differences in prevalence of complex diseases.     

High-throughput sequencing provides insights into genome variation and evolution in Salmonella Typhi

Isolates of Salmonella enterica serovar Typhi (Typhi), a human-restricted bacterial pathogen that causes typhoid, show limited genetic variation. We generated whole-genome sequences for 19 Typhi isolates using 454 (Roche) and Solexa (Illumina) technologies. Isolates, including the previously sequenced CT18 and Ty2 isolates, were selected to represent major nodes in the phylogenetic tree. Comparative analysis showed little evidence of purifying selection, antigenic variation or recombination between isolates. Rather, evolution in the Typhi population seems to be characterized by ongoing loss of gene function, consistent with a small effective population size. The lack of evidence for antigenic variation driven by immune selection is in contrast to strong adaptive selection for mutations conferring antibiotic resistance in Typhi. The observed patterns of genetic isolation and drift are consistent with the proposed key role of asymptomatic carriers of Typhi as the main reservoir of this pathogen, highlighting the need for identification and treatment of carriers.     

Chromosome-scale selective sweeps shape Caenorhabditis elegans genomic diversity

The nematode Caenorhabditis elegans is central to research in molecular, cell and developmental biology, but nearly all of this research has been conducted on a single strain of C. elegans. Little is known about the population genomic and evolutionary history of this species. We characterized C. elegans genetic variation using high-throughput selective sequencing of a worldwide collection of 200 wild strains and identified 41,188 SNPs. Notably, C. elegans genome variation is dominated by a set of commonly shared haplotypes on four of its six chromosomes, each spanning many megabases. Population genetic modeling showed that this pattern was generated by chromosome-scale selective sweeps that have reduced variation worldwide; at least one of these sweeps probably occurred in the last few hundred years. These sweeps, which we hypothesize to be a result of human activity, have drastically reshaped the global C. elegans population in the recent past.     

A framework for variation discovery and genotyping using next-generation DNA sequencing data

Recent advances in sequencing technology make it possible to comprehensively catalog genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious, and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (i) initial read mapping; (ii) local realignment around indels; (iii) base quality score recalibration; (iv) SNP discovery and genotyping to find all potential variants; and (v) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We here discuss the application of these tools, instantiated in the Genome Analysis Toolkit, to deep whole-genome, whole-exome capture and multi-sample low-pass (～4×) 1000 Genomes Project datasets.     

As normal as normal can be?

Two papers report that large-scale copy-number variations, ranging in size from 100 kb to 2 Mb, are distributed widely throughout the human genome, and that a high proportion of them encompass known genes. This unexpected level of genome variation has implications for our view of human genetic diversity and phenotypic variation.     

Human genome sequence variation and the influence of gene history,mutation and recombination

Variation in the human genome sequence is key to understanding susceptibility to disease in modern populations and the history of ancestral populations. Unlocking this information requires knowledge of the patterns and underlying causes of human sequence diversity. By applying a new population-genetic framework to two genome-wide polymorphism surveys, we find that the human genome contains sizeable regions (stretching over tens of thousands of base pairs) that have intrinsically high and low rates of sequence variation. We show that the primary determinant of these patterns is shared genealogical history. Only a fraction of the variation (at most 25%) is due to the local mutation rate. By measuring the average distance over which genealogical histories are typically preserved, these data provide the first genome-wide estimate of the average extent of correlation among variants (linkage disequilibrium). The results are best explained by extreme variability in the recombination rate at a fine scale, and provide the first empirical evidence that such recombination 'hot spots' are a general feature of the human genome and have a principal role in shaping genetic variation in the human population.