Recommendations of the 2006 Human Variome Project meeting

Lists of variations in genomic DNA and their effects have been kept for some time and have been used in diagnostics and research. Although these lists have been carefully gathered and curated, there has been little standardization and coordination, complicating their use. Given the myriad possible variations in the estimated 24,000 genes in the human genome, it would be useful to have standard criteria for databases of variation. Incomplete collection and ascertainment of variants demonstrates a need for a universally accessible system. These and other problems led to the World Heath Organization-cosponsored meeting on June 20-23, 2006 in Melbourne, Australia, which launched the Human Variome Project. This meeting addressed all areas of human genetics relevant to collection of information on variation and its effects. Members of each of eight sessions (the clinic and phenotype, the diagnostic laboratory, the research laboratory, curation and collection, informatics, relevance to the emerging world, integration and federation and funding and sustainability) developed a number of recommendations that were then organized into a total of 96 recommendations to act as a foundation for future work worldwide. Here we summarize the background of the project, the meeting and its recommendations.     

Crowdsourcing human mutations

The first Human Variome microattribution review shows that data citation and publication credit can work as incentives for systematic curation of gene variant and phenotype data. Analysis of the formal assertions in both databases and journal articles argues for better separation of data structures from narrative so that they can better support one another to communicate meaning.     

Genome-wide association analysis identifies 20 loci that influence adult height

Adult height is a model polygenic trait, but there has been limited success in identifying the genes underlying its normal variation. To identify genetic variants influencing adult human height, we used genome-wide association data from 13,665 individuals and genotyped 39 variants in an additional 16,482 samples. We identified 20 variants associated with adult height (P < 5 x 10(-7), with 10 reaching P < 1 x 10(-10)). Combined, the 20 SNPs explain approximately 3% of height variation, with a approximately 5 cm difference between the 6.2% of people with 17 or fewer 'tall' alleles compared to the 5.5% with 27 or more 'tall' alleles. The loci we identified implicate genes in Hedgehog signaling (IHH, HHIP, PTCH1), extracellular matrix (EFEMP1, ADAMTSL3, ACAN) and cancer (CDK6, HMGA2, DLEU7) pathways, and provide new insights into human growth and developmental processes. Finally, our results provide insights into the genetic architecture of a classic quantitative trait.     

Conserved noncoding sequences are selectively constrained and not mutation cold spots

Noncoding genetic variants are likely to influence human biology and disease, but recognizing functional noncoding variants is difficult. Approximately 3% of noncoding sequence is conserved among distantly related mammals, suggesting that these evolutionarily conserved noncoding regions (CNCs) are selectively constrained and contain functional variation. However, CNCs could also merely represent regions with lower local mutation rates. Here we address this issue and show that CNCs are selectively constrained in humans by analyzing HapMap genotype data. Specifically, new (derived) alleles of SNPs within CNCs are rarer than new alleles in nonconserved regions (P = 3 x 10(-18)), indicating that evolutionary pressure has suppressed CNC-derived allele frequencies. Intronic CNCs and CNCs near genes show greater allele frequency shifts, with magnitudes comparable to those for missense variants. Thus, conserved noncoding variants are more likely to be functional. Allele frequency distributions highlight selectively constrained genomic regions that should be intensively surveyed for functionally important variation.     

A road map for efficient and reliable human genome epidemiology

Networks of investigators have begun sharing best practices, tools and methods for analysis of associations between genetic variation and common diseases. A Network of Investigator Networks has been set up to drive the process, sponsored by the Human Genome Epidemiology Network. A workshop is planned to develop consensus guidelines for reporting results of genetic association studies. Published literature databases will be integrated, and unpublished data, including 'negative' studies, will be captured by online journals and through investigator networks. Systematic reviews will be expanded to include more meta-analyses of individual-level data and prospective meta-analyses. Field synopses will offer regularly updated overviews.     

Population genetic structure of variable drug response.

Geographic patterns of genetic variation, including variation at drug metabolizing enzyme (DME) loci and drug targets, indicate that geographic structuring of inter-individual variation in drug response may occur frequently. This raises two questions: how to represent human population genetic structure in the evaluation of drug safety and efficacy, and how to relate this structure to drug response. We address these by (i) inferring the genetic structure present in a heterogeneous sample and (ii) comparing the distribution of DME variants across the inferred genetic clusters of individuals. We find that commonly used ethnic labels are both insufficient and inaccurate representations of the inferred genetic clusters, and that drug-metabolizing profiles, defined by the distribution of DME variants, differ significantly among the clusters. We note, however, that the complexity of human demographic history means that there is no obvious natural clustering scheme, nor an obvious appropriate degree of resolution. Our comparison of drug-metabolizing profiles across the inferred clusters establishes a framework for assessing the appropriate level of resolution in relating genetic structure to drug response.     

Systematic mapping of genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse signaling pathways

Most heritable traits, including disease susceptibility, are affected by interactions between multiple genes. However, we understand little about how genes interact because very few possible genetic interactions have been explored experimentally. We have used RNA interference in Caenorhabditis elegans to systematically test approximately 65,000 pairs of genes for their ability to interact genetically. We identify approximately 350 genetic interactions between genes functioning in signaling pathways that are mutated in human diseases, including components of the EGF/Ras, Notch and Wnt pathways. Most notably, we identify a class of highly connected 'hub' genes: inactivation of these genes can enhance the phenotypic consequences of mutation of many different genes. These hub genes all encode chromatin regulators, and their activity as genetic hubs seems to be conserved across animals. We propose that these genes function as general buffers of genetic variation and that these hub genes may act as modifier genes in multiple, mechanistically unrelated genetic diseases in humans.     

Polygenic susceptibility to breast cancer and implications for prevention

The knowledge of human genetic variation that will come from the human genome sequence makes feasible a polygenic approach to disease prevention, in which it will be possible to identify individuals as susceptible by their genotype profile and to prevent disease by targeting interventions to those at risk. There is doubt, however, regarding the magnitude of these genetic effects and thus the potential to apply them to either individuals or populations. We have therefore examined the potential for prediction of risk based on common genetic variation using data from a population-based series of individuals with breast cancer. The data are compatible with a log-normal distribution of genetic risk in the population that is sufficiently wide to provide useful discrimination of high- and low-risk groups. Assuming all of the susceptibility genes could be identified, the half of the population at highest risk would account for 88% of all affected individuals. By contrast, if currently identified risk factors for breast cancer were used to stratify the population, the half of the population at highest risk would account for only 62% of all cases. These results suggest that the construction and use of genetic-risk profiles may provide significant improvements in the efficacy of population-based programs of intervention for cancers and other diseases.     

Characterization of single-nucleotide polymorphisms in coding regions of human genes.

A major goal in human genetics is to understand the role of common genetic variants in susceptibility to common diseases. This will require characterizing the nature of gene variation in human populations, assembling an extensive catalogue of single-nucleotide polymorphisms (SNPs) in candidate genes and performing association studies for particular diseases. At present, our knowledge of human gene variation remains rudimentary. Here we describe a systematic survey of SNPs in the coding regions of human genes. We identified SNPs in 106 genes relevant to cardiovascular disease, endocrinology and neuropsychiatry by screening an average of 114 independent alleles using 2 independent screening methods. To ensure high accuracy, all reported SNPs were confirmed by DNA sequencing. We identified 560 SNPs, including 392 coding-region SNPs (cSNPs) divided roughly equally between those causing synonymous and non-synonymous changes. We observed different rates of polymorphism among classes of sites within genes (non-coding, degenerate and non-degenerate) as well as between genes. The cSNPs most likely to influence disease, those that alter the amino acid sequence of the encoded protein, are found at a lower rate and with lower allele frequencies than silent substitutions. This likely reflects selection acting against deleterious alleles during human evolution. The lower allele frequency of missense cSNPs has implications for the compilation of a comprehensive catalogue, as well as for the subsequent application to disease association.     

Evidence of widespread selection on standing variation in Europe at height-associated SNPs

Strong signatures of positive selection at newly arising genetic variants are well documented in humans, but this form of selection may not be widespread in recent human evolution. Because many human traits are highly polygenic and partly determined by common, ancient genetic variation, an alternative model for rapid genetic adaptation has been proposed: weak selection acting on many pre-existing (standing) genetic variants, or polygenic adaptation. By studying height, a classic polygenic trait, we demonstrate the first human signature of widespread selection on standing variation. We show that frequencies of alleles associated with increased height, both at known loci and genome wide, are systematically elevated in Northern Europeans compared with Southern Europeans (P < 4.3 × 10(-4)). This pattern mirrors intra-European height differences and is not confounded by ancestry or other ascertainment biases. The systematic frequency differences are consistent with the presence of widespread weak selection (selection coefficients ～10(-3)-10(-5) per allele) rather than genetic drift alone (P < 10(-15)).     

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.     

Screening a large reference sample to identify very low frequency sequence variants: comparisons between two genes

Most human sequence variation is in the form of single-nucleotide polymorphisms (SNPs). It has been proposed that coding-region SNPs (cSNPs) be used for direct association studies to determine the genetic basis of complex traits. The success of such studies depends on the frequency of disease-associated alleles, and their distribution in different ethnic populations. If disease-associated alleles are frequent in most populations, then direct genotyping of candidate variants could show robust associations in manageable study samples. This approach is less feasible if the genetic risk from a given candidate gene is due to many infrequent alleles. Previous studies of several genes demonstrated that most variants are relatively infrequent (<0.05). These surveys genotyped small samples (n<75) and thus had limited ability to identify rare alleles. Here we evaluate the prevalence and distribution of such rare alleles by genotyping an ethnically diverse reference sample that is more than six times larger than those used in previous studies (n=450). We screened for variants in the complete coding sequence and intron-exon junctions of two candidate genes for neuropsychiatric phenotypes: SLC6A4, encoding the serotonin transporter; and SLC18A2, encoding the vesicular monoamine transporter. Both genes have unique roles in neuronal transmission, and variants in either gene might be associated with neurobehavioral phenotypes.     

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 meta-analyses and field synopsis of genetic association studies in schizophrenia: the SzGene database

In an effort to pinpoint potential genetic risk factors for schizophrenia, research groups worldwide have published over 1,000 genetic association studies with largely inconsistent results. To facilitate the interpretation of these findings, we have created a regularly updated online database of all published genetic association studies for schizophrenia ('SzGene'). For all polymorphisms having genotype data available in at least four independent case-control samples, we systematically carried out random-effects meta-analyses using allelic contrasts. Across 118 meta-analyses, a total of 24 genetic variants in 16 different genes (APOE, COMT, DAO, DRD1, DRD2, DRD4, DTNBP1, GABRB2, GRIN2B, HP, IL1B, MTHFR, PLXNA2, SLC6A4, TP53 and TPH1) showed nominally significant effects with average summary odds ratios of approximately 1.23. Seven of these variants had not been previously meta-analyzed. According to recently proposed criteria for the assessment of cumulative evidence in genetic association studies, four of the significant results can be characterized as showing 'strong' epidemiological credibility. Our project represents the first comprehensive online resource for systematically synthesized and graded evidence of genetic association studies in schizophrenia. As such, it could serve as a model for field synopses of genetic associations in other common and genetically complex disorders.     

Detection of large-scale variation in the human genome

We identified 255 loci across the human genome that contain genomic imbalances among unrelated individuals. Twenty-four variants are present in > 10% of the individuals that we examined. Half of these regions overlap with genes, and many coincide with segmental duplications or gaps in the human genome assembly. This previously unappreciated heterogeneity may underlie certain human phenotypic variation and susceptibility to disease and argues for a more dynamic human genome structure.     

Human genes that limit AIDS

Discernable genetic variation among people and populations has an important role in infectious disease epidemics, including that of acquired immune deficiency syndrome (AIDS). Genetic association analysis of several large AIDS cohorts implicate 14 AIDS restriction genes, polymorphic variants in loci that regulate HIV-1 cell entry, acquired and innate immunity, and cytokine defenses to HIV-1. The influence and translational impact of these genes on individual and population sensitivity to AIDS is considerable.     

Rare independent mutations in renal salt handling genes contribute to blood pressure variation

The effects of alleles in many genes are believed to contribute to common complex diseases such as hypertension. Whether risk alleles comprise a small number of common variants or many rare independent mutations at trait loci is largely unknown. We screened members of the Framingham Heart Study (FHS) for variation in three genes-SLC12A3 (NCCT), SLC12A1 (NKCC2) and KCNJ1 (ROMK)-causing rare recessive diseases featuring large reductions in blood pressure. Using comparative genomics, genetics and biochemistry, we identified subjects with mutations proven or inferred to be functional. These mutations, all heterozygous and rare, produce clinically significant blood pressure reduction and protect from development of hypertension. Our findings implicate many rare alleles that alter renal salt handling in blood pressure variation in the general population, and identify alleles with health benefit that are nonetheless under purifying selection. These findings have implications for the genetic architecture of hypertension and other common complex traits.