Hundreds of variants clustered in genomic loci and biological pathways affect human height

Genome-wide association studies: results from the first few years and potential implications for clinical medicine

Most common diseases and quantitative traits are heritable: determined in part by genetic variation within the population. The inheritance is typically polygenic in that combined effects of variants in numerous genes, plus nongenetic factors, determine outcome. The genes influencing common disease and quantitative traits remained largely unknown until the implementation in 2006 of genome-wide association (GWA) studies that comprehensively surveyed common genetic variation (frequency>5%). By 2010, GWA studies identified>1,000 genetic variants for polygenic traits. Typically, these variants together account for a modest fraction (10%-30%) of heritability, but they have highlighted genes in both known and new biological pathways and genes of unknown function. This initial effort prefigures new studies aimed at rarer variation and decades of functional work to decipher newly glimpsed biology. The greatest impact of GWA studies may not be in predictive medicine but rather in the development over the next decades of therapies based on novel biological insights.

Genome partitioning of genetic variation for complex traits using common SNPs

We estimate and partition genetic variation for height, body mass index (BMI), von Willebrand factor and QT interval (QTi) using 586,898 SNPs genotyped on 11,586 unrelated individuals. We estimate that ∼45%, ∼17%, ∼25% and ∼21% of the variance in height, BMI, von Willebrand factor and QTi, respectively, can be explained by all autosomal SNPs and a further ∼0.5-1% can be explained by X chromosome SNPs. We show that the variance explained by each chromosome is proportional to its length, and that SNPs in or near genes explain more variation than SNPs between genes. We propose a new approach to estimate variation due to cryptic relatedness and population stratification. Our results provide further evidence that a substantial proportion of heritability is captured by common SNPs, that height, BMI and QTi are highly polygenic traits, and that the additive variation explained by a part of the genome is approximately proportional to the total length of DNA contained within genes therein.

Delineating the Hemostaseome as an aid to individualize the analysis of the hereditary basis of thrombotic and bleeding disorders

Next-generation sequencing and genome-wide association studies represent powerful tools to identify genetic variants that confer disease risk within populations. On their own, however, they cannot provide insight into how these variants contribute to individual risk for diseases that exhibit complex inheritance, or alternatively confer health in a given individual. Even in the case of well-characterized variants that confer a significant disease risk, more healthy individuals carry the variant, with no apparent ill effect, than those who manifest disease. Access to low-cost genome sequence data promises to provide an unprecedentedly detailed view of the nature of the hereditary component of complex diseases, but requires the large-scale comparison of sequence data from individuals with and without disease to deliver a clinical calibration. The provision of informatics support remains problematic as there are currently no means to interpret the data generated. Here, we initiate this process, a prerequisite for such a study, by narrowing the focus from an entire genome to that of a single biological system. To this end, we examine the 'Hemostaseome,' and more specifically focus on DNA sequence changes pertaining to those human genes known to impact upon hemostasis and thrombosis that can be analyzed coordinately, and on an individual basis, to interrogate how specific combinations of variants act to confer disease predisposition. As a first step, we delineate known members of the Hemostaseome and explore the nature of the genetic variants that may cause disease in individuals whose hemostatic balance has become shifted toward either a prothrombotic or anticoagulant phenotype.

Indirect evidence for the genetic determination of short stature in African Pygmies

Central African Pygmy populations are known to be the shortest human populations worldwide. Many evolutionary hypotheses have been proposed to explain this short stature: adaptation to food limitations, climate, forest density, or high mortality rates. However, such hypotheses are difficult to test given the lack of long-term surveys and demographic data. Whether the short stature observed nowadays in African Pygmy populations as compared to their Non-Pygmy neighbors is determined by genetic factors remains widely unknown. Here, we study a uniquely large new anthropometrical dataset comprising more than 1,000 individuals from 10 Central African Pygmy and neighboring Non-Pygmy populations, categorized as such based on cultural criteria rather than height. We show that climate, or forest density may not play a major role in the difference in adult stature between existing Pygmies and Non-Pygmies, without ruling out the hypothesis that such factors played an important evolutionary role in the past. Furthermore, we analyzed the relationship between stature and neutral genetic variation in a subset of 213 individuals and found that the Pygmy individuals' stature was significantly positively correlated with levels of genetic similarity with the Non-Pygmy gene-pool for both men and women. Overall, we show that a Pygmy individual exhibiting a high level of genetic admixture with the neighboring Non-Pygmies is likely to be taller. These results show for the first time that the major morphological difference in stature found between Central African Pygmy and Non-Pygmy populations is likely determined by genetic factors.

Schizophrenia genetics: where next?

The purpose of this invited review is to summarize the state of genetic research into the etiology of schizophrenia (SCZ) and to consider options for progress. The fundamental uncertainty in SCZ genetics has always been the nature of the beast, the underlying genetic architecture. If this were known, studies using the appropriate technologies and sample sizes could be designed with an excellent chance of producing high-confidence results. Until recently, few pertinent data were available, and the field necessarily relied on speculation. However, for the first time in the complex and frustrating history of inquiry into the genetics of SCZ, we now have empirical data about the genetic basis of SCZ that implicate specific loci and that can be used to plan the next steps forward.

Next generation genome-wide association tool: design and coverage of a high-throughput European-optimized SNP array

The success of genome-wide association studies has paralleled the development of efficient genotyping technologies. We describe the development of a next-generation microarray based on the new highly-efficient Affymetrix Axiom genotyping technology that we are using to genotype individuals of European ancestry from the Kaiser Permanente Research Program on Genes, Environment and Health (RPGEH). The array contains 674,517 SNPs, and provides excellent genome-wide as well as gene-based and candidate-SNP coverage. Coverage was calculated using an approach based on imputation and cross validation. Preliminary results for the first 80,301 saliva-derived DNA samples from the RPGEH demonstrate very high quality genotypes, with sample success rates above 94% and over 98% of successful samples having SNP call rates exceeding 98%. At steady state, we have produced 462 million genotypes per week for each Axiom system. The new array provides a valuable addition to the repertoire of tools for large scale genome-wide association studies.

Improving human forensics through advances in genetics, genomics and molecular biology

Forensic DNA profiling currently allows the identification of persons already known to investigating authorities. Recent advances have produced new types of genetic markers with the potential to overcome some important limitations of current DNA profiling methods. Moreover, other developments are enabling completely new kinds of forensically relevant information to be extracted from biological samples. These include new molecular approaches for finding individuals previously unknown to investigators, and new molecular methods to support links between forensic sample donors and criminal acts. Such advances in genetics, genomics and molecular biology are likely to improve human forensic case work in the near future.

Variants modulating the expression of a chromosome domain encompassing PLAG1 influence bovine stature

We report mapping of a quantitative trait locus (QTL) with a major effect on bovine stature to a ∼780-kb interval using a Hidden Markov Model-based approach that simultaneously exploits linkage and linkage disequilibrium. We re-sequenced the interval in six sires with known QTL genotype and identified 13 clustered candidate quantitative trait nucleotides (QTNs) out of >9,572 discovered variants. We eliminated five candidate QTNs by studying the phenotypic effect of a recombinant haplotype identified in a breed diversity panel. We show that the QTL influences fetal expression of seven of the nine genes mapping to the ∼780-kb interval. We further show that two of the eight candidate QTNs, mapping to the PLAG1-CHCHD7 intergenic region, influence bidirectional promoter strength and affect binding of nuclear factors. By performing expression QTL analyses, we identified a splice site variant in CHCHD7 and exploited this naturally occurring null allele to exclude CHCHD7 as single causative gene.

VIZ-GRAIL: visualizing functional connections across disease loci

MOTIVATION

As disease loci are rapidly discovered, an emerging challenge is to identify common pathways and biological functionality across loci. Such pathways might point to potential disease mechanisms. One strategy is to look for functionally related or interacting genes across genetic loci. Previously, we defined a statistical strategy, Gene Relationships Across Implicated Loci (GRAIL), to identify whether pair-wise gene relationships defined using PubMed text similarity are enriched across loci. Here, we have implemented VIZ-GRAIL, a software tool to display those relationships and to depict the underlying biological patterns.

RESULTS

Our tool can seamlessly interact with the GRAIL web site to obtain the results of analyses and create easy to read visual displays. To most clearly display results, VIZ-GRAIL arranges genes and genetic loci to minimize intersecting pair-wise gene connections. VIZ-GRAIL can be easily applied to other types of functional connections, beyond those from GRAIL. This method should help investigators appreciate the presence of potentially important common functions across loci.

AVAILABILITY

The GRAIL algorithm is implemented online at http://www.broadinstitute.org/mpg/grail/grail.php. VIZ-GRAIL source-code is at http://www.broadinstitute.org/mpg/grail/vizgrail.html.

Meta-analysis of heterogeneous data sources for genome-scale identification of risk genes in complex phenotypes

Meta-analyses of large-scale association studies typically proceed solely within one data type and do not exploit the potential complementarities in other sources of molecular evidence. Here, we present an approach to combine heterogeneous data from genome-wide association (GWA) studies, protein-protein interaction screens, disease similarity, linkage studies, and gene expression experiments into a multi-layered evidence network which is used to prioritize the entire protein-coding part of the genome identifying a shortlist of candidate genes. We report specifically results on bipolar disorder, a genetically complex disease where GWA studies have only been moderately successful. We validate one such candidate experimentally, YWHAH, by genotyping five variations in 640 patients and 1,377 controls. We found a significant allelic association for the rs1049583 polymorphism in YWHAH (adjusted P = 5.6e-3) with an odds ratio of 1.28 [1.12-1.48], which replicates a previous case-control study. In addition, we demonstrate our approach's general applicability by use of type 2 diabetes data sets. The method presented augments moderately powered GWA data, and represents a validated, flexible, and publicly available framework for identifying risk genes in highly polygenic diseases. The method is made available as a web service at www.cbs.dtu.dk/services/metaranker.

Novel method to estimate the phenotypic variation explained by genome-wide association studies reveals large fraction of the missing heritability

Genome-wide association studies (GWAS) are conducted with the promise to discover novel genetic variants associated with diverse traits. For most traits, associated markers individually explain just a modest fraction of the phenotypic variation, but their number can well be in the hundreds. We developed a maximum likelihood method that allows us to infer the distribution of associated variants even when many of them were missed by chance. Compared to previous approaches, the novelty of our method is that it (a) does not require having an independent (unbiased) estimate of the effect sizes; (b) makes use of the complete distribution of P-values while allowing for the false discovery rate; (c) takes into account allelic heterogeneity and the SNP pruning strategy. We applied our method to the latest GWAS meta-analysis results of the GIANT consortium. It revealed that while the explained variance of genome-wide (GW) significant SNPs is around 1% for waist-hip ratio (WHR), the observed P-values provide evidence for the existence of variants explaining 10% (CI=[8.5-11.5%]) of the phenotypic variance in total. Similarly, the total explained variance likely to exist for height is estimated to be 29% (CI=[28-30%]), three times higher than what the observed GW significant SNPs give rise to. This methodology also enables us to predict the benefit of future GWA studies that aim to reveal more associated genetic markers via increased sample size.

Deep sequencing of GDF5 reveals the absence of rare variants at this important osteoarthritis susceptibility locus

OBJECTIVE

The common single nucleotide polymorphism (SNP) rs143383 in the 5' untranslated region (5'UTR) of growth and differentiation factor 5 (GDF5) is strongly associated with osteoarthritis (OA) and influences GDF5 allelic expression in vitro and in the joint tissues of OA patients. This effect is modulated in cis by another common SNP, also located within the 5'UTR, whilst a common SNP in the 3'UTR influences allelic expression independent of rs143383. DNA variants can be common, rare or extremely rare/unique. To therefore enhance our understanding of the allelic architecture of this very important OA susceptibility locus we sequenced the gene for potentially functional and novel rare variants.

METHOD

Using the Sanger method we sequenced GDF5 in 992 OA patients and 944 controls, with DNA changes identified by sequencing software. We encompassed the protein-coding region of the two GDF5 exons, both untranslated regions and approximately 100 bp of the proximal promoter of the gene.

RESULTS

We detected 13 variants. Six were extremely rare with minor allele frequencies (MAFs) of ≤ 0.0006. One is in a predicted transcription factor binding site in the GDF5 promoter whilst two substitute conserved amino acids. The remaining seven variants were common and are previously known variants, with MAFs ranging from 0.025 to 0.39. There was a complete absence of variants with frequencies in-between the extremely rare (n=6) and the common (n=7).

CONCLUSIONS

This is the first report of the deep sequencing of an OA susceptibility locus. The absence of rare variants informs us that within the regions of the gene that we have sequenced GDF5 does not harbour any novel variants that are able to contribute, at a population level, to the OA association signal mediated by rs143383 nor does it harbour, at a population level, any novel variants that can influence OA susceptibility independent of rs143383.

Beyond missing heritability: prediction of complex traits

Despite rapid advances in genomic technology, our ability to account for phenotypic variation using genetic information remains limited for many traits. This has unfortunately resulted in limited application of genetic data towards preventive and personalized medicine, one of the primary impetuses of genome-wide association studies. Recently, a large proportion of the "missing heritability" for human height was statistically explained by modeling thousands of single nucleotide polymorphisms concurrently. However, it is currently unclear how gains in explained genetic variance will translate to the prediction of yet-to-be observed phenotypes. Using data from the Framingham Heart Study, we explore the genomic prediction of human height in training and validation samples while varying the statistical approach used, the number of SNPs included in the model, the validation scheme, and the number of subjects used to train the model. In our training datasets, we are able to explain a large proportion of the variation in height (h(2) up to 0.83, R(2) up to 0.96). However, the proportion of variance accounted for in validation samples is much smaller (ranging from 0.15 to 0.36 depending on the degree of familial information used in the training dataset). While such R(2) values vastly exceed what has been previously reported using a reduced number of pre-selected markers (<0.10), given the heritability of the trait (∼ 0.80), substantial room for improvement remains.

Dog models of naturally occurring cancer

Studies using dogs provide an ideal solution to the gap in animal models for natural disease and translational medicine. This is evidenced by approximately 400 inherited disorders being characterized in domesticated dogs, most of which are relevant to humans. There are several hundred isolated populations of dogs (breeds) and each has a vastly reduced genetic variation compared with humans; this simplifies disease mapping and pharmacogenomics. Dogs age five- to eight-fold faster than do humans, share environments with their owners, are usually kept until old age and receive a high level of health care. Farseeing investigators recognized this potential and, over the past decade, have developed the necessary tools and infrastructure to utilize this powerful model of human disease, including the sequencing of the dog genome in 2005. Here, we review the nascent convergence of genetic and translational canine models of spontaneous disease, focusing on cancer.

Epigenetics of asthma

Asthma is caused by both heritable and environmental factors. It has become clear that genetic studies do not adequately explain the heritability and susceptibility to asthma. The study of epigenetics, heritable non-coding changes to DNA may help to explain the heritable component of asthma. Additionally, epigenetic modifications can be influenced by the environment, including pollution and cigarette smoking, which are known asthma risk factors. These environmental trigger-induced epigenetic changes may be involved in skewing the immune system towards a Th2 phenotype following in utero exposure and thereby enhancing the risk of asthma. Alternatively, they may directly or indirectly modulate the immune and inflammatory processes in asthmatics via effects on treatment responsiveness. The study of epigenetics may therefore play an important role in our understanding and possible treatment of asthma and other allergic diseases. This article is part of a Special Issue entitled: Biochemistry of Asthma.

Population-based resequencing of experimentally evolved populations reveals the genetic basis of body size variation in Drosophila melanogaster

Body size is a classic quantitative trait with evolutionarily significant variation within many species. Locating the alleles responsible for this variation would help understand the maintenance of variation in body size in particular, as well as quantitative traits in general. However, successful genome-wide association of genotype and phenotype may require very large sample sizes if alleles have low population frequencies or modest effects. As a complementary approach, we propose that population-based resequencing of experimentally evolved populations allows for considerable power to map functional variation. Here, we use this technique to investigate the genetic basis of natural variation in body size in Drosophila melanogaster. Significant differentiation of hundreds of loci in replicate selection populations supports the hypothesis that the genetic basis of body size variation is very polygenic in D. melanogaster. Significantly differentiated variants are limited to single genes at some loci, allowing precise hypotheses to be formed regarding causal polymorphisms, while other significant regions are large and contain many genes. By using significantly associated polymorphisms as a priori candidates in follow-up studies, these data are expected to provide considerable power to determine the genetic basis of natural variation in body size.

Understanding the causes and consequences of natural genetic variation is crucial to the characterization of biological evolution. Moreover, natural genetic variation is comprised of millions of perturbations, which are partially randomized across genotypes such that a small number of individuals can be used to combinatorially analyze a large number of differences, facilitating mechanistic understanding of biological systems. Here we demonstrate a powerful technique to parse genomic variation using artificial selection. By selecting replicate populations of Drosophila flies to become bigger and smaller, and then determining the evolutionary response at the genomic level, we have mapped hundreds of genes that respond to selection on body size. As our approach is powerful and cost-effective compared to existing approaches, we expect it to be a major component of diverse future efforts.

Genomic inflation factors under polygenic inheritance

Population structure, including population stratification and cryptic relatedness, can cause spurious associations in genome-wide association studies (GWAS). Usually, the scaled median or mean test statistic for association calculated from multiple single-nucleotide-polymorphisms across the genome is used to assess such effects, and 'genomic control' can be applied subsequently to adjust test statistics at individual loci by a genomic inflation factor. Published GWAS have clearly shown that there are many loci underlying genetic variation for a wide range of complex diseases and traits, implying that a substantial proportion of the genome should show inflation of the test statistic. Here, we show by theory, simulation and analysis of data that in the absence of population structure and other technical artefacts, but in the presence of polygenic inheritance, substantial genomic inflation is expected. Its magnitude depends on sample size, heritability, linkage disequilibrium structure and the number of causal variants. Our predictions are consistent with empirical observations on height in independent samples of ~4000 and ~133,000 individuals.

From expression QTLs to personalized transcriptomics

Approaches that combine expression quantitative trait loci (eQTLs) and genome-wide association (GWA) studies are offering new functional information about the aetiology of complex human traits and diseases. Improved study designs--which take into account technological advances in resolving the transcriptome, cell history and state, population of origin and diverse endophenotypes--are providing insights into the architecture of disease and the landscape of gene regulation in humans. Furthermore, these advances are helping to establish links between cellular effects and organismal traits.

Initial impact of the sequencing of the human genome

The sequence of the human genome has dramatically accelerated biomedical research. Here I explore its impact, in the decade since its publication, on our understanding of the biological functions encoded in the genome, on the biological basis of inherited diseases and cancer, and on the evolution and history of the human species. I also discuss the road ahead in fulfilling the promise of genomics for medicine.

Meta-analysis of genome-wide association studies in celiac disease and rheumatoid arthritis identifies fourteen non-HLA shared loci

Epidemiology and candidate gene studies indicate a shared genetic basis for celiac disease (CD) and rheumatoid arthritis (RA), but the extent of this sharing has not been systematically explored. Previous studies demonstrate that 6 of the established non-HLA CD and RA risk loci (out of 26 loci for each disease) are shared between both diseases. We hypothesized that there are additional shared risk alleles and that combining genome-wide association study (GWAS) data from each disease would increase power to identify these shared risk alleles. We performed a meta-analysis of two published GWAS on CD (4,533 cases and 10,750 controls) and RA (5,539 cases and 17,231 controls). After genotyping the top associated SNPs in 2,169 CD cases and 2,255 controls, and 2,845 RA cases and 4,944 controls, 8 additional SNPs demonstrated P<5×10⁻⁸ in a combined analysis of all 50,266 samples, including four SNPs that have not been previously confirmed in either disease: rs10892279 near the DDX6 gene (P_combined = 1.2×10⁻¹²), rs864537 near CD247 (P_combined = 2.2×10⁻¹¹), rs2298428 near UBE2L3 (P_combined = 2.5×10⁻¹⁰), and rs11203203 near UBASH3A (P_combined = 1.1×10⁻⁸). We also confirmed that 4 gene loci previously established in either CD or RA are associated with the other autoimmune disease at combined P<5×10⁻⁸ (SH2B3, 8q24, STAT4, and TRAF1-C5). From the 14 shared gene loci, 7 SNPs showed a genome-wide significant effect on expression of one or more transcripts in the linkage disequilibrium (LD) block around the SNP. These associations implicate antigen presentation and T-cell activation as a shared mechanism of disease pathogenesis and underscore the utility of cross-disease meta-analysis for identification of genetic risk factors with pleiotropic effects between two clinically distinct diseases.

Celiac disease (CD) and rheumatoid arthritis (RA) are two autoimmune diseases characterized by distinct clinical features but increased co-occurrence in families and individuals. Genome-wide association studies (GWAS) performed in CD and RA have identified the HLA region and 26 non-HLA genetic risk loci in each disease. Of the 26 CD and 26 RA risk loci, previous studies have shown that six are shared between the two diseases. In this study we aimed to identify additional shared risk alleles and, in doing so, gain more insight into shared disease pathogenesis. We first empirically investigated the distribution of putative risk alleles from GWAS across both diseases (after removing known risk loci for both diseases). We found that CD risk alleles are non-randomly distributed in the RA GWAS (and vice versa), indicating that CD risk alleles have an increased prior probability of being associated with RA (and vice versa). Next, we performed a GWAS meta-analysis to search for shared risk alleles by combing the RA and CD GWAS, performing both directional and opposite allelic effect analyses, followed by replication testing in independent case-control datasets in both diseases. In addition to the already established six non-HLA shared risk loci, we observed statistically robust associations at eight SNPs, thereby increasing the number of shared non-HLA risk loci to fourteen. Finally, we used gene expression studies and pathway analysis tools to identify the plausible candidate genes in the fourteen associated loci. We observed remarkable overrepresentation of T-cell signaling molecules among the shared genes.

The domestic dog: man's best friend in the genomic era

The domestic dog genome--shaped by domestication, adaptation to human-dominated environments and artificial selection--encodes tremendous phenotypic diversity. Recent developments have improved our understanding of the genetics underlying this diversity, unleashing the dog as an important model organism for complex-trait analysis.

Promise and pitfalls of the Immunochip

Genomewide association studies (GWAS) have proven a powerful hypothesis-free method to identify common disease-associated variants. Even quite large GWAS, however, have only at best identified moderate proportions of the genetic variants contributing to disease heritability. To provide cost-effective genotyping of common and rare variants to map the remaining heritability and to fine-map established loci, the Immunochip Consortium has developed a 200,000 SNP chip that has been produced in very large numbers for a fraction of the cost of GWAS chips. This chip provides a powerful tool for immunogenetics gene mapping.

Progress and promise of genome-wide association studies for human complex trait genetics

Enormous progress in mapping complex traits in humans has been made in the last 5 yr. There has been early success for prevalent diseases with complex phenotypes. These studies have demonstrated clearly that, while complex traits differ in their underlying genetic architectures, for many common disorders the predominant pattern is that of many loci, individually with small effects on phenotype. For some traits, loci of large effect have been identified. For almost all complex traits studied in humans, the sum of the identified genetic effects comprises only a portion, generally less than half, of the estimated trait heritability. A variety of hypotheses have been proposed to explain why this might be the case, including untested rare variants, and gene-gene and gene-environment interaction. Effort is currently being directed toward implementation of novel analytic approaches and testing rare variants for association with complex traits using imputed variants from the publicly available 1000 Genomes Project resequencing data and from direct resequencing of clinical samples. Through integration with annotations and functional genomic data as well as by in vitro and in vivo experimentation, mapping studies continue to characterize functional variants associated with complex traits and address fundamental issues such as epistasis and pleiotropy. This review focuses primarily on the ways in which genome-wide association studies (GWASs) have revolutionized the field of human quantitative genetics.

Genome-wide association studies of atrial fibrillation: past, present, and future

Genome-wide association studies (GWAS) for atrial fibrillation (AF) have identified three distinct genetic loci on chromosomes 1q21, 4q25, and 16q22 that are associated with the arrhythmia. Susceptibility loci also have been identified by GWAS for PR interval duration, a quantitative phenotype related to AF. In this review article, we have sought to summarize the latest findings for population-based genetic studies of AF, to highlight ongoing functional studies, and to explore the future directions of genetic research on AF.

Meta-analysis of Dense Genecentric Association Studies Reveals Common and Uncommon Variants Associated with Height

Height is a classic complex trait with common variants in a growing list of genes known to contribute to the phenotype. Using a genecentric genotyping array targeted toward cardiovascular-related loci, comprising 49,320 SNPs across approximately 2000 loci, we evaluated the association of common and uncommon SNPs with adult height in 114,223 individuals from 47 studies and six ethnicities. A total of 64 loci contained a SNP associated with height at array-wide significance (p < 2.4 × 10(-6)), with 42 loci surpassing the conventional genome-wide significance threshold (p < 5 × 10(-8)). Common variants with minor allele frequencies greater than 5% were observed to be associated with height in 37 previously reported loci. In individuals of European ancestry, uncommon SNPs in IL11 and SMAD3, which would not be genotyped with the use of standard genome-wide genotyping arrays, were strongly associated with height (p < 3 × 10(-11)). Conditional analysis within associated regions revealed five additional variants associated with height independent of lead SNPs within the locus, suggesting allelic heterogeneity. Although underpowered to replicate findings from individuals of European ancestry, the direction of effect of associated variants was largely consistent in African American, South Asian, and Hispanic populations. Overall, we show that dense coverage of genes for uncommon SNPs, coupled with large-scale meta-analysis, can successfully identify additional variants associated with a common complex trait.

The genetics of breast cancer: risk factors for disease

The genetic factors known to be involved in breast cancer risk comprise about 30 genes. These include the high-penetrance early-onset breast cancer genes, BRCA1 and BRCA2, a number of rare cancer syndrome genes, and rare genes with more moderate penetrance. A larger group of common variants has more recently been identified through genome-wide association studies. Quite a number of these common variants are mapped to genomic regions without being firmly associated with specific genes. It is thought that most of these variants have gene regulatory functions, but their precise roles in disease susceptibility are not well understood. Common variants account for only a small percentage of the risk of disease because they have low penetrance. Collectively, the breast cancer genes identified to date contribute only ~30% of the familial risk. Therefore, there is much interest in accounting for the missing heritability, and possible sources include loss of information through ignoring phenotype heterogeneity (disease subtypes have genetic differences), gene–gene and gene–environment interaction, and rarer forms of variation. Identification of these rarer variations in coding regions is now feasible and cost effective through exome sequencing, which has already identified high-penetrance variants for some rare diseases. Targeting more ‘extreme’ breast cancer phenotypes, particularly cases with early-onset disease, a strong family history (not accounted for by BRCA mutations), and with specific tumor subtypes, provides a route to progress using next-generation sequencing methods.

GCTA: a tool for genome-wide complex trait analysis

For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the "missing heritability" problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.

Molecular Genetics and Economics

The costs of comprehensively genotyping human subjects have fallen to the point where major funding bodies, even in the social sciences, are beginning to incorporate genetic and biological markers into major social surveys. How, if at all, should economists use and combine molecular genetic and economic data from these surveys? What challenges arise when analyzing genetically informative data? To illustrate, we present results from a “genome-wide association study” of educational attainment. We use a sample of 7,500 individuals from the Framingham Heart Study; our dataset contains over 360,000 genetic markers per person. We get some initially promising results linking genetic markers to educational attainment, but these fail to replicate in a second large sample of 9,500 people from the Rotterdam Study. Unfortunately such failure is typical in molecular genetic studies of this type, so the example is also cautionary. We discuss a number of methodological challenges that face researchers who use molecular genetics to reliably identify genetic associates of economic traits. Our overall assessment is cautiously optimistic: this new data source has potential in economics. But researchers and consumers of the genoeconomic literature should be wary of the pitfalls, most notably the difficulty of doing reliable inference when faced with multiple hypothesis problems on a scale never before encountered in social science.

Planning a genome-wide association study: points to consider

It is well established that genetic diversity combined with specific environmental exposures contributes to disease susceptibility. However, it has turned out to be challenging to isolate the genes underlying the genetic component conferring susceptibility to most complex disorders. Traditional candidate gene and family-based linkage studies, which dominated gene discovery efforts for many years, were largely unsuccessful in unraveling the genetics of these traits due to the relatively limited information gained. Within the last 5 years, new advances in high-throughput methods have allowed for large volumes of single nucleotide polymorphisms (SNPs) throughout the genome to be genotyped across large and comprehensively phenotyped patient cohorts. Unlike previous approaches, these 'genome-wide association studies' (GWAS) have extensively delivered on the promise of uncovering genetic determinants of complex diseases, with hundreds of novel disease-associated variants being largely replicated by independent groups. This review provides an overview of these recent breakthroughs in the context of the pitfalls and challenges related to designing and carrying out a successful GWAS.

Analysing biological pathways in genome-wide association studies

Genome-wide association (GWA) studies have typically focused on the analysis of single markers, which often lacks the power to uncover the relatively small effect sizes conferred by most genetic variants. Recently, pathway-based approaches have been developed, which use prior biological knowledge on gene function to facilitate more powerful analysis of GWA study data sets. These approaches typically examine whether a group of related genes in the same functional pathway are jointly associated with a trait of interest. Here we review the development of pathway-based approaches for GWA studies, discuss their practical use and caveats, and suggest that pathway-based approaches may also be useful for future GWA studies with sequencing data.

Characterizing allelic association in the genome era

Whole genome data are allowing the estimation of population genetic parameters with an accuracy not imagined 50 years ago. Variation in these parameters along the genome is being found empirically where once only approximate theoretical values were available. Along with increased information, however, has come the issue of multiple testing and the realization that high values of the coefficients of variation of quantities such as relatedness measures may make it difficult to draw inferences. This review concentrates on measures of allelic association within and between individuals and within and between populations.

A genome-wide association study of self-rated health

Self-rated health questions have been proven to be a highly reliable and valid measure of overall health as measured by other indicators in many population groups. It also has been shown to be a very good predictor of mortality, chronic or severe diseases, and the need for services, and is positively correlated with clinical assessments. Genetic factors have been estimated to account for 25-64% of the variance in the liability of self-rated health. The aim of the present study was to identify Single Nucleotide Polymorphisms (SNPs) underlying the heritability of self-rated health by conducting a genome-wide association analysis in a large sample of 6,706 Australian individuals aged 18-92. No genome wide significant SNPs associated with self-rated health could be identified, indicating that self-rated health may be influenced by a large number of SNPs with very small effect size. A very large sample will be needed to identify these SNPs.