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

Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture

Improvements in technology have reduced the cost of DNA sequencing to the point that the limiting factor for many experiments is the time and reagent cost of sample preparation. We present an approach in which 192 sequencing libraries can be produced in a single day of technician time at a cost of about $15 per sample. These libraries are effective not only for low-pass whole-genome sequencing, but also for simultaneously enriching them in pools of approximately 100 individually barcoded samples for a subset of the genome without substantial loss in efficiency of target capture. We illustrate the power and effectiveness of this approach on about 2000 samples from a prostate cancer study.

Rare and common variants: twenty arguments

Genome-wide association studies have greatly improved our understanding of the genetic basis of disease risk. The fact that they tend not to identify more than a fraction of the specific causal loci has led to divergence of opinion over whether most of the variance is hidden as numerous rare variants of large effect or as common variants of very small effect. Here I review 20 arguments for and against each of these models of the genetic basis of complex traits and conclude that both classes of effect can be readily reconciled.

Five years of GWAS discovery

The past five years have seen many scientific and biological discoveries made through the experimental design of genome-wide association studies (GWASs). These studies were aimed at detecting variants at genomic loci that are associated with complex traits in the population and, in particular, at detecting associations between common single-nucleotide polymorphisms (SNPs) and common diseases such as heart disease, diabetes, auto-immune diseases, and psychiatric disorders. We start by giving a number of quotes from scientists and journalists about perceived problems with GWASs. We will then briefly give the history of GWASs and focus on the discoveries made through this experimental design, what those discoveries tell us and do not tell us about the genetics and biology of complex traits, and what immediate utility has come out of these studies. Rather than giving an exhaustive review of all reported findings for all diseases and other complex traits, we focus on the results for auto-immune diseases and metabolic diseases. We return to the perceived failure or disappointment about GWASs in the concluding section.

A multi-cohort study of polymorphisms in the GH/IGF axis and physical capability: the HALCyon programme

BACKGROUND

Low muscle mass and function have been associated with poorer indicators of physical capability in older people, which are in-turn associated with increased mortality rates. The growth hormone/insulin-like growth factor (GH/IGF) axis is involved in muscle function and genetic variants in genes in the axis may influence measures of physical capability.

METHODS

As part of the Healthy Ageing across the Life Course (HALCyon) programme, men and women from seven UK cohorts aged between 52 and 90 years old were genotyped for six polymorphisms: rs35767 (IGF1), rs7127900 (IGF2), rs2854744 (IGFBP3), rs2943641 (IRS1), rs2665802 (GH1) and the exon-3 deletion of GHR. The polymorphisms have previously been robustly associated with age-related traits or are potentially functional. Meta-analysis was used to pool within-study genotypic effects of the associations between the polymorphisms and four measures of physical capability: grip strength, timed walk or get up and go, chair rises and standing balance.

RESULTS

Few important associations were observed among the several tests. We found evidence that rs2665802 in GH1 was associated with inability to balance for 5 s (pooled odds ratio per minor allele = 0.90, 95% CI: 0.82-0.98, p-value = 0.01, n = 10,748), after adjusting for age and sex. We found no evidence for other associations between the polymorphisms and physical capability traits.

CONCLUSION

Our findings do not provide evidence for a substantial influence of these common polymorphisms in the GH/IGF axis on objectively measured physical capability levels in older adults.

Cohort Profile: TwinsUK and healthy ageing twin study

The UK's largest registry of adult twins, or TwinsUK Registry, started in 1992 and encompasses about 12000 volunteer twins from all over the United Kingdom. More than 70% of the registered twins have filled at least one detailed health questionnaire and about half of them undergone a baseline comprehensive assessment and two follow-up clinical evaluations. The most recent follow-up visit, known as Healthy Ageing Twin Study (HATS), involved 3125 female twins aged >40 years with at least one previous clinical assessment to enable inspection of longitudinal changes in ageing traits and their genetic and environmental components. The study benefits from several state-of-the-art OMICs studies including genome-wide association, next-generation genome and transcriptome sequencing, and epigenetic and metabolomic profiles. This makes our cohort as one of the most deeply phenotyped and genotyped in the world. Several collaborative projects in the field of epidemiology of complex disorders are ongoing in our cohort and interested researchers are encouraged to get in contact for future collaborations.

Fast identification of biological pathways associated with a quantitative trait using group lasso with overlaps

Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within biological pathways, the incorporation of prior pathways information into a statistical model is expected to increase the power to detect true associations in a genetic association study. Most existing pathways-based methods rely on marginal SNP statistics and do not fully exploit the dependence patterns among SNPs within pathways.We use a sparse regression model, with SNPs grouped into pathways, to identify causal pathways associated with a quantitative trait. Notable features of our "pathways group lasso with adaptive weights" (P-GLAW) algorithm include the incorporation of all pathways in a single regression model, an adaptive pathway weighting procedure that accounts for factors biasing pathway selection, and the use of a bootstrap sampling procedure for the ranking of important pathways. P-GLAW takes account of the presence of overlapping pathways and uses a novel combination of techniques to optimise model estimation, making it fast to run, even on whole genome datasets.In a comparison study with an alternative pathways method based on univariate SNP statistics, our method demonstrates high sensitivity and specificity for the detection of important pathways, showing the greatest relative gains in performance where marginal SNP effect sizes are small.

Quantile-specific penetrance of genes affecting lipoproteins, adiposity and height

Quantile-dependent penetrance is proposed to occur when the phenotypic expression of a SNP depends upon the population percentile of the phenotype. To illustrate the phenomenon, quantiles of height, body mass index (BMI), and plasma lipids and lipoproteins were compared to genetic risk scores (GRS) derived from single nucleotide polymorphisms (SNP)s having established genome-wide significance: 180 SNPs for height, 32 for BMI, 37 for low-density lipoprotein (LDL)-cholesterol, 47 for high-density lipoprotein (HDL)-cholesterol, 52 for total cholesterol, and 31 for triglycerides in 1930 subjects. Both phenotypes and GRSs were adjusted for sex, age, study, and smoking status. Quantile regression showed that the slope of the genotype-phenotype relationships increased with the percentile of BMI (P = 0.002), LDL-cholesterol (P = 3×10⁻⁸), HDL-cholesterol (P = 5×10⁻⁶), total cholesterol (P = 2.5×10⁻⁶), and triglyceride distribution (P = 7.5×10⁻⁶), but not height (P = 0.09). Compared to a GRS's phenotypic effect at the 10^th population percentile, its effect at the 90^th percentile was 4.2-fold greater for BMI, 4.9-fold greater for LDL-cholesterol, 1.9-fold greater for HDL-cholesterol, 3.1-fold greater for total cholesterol, and 3.3-fold greater for triglycerides. Moreover, the effect of the rs1558902 (FTO) risk allele was 6.7-fold greater at the 90^th than the 10^th percentile of the BMI distribution, and that of the rs3764261 (CETP) risk allele was 2.4-fold greater at the 90^th than the 10^th percentile of the HDL-cholesterol distribution. Conceptually, it maybe useful to distinguish environmental effects on the phenotype that in turn alters a gene's phenotypic expression (quantile-dependent penetrance) from environmental effects affecting the gene's phenotypic expression directly (gene-environment interaction).

Mixed Modeling with Whole Genome Data

Objective. We consider the need for a modeling framework for related individuals and various sources of variations. The relationships could either be among relatives in families or among unrelated individuals in a general population with cryptic relatedness; both could be refined or derived with whole genome data. As with variations they can include oliogogenes, polygenes, single nucleotide polymorphism (SNP), and covariates.Methods. We describe mixed models as a coherent theoretical framework to accommodate correlations for various types of outcomes in relation to many sources of variations. The framework also extends to consortium meta-analysis involving both population-based and family-based studies.Results. Through examples we show that the framework can be furnished with general statistical packages whose great advantage lies in simplicity and exibility to study both genetic and environmental effects. Areas which require further work are also indicated.Conclusion. Mixed models will play an important role in practical analysis of data on both families and unrelated individuals when whole genome information is available.

Uses of genomics in livestock agriculture

World demand for livestock products is likely to increase in coming decades but the cost of production could escalate faster than the price due to competition for land, water, grain and fertiliser and the effects of climate change and its mitigation. To remain competitive for these resources, livestock agriculture has to dramatically increase in efficiency of production. Genetic gain is one mechanism to achieve increased efficiency and there is the opportunity to utilise the scientific advances in genomics. Three ways in which genomics can be used are in additive genetic improvement, exploitation of non-additive genetic variance and management which exploits genotype by environment interactions to optimise management. Genomic selection is already being widely implemented in dairy cattle and beef cattle and sheep will follow in the future once the accuracy of genomic selection is high enough. The accuracy of equations that predict breeding value from DNA genotypes can be increased by increasing the size of the reference population from which the equations are estimated, increasing the density of markers, using genome sequences instead of markers, using more appropriate statistical procedures and incorporating biological information into the prediction. In the long term, genomic selection combined with reproductive technology that reduces the minimum age at breeding will greatly increase the rate of genetic gain. This will allow long-term increases in biological efficiency and short-term tailoring of livestock to meet the demands of particular markets and opportunities.

Behavioral genetics of affective and anxiety disorders

As shown by clinical genetic studies, affective and anxiety disorders are complex genetic disorders with genetic and environmental factors interactively determining their respective pathomechanism. Advances in molecular genetic techniques including linkage studies, association studies, and genome-wide association studies allow for the detailed dissection of the genetic influence on the development of these disorders. Besides the molecular genetic investigation of categorical entities according to standardized diagnostic criteria, intermediate phenotypes comprising neurobiological or neuropsychological traits (e.g., neuronal correlates of emotional processing) that are linked to the disease of interest and that are heritable, have been proposed to be closer to the underlying genotype than the overall disease phenotype. These intermediate phenotypes are dimensional and more precisely defined than the categorical disease phenotype, and therefore have attracted much interest in the genetic investigation of affective and anxiety disorders. Given the complex genetic nature of affective and anxiety disorders with an interaction of multiple risk genes and environmental influences, the interplay of genetic factors with environmental factors is investigated by means of gene-environment interaction (GxE) studies. Pharmacogenetic studies aid in the dissection of the genetically influenced heterogeneity of psychotropic drug response and may contribute to the development of a more individualized treatment of affective and anxiety disorders. Finally, there is some evidence for genetic factors potentially shared between affective and anxiety disorders pointing to a possible overlapping phenotype between anxiety disorders and depression.

Next steps in cardiovascular disease genomic research--sequencing, epigenetics, and transcriptomics

BACKGROUND

Genomic research in cardiovascular disease (CVD) has progressed rapidly over the last 5 years. In most cases, however, these groundbreaking observations have not yet been accompanied by clinically applicable tools for risk prediction, diagnosis, or therapeutic interventions.

CONTENT

We reviewed the scientific literature published in English for novel methods and promising genomic targets that would permit large-scale screening and follow-up of recent genomic findings for CVD. We anticipate that advances in 3 key areas will be critical for the success of these projects. First, exome-centered and whole-genome next-generation sequencing will identify rare and novel genetic variants associated with CVD and its risk factors. Improvements in methods will also greatly advance the field of epigenetics and gene expression in humans. Second, research is increasingly acknowledging that static DNA sequence variation explains only a fraction of the inherited phenotype. Therefore, we expect that multiple epigenetic and gene expression signatures will be related to CVD in experimental and clinical settings. Leveraging existing large-scale consortia and clinical biobanks in combination with electronic health records holds promise for integrating epidemiological and clinical genomics data. Finally, a systems biology approach will be needed to integrate the accumulated multidimensional data.

SUMMARY

Novel methods in sequencing, epigenetics, and transcriptomics, plus unprecedented large-scale cooperative efforts, promise to generate insights into the complexity of CVD. The rapid accumulation and integration of knowledge will shed light on a considerable proportion of the missing heritability for CVD.

Opportunities and challenges for genome sequencing in the clinic

Human genome sequencing technology is developing rapidly. These developments are providing exciting opportunities for genetic mapping of human traits, ranging from accelerated discovery of mutations underlying relatively simple Mendelian disorders to more genetically complex human diseases. This chapter outlines the development of whole-genome sequencing in a historical context of genetic mapping and explores the impact that sequencing is having on gene discovery study design. Using the example of epilepsy, the authors outline the opportunities and barriers for the translation of genetic predictors from discovery to the clinic. Finally, the authors discuss the practical challenges of actual implementation of whole-genome sequencing to the clinic.

Genetics and epigenetics of osteoarthritis

Osteoarthritis (OA) is a common age-related disease that affects the tissues of the synovial joint, leading to loss of function and pain. It impacts on both patient morbidity and mortality. It is a complex, polygenic disease that lacks any large-effect susceptibility loci. Instead, OA susceptibility alleles individually contribute only modestly to the overall disease risk, making their identification challenging. Despite this, breakthroughs have occurred with compelling associations so far reported to polymorphisms within the genes GDF5 and MCF2L and to the genomic region 7q22. The latter two have emerged from genome-wide association scans, which are likely to yield more hits in the near future. As for many complex diseases, it is now apparent that epigenetic effects are also important mediators of disease biology, with DNA methylation, histone modifications and non-coding RNAs all having a role. At present, much of the epigenetic focus has been on cartilage, the tissue at the center of the OA disease process. If we are to get close to a qualitative and quantitative understanding of the impact of epigenetics on OA, then in future the other tissues of the joint will also need to be investigated. One of the more exciting insights to have emerged recently is the fact that epigenetic effects can impact on OA genetic effects and this may be a particularly fruitful avenue for integrating both as we move toward a clearer understanding of the pathophysiology of this intriguing disease.

Extent of height variability explained by known height-associated genetic variants in an isolated population of the Adriatic coast of Croatia

BACKGROUND

Human height is a classical example of a polygenic quantitative trait. Recent large-scale genome-wide association studies (GWAS) have identified more than 200 height-associated loci, though these variants explain only 2∼10% of overall variability of normal height. The objective of this study was to investigate the variance explained by these loci in a relatively isolated population of European descent with limited admixture and homogeneous genetic background from the Adriatic coast of Croatia.

METHODOLOGY/PRINCIPAL FINDINGS

In a sample of 1304 individuals from the island population of Hvar, Croatia, we performed genome-wide SNP typing and assessed the variance explained by genetic scores constructed from different panels of height-associated SNPs extracted from five published studies. The combined information of the 180 SNPs reported by Lango Allen el al. explained 7.94% of phenotypic variation in our sample. Genetic scores based on 20~50 SNPs reported by the remaining individual GWA studies explained 3~5% of height variance. These percentages of variance explained were within ranges comparable to the original studies and heterogeneity tests did not detect significant differences in effect size estimates between our study and the original reports, if the estimates were obtained from populations of European descent.

CONCLUSIONS/SIGNIFICANCE

We have evaluated the portability of height-associated loci and the overall fitting of estimated effect sizes reported in large cohorts to an isolated population. We found proportions of explained height variability were comparable to multiple reference GWAS in cohorts of European descent. These results indicate similar genetic architecture and comparable effect sizes of height loci among populations of European descent.

Genome-wide association studies: Where we are heading?

We have witnessed tremendous success in genome-wide association studies (GWAS) in recent years. Since the identification of variants in the complement factor H gene on the risk of age-related macular degeneration, GWAS have become ubiquitous in genetic studies and have led to the identification of genetic variants that are associated with a variety of complex human diseases and traits. These discoveries have changed our understanding of the biological architecture of common, complex diseases and have also provided new hypotheses to test. New tools, such as next-generation sequencing, will be an important part of the future of genetics research; however, GWAS studies will continue to play an important role in disease gene discovery. Many traits have yet to be explored by GWAS, especially in minority populations, and large collaborative studies are currently being conducted to maximize the return from existing GWAS data. In addition, GWAS technology continues to improve, increasing genomic coverage for major global populations and decreasing the cost of experiments. Although much of the variance attributable to genetic factors for many important traits is still unexplained, GWAS technology has been instrumental in mapping over a thousand genes to hundreds of traits. More discoveries are made each month and the scale, quality and quantity of current work has a steady trend upward. We briefly review the current key trends in GWAS, which can be summarized with three goals: increase power, increase collaborations and increase populations.

Comparison of strategies to detect epistasis from eQTL data

Genome-wide association studies have been instrumental in identifying genetic variants associated with complex traits such as human disease or gene expression phenotypes. It has been proposed that extending existing analysis methods by considering interactions between pairs of loci may uncover additional genetic effects. However, the large number of possible two-marker tests presents significant computational and statistical challenges. Although several strategies to detect epistasis effects have been proposed and tested for specific phenotypes, so far there has been no systematic attempt to compare their performance using real data. We made use of thousands of gene expression traits from linkage and eQTL studies, to compare the performance of different strategies. We found that using information from marginal associations between markers and phenotypes to detect epistatic effects yielded a lower false discovery rate (FDR) than a strategy solely using biological annotation in yeast, whereas results from human data were inconclusive. For future studies whose aim is to discover epistatic effects, we recommend incorporating information about marginal associations between SNPs and phenotypes instead of relying solely on biological annotation. Improved methods to discover epistatic effects will result in a more complete understanding of complex genetic effects.

Genetics of osteoarthritis

PURPOSE OF REVIEW

A number of reasonably powered osteoarthritis genome-wide association scans are now in the final phases of their analysis, leaving us all with baited breath. This review highlights some of the osteoarthritis signals and subsequent insights that have emerged from the candidate studies and smaller scale scans that have preceded these more powered studies, and which could therefore be considered as appetizers to the hopeful treats to follow.

RECENT FINDINGS

If one applies the strict criteria of genome-wide significance thresholds, only two current signals pass muster: GDF5 and 7p22. If one relaxes slightly, other signals emerge, such as DIO2, SMAD3 and ASPN. After these, however, we enter the realm where faith takes precedence.

SUMMARY

The search for osteoarthritis susceptibility loci has not been as successful as many had anticipated. This reflects many factors, including the heterogeneous nature of the disease, the tendency to use less severe phenotypes in genetic searches and the reliance on underpowered studies. We do, however, have some successes and in the very near future others will emerge from the more powered scans. Hopefully, combining the current with the new will help our attempts to understand the cause of this complex, common arthritis.

Epidemiology, epigenetics and the 'Gloomy Prospect': embracing randomness in population health research and practice

Epidemiologists aim to identify modifiable causes of disease, this often being a prerequisite for the application of epidemiological findings in public health programmes, health service planning and clinical medicine. Despite successes in identifying causes, it is often claimed that there are missing additional causes for even reasonably well-understood conditions such as lung cancer and coronary heart disease. Several lines of evidence suggest that largely chance events, from the biographical down to the sub-cellular, contribute an important stochastic element to disease risk that is not epidemiologically tractable at the individual level. Epigenetic influences provide a fashionable contemporary explanation for such seemingly random processes. Chance events-such as a particular lifelong smoker living unharmed to 100 years-are averaged out at the group level. As a consequence population-level differences (for example, secular trends or differences between administrative areas) can be entirely explicable by causal factors that appear to account for only a small proportion of individual-level risk. In public health terms, a modifiable cause of the large majority of cases of a disease may have been identified, with a wild goose chase continuing in an attempt to discipline the random nature of the world with respect to which particular individuals will succumb. The quest for personalized medicine is a contemporary manifestation of this dream. An evolutionary explanation of why randomness exists in the development of organisms has long been articulated, in terms of offering a survival advantage in changing environments. Further, the basic notion that what is near-random at one level may be almost entirely predictable at a higher level is an emergent property of many systems, from particle physics to the social sciences. These considerations suggest that epidemiological approaches will remain fruitful as we enter the decade of the epigenome.

Genome-wide association of copy-number variation reveals an association between short stature and the presence of low-frequency genomic deletions

Height is a model polygenic trait that is highly heritable. Genome-wide association studies have identified hundreds of single-nucleotide polymorphisms associated with stature, but the role of structural variation in determining height is largely unknown. We performed a genome-wide association study of copy-number variation and stature in a clinical cohort of children who had undergone comparative genomic hybridization (CGH) microarray analysis for clinical indications. We found that subjects with short stature had a greater global burden of copy-number variants (CNVs) and a greater average CNV length than did controls (p < 0.002). These associations were present for lower-frequency (<5%) and rare (<1%) deletions, but there were no significant associations seen for duplications. Known gene-deletion syndromes did not account for our findings, and we saw no significant associations with tall stature. We then extended our findings into a population-based cohort and found that, in agreement with the clinical cohort study, an increased burden of lower-frequency deletions was associated with shorter stature (p = 0.015). Our results suggest that in individuals undergoing copy-number analysis for clinical indications, short stature increases the odds that a low-frequency deletion will be found. Additionally, copy-number variation might contribute to genetic variation in stature in the general population.

An assessment of the individual and collective effects of variants on height using twins and a developmentally informative study design

In a sample of 3,187 twins and 3,294 of their parents, we sought to investigate association of both individual variants and a genotype-based height score involving 176 of the 180 common genetic variants with adult height identified recently by the GIANT consortium. First, longitudinal observations on height spanning pre-adolescence through adulthood in the twin sample allowed us to investigate the separate effects of the previously identified SNPs on pre-pubertal height and pubertal growth spurt. We show that the effect of SNPs identified by the GIANT consortium is primarily on prepubertal height. Only one SNP, rs7759938 in LIN28B, approached a significant association with pubertal growth. Second, we show how using the twin data to control statistically for environmental variance can provide insight into the ultimate magnitude of SNP effects and consequently the genetic architecture of a phenotype. Specifically, we computed a genetic score by weighting SNPs according to their effects as assessed via meta-analysis. This weighted score accounted for 9.2% of the phenotypic variance in height, but 14.3% of the corresponding genetic variance. Longitudinal samples will be needed to understand the developmental context of common genetic variants identified through GWAS, while genetically informative designs will be helpful in accurately characterizing the extent to which these variants account for genetic, and not just phenotypic, variance.

We evaluated the developmental specificity of 176 SNPs known to affect adult height based on meta-analysis from the GIANT consortium. First, longitudinal observations on height spanning pre-adolescence through adulthood in a twin sample allowed us to investigate the individual effects of the previously identified SNPs on both pre-pubertal height and pubertal growth spurt. We show that the effect of the SNPs identified by the GIANT consortium is primarily on prepubertal height. Only one SNP, rs7759938 in LIN28B, approached a significant association with pubertal growth. Second, using standard twin heritability models, we investigated the extent to which the collective effect of these SNPs explained genetic variance in height—as opposed to phenotypic variance, as other studies have done. We computed a genetic score by weighting SNPs according to their effects as assessed via meta-analysis. We show that, while the score accounts for ∼9% of the phenotypic variance in height (i.e., the overall variance), it accounts for ∼14% of the corresponding genetic variance. Longitudinal samples are necessary to understand the developmental context of common genetic variants identified through GWAS, while twin samples will be helpful in accurately characterizing the extent to which these variants account for genetic, and not just phenotypic, variance.

Genetic variation in PLAG1 associates with early life body weight and peripubertal weight and growth in Bos taurus

Variation at the pleiomorphic adenoma gene 1 (PLAG1) locus has recently been implicated in the regulation of stature and weight in Bos taurus. Using a population of 942 outbred Holstein-Friesian dairy calves, we report confirmation of this effect, demonstrating strong association of early life body weight with PLAG1 genotype. Peripubertal body weight and growth rate were also significantly associated with PLAG1 genotype. Growth rate per kilogram of body weight, daily feed intake, gross feed efficiency and residual feed intake were not significantly associated with PLAG1 genotype. This study supports the status of PLAG1 as a key regulator of mammalian growth. Further, the data indicate the utility of PLAG1 polymorphisms for the selection of animals to achieve enhanced weight gain or conversely to aid the selection of animals with lower mature body weight and thus lower maintenance energy requirements.

What should the genome-wide significance threshold be? Empirical replication of borderline genetic associations

BACKGROUND

Robust replication is a sine qua non for the rigorous documentation of proposed associations in the genome-wide association (GWA) setting. Currently, associations of common variants reaching P ≤ 5 × 10(-8) are considered replicated. However, there is some ambiguity about the most suitable threshold for claiming genome-wide significance.

METHODS

We defined as 'borderline' associations those with P > 5 × 10(-8) and P ≤ 1 × 10(-7). The eligible associations were retrieved using the 'Catalog of Published Genome-Wide Association Studies'. For each association we assessed whether it reached P ≤ 5 × 10(-8) with inclusion of additional data from subsequent GWA studies.

RESULTS

Thirty-four eligible genotype-phenotype associations were evaluated with data and clarifications contributed from diverse investigators. Replication data from subsequent GWA studies could be obtained for 26 of them. Of those, 19 associations (73%) reached P ≤ 5 × 10(-8) for the same or a related trait implicating either the exact same allele or one in very high linkage disequilibrium and 17 reached P < 10(-8). If the seven associations that did not reach P ≤ 5 × 10(-8) when additional data were considered are assumed to have been false-positives, the false-discovery rate for borderline associations is estimated to be 27% [95% confidence interval (CI) 12-48%]. For five associations, the current P-value is > 10(-6) [corresponding false-discovery rate 19% (95% CI 7-39%)].

CONCLUSION

A substantial proportion, but not all, of the associations with borderline genome-wide significance represent replicable, possibly genuine associations. Our empirical evaluation suggests a possible relaxation in the current GWS threshold.

Heritability of volumetric brain changes and height in children entering puberty

The human brain undergoes structural changes in children entering puberty, while simultaneously children increase in height. It is not known if brain changes are under genetic control, and whether they are related to genetic factors influencing the amount of overall increase in height. Twins underwent magnetic resonance imaging brain scans at age 9 (N = 190) and 12 (N = 125). High heritability estimates were found at both ages for height and brain volumes (49-96%), and high genetic correlation between ages were observed (r(g) > 0.89). With increasing age, whole brain (+1.1%), cerebellum (+4.2%), cerebral white matter (+5.1%), and lateral ventricle (+9.4%) volumes increased, and third ventricle (-4.0%) and cerebral gray matter (-1.6%) volumes decreased. Children increased on average 13.8 cm in height (9.9%). Genetic influences on individual difference in volumetric brain and height changes were estimated, both within and across traits. The same genetic factors influenced both cerebral (20% heritable) and cerebellar volumetric changes (45%). Thus, the extent to which changes in cerebral and cerebellar volumes are heritable in children entering puberty are due to the same genes that influence change in both structures. The increase in height was heritable (73%), and not associated with cerebral volumetric change, but positively associated with cerebellar volume change (r(p) = 0.24). This association was explained by a genetic correlation (r(g) = 0.48) between height and cerebellar change. Brain and body each expand at their own pace and through separate genetic pathways. There are distinct genetic processes acting on structural brain development, which cannot be explained by genetic increase in height.

Rapid testing of gene-gene interactions in genome-wide association studies of binary and quantitative phenotypes

Genome-wide association (GWA) studies have been extremely successful in identifying novel loci contributing effects to a wide range of complex human traits. However, despite this success, the joint marginal effects of these loci account for only a small proportion of the heritability of these traits. Interactions between variants in different loci are not typically modelled in traditional GWA analysis, but may account for some of the missing heritability in humans, as they do in other model organisms. One of the key challenges in performing gene-gene interaction studies is the computational burden of the analysis. We propose a two-stage interaction analysis strategy to address this challenge in the context of both quantitative traits and dichotomous phenotypes. We have performed simulations to demonstrate only a negligible loss in power of this two-stage strategy, while minimizing the computational burden. Application of this interaction strategy to GWA studies of T2D and obesity highlights potential novel signals of association, which warrant follow-up in larger cohorts.

Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci

OBJECTIVE

To perform a 1-stage meta-analysis of genome-wide association studies (GWAS) of multiple sclerosis (MS) susceptibility and to explore functional consequences of new susceptibility loci.

METHODS

We synthesized 7 MS GWAS. Each data set was imputed using HapMap phase II, and a per single nucleotide polymorphism (SNP) meta-analysis was performed across the 7 data sets. We explored RNA expression data using a quantitative trait analysis in peripheral blood mononuclear cells (PBMCs) of 228 subjects with demyelinating disease.

RESULTS

We meta-analyzed 2,529,394 unique SNPs in 5,545 cases and 12,153 controls. We identified 3 novel susceptibility alleles: rs170934(T) at 3p24.1 (odds ratio [OR], 1.17; p = 1.6 × 10(-8)) near EOMES, rs2150702(G) in the second intron of MLANA on chromosome 9p24.1 (OR, 1.16; p = 3.3 × 10(-8)), and rs6718520(A) in an intergenic region on chromosome 2p21, with THADA as the nearest flanking gene (OR, 1.17; p = 3.4 × 10(-8)). The 3 new loci do not have a strong cis effect on RNA expression in PBMCs. Ten other susceptibility loci had a suggestive p < 1 × 10(-6) , some of these loci have evidence of association in other inflammatory diseases (ie, IL12B, TAGAP, PLEK, and ZMIZ1).

INTERPRETATION

We have performed a meta-analysis of GWAS in MS that more than doubles the size of previous gene discovery efforts and highlights 3 novel MS susceptibility loci. These and additional loci with suggestive evidence of association are excellent candidates for further investigations to refine and validate their role in the genetic architecture of MS.

Common polymorphisms in the GH/IGF-1 axis contribute to growth in extremely tall subjects

CONTEXT/OBJECTIVE

The growth hormone (GH)/insulin-like growth factor-1(IGF-1) axis is the key regulator of somatic growth in humans and its genes are plausible candidates to study the genetics of height variation. Here, we studied polymorphic variation in the GH/IGF-1 axis in the extremely tall Dutch.

METHODS

Case-control study of 166 tall cases with height >2 SDS and 206 controls with normally distributed height <2 SDS. Excluded were subjects with endocrine disorders or growth syndromes. We analyzed genomic DNA at 7 common polymorphisms in the GH-1, GH receptor (GHR), IGF-1 and IGFBP-3 genes.

RESULTS

The association of the GH-1 1663 SNP with tall stature approached statistical significance, with the T-allele more present in the tall (allele frequency (AF): 0.44 vs. 0.36; p=0.084). Moreover, haplotype frequencies at this locus were significantly different between cases and controls, with the GGT haplotype most commonly seen in cases (p=0.01). Allele frequencies of GHR polymorphisms were not different. For the IGF-1 CA-repeat we observed a higher frequency of homozygous 192-bp carriers among tall males compared to control males (AF: 0.62 vs. 0.55; p=0.02). The IGFBP-3 -202 C-allele occurred more frequently in cases than in controls (AF: 0.58 vs. 0.50; p=0.002). Within cases, those carrying one or two copies of the -202 C-allele were significantly taller than AA genotype carriers (AC, p=0.028 and CC, p=0.009). Serum IGFBP-3 levels were highest in AA genotype carriers, the -202 SNP explained 5.8% of the variation.

CONCLUSION

Polymorphic variation in the GH-1, IGF-1 and IGFBP-3 genes is associated with extremely tall stature. In particular, the IGFBP-3 -202 SNP is associated not only with being very tall but also with height variation within the tall.

Common variants show predicted polygenic effects on height in the tails of the distribution, except in extremely short individuals

Common genetic variants have been shown to explain a fraction of the inherited variation for many common diseases and quantitative traits, including height, a classic polygenic trait. The extent to which common variation determines the phenotype of highly heritable traits such as height is uncertain, as is the extent to which common variation is relevant to individuals with more extreme phenotypes. To address these questions, we studied 1,214 individuals from the top and bottom extremes of the height distribution (tallest and shortest ∼1.5%), drawn from ∼78,000 individuals from the HUNT and FINRISK cohorts. We found that common variants still influence height at the extremes of the distribution: common variants (49/141) were nominally associated with height in the expected direction more often than is expected by chance (p<5×10⁻²⁸), and the odds ratios in the extreme samples were consistent with the effects estimated previously in population-based data. To examine more closely whether the common variants have the expected effects, we calculated a weighted allele score (WAS), which is a weighted prediction of height for each individual based on the previously estimated effect sizes of the common variants in the overall population. The average WAS is consistent with expectation in the tall individuals, but was not as extreme as expected in the shortest individuals (p<0.006), indicating that some of the short stature is explained by factors other than common genetic variation. The discrepancy was more pronounced (p<10⁻⁶) in the most extreme individuals (height<0.25 percentile). The results at the extreme short tails are consistent with a large number of models incorporating either rare genetic non-additive or rare non-genetic factors that decrease height. We conclude that common genetic variants are associated with height at the extremes as well as across the population, but that additional factors become more prominent at the shorter extreme.

A statistical method for region-based meta-analysis of genome-wide association studies in genetically diverse populations

Genome-wide association studies (GWAS) have become the preferred experimental design in exploring the genetic etiology of complex human traits and diseases. Standard SNP-based meta-analytic approaches have been utilized to integrate the results from multiple experiments. This fundamentally assumes that the patterns of linkage disequilibrium (LD) between the underlying causal variants and the directly genotyped SNPs are similar across the populations for the same SNPs to emerge with surrogate evidence of disease association. We introduce a novel strategy for assessing regional evidence of phenotypic association that explicitly incorporates the extent of LD in the region. This provides a natural framework for combining evidence from multi-ethnic studies of both dichotomous and quantitative traits that (i) accommodates different patterns of LD, (ii) integrates different genotyping platforms and (iii) allows for the presence of allelic heterogeneity between the populations. Our method can also be generalized to perform gene-based or pathway-based analyses. Applying this method on real GWAS data in type 2 diabetes (T2D) boosted the association evidence in regions well-established for T2D etiology in three diverse South-East Asian populations, as well as identified two novel gene regions and a biologically convincing pathway that are subsequently validated with data from the Wellcome Trust Case Control Consortium.

New gene functions in megakaryopoiesis and platelet formation

Platelets are the second most abundant cell type in blood and are essential for maintaining haemostasis. Their count and volume are tightly controlled within narrow physiological ranges, but there is only limited understanding of the molecular processes controlling both traits. Here we carried out a high-powered meta-analysis of genome-wide association studies (GWAS) in up to 66,867 individuals of European ancestry, followed by extensive biological and functional assessment. We identified 68 genomic loci reliably associated with platelet count and volume mapping to established and putative novel regulators of megakaryopoiesis and platelet formation. These genes show megakaryocyte-specific gene expression patterns and extensive network connectivity. Using gene silencing in Danio rerio and Drosophila melanogaster, we identified 11 of the genes as novel regulators of blood cell formation. Taken together, our findings advance understanding of novel gene functions controlling fate-determining events during megakaryopoiesis and platelet formation, providing a new example of successful translation of GWAS to function.

Mechanisms and pathways of growth failure in primordial dwarfism

The greatest difference between species is size; however, the developmental mechanisms determining organism growth remain poorly understood. Primordial dwarfism is a group of human single-gene disorders with extreme global growth failure (which includes Seckel syndrome, microcephalic osteodysplastic primordial dwarfism I [MOPD] types I and II, and Meier-Gorlin syndrome). Ten genes have now been identified for microcephalic primordial dwarfism, encoding proteins involved in fundamental cellular processes including genome replication (ORC1 [origin recognition complex 1], ORC4, ORC6, CDT1, and CDC6), DNA damage response (ATR [ataxia-telangiectasia and Rad3-related]), mRNA splicing (U4atac), and centrosome function (CEP152, PCNT, and CPAP). Here, we review the cellular and developmental mechanisms underlying the pathogenesis of these conditions and address whether further study of these genes could provide novel insight into the physiological regulation of organism growth.

Distinct genetic architectures for male and female inflorescence traits of maize

We compared the genetic architecture of thirteen maize morphological traits in a large population of recombinant inbred lines. Four traits from the male inflorescence (tassel) and three traits from the female inflorescence (ear) were measured and studied using linkage and genome-wide association analyses and compared to three flowering and three leaf traits previously studied in the same population. Inflorescence loci have larger effects than flowering and leaf loci, and ear effects are larger than tassel effects. Ear trait models also have lower predictive ability than tassel, flowering, or leaf trait models. Pleiotropic loci were identified that control elongation of ear and tassel, consistent with their common developmental origin. For these pleiotropic loci, the ear effects are larger than tassel effects even though the same causal polymorphisms are likely involved. This implies that the observed differences in genetic architecture are not due to distinct features of the underlying polymorphisms. Our results support the hypothesis that genetic architecture is a function of trait stability over evolutionary time, since the traits that changed most during the relatively recent domestication of maize have the largest effects.

Genetic architecture is of broad interest in evolutionary biology, plant and animal breeding, and medicine, because it influences both the response to selection and the success of trait mapping. Results from the most rigorously studied genetic systems suggest a similar genetic architecture across all species and traits studied, with many loci of small effect. A few strongly selected traits in domesticated organisms show unusual genetic architecture, for reasons that are unclear. We compare maize inflorescence, flowering, and leaf traits and show that inflorescence traits have distinct genetic architectures characterized by larger effects. Female inflorescences (ears) have larger effects than male inflorescences (tassels) even though the two structures have similar developmental origins. Analysis of pleiotropic loci shows that these larger effects are not inherent features of the underlying polymorphisms. Rather, maize inflorescences appear to be exceptionally labile, with female inflorescences more labile than male inflorescences. These results support the canalization hypothesis, which predicts that rapidly changing traits will have larger effects. We suggest that maize inflorescence traits, and ear traits in particular, have larger effects than flowering or leaf traits as a result of strong directional selection during maize domestication.

Two patients walk into a clinic...a genomics perspective on the future of schizophrenia

Progress is being made in schizophrenia genomics, suggesting that this complex brain disorder involves rare, moderate to high-risk mutations and the cumulative impact of small genetic effects, coupled with environmental factors. The genetic heterogeneity underlying schizophrenia and the overlap with other neurodevelopmental disorders suggest that it will not continue to be viewed as a single disease. This has radical implications for clinical practice, as diagnosis and treatment will be guided by molecular etiology rather than clinical diagnostic criteria.

Signs of selective pressure on genetic variants affecting human height

Many decades of scientific investigation have proved the role of selective pressure in Homo Sapiens at least at the level of individual genes or loci. Nevertheless, there are examples of polygenic traits that are bound to be under selection, but studies devoted to apply population genetics methods to unveil such occurrence are still lacking. Stature provides a relevant example of well-studied polygenic trait for which is now available a genome-wide association study which has identified the genes involved in this trait, and which is known to be under selection. We studied the behavior of F(ST) in a simulated toy model to detect population differentiation on a generic polygenic phenotype under selection. The simulations showed that the set of alleles involved in the trait has a higher mean F(ST) value than those undergoing genetic drift only. In view of this we looked for an increase in the mean F(ST) value of the 180 variants associated to human height. For this set of alleles we found F(ST) to be significantly higher than the genomic background (p = 0.0356). On the basis of a statistical analysis we excluded that the increase was just due to the presence of outliers. We also proved as marginal the role played by local adaptation phenomena, even on different phenotypes in linkage disequilibrium with genetic variants involved in height. The increase of F(ST) for the set of alleles involved in a polygenic trait seems to provide an example of symmetry breaking phenomenon concerning the population differentiation. The splitting in the allele frequencies would be driven by the initial conditions in the population dynamics which are stochastically modified by events like drift, bottlenecks, etc, and other stochastic events like the born of new mutations.

Dense genotyping identifies and localizes multiple common and rare variant association signals in celiac disease

Using variants from the 1000 Genomes Project pilot European CEU dataset and data from additional resequencing studies, we densely genotyped 183 non-HLA risk loci previously associated with immune-mediated diseases in 12,041 individuals with celiac disease (cases) and 12,228 controls. We identified 13 new celiac disease risk loci reaching genome-wide significance, bringing the number of known loci (including the HLA locus) to 40. We found multiple independent association signals at over one-third of these loci, a finding that is attributable to a combination of common, low-frequency and rare genetic variants. Compared to previously available data such as those from HapMap3, our dense genotyping in a large sample collection provided a higher resolution of the pattern of linkage disequilibrium and suggested localization of many signals to finer scale regions. In particular, 29 of the 54 fine-mapped signals seemed to be localized to single genes and, in some instances, to gene regulatory elements. Altogether, we define the complex genetic architecture of the risk regions of and refine the risk signals for celiac disease, providing the next step toward uncovering the causal mechanisms of the disease.

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.

Genetic studies on the ghrelin, growth hormone secretagogue receptor (GHSR) and ghrelin O-acyl transferase (GOAT) genes

Ghrelin is a 28 amino acid peptide hormone that is produced both centrally and peripherally. Regulated by the ghrelin O-acyl transferase enzyme, ghrelin exerts its action through the growth hormone secretagogue receptor, and is implicated in a diverse range of physiological processes. These implications have placed the ghrelin signaling pathway at the center of a large number of candidate gene and genome-wide studies which aim to identify the genetic basis of human heterogeneity. In this review we summarize the available data on the genetic variability of ghrelin, its receptor and its regulatory enzyme, and their association with obesity, stature, type 2 diabetes, cardiovascular disease, eating disorders, and reward seeking behavior.

Missing heritability in the tails of quantitative traits? A simulation study on the impact of slightly altered true genetic models

OBJECTIVE

Genome-wide association studies have identified robust associations between single nucleotide polymorphisms and complex traits. As the proportion of phenotypic variance explained is still limited for most of the traits, larger and larger meta-analyses are being conducted to detect additional associations. Here we investigate the impact of the study design and the underlying assumption about the true genetic effect in a bimodal mixture situation on the power to detect associations.

METHODS

We performed simulations of quantitative phenotypes analysed by standard linear regression and dichotomized case-control data sets from the extremes of the quantitative trait analysed by standard logistic regression.

RESULTS

Using linear regression, markers with an effect in the extremes of the traits were almost undetectable, whereas analysing extremes by case-control design had superior power even for much smaller sample sizes. Two real data examples are provided to support our theoretical findings and to explore our mixture and parameter assumption.

CONCLUSIONS

Our findings support the idea to re-analyse the available meta-analysis data sets to detect new loci in the extremes. Moreover, our investigation offers an explanation for discrepant findings when analysing quantitative traits in the general population and in the extremes.

Adult height variants affect birth length and growth rate in children

Previous studies identified 180 single nucleotide polymorphisms (SNPs) associated with adult height, explaining ∼10% of the variance. The age at which these begin to affect growth is unclear. We modelled the effect of these SNPs on birth length and childhood growth. A total of 7768 participants in the Avon Longitudinal Study of Parents and Children had data available. Individual growth trajectories from 0 to 10 years were estimated using mixed-effects linear spline models and differences in trajectories by individual SNPs and allelic score were determined. The allelic score was associated with birth length (0.026 cm increase per 'tall' allele, SE = 0.003, P = 1 × 10(-15), equivalent to 0.017 SD). There was little evidence of association between the allelic score and early infancy growth (0-3 months), but there was evidence of association between the allelic score and later growth. This association became stronger with each consecutive growth period, per 'tall' allele per month effects were 0.015 SD (3 months-1 year, SE = 0.004), 0.023 SD (1-3 years, SE = 0.003) and 0.028 SD (3-10 years, SE = 0.003). By age 10, the mean height difference between individuals with ≤170 versus ≥191 'tall' alleles (the top and bottom 10%) was 4.7 cm (0.8 SD), explaining ∼5% of the variance. There was evidence of associations with specific growth periods for some SNPs (rs3791675, EFEMP1 and rs6569648, L3MBTL3) and supportive evidence for previously reported age-dependent effects of HHIP and SOCS2 SNPs. SNPs associated with adult height influence birth length and have an increasing effect on growth from late infancy through to late childhood. By age 10, they explain half the height variance (∼5%) of that explained in adults (∼10%).

Thirty-five common variants for coronary artery disease: the fruits of much collaborative labour

Coronary artery disease (CAD) is the leading cause of death worldwide. Affected individuals cluster in families in patterns that reflect the sharing of numerous susceptibility genes. Genome-wide and large-scale gene-centric genotyping studies that involve tens of thousands of cases and controls have now mapped common disease variants to 34 distinct loci. Some coronary disease common variants show allelic heterogeneity or copy number variation. Some of the loci include candidate genes that imply conventional or emerging risk factor-mediated mechanisms of disease pathogenesis. Quantitative trait loci associations with risk factors have been informative in Mendelian randomization studies as well as fine-mapping of causative variants. But, for most loci, plausible mechanistic links are uncertain or obscure at present but provide potentially novel directions for research into this disease's pathogenesis. The common variants explain ~4% of inter-individual variation in disease risk and no more than 13% of the total heritability of coronary disease. Although many CAD genes are presently undiscovered, it is likely that larger collaborative genome-wide association studies will map further common/low-penetrance variants and hoped that low-frequency or rare high-penetrance variants will also be identified in medical resequencing experiments.

Distribution of allele frequencies and effect sizes and their interrelationships for common genetic susceptibility variants

Recent discoveries of hundreds of common susceptibility SNPs from genome-wide association studies provide a unique opportunity to examine population genetic models for complex traits. In this report, we investigate distributions of various population genetic parameters and their interrelationships using estimates of allele frequencies and effect-size parameters for about 400 susceptibility SNPs across a spectrum of qualitative and quantitative traits. We calibrate our analysis by statistical power for detection of SNPs to account for overrepresentation of variants with larger effect sizes in currently known SNPs that are expected due to statistical power for discovery. Across all qualitative disease traits, minor alleles conferred "risk" more often than "protection." Across all traits, an inverse relationship existed between "regression effects" and allele frequencies. Both of these trends were remarkably strong for type I diabetes, a trait that is most likely to be influenced by selection, but were modest for other traits such as human height or late-onset diseases such as type II diabetes and cancers. Across all traits, the estimated effect-size distribution suggested the existence of increasingly large numbers of susceptibility SNPs with decreasingly small effects. For most traits, the set of SNPs with intermediate minor allele frequencies (5-20%) contained an unusually small number of susceptibility loci and explained a relatively small fraction of heritability compared with what would be expected from the distribution of SNPs in the general population. These trends could have several implications for future studies of common and uncommon variants.

Genome-wide association studies and type 2 diabetes

In recent years, the search for genetic determinants of type 2 diabetes (T2D) has changed dramatically. Although linkage and small-scale candidate gene studies were highly successful in the identification of genes, which, when mutated, caused monogenic forms of T2D, they were largely unsuccessful when applied to the more common forms of the disease. To date, these approaches have only identified two loci (PPARG, KCNJ11) robustly implicated in T2D susceptibility. The ability to perform large-scale association analysis, including genome-wide association studies (GWAS) in many thousands of samples from different populations, and subsequently, the shift to form large international collaborations to perform meta-analyses across many studies has taken the number of independent loci showing genome-wide significant associations with T2D to 44. This number includes six loci identified initially through the analysis of quantitative glycaemic phenotypes, illustrating the usefulness of this approach both to identify new disease genes and gain insight into the mechanisms leading to disease. Combined, these loci still only account for ∼10% of the observed familial clustering in Europeans, leaving much of the variance unexplained. In this review, we will describe what GWAS have taught us about the genetic basis of T2D and discuss possible next steps to uncover the remaining heritability.

Integrating autoimmune risk loci with gene-expression data identifies specific pathogenic immune cell subsets

Although genome-wide association studies have implicated many individual loci in complex diseases, identifying the exact causal alleles and the cell types within which they act remains greatly challenging. To ultimately understand disease mechanism, researchers must carefully conceive functional studies in relevant pathogenic cell types to demonstrate the cellular impact of disease-associated genetic variants. This challenge is highlighted in autoimmune diseases, such as rheumatoid arthritis, where any of a broad range of immunological cell types might potentially be impacted by genetic variation to cause disease. To this end, we developed a statistical approach to identify potentially pathogenic cell types in autoimmune diseases by using a gene-expression data set of 223 murine-sorted immune cells from the Immunological Genome Consortium. We found enrichment of transitional B cell genes in systemic lupus erythematosus (p = 5.9 × 10(-6)) and epithelial-associated stimulated dendritic cell genes in Crohn disease (p = 1.6 × 10(-5)). Finally, we demonstrated enrichment of CD4+ effector memory T cell genes within rheumatoid arthritis loci (p < 10(-6)). To further validate the role of CD4+ effector memory T cells within rheumatoid arthritis, we identified 436 loci that were not yet known to be associated with the disease but that had a statistically suggestive association in a recent genome-wide association study (GWAS) meta-analysis (p(GWAS) < 0.001). Even among these putative loci, we noted a significant enrichment for genes specifically expressed in CD4+ effector memory T cells (p = 1.25 × 10(-4)). These cell types are primary candidates for future functional studies to reveal the role of risk alleles in autoimmunity. Our approach has application in other phenotypes, outside of autoimmunity, where many loci have been discovered and high-quality cell-type-specific gene expression is available.

Integrating autoimmune risk loci with gene-expression data identifies specific pathogenic immune cell subsets

Although genome-wide association studies have implicated many individual loci in complex diseases, identifying the exact causal alleles and the cell types within which they act remains greatly challenging. To ultimately understand disease mechanism, researchers must carefully conceive functional studies in relevant pathogenic cell types to demonstrate the cellular impact of disease-associated genetic variants. This challenge is highlighted in autoimmune diseases, such as rheumatoid arthritis, where any of a broad range of immunological cell types might potentially be impacted by genetic variation to cause disease. To this end, we developed a statistical approach to identify potentially pathogenic cell types in autoimmune diseases by using a gene-expression data set of 223 murine-sorted immune cells from the Immunological Genome Consortium. We found enrichment of transitional B cell genes in systemic lupus erythematosus (p = 5.9 × 10(-6)) and epithelial-associated stimulated dendritic cell genes in Crohn disease (p = 1.6 × 10(-5)). Finally, we demonstrated enrichment of CD4+ effector memory T cell genes within rheumatoid arthritis loci (p < 10(-6)). To further validate the role of CD4+ effector memory T cells within rheumatoid arthritis, we identified 436 loci that were not yet known to be associated with the disease but that had a statistically suggestive association in a recent genome-wide association study (GWAS) meta-analysis (p(GWAS) < 0.001). Even among these putative loci, we noted a significant enrichment for genes specifically expressed in CD4+ effector memory T cells (p = 1.25 × 10(-4)). These cell types are primary candidates for future functional studies to reveal the role of risk alleles in autoimmunity. Our approach has application in other phenotypes, outside of autoimmunity, where many loci have been discovered and high-quality cell-type-specific gene expression is available.

Genome-wide association studies (GWAS): impact on elucidating the aetiology of diabetes

It has proven to be challenging to isolate the genes underlying the genetic components conferring susceptibility to type 1 and type 2 diabetes. Unlike previous approaches, 'genome-wide association studies' have extensively delivered on the promise of uncovering genetic determinants of complex diseases, with a number of novel disease-associated variants being largely replicated by independent groups. This review provides an overview of these recent breakthroughs in the context of type 1 and type 2 diabetes, and outlines strategies on how these findings will be applied to impact clinical care for these two highly prevalent disorders.