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

The ghrelin axis--does it have an appetite for cancer progression?

Ghrelin, the endogenous ligand for the GH secretagogue receptor (GHSR), is a peptide hormone with diverse physiological roles. Ghrelin regulates GH release, appetite and feeding, gut motility, and energy balance and also has roles in the cardiovascular, immune, and reproductive systems. Ghrelin and the GHSR are expressed in a wide range of normal and tumor tissues, and a fluorescein-labeled, truncated form of ghrelin is showing promise as a biomarker for prostate cancer. Plasma ghrelin levels are generally inversely related to body mass index and are unlikely to be useful as a biomarker for cancer, but may be useful as a marker for cancer cachexia. Some single nucleotide polymorphisms in the ghrelin and GHSR genes have shown associations with cancer risk; however, larger studies are required. Ghrelin regulates processes associated with cancer, including cell proliferation, apoptosis, cell migration, cell invasion, inflammation, and angiogenesis; however, the role of ghrelin in cancer is currently unclear. Ghrelin has predominantly antiinflammatory effects and may play a role in protecting against cancer-related inflammation. Ghrelin and its analogs show promise as treatments for cancer-related cachexia. Further studies using in vivo models are required to determine whether ghrelin has a role in cancer progression.

Gene-environment interactions and obesity traits among postmenopausal African-American and Hispanic women in the Women's Health Initiative SHARe Study

Genome-wide association studies (GWAS) of obesity measures have identified associations with single nucleotide polymorphisms (SNPs). However, no large-scale evaluation of gene-environment interactions has been performed. We conducted a search of gene-environment (G × E) interactions in post-menopausal African-American and Hispanic women from the Women's Health Initiative SNP Health Association Resource GWAS study. Single SNP linear regression on body mass index (BMI) and waist-to-hip circumference ratio (WHR) adjusted for multidimensional-scaling-derived axes of ancestry and age was run in race-stratified data with 871,512 SNPs available from African-Americans (N = 8,203) and 786,776 SNPs from Hispanics (N = 3,484). Tests of G × E interaction at all SNPs for recreational physical activity (m h/week), dietary energy intake (kcal/day), alcohol intake (categorical), cigarette smoking years, and cigarette smoking (ever vs. never) were run in African-Americans and Hispanics adjusted for ancestry and age at interview, followed by meta-analysis of G × E interaction terms. The strongest evidence for concordant G × E interactions in African-Americans and Hispanics was for smoking and marker rs10133840 (Q statistic P = 0.70, beta = -0.01, P = 3.81 × 10(-7)) with BMI as the outcome. The strongest evidence for G × E interaction within a cohort was in African-Americans with WHR as outcome for dietary energy intake and rs9557704 (SNP × kcal = -0.04, P = 2.17 × 10(-7)). No results exceeded the Bonferroni-corrected statistical significance threshold.

Alzheimer's therapeutics: continued clinical failures question the validity of the amyloid hypothesis-but what lies beyond?

The worldwide incidence of Alzheimer's disease (AD) is increasing with estimates that 115 million individuals will have AD by 2050, creating an unsustainable healthcare challenge due to a lack of effective treatment options highlighted by multiple clinical failures of agents designed to reduce the brain amyloid burden considered synonymous with the disease. The amyloid hypothesis that has been the overarching focus of AD research efforts for more than two decades has been questioned in terms of its causality but has not been unequivocally disproven despite multiple clinical failures, This is due to issues related to the quality of compounds advanced to late stage clinical trials and the lack of validated biomarkers that allow the recruitment of AD patients into trials before they are at a sufficiently advanced stage in the disease where therapeutic intervention is deemed futile. Pursuit of a linear, reductionistic amyloidocentric approach to AD research, which some have compared to a religious faith, has resulted in other, equally plausible but as yet unvalidated AD hypotheses being underfunded leading to a disastrous roadblock in the search for urgently needed AD therapeutics. Genetic evidence supporting amyloid causality in AD is reviewed in the context of the clinical failures, and progress in tau-based and alternative approaches to AD, where an evolving modus operandi in biomedical research fosters excessive optimism and a preoccupation with unproven, and often ephemeral, biomarker/genome-based approaches that override transparency, objectivity and data-driven decision making, resulting in low probability environments where data are subordinate to self propagating hypotheses.

Strong signatures of selection in the domestic pig genome

Domestication of wild boar (Sus scrofa) and subsequent selection have resulted in dramatic phenotypic changes in domestic pigs for a number of traits, including behavior, body composition, reproduction, and coat color. Here we have used whole-genome resequencing to reveal some of the loci that underlie phenotypic evolution in European domestic pigs. Selective sweep analyses revealed strong signatures of selection at three loci harboring quantitative trait loci that explain a considerable part of one of the most characteristic morphological changes in the domestic pig--the elongation of the back and an increased number of vertebrae. The three loci were associated with the NR6A1, PLAG1, and LCORL genes. The latter two have repeatedly been associated with loci controlling stature in other domestic animals and in humans. Most European domestic pigs are homozygous for the same haplotype at these three loci. We found an excess of derived nonsynonymous substitutions in domestic pigs, most likely reflecting both positive selection and relaxed purifying selection after domestication. Our analysis of structural variation revealed four duplications at the KIT locus that were exclusively present in white or white-spotted pigs, carrying the Dominant white, Patch, or Belt alleles. This discovery illustrates how structural changes have contributed to rapid phenotypic evolution in domestic animals and how alleles in domestic animals may evolve by the accumulation of multiple causative mutations as a response to strong directional selection.

Association of LIN28B with adult adiposity-related traits in females

Context

Pubertal timing is under strong genetic control and its early onset associates with several adverse health outcomes in adulthood, including obesity, type 2 diabetes and cardiovascular disease. Recent data indicate strong association between pubertal timing and genetic variants near LIN28B, but it is currently unknown whether the gene contributes to the association between puberty and adult disease.

Objective

To elucidate the putative genetic link between early puberty and adult disease risk, we examined the association of two genetic variants near LIN28B with adult body size and metabolic profiles in randomly ascertained adult Finnish males and females.

Methods

Two single nucleotide polymorphisms (SNPs), rs7759938, the lead SNP previously associated with pubertal timing and height, and rs314279, previously also associated with menarcheal age but only partially correlated with rs7759938 (r² = 0.30), were genotyped in 26,636 study subjects participating in the Finnish population survey FINRISK. Marker associations with adult height, weight, body mass index (BMI), hip and waist circumference, blood glucose, serum insulin and lipid/lipoprotein levels were determined by linear regression analyses.

Results

Both rs7759938 and rs314279 associated with adult height in both sexes (p = 2×10⁻⁶ and p = 0.001). Furthermore, rs314279 associated with increased weight in females (p = 0.001). Conditioned analyses including both SNPs in the regression model verified that rs314279 independently associates with adult female weight, BMI and hip circumference (p<0.005). Neither SNP associated with glucose, lipid, or lipoprotein levels.

Conclusion

Genetic variants near the puberty-associated gene LIN28B associate with adult weight and body shape in females, suggesting that the gene may tag molecular pathways influencing adult adiposity-related traits.

Gene-based copy number variation study reveals a microdeletion at 12q24 that influences height in the Korean population

Height is a classic polygenic trait with high heritability (h(2)=0.8). Recent genome-wide association studies have revealed many independent loci associated with human height. In addition, although many studies have reported an association between copy number variation (CNV) and complex diseases, few have explored the relationship between CNV and height. Recent studies reported that single nucleotide polymorphisms (SNPs) are highly correlated with common CNVs, suggesting that it is warranted to survey CNVs to identify additional genetic factors affecting heritable traits such as height. This study tested the hypothesis that there would be CNV regions (CNVRs) associated with height nearby genes from the GWASs known to affect height. We identified regions containing >1% copy number deletion frequency from 3667 population-based cohort samples using the Illumina HumanOmni1-Quad BeadChip. Among the identified CNVRs, we selected 15 candidate regions that were located within 1Mb of 283 previously reported genes. To assess the effect of these CNVRs on height, statistical analyses were conducted with samples from a case group of 370 taller (upper 10%) individuals and a control group of 1828 individuals (lower 50%). We found that a newly identified 17.7 kb deletion at chromosomal position 12q24.33, approximately 171.6 kb downstream of GPR133, significantly correlated with height; this finding was validated using quantitative PCR. These results suggest that CNVs are potentially important in determining height and may contribute to height variation in human populations.

Interpreting noncoding genetic variation in complex traits and human disease

Association studies provide genome-wide information about the genetic basis of complex disease, but medical research has primarily focused on protein-coding variants, due to the difficulty of interpreting non-coding mutations. This picture has changed with advances in the systematic annotation of functional non-coding elements. Evolutionary conservation, functional genomics, chromatin state, sequence motifs, and molecular quantitative trait loci all provide complementary information about non-coding function. These functional maps can help prioritize variants on risk haplotypes, filter mutations encountered in the clinic, and perform systems-level analyses to reveal processes underlying disease associations. Advances in predictive modeling can enable dataset integration to reveal pathways shared across loci and alleles, and richer regulatory models can guide the search for epistatic interactions. Lastly, new massively parallel reporter experiments can systematically validate regulatory predictions. Ultimately, advances in regulatory and systems genomics can help unleash the value of whole-genome sequencing for personalized genomic risk assessment, diagnosis, and treatment.

Human complex trait genetics: lifting the lid of the genomics toolbox - from pathways to prediction

During the initial stages of the genome revolution human genetics was hugely successful in discovering the underlying genes for monogenic diseases. Over 3,000 monogenic diseases have been discovered with simple patterns of inheritance. The unravelling and identification of the genetic variants underlying complex or multifactorial traits, however, is proving much more elusive. There have been over 1,000 significant variants found for many quantitative and binary traits yet they explain very little of the estimated genetic variance or heritability evident from family analysis. There are many hypotheses as to why this might be the case. This apparent lack of information is holding back the clinical application of genetics and shedding doubt on whether more of the same will reveal where the remainder of the variation lies. Here we explore the current state of play, the types of variants we can detect and how they are currently exploited. Finally we look at the future challenges we must face to persuade the human genome to yield its secrets.

A multi-SNP locus-association method reveals a substantial fraction of the missing heritability

There are many known examples of multiple semi-independent associations at individual loci; such associations might arise either because of true allelic heterogeneity or because of imperfect tagging of an unobserved causal variant. This phenomenon is of great importance in monogenic traits but has not yet been systematically investigated and quantified in complex-trait genome-wide association studies (GWASs). Here, we describe a multi-SNP association method that estimates the effect of loci harboring multiple association signals by using GWAS summary statistics. Applying the method to a large anthropometric GWAS meta-analysis (from the Genetic Investigation of Anthropometric Traits consortium study), we show that for height, body mass index (BMI), and waist-to-hip ratio (WHR), 3%, 2%, and 1%, respectively, of additional phenotypic variance can be explained on top of the previously reported 10% (height), 1.5% (BMI), and 1% (WHR). The method also permitted a substantial increase (by up to 50%) in the number of loci that replicate in a discovery-validation design. Specifically, we identified 74 loci at which the multi-SNP, a linear combination of SNPs, explains significantly more variance than does the best individual SNP. A detailed analysis of multi-SNPs shows that most of the additional variability explained is derived from SNPs that are not in linkage disequilibrium with the lead SNP, suggesting a major contribution of allelic heterogeneity to the missing heritability.

Genome-wide association studies in mice

Genome-wide association studies (GWASs) have transformed the field of human genetics and have led to the discovery of hundreds of genes that are implicated in human disease. The technological advances that drove this revolution are now poised to transform genetic studies in model organisms, including mice. However, the design of GWASs in mouse strains is fundamentally different from the design of human GWASs, creating new challenges and opportunities. This Review gives an overview of the novel study designs for mouse GWASs, which dramatically improve both the statistical power and resolution compared to classical gene-mapping approaches.

Confluence of genes, environment, development, and behavior in a post Genome-Wide Association Study world

This article serves to outline a research paradigm to investigate main effects and interactions of genes, environment, and development on behavior and psychiatric illness. We provide a historical context for candidate gene studies and genome-wide association studies, including benefits, limitations, and expected payoffs. Using substance use and abuse as our driving example, we then turn to the importance of etiological psychological theory in guiding genetic, environmental, and developmental research, as well as the utility of refined phenotypic measures, such as endophenotypes, in the pursuit of etiological understanding and focused tests of genetic and environmental associations. Phenotypic measurement has received considerable attention in the history of psychology and is informed by psychometrics, whereas the environment remains relatively poorly measured and is often confounded with genetic effects (i.e., gene-environment correlation). Genetically informed designs, which are no longer limited to twin and adoption studies thanks to ever-cheaper genotyping, are required to understand environmental influences. Finally, we outline the vast amount of individual difference in structural genomic variation, most of which remains to be leveraged in genetic association tests. Although the genetic data can be massive and burdensome (tens of millions of variants per person), we argue that improved understanding of genomic structure and function will provide investigators with new tools to test specific a priori hypotheses derived from etiological psychological theory, much like current candidate gene research but with less confusion and more payoff than candidate gene research has to date.

Twins and the mystery of missing heritability: the contribution of gene-environment interactions

Since 2006, the advent of increasingly larger genome-wide association studies and their meta-analyses have led to numerous, replicated findings of genetic polymorphisms associated with many diseases and traits. Early studies suggested that the identified loci generally accounted for a small fraction of the genetic variance estimated from twin and family studies. This led to the concept of 'missing heritability'. Here, the progress in accounting for a greater proportion of the variance is reviewed. In particular, gene-environment interactions can, for some traits and in certain circumstances, explain part of this missing heritability.

Variants affecting exon skipping contribute to complex traits

DNA variants that affect alternative splicing and the relative quantities of different gene transcripts have been shown to be risk alleles for some Mendelian diseases. However, for complex traits characterized by a low odds ratio for any single contributing variant, very few studies have investigated the contribution of splicing variants. The overarching goal of this study is to discover and characterize the role that variants affecting alternative splicing may play in the genetic etiology of complex traits, which include a significant number of the common human diseases. Specifically, we hypothesize that single nucleotide polymorphisms (SNPs) in splicing regulatory elements can be characterized in silico to identify variants affecting splicing, and that these variants may contribute to the etiology of complex diseases as well as the inter-individual variability in the ratios of alternative transcripts. We leverage high-throughput expression profiling to 1) experimentally validate our in silico predictions of skipped exons and 2) characterize the molecular role of intronic genetic variations in alternative splicing events in the context of complex human traits and diseases. We propose that intronic SNPs play a role as genetic regulators within splicing regulatory elements and show that their associated exon skipping events can affect protein domains and structure. We find that SNPs we would predict to affect exon skipping are enriched among the set of SNPs reported to be associated with complex human traits.

Alternative splicing is a common eukaryotic cellular mechanism that allows for the production of multiple proteins from one gene and occurs in 40%–90% of all human genes. Alternative splicing has been shown to be important for many critical biological processes, including development, evolution, and even psychological behavior. Additionally, alternative splicing has been associated with 15%–50% of human genetic diseases, including breast cancer; however, the precise mechanism by which genetic variations regulate this process remains to be fully elucidated. In this study, we develop an integrative approach that utilizes sequence-based analysis and genome-wide expression profiling to identify genetic variations that may affect alternative splicing. We also evaluate their enrichment among established disease-associated variations. Our study provides insights into the functionality of these variations and emphasizes their importance for complex human traits and diseases.

SHAVE: shrinkage estimator measured for multiple visits increases power in GWAS of quantitative traits

Measurement error and biological variability generate distortions in quantitative phenotypic data. In longitudinal studies with repeated measurements, the multiple measurements provide a route to reduce noise and correspondingly increase the strength of signals in genome-wide association studies (GWAS).To optimize noise correction, we have developed Shrunken Average (SHAVE), an approach using a Bayesian Shrinkage estimator. This estimator uses regression toward the mean for every individual as a function of (1) their average across visits; (2) their number of visits; and (3) the correlation between visits. Computer simulations support an increase in power, with results very similar to those expected by the assumptions of the model. The method was applied to a real data set for 14 anthropomorphic traits in ∼6000 individuals enrolled in the SardiNIA project, with up to three visits (measurements) for each participant. Results show that additional measurements have a large impact on the strength of GWAS signals, especially when participants have different number of visits, with SHAVE showing a clear increase in power relative to single visits. In addition, we have derived a relation to assess the improvement in power as a function of number of visits and correlation between visits. It can also be applied in the optimization of experimental designs or usage of measuring devices. SHAVE is fast and easy to run, written in R and freely available online.

Behavior genetics and postgenomics

The science of genetics is undergoing a paradigm shift. Recent discoveries, including the activity of retrotransposons, the extent of copy number variations, somatic and chromosomal mosaicism, and the nature of the epigenome as a regulator of DNA expressivity, are challenging a series of dogmas concerning the nature of the genome and the relationship between genotype and phenotype. According to three widely held dogmas, DNA is the unchanging template of heredity, is identical in all the cells and tissues of the body, and is the sole agent of inheritance. Rather than being an unchanging template, DNA appears subject to a good deal of environmentally induced change. Instead of identical DNA in all the cells of the body, somatic mosaicism appears to be the normal human condition. And DNA can no longer be considered the sole agent of inheritance. We now know that the epigenome, which regulates gene expressivity, can be inherited via the germline. These developments are particularly significant for behavior genetics for at least three reasons: First, epigenetic regulation, DNA variability, and somatic mosaicism appear to be particularly prevalent in the human brain and probably are involved in much of human behavior; second, they have important implications for the validity of heritability and gene association studies, the methodologies that largely define the discipline of behavior genetics; and third, they appear to play a critical role in development during the perinatal period and, in particular, in enabling phenotypic plasticity in offspring. I examine one of the central claims to emerge from the use of heritability studies in the behavioral sciences, the principle of minimal shared maternal effects, in light of the growing awareness that the maternal perinatal environment is a critical venue for the exercise of adaptive phenotypic plasticity. This consideration has important implications for both developmental and evolutionary biology.

Androgen insensitivity syndrome

Androgen insensitivity syndrome in its complete form is a disorder of hormone resistance characterised by a female phenotype in an individual with an XY karyotype and testes producing age-appropriate normal concentrations of androgens. Pathogenesis is the result of mutations in the X-linked androgen receptor gene, which encodes for the ligand-activated androgen receptor--a transcription factor and member of the nuclear receptor superfamily. This Seminar describes the clinical manifestations of androgen insensitivity syndrome from infancy to adulthood, reviews the mechanism of androgen action, and shows examples of how mutations of the androgen receptor gene cause the syndrome. Management of androgen insensitivity syndrome should be undertaken by a multidisciplinary team and include gonadectomy to avoid gonad tumours in later life, appropriate sex-hormone replacement at puberty and beyond, and an emphasis on openness in disclosure.

Insights from genome-wide association studies of drug response

Early genome-wide association studies (GWAS) using relatively small samples have identified both rare and common genetic variants with large impact on severe adverse drug reactions, dosing, and efficacy. Here we outline the challenges and recent successes of the GWAS approach in disease genetics and the ways in which these can be applied to pharmacogenomics for biological discovery, determination of heritability, and personalized treatment. We highlight that the genetic architecture of drug efficacy reflects a complex trait yet that of adverse drug reactions more closely mirrors the architecture of Mendelian diseases and how this difference affects future study design. Given that multiple layers of biological data are increasingly available on large samples from biorepositories linked to electronic medical records, GWAS will remain a key component of the systems biology approach to uncovering small to moderate genetic determinants of drug response; these discoveries should move us closer to a personalized approach to health care.

Localising loci underlying complex trait variation using Regional Genomic Relationship Mapping

The limited proportion of complex trait variance identified in genome-wide association studies may reflect the limited power of single SNP analyses to detect either rare causative alleles or those of small effect. Motivated by studies that demonstrate that loci contributing to trait variation may contain a number of different alleles, we have developed an analytical approach termed Regional Genomic Relationship Mapping that, like linkage-based family methods, integrates variance contributed by founder gametes within a pedigree. This approach takes advantage of very distant (and unrecorded) relationships, and this greatly increases the power of the method, compared with traditional pedigree-based linkage analyses. By integrating variance contributed by founder gametes in the population, our approach provides an estimate of the Regional Heritability attributable to a small genomic region (e.g. 100 SNP window covering ca. 1 Mb of DNA in a 300000 SNP GWAS) and has the power to detect regions containing multiple alleles that individually contribute too little variance to be detectable by GWAS as well as regions with single common GWAS-detectable SNPs. We use genome-wide SNP array data to obtain both a genome-wide relationship matrix and regional relationship (“identity by state" or IBS) matrices for sequential regions across the genome. We then estimate a heritability for each region sequentially in our genome-wide scan. We demonstrate by simulation and with real data that, when compared to traditional (“individual SNP") GWAS, our method uncovers new loci that explain additional trait variation. We analysed data from three Southern European populations and from Orkney for exemplar traits – serum uric acid concentration and height. We show that regional heritability estimates are correlated with results from genome-wide association analysis but can capture more of the genetic variance segregating in the population and identify additional trait loci.

Exome sequencing and complex disease: practical aspects of rare variant association studies

Genetic association and linkage studies can provide insights into complex disease biology, guiding the development of new diagnostic and therapeutic strategies. Over the past decade, genetic association studies have largely focused on common, easy to measure genetic variants shared between many individuals. These common variants typically have subtle functional consequence and translating the resulting association signals into biological insights can be challenging. In the last few years, exome sequencing has emerged as a cost-effective strategy for extending these studies to include rare coding variants, which often have more marked functional consequences. Here, we provide practical guidance in the design and analysis of complex trait association studies focused on rare, coding variants.

FTO genotype is associated with phenotypic variability of body mass index

There is evidence across several species for genetic control of phenotypic variation of complex traits, such that the variance among phenotypes is genotype dependent. Understanding genetic control of variability is important in evolutionary biology, agricultural selection programmes and human medicine, yet for complex traits, no individual genetic variants associated with variance, as opposed to the mean, have been identified. Here we perform a meta-analysis of genome-wide association studies of phenotypic variation using ∼170,000 samples on height and body mass index (BMI) in human populations. We report evidence that the single nucleotide polymorphism (SNP) rs7202116 at the FTO gene locus, which is known to be associated with obesity (as measured by mean BMI for each rs7202116 genotype), is also associated with phenotypic variability. We show that the results are not due to scale effects or other artefacts, and find no other experiment-wise significant evidence for effects on variability, either at loci other than FTO for BMI or at any locus for height. The difference in variance for BMI among individuals with opposite homozygous genotypes at the FTO locus is approximately 7%, corresponding to a difference of ∼0.5 kilograms in the standard deviation of weight. Our results indicate that genetic variants can be discovered that are associated with variability, and that between-person variability in obesity can partly be explained by the genotype at the FTO locus. The results are consistent with reported FTO by environment interactions for BMI, possibly mediated by DNA methylation. Our BMI results for other SNPs and our height results for all SNPs suggest that most genetic variants, including those that influence mean height or mean BMI, are not associated with phenotypic variance, or that their effects on variability are too small to detect even with samples sizes greater than 100,000.

The role of GHR and IGF1 genes in the genetic determination of African pygmies' short stature

African pygmies are at the lower extreme of human variation in adult stature and many evolutionary hypotheses have been proposed to explain this phenotype. We showed in a recent study that the difference in average stature of about 10 cm observed between contemporary pygmies and neighboring non-pygmies has a genetic component. Nevertheless, the genetic basis of African pygmies' short stature remains unknown. Using a candidate-gene approach, we show that intronic polymorphisms in GH receptor (GHR) and insulin-like growth factor 1 (IGF1) genes present outlying values of the genetic distance between Baka pygmies and their non-pygmy Nzimé neighbors. We further show that GHR and IGF1 genes have experienced divergent natural selection pressures between pygmies and non-pygmies throughout evolution. In addition, these SNPs are associated with stature in a sample composed of 60 pygmies and 30 non-pygmies and this association remains significant when correcting for population structure for the GHR locus. We conclude that the GHR and IGF1 genes may have a role in African pygmies' short stature. The use of phenotypically contrasted populations is a promising strategy to identify new variants associated with complex traits in humans.

Effect of genetic variants, especially CYP2C9 and VKORC1, on the pharmacology of warfarin

The genes encoding the cytochrome P450 2C9 enzyme (CYP2C9) and vitamin K-epoxide reductase complex unit 1 (VKORC1) are major determinants of anticoagulant response to warfarin. Together with patient demographics and clinical information, they account for approximately one-half of the warfarin dose variance in individuals of European descent. Recent prospective and randomized controlled trial data support pharmacogenetic guidance with their use in warfarin dose initiation and titration. Benefits from pharmacogenetics-guided warfarin dosing have been reported to extend beyond the period of initial dosing, with supportive data indicating benefits to at least 3 months. The genetic effects of VKORC1 and CYP2C9 in African and Asian populations are concordant with those in individuals of European ancestry; however, frequency distribution of allelic variants can vary considerably between major populations. Future randomized controlled trials in multiethnic settings using population-specific dosing algorithms will allow us to further ascertain the generalizability and cost-effectiveness of pharmacogenetics-guided warfarin therapy. Additional genome-wide association studies may help us to improve and refine dosing algorithms and potentially identify novel biological pathways.

Estimating genetic effects and quantifying missing heritability explained by identified rare-variant associations

Next-generation sequencing has led to many complex-trait rare-variant (RV) association studies. Although single-variant association analysis can be performed, it is grossly underpowered. Therefore, researchers have developed many RV association tests that aggregate multiple variant sites across a genetic region (e.g., gene), and test for the association between the trait and the aggregated genotype. After these aggregate tests detect an association, it is only possible to estimate the average genetic effect for a group of RVs. As a result of the "winner's curse," such an estimate can be biased. Although for common variants one can obtain unbiased estimates of genetic parameters by analyzing a replication sample, for RVs it is desirable to obtain unbiased genetic estimates for the study where the association is identified. This is because there can be substantial heterogeneity of RV sites and frequencies even among closely related populations. In order to obtain an unbiased estimate for aggregated RV analysis, we developed bootstrap-sample-split algorithms to reduce the bias of the winner's curse. The unbiased estimates are greatly important for understanding the population-specific contribution of RVs to the heritability of complex traits. We also demonstrate both theoretically and via simulations that for aggregate RV analysis the genetic variance for a gene or region will always be underestimated, sometimes substantially, because of the presence of noncausal variants or because of the presence of causal variants with effects of different magnitudes or directions. Therefore, even if RVs play a major role in the complex-trait etiologies, a portion of the heritability will remain missing, and the contribution of RVs to the complex-trait etiologies will be underestimated.

Functional analysis of HapMap SNPs

Genome-wide association studies (GWAS) have successfully identified many genetic variants associated with complex diseases and traits. However, functional consequence of genetic variants studied in GWAS is not yet fully investigated, which would hinder the application of GWAS. We therefore performed a systematic functional analysis of HapMap SNPs, which have been most commonly used as the reference panel for GWAS. Our study highlights several characteristics of HapMap SNPs and identifies subsets of genetic variants with interesting functional implication. The results show that HapMap SNPs have good coverage within RefSeq genes, especially within known disease-related genes. On the other hand, only a small percentage of SNPs are non-synonymous SNPs while many SNPs are actually located at gene deserts. Moreover, many functionally important variants are not yet still interrogated. A redesigned SNP reference panel with additional functionally important variants would be useful to identify disease-causal variants in the future genome-wide studies.

Human β cell transcriptome analysis uncovers lncRNAs that are tissue-specific, dynamically regulated, and abnormally expressed in type 2 diabetes

A significant portion of the genome is transcribed as long noncoding RNAs (lncRNAs), several of which are known to control gene expression. The repertoire and regulation of lncRNAs in disease-relevant tissues, however, has not been systematically explored. We report a comprehensive strand-specific transcriptome map of human pancreatic islets and β cells, and uncover >1100 intergenic and antisense islet-cell lncRNA genes. We find islet lncRNAs that are dynamically regulated and show that they are an integral component of the β cell differentiation and maturation program. We sequenced the mouse islet transcriptome and identify lncRNA orthologs that are regulated like their human counterparts. Depletion of HI-LNC25, a β cell-specific lncRNA, downregulated GLIS3 mRNA, thus exemplifying a gene regulatory function of islet lncRNAs. Finally, selected islet lncRNAs were dysregulated in type 2 diabetes or mapped to genetic loci underlying diabetes susceptibility. These findings reveal a new class of islet-cell genes relevant to β cell programming and diabetes pathophysiology.

Contribution of human growth hormone-releasing hormone receptor (GHRHR) gene sequence variation to isolated severe growth hormone deficiency (ISGHD) and normal adult height

OBJECTIVE

Molecular causes of isolated severe growth hormone deficiency (ISGHD) in several genes have been established. The aim of this study was to analyse the contribution of growth hormone-releasing hormone receptor (GHRHR) gene sequence variation to GH deficiency in a series of prepubertal ISGHD patients and to normal adult height.

DESIGN, SUBJECTS AND MEASUREMENTS

A systematic GHRHR gene sequence analysis was performed in 69 ISGHD patients and 60 normal adult height controls (NAHC). Four GHRHR single-nucleotide polymorphisms (SNPs) were genotyped in 248 additional NAHC. An analysis was performed on individual SNPs and combined genotype associations with diagnosis in ISGHD patients and with height-SDS in NAHC.

RESULTS

Twenty-one SNPs were found. P3, P13, P15 and P20 had not been previously described. Patients and controls shared 12 SNPs (P1, P2, P4-P11, P16 and P21). Significantly different frequencies of the heterozygous genotype and alternate allele were detected in P9 (exon 4, rs4988498) and P12 (intron 6, rs35609199); P9 heterozygous genotype frequencies were similar in patients and the shortest control group (heights between -2 and -1 SDS) and significantly different in controls (heights between -1 and +2 SDS). GHRHR P9 together with 4 GH1 SNP genotypes contributed to 6·2% of height-SDS variation in the entire 308 NAHC.

CONCLUSIONS

This study established the GHRHR gene sequence variation map in ISGHD patients and NAHC. No evidence of GHRHR mutation contribution to ISGHD was found in this population, although P9 and P12 SNP frequencies were significantly different between ISGHD and NAHC. Thus, the gene sequence may contribute to normal adult height, as demonstrated in NAHC.

Allelic variations in the vitamin D receptor gene, insulin secretion and parents' heights are independently associated with height in obese children and adolescents

Polymorphisms in the VDR gene were reported to be associated with variations in intrauterine and postnatal growth and with adult height, but also with other traits that are strongly correlated such as the BMI, insulin sensitivity, insulin secretion and hyperglycemia. Here, we assessed the impact of VDR polymorphisms on body height and its interactions with obesity- and glucose tolerance-related traits in obese children and adolescents. We studied 173 prepubertal (Tanner's stage 1) and 146 pubertal (Tanner's stages 2-5) obese children who were referred for a weight-loss program. Three single nucleotide polymorphisms were genotyped: rs1544410 (BsmI), rs7975232 (ApaI) and rs731236 (TaqI). BsmI and TaqI genotypes were significantly associated with height in pubertal children, but the associations did not reach statistical significance in prepubertal children. In stepwise regression analyses, the lean body mass, insulin secretion, BsmI or TaqI genotypes and the father's and the mother's height were independently and positively associated with height in pubertal children. These covariables accounted for 46% of the trait variance. The height of homozygous carriers of the minor allele of BsmI was 0.65 z-scores (4cm) higher than the height of homozygous carriers of the major allele (P=.0006). Haplotype analyses confirmed the associations of the minor alleles of BsmI and TaqI with increased height. In conclusion, VDR genotypes were significantly associated with height in pubertal obese children. The associations were independent from the effects of confounding traits, such as the body fat mass, insulin secretion, insulin sensitivity and glucose tolerance.

Clinical review: Genome-wide association studies of skeletal phenotypes: what we have learned and where we are headed

CONTEXT

The primary goals of genome-wide association studies (GWAS) are to discover new molecular and biological pathways involved in the regulation of bone metabolism that can be leveraged for drug development. In addition, the identified genetic determinants may be used to enhance current risk factor profiles.

EVIDENCE ACQUISITION

There have been more than 40 published GWAS on skeletal phenotypes, predominantly focused on dual-energy x-ray absorptiometry-derived bone mineral density (BMD) of the hip and spine.

EVIDENCE SYNTHESIS

Sixty-six BMD loci have been replicated across all the published GWAS, confirming the highly polygenic nature of BMD variation. Only seven of the 66 previously reported genes (LRP5, SOST, ESR1, TNFRSF11B, TNFRSF11A, TNFSF11, PTH) from candidate gene association studies have been confirmed by GWAS. Among 59 novel BMD GWAS loci that have not been reported by previous candidate gene association studies, some have been shown to be involved in key biological pathways involving the skeleton, particularly Wnt signaling (AXIN1, LRP5, CTNNB1, DKK1, FOXC2, HOXC6, LRP4, MEF2C, PTHLH, RSPO3, SFRP4, TGFBR3, WLS, WNT3, WNT4, WNT5B, WNT16), bone development: ossification (CLCN7, CSF1, MEF2C, MEPE, PKDCC, PTHLH, RUNX2, SOX6, SOX9, SPP1, SP7), mesenchymal-stem-cell differentiation (FAM3C, MEF2C, RUNX2, SOX4, SOX9, SP7), osteoclast differentiation (JAG1, RUNX2), and TGF-signaling (FOXL1, SPTBN1, TGFBR3). There are still 30 BMD GWAS loci without prior molecular or biological evidence of their involvement in skeletal phenotypes. Other skeletal phenotypes that either have been or are being studied include hip geometry, bone ultrasound, quantitative computed tomography, high-resolution peripheral quantitative computed tomography, biochemical markers, and fractures such as vertebral, nonvertebral, hip, and forearm.

CONCLUSIONS

Although several challenges lie ahead as GWAS moves into the next generation, there are prospects of new discoveries in skeletal biology. This review integrates findings from previous GWAS and provides a roadmap for future directions building on current GWAS successes.

Adult height and the risk of cause-specific death and vascular morbidity in 1 million people: individual participant meta-analysis

BACKGROUND

The extent to which adult height, a biomarker of the interplay of genetic endowment and early-life experiences, is related to risk of chronic diseases in adulthood is uncertain.

METHODS

We calculated hazard ratios (HRs) for height, assessed in increments of 6.5 cm, using individual-participant data on 174374 deaths or major non-fatal vascular outcomes recorded among 1085949 people in 121 prospective studies.

RESULTS

For people born between 1900 and 1960, mean adult height increased 0.5-1 cm with each successive decade of birth. After adjustment for age, sex, smoking and year of birth, HRs per 6.5 cm greater height were 0.97 (95% confidence interval: 0.96-0.99) for death from any cause, 0.94 (0.93-0.96) for death from vascular causes, 1.04 (1.03-1.06) for death from cancer and 0.92 (0.90-0.94) for death from other causes. Height was negatively associated with death from coronary disease, stroke subtypes, heart failure, stomach and oral cancers, chronic obstructive pulmonary disease, mental disorders, liver disease and external causes. In contrast, height was positively associated with death from ruptured aortic aneurysm, pulmonary embolism, melanoma and cancers of the pancreas, endocrine and nervous systems, ovary, breast, prostate, colorectum, blood and lung. HRs per 6.5 cm greater height ranged from 1.26 (1.12-1.42) for risk of melanoma death to 0.84 (0.80-0.89) for risk of death from chronic obstructive pulmonary disease. HRs were not appreciably altered after further adjustment for adiposity, blood pressure, lipids, inflammation biomarkers, diabetes mellitus, alcohol consumption or socio-economic indicators.

CONCLUSION

Adult height has directionally opposing relationships with risk of death from several different major causes of chronic diseases.

Common variants at the MHC locus and at chromosome 16q24.1 predispose to Barrett's esophagus

Barrett's esophagus is an increasingly common disease that is strongly associated with reflux of stomach acid and usually a hiatus hernia, and it strongly predisposes to esophageal adenocarcinoma (EAC), a tumor with a very poor prognosis. We report the first genome-wide association study on Barrett's esophagus, comprising 1,852 UK cases and 5,172 UK controls in the discovery stage and 5,986 cases and 12,825 controls in the replication stage. Variants at two loci were associated with disease risk: chromosome 6p21, rs9257809 (Pcombined=4.09×10(-9); odds ratio (OR)=1.21, 95% confidence interval (CI)=1.13-1.28), within the major histocompatibility complex locus, and chromosome 16q24, rs9936833 (Pcombined=2.74×10(-10); OR=1.14, 95% CI=1.10-1.19), for which the closest protein-coding gene is FOXF1, which is implicated in esophageal development and structure. We found evidence that many common variants of small effect contribute to genetic susceptibility to Barrett's esophagus and that SNP alleles predisposing to obesity also increase risk for Barrett's esophagus.

Genome-wide association studies of asthma in population-based cohorts confirm known and suggested loci and identify an additional association near HLA

RATIONALE

Asthma has substantial morbidity and mortality and a strong genetic component, but identification of genetic risk factors is limited by availability of suitable studies.

OBJECTIVES

To test if population-based cohorts with self-reported physician-diagnosed asthma and genome-wide association (GWA) data could be used to validate known associations with asthma and identify novel associations.

METHODS

The APCAT (Analysis in Population-based Cohorts of Asthma Traits) consortium consists of 1,716 individuals with asthma and 16,888 healthy controls from six European-descent population-based cohorts. We examined associations in APCAT of thirteen variants previously reported as genome-wide significant (P<5 x 10(-8)) and three variants reported as suggestive (P<5× 10(-7)). We also searched for novel associations in APCAT (Stage 1) and followed-up the most promising variants in 4,035 asthmatics and 11,251 healthy controls (Stage 2). Finally, we conducted the first genome-wide screen for interactions with smoking or hay fever.

MAIN RESULTS

We observed association in the same direction for all thirteen previously reported variants and nominally replicated ten of them. One variant that was previously suggestive, rs11071559 in RORA, now reaches genome-wide significance when combined with our data (P = 2.4 × 10(-9)). We also identified two genome-wide significant associations: rs13408661 near IL1RL1/IL18R1 (P(Stage1+Stage2) = 1.1x10(-9)), which is correlated with a variant recently shown to be associated with asthma (rs3771180), and rs9268516 in the HLA region (P(Stage1+Stage2) = 1.1x10(-8)), which appears to be independent of previously reported associations in this locus. Finally, we found no strong evidence for gene-environment interactions with smoking or hay fever status.

CONCLUSIONS

Population-based cohorts with simple asthma phenotypes represent a valuable and largely untapped resource for genetic studies of asthma.

Most reported genetic associations with general intelligence are probably false positives

General intelligence (g) and virtually all other behavioral traits are heritable. Associations between g and specific single-nucleotide polymorphisms (SNPs) in several candidate genes involved in brain function have been reported. We sought to replicate published associations between g and 12 specific genetic variants (in the genes DTNBP1, CTSD, DRD2, ANKK1, CHRM2, SSADH, COMT, BDNF, CHRNA4, DISC1, APOE, and SNAP25) using data sets from three independent, well-characterized longitudinal studies with samples of 5,571, 1,759, and 2,441 individuals. Of 32 independent tests across all three data sets, only 1 was nominally significant. By contrast, power analyses showed that we should have expected 10 to 15 significant associations, given reasonable assumptions for genotype effect sizes. For positive controls, we confirmed accepted genetic associations for Alzheimer's disease and body mass index, and we used SNP-based calculations of genetic relatedness to replicate previous estimates that about half of the variance in g is accounted for by common genetic variation among individuals. We conclude that the molecular genetics of psychology and social science requires approaches that go beyond the examination of candidate genes.

Extracting actionable information from genome scans

Genome-wide association studies discovered numerous genetic variants significantly associated with various phenotypes. However, significant signals explain only a small portion of the variation in many traits. One explanation is that missing variation is found in "suggestive signals," i.e., variants with reasonably small P-values. However, it is not clear how to capture this information and use it optimally to design and analyze future studies. We propose to extract the available information from a genome scan by accurately estimating the means of univariate statistics. The means are estimated by: (i) computing the sum of squares (SS) of a genome scan's univariate statistics, (ii) using SS to estimate the expected SS for the means (SSM) of univariate statistics, and (iii) constructing accurate soft threshold (ST) estimators for means of univariate statistics by requiring that the SS of these estimators equals the SSM. When compared to competitors, ST estimators explain a substantially higher fraction of the variability in true means. The accuracy of proposed estimators can be used to design two-tier follow-up studies in which regions close to variants having ST-estimated means above a certain threshold are sequenced at high coverage and the rest of the genome is sequenced at low coverage. This follow-up approach reduces the sequencing burden by at least an order of magnitude when compared to a high coverage sequencing of the whole genome. Finally, we suggest ways in which ST methodology can be used to improve signal detection in future sequencing studies and to perform general statistical model selection.

The genetic architecture of growth rate in juvenile Takifugu species

Closely related species have often evolved dramatic differences in body size. Takifugu rubripes (fugu) is a large marine pufferfish whose genome has been sequenced, whereas T. niphobles is the smallest species among Takifugu. We show that, unsurprisingly, the juvenile growth rate of T. rubripes is higher than that of T. niphobles in a laboratory setting. We produced F(2) progenies of their F(1) hybrids and found one quantitative trait locus (QTL) significantly associated with variation in juvenile body size. This QTL region (3.5 Mb) contains no known genes directly related to growth phenotype (such as IGFs) except Fgf21, which inhibits growth hormone signaling in mouse. The QTL in Takifugu spp. is distinct from the region previously known to control body size variations in stickleback or tilapia. Our results suggest that in the fish tested herein, genomic regions underlying body size evolution might have different genetic origins. They also suggest that many diverse traits in Takifugu spp. are amenable to genetic mapping.

Integration of genome-wide association studies with biological knowledge identifies six novel genes related to kidney function

In conducting genome-wide association studies (GWAS), analytical approaches leveraging biological information may further understanding of the pathophysiology of clinical traits. To discover novel associations with estimated glomerular filtration rate (eGFR), a measure of kidney function, we developed a strategy for integrating prior biological knowledge into the existing GWAS data for eGFR from the CKDGen Consortium. Our strategy focuses on single nucleotide polymorphism (SNPs) in genes that are connected by functional evidence, determined by literature mining and gene ontology (GO) hierarchies, to genes near previously validated eGFR associations. It then requires association thresholds consistent with multiple testing, and finally evaluates novel candidates by independent replication. Among the samples of European ancestry, we identified a genome-wide significant SNP in FBXL20 (P = 5.6 × 10(-9)) in meta-analysis of all available data, and additional SNPs at the INHBC, LRP2, PLEKHA1, SLC3A2 and SLC7A6 genes meeting multiple-testing corrected significance for replication and overall P-values of 4.5 × 10(-4)-2.2 × 10(-7). Neither the novel PLEKHA1 nor FBXL20 associations, both further supported by association with eGFR among African Americans and with transcript abundance, would have been implicated by eGFR candidate gene approaches. LRP2, encoding the megalin receptor, was identified through connection with the previously known eGFR gene DAB2 and extends understanding of the megalin system in kidney function. These findings highlight integration of existing genome-wide association data with independent biological knowledge to uncover novel candidate eGFR associations, including candidates lacking known connections to kidney-specific pathways. The strategy may also be applicable to other clinical phenotypes, although more testing will be needed to assess its potential for discovery in general.

Bag of Naïve Bayes: biomarker selection and classification from genome-wide SNP data

BACKGROUND

Multifactorial diseases arise from complex patterns of interaction between a set of genetic traits and the environment. To fully capture the genetic biomarkers that jointly explain the heritability component of a disease, thus, all SNPs from a genome-wide association study should be analyzed simultaneously.

RESULTS

In this paper, we present Bag of Naïve Bayes (BoNB), an algorithm for genetic biomarker selection and subjects classification from the simultaneous analysis of genome-wide SNP data. BoNB is based on the Naïve Bayes classification framework, enriched by three main features: bootstrap aggregating of an ensemble of Naïve Bayes classifiers, a novel strategy for ranking and selecting the attributes used by each classifier in the ensemble and a permutation-based procedure for selecting significant biomarkers, based on their marginal utility in the classification process. BoNB is tested on the Wellcome Trust Case-Control study on Type 1 Diabetes and its performance is compared with the ones of both a standard Naïve Bayes algorithm and HyperLASSO, a penalized logistic regression algorithm from the state-of-the-art in simultaneous genome-wide data analysis.

CONCLUSIONS

The significantly higher classification accuracy obtained by BoNB, together with the significance of the biomarkers identified from the Type 1 Diabetes dataset, prove the effectiveness of BoNB as an algorithm for both classification and biomarker selection from genome-wide SNP data.

AVAILABILITY

Source code of the BoNB algorithm is released under the GNU General Public Licence and is available at http://www.dei.unipd.it/~sambofra/bonb.html.

A genome-wide association study identifies five loci influencing facial morphology in Europeans

Inter-individual variation in facial shape is one of the most noticeable phenotypes in humans, and it is clearly under genetic regulation; however, almost nothing is known about the genetic basis of normal human facial morphology. We therefore conducted a genome-wide association study for facial shape phenotypes in multiple discovery and replication cohorts, considering almost ten thousand individuals of European descent from several countries. Phenotyping of facial shape features was based on landmark data obtained from three-dimensional head magnetic resonance images (MRIs) and two-dimensional portrait images. We identified five independent genetic loci associated with different facial phenotypes, suggesting the involvement of five candidate genes--PRDM16, PAX3, TP63, C5orf50, and COL17A1--in the determination of the human face. Three of them have been implicated previously in vertebrate craniofacial development and disease, and the remaining two genes potentially represent novel players in the molecular networks governing facial development. Our finding at PAX3 influencing the position of the nasion replicates a recent GWAS of facial features. In addition to the reported GWA findings, we established links between common DNA variants previously associated with NSCL/P at 2p21, 8q24, 13q31, and 17q22 and normal facial-shape variations based on a candidate gene approach. Overall our study implies that DNA variants in genes essential for craniofacial development contribute with relatively small effect size to the spectrum of normal variation in human facial morphology. This observation has important consequences for future studies aiming to identify more genes involved in the human facial morphology, as well as for potential applications of DNA prediction of facial shape such as in future forensic applications.

The evolution of human adiposity and obesity: where did it all go wrong?

Because obesity is associated with diverse chronic diseases, little attention has been directed to the multiple beneficial functions of adipose tissue. Adipose tissue not only provides energy for growth, reproduction and immune function, but also secretes and receives diverse signaling molecules that coordinate energy allocation between these functions in response to ecological conditions. Importantly, many relevant ecological cues act on growth and physique, with adiposity responding as a counterbalancing risk management strategy. The large number of individual alleles associated with adipose tissue illustrates its integration with diverse metabolic pathways. However, phenotypic variation in age, sex, ethnicity and social status is further associated with different strategies for storing and using energy. Adiposity therefore represents a key means of phenotypic flexibility within and across generations, enabling a coherent life-history strategy in the face of ecological stochasticity. The sensitivity of numerous metabolic pathways to ecological cues makes our species vulnerable to manipulative globalized economic forces. The aim of this article is to understand how human adipose tissue biology interacts with modern environmental pressures to generate excess weight gain and obesity. The disease component of obesity might lie not in adipose tissue itself, but in its perturbation by our modern industrialized niche. Efforts to combat obesity could be more effective if they prioritized 'external' environmental change rather than attempting to manipulate 'internal' biology through pharmaceutical or behavioral means.

Small- and large-effect quantitative trait locus interactions underlie variation in yeast sporulation efficiency

Quantitative trait loci (QTL) with small effects on phenotypic variation can be difficult to detect and analyze. Because of this a large fraction of the genetic architecture of many complex traits is not well understood. Here we use sporulation efficiency in Saccharomyces cerevisiae as a model complex trait to identify and study small-effect QTL. In crosses where the large-effect quantitative trait nucleotides (QTN) have been genetically fixed we identify small-effect QTL that explain approximately half of the remaining variation not explained by the major effects. We find that small-effect QTL are often physically linked to large-effect QTL and that there are extensive genetic interactions between small- and large-effect QTL. A more complete understanding of quantitative traits will require a better understanding of the numbers, effect sizes, and genetic interactions of small-effect QTL.

A rare variant in the osteoarthritis-associated locus GDF5 is functional and reveals a site that can be manipulated to modulate GDF5 expression

Osteoarthritis (OA) is a polygenic disease characterized by cartilage loss, with the single-nucleotide polymorphism (SNP) rs143383 (C/T) influencing OA susceptibility across a range of ethnic groups. The SNP resides within the 5′-UTR of the growth and differentiation factor 5 gene (GDF5), with the OA-associated T-allele mediating reduced GDF5 expression. As GDF5 codes for a cartilage anabolic protein, this reduced expression may explain why the T-allele of rs143383 is an OA risk factor. Our deep sequencing of GDF5 identified a C/A transversion located −41 bp relative to the gene's transcription start site. This promoter variant is predicted to affect transcription factor binding and it may therefore highlight a regulatory site that could be exploited to manipulate GDF5 expression and alleviate the detrimental effect mediated by the T-allele of rs143383. Here, we describe our functional assessment of the −41 bp variant. Using reporter constructs we demonstrated that the transversion leads to increased gene expression to such a degree that the A-allele is able to compensate for the reduced expression mediated by the T-allele of rs143383. Using electrophoretic mobility shift assays we identified YY1 as a trans-acting factor that differentially binds to the alleles of the −41 bp variant, with more avid binding to allele A. Knockdown of YY1 led to a significant reduction in GDF5 expression, supporting YY1 as a GDF5 activator. In conclusion, we demonstrated that the −41 bp variant is functional and we have identified a regulatory region of GDF5 that can be exploited to overcome the OA genetic deficit mediated by the T-allele of rs143383.

The HIrisPlex system for simultaneous prediction of hair and eye colour from DNA

Recently, the field of predicting phenotypes of externally visible characteristics (EVCs) from DNA genotypes with the final aim of concentrating police investigations to find persons completely unknown to investigating authorities, also referred to as Forensic DNA Phenotyping (FDP), has started to become established in forensic biology. We previously developed and forensically validated the IrisPlex system for accurate prediction of blue and brown eye colour from DNA, and recently showed that all major hair colour categories are predictable from carefully selected DNA markers. Here, we introduce the newly developed HIrisPlex system, which is capable of simultaneously predicting both hair and eye colour from DNA. HIrisPlex consists of a single multiplex assay targeting 24 eye and hair colour predictive DNA variants including all 6 IrisPlex SNPs, as well as two prediction models, a newly developed model for hair colour categories and shade, and the previously developed IrisPlex model for eye colour. The HIrisPlex assay was designed to cope with low amounts of template DNA, as well as degraded DNA, and preliminary sensitivity testing revealed full DNA profiles down to 63pg input DNA. The power of the HIrisPlex system to predict hair colour was assessed in 1551 individuals from three different parts of Europe showing different hair colour frequencies. Using a 20% subset of individuals, while 80% were used for model building, the individual-based prediction accuracies employing a prediction-guided approach were 69.5% for blond, 78.5% for brown, 80% for red and 87.5% for black hair colour on average. Results from HIrisPlex analysis on worldwide DNA samples imply that HIrisPlex hair colour prediction is reliable independent of bio-geographic ancestry (similar to previous IrisPlex findings for eye colour). We furthermore demonstrate that it is possible to infer with a prediction accuracy of >86% if a brown-eyed, black-haired individual is of non-European (excluding regions nearby Europe) versus European (including nearby regions) bio-geographic origin solely from the strength of HIrisPlex eye and hair colour probabilities, which can provide extra intelligence for future forensic applications. The HIrisPlex system introduced here, including a single multiplex test assay, an interactive tool and prediction guide, and recommendations for reporting final outcomes, represents the first tool for simultaneously establishing categorical eye and hair colour of a person from DNA. The practical forensic application of the HIrisPlex system is expected to benefit cases where other avenues of investigation, including STR profiling, provide no leads on who the unknown crime scene sample donor or the unknown missing person might be.

Gene balance hypothesis: connecting issues of dosage sensitivity across biological disciplines

We summarize, in this review, the evidence that genomic balance influences gene expression, quantitative traits, dosage compensation, aneuploid syndromes, population dynamics of copy number variants and differential evolutionary fate of genes after partial or whole-genome duplication. Gene balance effects are hypothesized to result from stoichiometric differences among members of macromolecular complexes, the interactome, and signaling pathways. The implications of gene balance are discussed.

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

Strong signatures of positive selection at newly arising genetic variants are well-documented in humans^1–8, but this form of selection may not be widespread in recent human evolution⁹. Because many human traits are highly polygenic and partly determined by common, ancient genetic variation, an alternative model for rapid genetic adaptation has been proposed: weak selection acting on many pre-existing (standing) genetic variants, or polygenic adaptation^10–12. By studying height, a classic polygenic trait, we demonstrate the first human signature of widespread selection on standing variation. We show that frequencies of alleles associated with increased height, both at known loci and genome-wide, are systematically elevated in Northern Europeans compared with Southern Europeans (p<4.3×10⁻⁴). This pattern mirrors intra-European height differences and is not confounded by ancestry or other ascertainment biases. The systematic frequency differences are consistent with the presence of widespread weak selection (selection coefficients ~10⁻³–10⁻⁵ per allele) rather than genetic drift alone (p<10⁻¹⁵).

Understanding and predicting complex traits: knowledge from cattle

The genetic architecture of complex traits in cattle includes very large numbers of loci affecting any given trait. Most of these loci have small effects but occasionally there are loci with moderate-to-large effects segregating due to recent selection for the mutant allele. Genomic markers capture most but not all of the additive genetic variance for traits, probably because there are causal mutations with low allele frequency and therefore in incomplete linkage disequilibrium with the markers. The prediction of genetic value from genomic markers can achieve high accuracy by using statistical models that include all markers and assuming that marker effects are random variables drawn from a specified prior distribution. Recent effective population size is in the order of 100 within cattle breeds and ≈ 2500 animals with genotypes and phenotypes are sufficient to predict the genetic value of animals with an accuracy of 0.65. Recent effective population size for humans is much larger, in the order of 10,000-15,000, and more than 145,000 records would be required to reach a similar accuracy for people. However, our calculations assume that genomic markers capture all the genetic variance. This may be possible in the future as causal polymorphisms are genotyped using genome sequence data.

Common genetic variants and gene-expression changes associated with bipolar disorder are over-represented in brain signaling pathway genes

BACKGROUND

Despite high heritability, the genetic variants influencing bipolar disorder (BD) susceptibility remain largely unknown. Low statistical power to detect the small effect-size alleles believed to underlie much of the genetic risk and possible heterogeneity between cohorts are an increasing concern. Integrative biology approaches might offer advantages over genetic analysis alone by combining different genomic datasets at the higher level of biological processes rather than the level of specific genetic variants or genes. We employed this strategy to identify biological processes involved in BD etiopathology.

METHOD

Three genome-wide association studies and a brain gene-expression study were combined with the Human Protein Reference Database protein-protein interaction network data. We used bioinformatic analysis to search for biological networks with evidence of association on the basis of enrichment among both genetic and differential-expression associations with BD.

RESULTS

We identified association with gene networks involved in transmission of nerve impulse, Wnt, and Notch signaling. Three features stand out among these genes: 1) they localized to the human postsynaptic density, which is crucial for neuronal function; 2) their mouse knockouts present altered behavioral phenotypes; and 3) some are known targets of the pharmacological treatments for BD.

CONCLUSIONS

Genetic and gene-expression associations of BD cluster in discrete regions of the protein-protein interaction network. We found replicated evidence for association for networks involving several interlinked signaling pathways. These genes are promising candidates to generate animal models and pharmacological interventions. Our results demonstrate the potential advantage of integrative biology analyses of BD datasets.

Identifying the genetic variation of gene expression using gene sets: application of novel gene Set eQTL approach to PharmGKB and KEGG

Genetic variation underlying the regulation of mRNA gene expression in humans may provide key insights into the molecular mechanisms of human traits and complex diseases. Current statistical methods to map genetic variation associated with mRNA gene expression have typically applied standard linkage and/or association methods; however, when genome-wide SNP and mRNA expression data are available performing all pair wise comparisons is computationally burdensome and may not provide optimal power to detect associations. Consideration of different approaches to account for the high dimensionality and multiple testing issues may provide increased efficiency and statistical power. Here we present a novel approach to model and test the association between genetic variation and mRNA gene expression levels in the context of gene sets (GSs) and pathways, referred to as gene set - expression quantitative trait loci analysis (GS-eQTL). The method uses GSs to initially group SNPs and mRNA expression, followed by the application of principal components analysis (PCA) to collapse the variation and reduce the dimensionality within the GSs. We applied GS-eQTL to assess the association between SNP and mRNA expression level data collected from a cell-based model system using PharmGKB and KEGG defined GSs. We observed a large number of significant GS-eQTL associations, in which the most significant associations arose between genetic variation and mRNA expression from the same GS. However, a number of associations involving genetic variation and mRNA expression from different GSs were also identified. Our proposed GS-eQTL method effectively addresses the multiple testing limitations in eQTL studies and provides biological context for SNP-expression associations.

So many doggone traits: mapping genetics of multiple phenotypes in the domestic dog

The worldwide dog population is fragmented into >350 domestic breeds. Breeds share a common ancestor, the gray wolf. The intense artificial selection imposed by humans to develop breeds with particular behaviors and phenotypic traits has occurred primarily in the last 200-300 years. As a result, the number of genes controlling the major differences in body size, leg length, head shape, etc. that define each dog is small, and genetically tractable. This is in comparison to many human complex traits where small amounts of variance are controlled by literally hundreds of genes. We have been interested in disentangling the genetic mechanisms controlling breed-defining morphological traits in the domestic dog. The structure of the dog population, comprised large numbers of pure breeding populations, makes this task surprisingly doable. In this review, we summarize recent work on the genetics of body size, leg length and skull shape, while setting the stage for tackling other traits. It is our expectation that these results will contribute to a better understanding of mammalian developmental processes overall.

Candidate and non-candidate genes in behavior genetics

In this review we discuss recent developments in psychiatric genetics: on the one hand, studies using whole genome approaches (genome-wide association studies (GWAS) and exome sequencing) are coming close to finding genes and molecular variants that contribute to disease susceptibility; on the other candidate genes, such as the serotonin transporter, continue to dominate in genetic studies of brain imaging phenotypes and in protracted searches for gene by environment interactions. These two areas intersect, in that new information about genetic effects from whole genome approaches, should (but does not always) inform the single locus analyses.