Large-scale Association Analyses Research Articles

Variant effect predictors: a systematic review and practical guide.

Large-scale association analyses using whole-genome sequence data have become feasible, but understanding the functional impacts of these associations remains challenging. Although many tools are available to predict the functional impacts of genetic variants, it is unclear which tool should be used in practice. This work provides a practical guide to assist in selecting appropriate tools for variant annotation. We conducted a MEDLINE search up to November 10, 2023, and included tools that are applicable to a broad range of phenotypes, can be used locally, and have been recently updated. Tools were categorized based on the types of variants they accept and the functional impacts they predict. Sequence Ontology terms were used for standardization. We identified 118 databases and software packages, encompassing 36 variant types and 161 functional impacts. Combining only three tools, namely SnpEff, FAVOR, and SparkINFERNO, allows predicting 99 (61%) distinct functional impacts. Thirty-seven tools predict 89 functional impacts that are not supported by any other tool, while 75 tools predict pathogenicity and can be used within the ACMG/AMP guidelines in a clinical context. We launched a website allowing researchers to select tools based on desired variants and impacts. In summary, more than 100 tools are already available to predict approximately 160 functional impacts. About 60% of the functional impacts can be predicted by the combination of three tools. Unexpectedly, recent tools do not predict more impacts than older ones. Future research should allow predicting the functionality of so far unsupported variant types, such as gene fusions.URL: https://cardio-care.shinyapps.io/VEP_Finder/ .Registration: OSF Registries on November 10, 2023, https://osf.io/s2gct .

A Shared Genetic Signature for Common Chronic Pain Conditions and its Impact on Biopsychosocial Traits

The multiple comorbidities & dimensions of chronic pain present a formidable challenge in disentangling its aetiology. Here, we performed genome-wide association studies of 8 chronic pain types using UK Biobank data (N =4,037–79,089 cases; N = 239,125 controls), followed by bivariate linkage disequilibrium-score regression and latent causal variable analyses to determine (respectively) their genetic correlations and genetic causal proportion (GCP) parameters with 1,492 other complex traits. We report evidence of a shared genetic signature across chronic pain types as their genetic correlations and GCP directions were broadly consistent across an array of biopsychosocial traits. Across 5,942 significant genetic correlations, 570 trait pairs could be explained by a causal association (|GCP| >0.6; 5% false discovery rate), including 82 traits affected by pain while 410 contributed to an increased risk of chronic pain (cf. 78 with a decreased risk) such as certain somatic pathologies (eg, musculoskeletal), psychiatric traits (eg, depression), socioeconomic factors (eg, occupation) and medical comorbidities (eg, cardiovascular disease). This data-driven phenome-wide association analysis has demonstrated a novel and efficient strategy for identifying genetically supported risk & protective traits to enhance the design of interventional trials targeting underlying causal factors and accelerate the development of more effective treatments with broader clinical utility. PerspectiveThrough large-scale phenome-wide association analyses of >1,400 biopsychosocial traits, this article provides evidence for a shared genetic signature across 8 common chronic pain types. It lays the foundation for further translational studies focused on identifying causal genetic variants and pathophysiological pathways to develop novel diagnostic & therapeutic technologies and strategies.

Genetic evidence for association of NOTCH4 variant rs2071287 with schizophrenia susceptibility in the North Indian population.

Neurogenic locus notch homolog 4 (NOTCH4) regulates signaling pathways associated with neuronal maturation, a process involved in the development and patterning of the central nervous system. The NOTCH4 gene has also been identified as a possible susceptibility gene for schizophrenia (SCZ). The study aimed to determine the association of NOTCH4 polymorphisms with the risk of SCZ in the North Indian population of the Jammu region. The single nucleotide polymorphism genotyping for NOTCH4 variant rs2071287 was done by Sanger's sequencing method, and the other variant rs3131296 was done by TaqMan assay method for 207 SCZ cases and 304 healthy controls of North Indian origin. This association study suggested that the rs2071287 was found to be significantly associated with SCZ. Moreover, the GG genotype of rs2071287 was observed to be associated with a higher risk for SCZ (P-value = 6.45 × 10 - 5; OR = 1.71; 95% CI, 1.31-2.24). To establish the potential biomarker role of this variant, large-scale association analyses in other populations is required.

The genetic architecture of sporadic and multiple consecutive miscarriage

Miscarriage is a common, complex trait affecting ~15% of clinically confirmed pregnancies. Here we present the results of large-scale genetic association analyses with 69,054 cases from five different ancestries for sporadic miscarriage, 750 cases of European ancestry for multiple (≥3) consecutive miscarriage, and up to 359,469 female controls. We identify one genome-wide significant association (rs146350366, minor allele frequency (MAF) 1.2%, P = 3.2 × 10−8, odds ratio (OR) = 1.4) for sporadic miscarriage in our European ancestry meta-analysis and three genome-wide significant associations for multiple consecutive miscarriage (rs7859844, MAF = 6.4%, P = 1.3 × 10−8, OR = 1.7; rs143445068, MAF = 0.8%, P = 5.2 × 10−9, OR = 3.4; rs183453668, MAF = 0.5%, P = 2.8 × 10−8, OR = 3.8). We further investigate the genetic architecture of miscarriage with biobank-scale Mendelian randomization, heritability, and genetic correlation analyses. Our results show that miscarriage etiopathogenesis is partly driven by genetic variation potentially related to placental biology, and illustrate the utility of large-scale biobank data for understanding this pregnancy complication.

The Role of Chromosome X in Intraocular Pressure Variation and Sex-Specific Effects.

PurposeThe purpose of this study was to identify genetic variants on chromosome X associated with intraocular pressure (IOP) and determine if they possess any sex-specific effects.MethodsAssociation analyses were performed across chromosome X using 102,407 participants from the UK Biobank. Replication and validation analyses were conducted in an additional 6599 participants from the EPIC-Norfolk cohort, and an independent 331,682 participants from the UK Biobank.ResultsWe identified three loci associated with IOP at genomewide significance (P < 5 × 10−8), located within or near the following genes: MXRA5 (rs2107482, P = 7.1 × 10−11), GPM6B (rs66819623, P = 6.9 × 10−10), NDP, and EFHC2 (rs12558081, P = 4.9 × 10−11). Alleles associated with increased IOP were also associated with increased risk for primary open-angle glaucoma in an independent sample. Finally, our results indicate that chromosome X genetics most likely do not illicit sex-specific effects on IOP.ConclusionsIn this study, we report the results of genomewide levels of association of three loci on chromosome X with IOP, and provide a framework to include chromosome X in large-scale genomewide association analyses for complex phenotypes.

Scalable interpretable learning for multi-response error-in-variables regression

Corrupted data sets containing noisy or missing observations are prevalent in various contemporary applications such as economics, finance and bioinformatics. Despite the recent methodological and algorithmic advances in high-dimensional multi-response regression, how to achieve scalable and interpretable estimation under contaminated covariates is unclear. In this paper, we develop a new methodology called convex conditioned sequential sparse learning (COSS) for error-in-variables multi-response regression under both additive measurement errors and random missing data. It combines the strengths of the recently developed sequential sparse factor regression and the nearest positive semi-definite matrix projection, thus enjoying stepwise convexity and scalability in large-scale association analyses. Comprehensive theoretical guarantees are provided and we demonstrate the effectiveness of the proposed methodology through numerical studies.

A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma.

Molecular quantitative trait locus (QTL) analyses are increasingly popular to explore the genetic architecture of complex traits, but existing studies do not leverage shared regulatory patterns and suffer from a large multiplicity burden, which hampers the detection of weak signals such as trans associations. Here, we present a fully multivariate proteomic QTL (pQTL) analysis performed with our recently proposed Bayesian method LOCUS on data from two clinical cohorts, with plasma protein levels quantified by mass-spectrometry and aptamer-based assays. Our two-stage study identifies 136 pQTL associations in the first cohort, of which >80% replicate in the second independent cohort and have significant enrichment with functional genomic elements and disease risk loci. Moreover, 78% of the pQTLs whose protein abundance was quantified by both proteomic techniques are confirmed across assays. Our thorough comparisons with standard univariate QTL mapping on (1) these data and (2) synthetic data emulating the real data show how LOCUS borrows strength across correlated protein levels and markers on a genome-wide scale to effectively increase statistical power. Notably, 15% of the pQTLs uncovered by LOCUS would be missed by the univariate approach, including several trans and pleiotropic hits with successful independent validation. Finally, the analysis of extensive clinical data from the two cohorts indicates that the genetically-driven proteins identified by LOCUS are enriched in associations with low-grade inflammation, insulin resistance and dyslipidemia and might therefore act as endophenotypes for metabolic diseases. While considerations on the clinical role of the pQTLs are beyond the scope of our work, these findings generate useful hypotheses to be explored in future research; all results are accessible online from our searchable database. Thanks to its efficient variational Bayes implementation, LOCUS can analyze jointly thousands of traits and millions of markers. Its applicability goes beyond pQTL studies, opening new perspectives for large-scale genome-wide association and QTL analyses.Diet, Obesity and Genes (DiOGenes) trial registration number: NCT00390637.

An analytic framework for exploring sampling and observation process biases in genome and phenome-wide association studies using electronic health records.

Large-scale association analyses based on observational health care databases such as electronic health records have been a topic of increasing interest in the scientific community. However, challenges due to nonprobability sampling and phenotype misclassification associated with the use of these data sources are often ignored in standard analyses. The extent of the bias introduced by ignoring these factors is not well-characterized. In this paper, we develop an analytic framework for characterizing the bias expected in disease-gene association studies based on electronic health records when disease status misclassification and the sampling mechanism are ignored. Through a sensitivity analysis approach, this framework can be used to obtain plausible values for parameters of interest given summary results from standard analysis. We develop an online tool for performing this sensitivity analysis. Simulations demonstrate promising properties of the proposed method. We apply our approach to study bias in disease-gene association studies using electronic health record data from the Michigan Genomics Initiative, a longitudinal biorepository effort within The University Michigan health system.

Genetic variants of ADAMTS7 confer risk for ischaemic stroke in the Chinese population.

Large-scale genome-wide association analyses show an association between ADAMTS7 variations and coronary risk. However, the link between ADAMTS7 variability and ischaemic stroke (IS) has yet to be determined. This study evaluated ADAMTS7 variants with respect to the risk of IS. Genetic association analyses were performed in two independent case-control cohorts with 1279 patients with IS and 1268 age-matched healthy controls. Four variant genotypes of the ADAMTS7 gene were identified using the Multiplex SNaPshot assay. The rs3825807, rs11634042, and rs7173743 variants of ADAMTS7 were related to lower IS risk in both initial and replication cohort. The G-T-T-C and G-T-C-C haplotypes are significantly less prevalent in the IS group than in the control group. Further stratification according to IS subtypes indicated that carriers with the variant alleles of the rs3825807, rs11634042 and rs7173743 variants of ADAMTS7conferred a lower risk of developing large-artery atherosclerosis stroke subtype. Also, the mutated rs3825807 G allele, as well as the mutated rs11634042 T allele of ADAMTS7, are linked to a significant reduction of ADAMTS7 in patients with IS. Our findings confirm the role of ADAMTS7 in the pathophysiology of IS, with potentially significant implications for the prevention, treatment, and development of novel therapies for IS.

Expression QTLs Mapping and Analysis: A Bayesian Perspective.

The aim of expression Quantitative Trait Locus (eQTL) mapping is the identification of DNA sequence variants that explain variation in gene expression. Given the recent yield of trait-associated genetic variants identified by large-scale genome-wide association analyses (GWAS), eQTL mapping has become a useful tool to understand the functional context where these variants operate and eventually narrow down functional gene targets for disease. Despite its extensive application to complex (polygenic) traits and disease, the majority of eQTL studies still rely on univariate data modeling strategies, i.e., testing for association of all transcript-marker pairs. However these "one at-a-time" strategies are (1) unable to control the number of false-positives when an intricate Linkage Disequilibrium structure is present and (2) are often underpowered to detect the full spectrum of trans-acting regulatory effects. Here we present our viewpoint on the most recent advances on eQTL mapping approaches, with a focus on Bayesian methodology. We review the advantages of the Bayesian approach over frequentist methods and provide an empirical example of polygenic eQTL mapping to illustrate the different properties of frequentist and Bayesian methods. Finally, we discuss how multivariate eQTL mapping approaches have distinctive features with respect to detection of polygenic effects, accuracy, and interpretability of the results.

Imputation-based population genetics analysis of Plasmodium falciparum malaria parasites.

Whole-genome sequencing technologies are being increasingly applied to Plasmodium falciparum clinical isolates to identify genetic determinants of malaria pathogenesis. However, genome-wide discovery methods, such as haplotype scans for signatures of natural selection, are hindered by missing genotypes in sequence data. Poor correlation between single nucleotide polymorphisms (SNPs) in the P. falciparum genome complicates efforts to apply established missing-genotype imputation methods that leverage off patterns of linkage disequilibrium (LD). The accuracy of state-of-the-art, LD-based imputation methods (IMPUTE, Beagle) was assessed by measuring allelic r2 for 459 P. falciparum samples from malaria patients in 4 countries: Thailand, Cambodia, Gambia, and Malawi. In restricting our analysis to 86k high-quality SNPs across the populations, we found that the complete-case analysis was restricted to 21k SNPs (24.5%), despite no single SNP having more than 10% missing genotypes. The accuracy of Beagle in filling in missing genotypes was consistently high across all populations (allelic r2, 0.87-0.96), but the performance of IMPUTE was mixed (allelic r2, 0.34-0.99) depending on reference haplotypes and population. Positive selection analysis using Beagle-imputed haplotypes identified loci involved in resistance to chloroquine (crt) in Thailand, Cambodia, and Gambia, sulfadoxine-pyrimethamine (dhfr, dhps) in Cambodia, and artemisinin (kelch13) in Cambodia. Tajima’s D-based analysis identified genes under balancing selection that encode well-characterized vaccine candidates: apical merozoite antigen 1 (ama1) and merozoite surface protein 1 (msp1). In contrast, the complete-case analysis failed to identify any well-validated drug resistance or candidate vaccine loci, except kelch13. In a setting of low LD and modest levels of missing genotypes, using Beagle to impute P. falciparum genotypes is a viable strategy for conducting accurate large-scale population genetics and association analyses, and supporting global surveillance for drug resistance markers and candidate vaccine antigens.

The Effects of Genetic Background on Psychiatric Behaviors in CACNA1C +/‐ Mice

Bipolar disorder and schizophrenia are genetic‐based psychiatric disorders. The heritability of bipolar disorder and schizophrenia, around 70% ‐ 90% and 83%, respectively, shows that the genetic variation in certain genes help leads to the development of these psychiatric disorders and that studying these genetic variations will help us understand the physiological mechanisms that lead to these psychiatric disorders. Among other genes, CACNA1C was identified as a gene, via large‐scale genome‐wide association analyses, that contributes to a person's risk for these disorders. However, the effects of the genes found only account for a small percentage of the heritable risk of these psychiatric diseases. There has been evidence that the interactions between genes, or epistasis, may account for a significant amount of the heritability. We identified different genetic interactions with CACNA1C by utilizing mouse genetics. The behavioral phenotypes of both wild‐type and CACNA1C +/‐ mice of 19 different genetic backgrounds were observed via a prepulse inhibition test. Each test measures specific behaviors that correlate to neurobiological characteristics of bipolar disorder and schizophrenia. Our data suggests that the CACNA1C +/‐ mutation has different effects on the mouse's prepulse inhibition, depending on the genetic background. The data also suggests that the CACNA1C +/‐ mutation trends toward a decreased startle response in females, while not in males. This allows us to suggest that the CACNA1C gene does undergo epistasis. In the future, we will conduct a genome‐wide association study for both trait markers and modifier markers. We will then identify high‐priority candidate modifier genes that we may be able to confirm in human data sets.

Association of the NOTCH4 Gene Polymorphism rs204993 with Schizophrenia in the Chinese Han Population.

NOTCH4 regulates signaling pathways associated with neuronal maturation, a process involved in the development and patterning of the central nervous system. The NOTCH4 gene has also been identified as a possible susceptibility gene for schizophrenia (SCZ). The objective of this study was to examine the relationship between NOTCH4 polymorphisms and SCZ in the Chinese Han population. The rs2071287 and rs204993 polymorphisms of the NOTCH4 gene were analyzed in 443 patients with SCZ and 628 controls of Han Chinese descent. Single SNP allele-, genotype-, and gender-specific associations were analyzed using different models (i.e., additive, dominant, and recessive models). This association study revealed that the rs204993 polymorphism is significantly associated with susceptibility for SCZ and that the AA genotype of rs204993 is associated with a higher risk for SCZ (P = 0.027; OR = 1.460; 95% CI, 1.043–2.054). Our data are consistent with those obtained in previous studies that suggested that rs204993 is associated with SCZ and that the AA genotype of rs204993 demonstrates a higher risk. Further large-scale association analyses in Han Chinese populations are warranted.

Meta-analysis of genome-wide association studies of adult height in East Asians identifies 17 novel loci.

Human height is associated with risk of multiple diseases and is profoundly determined by an individual's genetic makeup and shows a high degree of ethnic heterogeneity. Large-scale genome-wide association (GWA) analyses of adult height in Europeans have identified nearly 180 genetic loci. A recent study showed high replicability of results from Europeans-based GWA studies in Asians; however, population-specific loci may exist due to distinct linkage disequilibrium patterns. We carried out a GWA meta-analysis in 93 926 individuals from East Asia. We identified 98 loci, including 17 novel and 81 previously reported loci, associated with height at P < 5 × 10(-8), together explaining 8.89% of phenotypic variance. Among the newly identified variants, 10 are commonly distributed (minor allele frequency, MAF > 5%) in Europeans, with comparable frequencies with in Asians, and 7 single-nucleotide polymorphisms are with low frequency (MAF < 5%) in Europeans. In addition, our data suggest that novel biological pathway such as the protein tyrosine phosphatase family is involved in regulation of height. The findings from this study considerably expand our knowledge of the genetic architecture of human height in Asians.

Lack of association of a genetic variant in the long intergenic noncoding RNA (linc01080) with Alzheimer's disease and amnestic mild cognitive impairment in Han Chinese

Alzheimer's disease (AD) is a complex neurodegenerative disorder that is the most common form of dementia in the elderly and is characterized by progressive memory loss and cognitive dysfunction. Although the pathogenetic mechanism of AD is still unknown, genetic variants play a critical role in the pathogenesis of AD. Increasing evidence has suggested that noncoding RNAs (ncRNAs) may be associated with the development of AD. A previous study analyzing human single-nucleotide polymorphisms (SNPs) of long intergenic noncoding RNA (lincRNA) at the genome level indicated that a genetic variant, rs7990916, in the lincRNA 01080 (linc01080) may play an important role in the physiology and pathophysiology of the human brain. To determine whether this SNP alters the risk for sporadic AD (SAD) or amnestic mild cognitive impairment (aMCI) susceptibility, we conducted a case–control study in a Han Chinese population with 106 SAD patients, 67 aMCI patients, and 179 healthy controls. Using the LDR-PCR genotyping method, we found no significant difference in allele or genotype frequency in the SNP rs7990916 between patients and controls (p > 0.05). Our results do not support previous findings, suggesting that further studies by using large-scale association analyses are warranted in Han Chinese populations.

Analysis of metabolic syndrome components in >15 000 african americans identifies pleiotropic variants: results from the population architecture using genomics and epidemiology study.

Metabolic syndrome (MetS) refers to the clustering of cardiometabolic risk factors, including dyslipidemia, central adiposity, hypertension, and hyperglycemia, in individuals. Identification of pleiotropic genetic factors associated with MetS traits may shed light on key pathways or mediators underlying MetS. Using the Metabochip array in 15 148 African Americans from the Population Architecture using Genomics and Epidemiology (PAGE) study, we identify susceptibility loci and investigate pleiotropy among genetic variants using a subset-based meta-analysis method, ASsociation-analysis-based-on-subSETs (ASSET). Unlike conventional models that lack power when associations for MetS components are null or have opposite effects, Association-analysis-based-on-subsets uses 1-sided tests to detect positive and negative associations for components separately and combines tests accounting for correlations among components. With Association-analysis-based-on-subsets, we identify 27 single nucleotide polymorphisms in 1 glucose and 4 lipids loci (TCF7L2, LPL, APOA5, CETP, and APOC1/APOE/TOMM40) significantly associated with MetS components overall, all P<2.5e-7, the Bonferroni adjusted P value. Three loci replicate in a Hispanic population, n=5172. A novel African American-specific variant, rs12721054/APOC1, and rs10096633/LPL are associated with ≥3 MetS components. We find additional evidence of pleiotropy for APOE, TOMM40, TCF7L2, and CETP variants, many with opposing effects (eg, the same rs7901695/TCF7L2 allele is associated with increased odds of high glucose and decreased odds of central adiposity). We highlight a method to increase power in large-scale genomic association analyses and report a novel variant associated with all MetS components in African Americans. We also identify pleiotropic associations that may be clinically useful in patient risk profiling and for informing translational research of potential gene targets and medications.

An efficient approach to large-scale genotype–phenotype association analyses

Modern molecular biotechnology generates a great deal of intermediate information, such as transcriptional and metabolic products in bridging DNA and complex traits. In genome-wide linkage analysis and genome-wide association study, regression analysis for large-scale correlated phenotypes is applied to map genes for those by-products that are regarded as quantitative traits. For a single trait, least absolute shrinkage and selection operator with coordinate descent step can be employed to efficiently shrink sparse non-zero genetic effects of quantitative trait loci (QTLs). However, regression analyses in a trait-by-trait basis do not take account of the correlations among the analyzed traits. In this study, conditional phenotype of each trait is defined, given other traits. Large-scale genotype-phenotype association analyses are therefore transformed to separate genotype-conditional phenotype ones. Meanwhile, the correlation architecture between each trait and other traits can also be provided by shrinkage estimation for each conditional phenotype. Simulation demonstrates that the proposed conditional mapping method is generally identical to joint mapping method based on multivariate analysis in terms of statistical detection power and parameter estimation. Application of the method is provided to locate eQTL in yeast.

Modeling temporal relationships in large scale clinical associations

We describe an approach for modeling temporal relationships in a large scale association analysis of electronic health record data. The addition of temporal information can inform hypothesis generation and help to explain the relationships. We applied this approach on a dataset containing 41.2 million time-stamped International Classification of Diseases, Ninth Revision (ICD-9) codes from 1.6 million patients. We performed two independent analyses including a pairwise association analysis using a χ(2) test and a temporal analysis using a binomial test. Data were visualized using network diagrams and reviewed for clinical significance. We found nearly 400 000 highly associated pairs of ICD-9 codes with varying numbers of strong temporal associations ranging from ≥1 day to ≥10 years apart. Most of the findings were not considered clinically novel, although some, such as an association between Helicobacter pylori infection and diabetes, have recently been reported in the literature. The temporal analysis in our large cohort, however, revealed that diabetes usually preceded the diagnoses of H pylori, raising questions about possible cause and effect. Such analyses have significant limitations, some of which are due to known problems with ICD-9 codes and others to potentially incomplete data even at a health system level. Nevertheless, large scale association analyses with temporal modeling can help provide a mechanism for novel discovery in support of hypothesis generation. Temporal relationships can provide an additional layer of meaning in identifying and interpreting clinical associations.

Interest, rationale, and potential clinical applications of genetic testing for mood disorders: A survey of stakeholders

Bipolar disorder (BP) and major depressive disorder (MDD) are among the most severe, persistent, and debilitating illnesses occurring in young people, with an estimated lifetime prevalence of approximately 1-1.5% for BP (Kessler et al., 2005; Kato, 2007; Merikangas et al. 2007; Braff and Freedman, 2008) and 9.2–19.6% for MDD (Kessler et al., 2005; Vicente et al. 2006; Kao et al. 2011). Although genetic testing for psychiatric disorders has been the goal of psychiatrists and geneticists for years (Braff and Freedman, 2008) this goal has been difficult to attain since the impact of individual genes on risk for psychiatric illness is small, often nonspecific and embedded in intricate causal pathways (Kendler, 2005). In contrast to Mendelian disorders, tests for complex conditions such as MDD and BP may yield only weak estimates of risk, as such conditions appear to be caused by a complicated interplay of multiple genetic and environmental factors, and the associated genetic variants may be common and confer only a small increment of increased or decreased risk (Wright and Kroese, 2010). Major depressive disorder and BP clearly have an important genetic component (Trippitelli et al. 1998; Bienvenu et al., 2010), yet results from genetic testing research have been largely confusing and contradictory (Jordan and Tsai, 2010). However, recent progress in the identification of millions of genetic markers and in powerful detection technologies has made it feasible to perform large-scale association analyses that have enough statistical power to identify genes exerting a relatively minor effect on the likelihood of a particular ailment (Bienvenu et al., 2010; Jordan and Tsai, 2010). In the current DNA-centered mindset, genetic tests are often seen as the first step towards new therapies, and many patient groups are lobbying for their development in the belief that such testing can be helpful in selecting the best treatment (as may occasionally be the case) or in reassuring anxious parents (Jordan and Tsai, 2010). Companies involved in molecular diagnostics, a $20 billion market in the United States, strive to develop diagnostic kits and systems, (Jordan and Tsai, 2010). Tests claiming to indicate increased genetic risks for BP and antidepressant response are already available commercially from companies, such as 23andMe. Great hopes are attached to psychiatric genetics with the goal of both better treatment and prevention and the anticipated de-stigmatizing effect of attributing mental illness to biological causes (Smits et al., 2007; Braff and Freedman, 2008; Couzin, 2008; Laegsgaard et al., 2009; Hoop et al., 2010; Facio et al., 2011). For carriers of variants that influence risk, genetic testing provides the potential to improve outcomes through prevention, early detection, tailored medication, and a better-quality basis for making reproductive decisions (Meiser et al., 2008). Studies have found positive attitudes towards predictive genetic testing for predisposition to psychiatric disorders among various stakeholders. These stakeholders have included: people with an unspecified psychiatric diagnosis (Laegsgaard and Mors, 2008), individuals affected by BP (Trippitelli et al., 1998; Laegsgaard et al., 2009) or MDD (Laegsgaard et al., 2009), people with multiple relatives affected by BP (Jones et al., 2002; Meiser et al., 2005, 2008), psychiatrists (Jones et al., 2002; Hoop et al., 2010), psychology students (Laegsgaard and Mors, 2008), and the general public (Wilde et al., 2010). This interest in psychiatric genetic testing seems to remain even without available preventative treatment options (Laegsgaard and Mors, 2008, 2009) and even if results offer only a probabilistic rather than definitive risk (Wilde et al., 2010). In spite of the positive attitudes toward genetic testing for mood disorders demonstrated by these studies, key related topics have not been explored in depth. There is limited research examining: specific reasons why individuals are interested in genetic tests for mood disorders (Trippitelli et al., 1998; Mesier et al., 2008; Wilhelm et al., 2009), how the level of test predictability affects interest in genetic testing for mood disorders, and the preferred manner to receive genetic test results for mood disorders (Wilde et al., 2010; Borry et al., 2010). This study explored these issues with individuals with a personal and/or family history of mood disorders as well as individuals with no personal or family history of mood disorders. The study examined potential reasons why stakeholders might be interested in genetic testing for risk of mood disorders, how predictive value affects level of interest in testing and preferences for the best manner to receive test results. It also hopes to add further insights to the ongoing debate about the potential impact of genetic attribution on the stigma associated with mental illness (Mechanic et al., 1994; Meiser et al., 2007; Laegsgaard and Mors, 2008; Meiser et al., 2008; Wilde et al., 2010).

Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.

Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have raised the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional follow-up of these newly discovered loci will further improve our understanding of glycemic control.