Detecting epistasis with restricted response patterns in pairs of biallelic loci.

Well-established examples of genetic epistasis between a pair of loci typically show characteristic patterns of phenotypic distributions in joint genotype tables. However, inferring epistasis given such data is difficult due to the lack of power in commonly used approaches, which decompose the epistatic patterns into main plus interaction effects followed by testing the interaction term. Testing additive-only or all terms may have more power, but they are sensitive to nonepistatic patterns. Alternatively, the epistatic patterns of interest can be enumerated and the best matching one is found by searching through the possibilities. Although this approach requires multiple testing correction over possible patterns, each pattern can be fitted with a regression model with just one degree of freedom and thus the overall power can still be high, if the number of possible patterns is limited. Here we compare the power of the linear decomposition and pattern search methods, by applying them to simulated data generated under several patterns of joint genotype effects with simple biological interpretations. Interaction-only tests are the least powerful; while pattern search approach is the most powerful if the range of possibilities is restricted, but still includes the true pattern.

Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies.

To identify loci for age at menarche, we performed a meta-analysis of 32 genome-wide association studies in 87,802 women of European descent, with replication in up to 14,731 women. In addition to the known loci at LIN28B (P = 5.4 × 10⁻⁶⁰) and 9q31.2 (P = 2.2 × 10⁻³³), we identified 30 new menarche loci (all P < 5 × 10⁻⁸) and found suggestive evidence for a further 10 loci (P < 1.9 × 10⁻⁶). The new loci included four previously associated with body mass index (in or near FTO, SEC16B, TRA2B and TMEM18), three in or near other genes implicated in energy homeostasis (BSX, CRTC1 and MCHR2) and three in or near genes implicated in hormonal regulation (INHBA, PCSK2 and RXRG). Ingenuity and gene-set enrichment pathway analyses identified coenzyme A and fatty acid biosynthesis as biological processes related to menarche timing.

Genome-wide association and genetic functional studies identify autism susceptibility candidate 2 gene (AUTS2) in the regulation of alcohol consumption.

Alcohol consumption is a moderately heritable trait, but the genetic basis in humans is largely unknown, despite its clinical and societal importance. We report a genome-wide association study meta-analysis of ∼2.5 million directly genotyped or imputed SNPs with alcohol consumption (gram per day per kilogram body weight) among 12 population-based samples of European ancestry, comprising 26,316 individuals, with replication genotyping in an additional 21,185 individuals. SNP rs6943555 in autism susceptibility candidate 2 gene (AUTS2) was associated with alcohol consumption at genome-wide significance (P = 4 × 10(-8) to P = 4 × 10(-9)). We found a genotype-specific expression of AUTS2 in 96 human prefrontal cortex samples (P = 0.026) and significant (P < 0.017) differences in expression of AUTS2 in whole-brain extracts of mice selected for differences in voluntary alcohol consumption. Down-regulation of an AUTS2 homolog caused reduced alcohol sensitivity in Drosophila (P < 0.001). Our finding of a regulator of alcohol consumption adds knowledge to our understanding of genetic mechanisms influencing alcohol drinking behavior.

A genome-wide association study of early menopause and the combined impact of identified variants.

Early menopause (EM) affects up to 10% of the female population, reducing reproductive lifespan considerably. Currently, it constitutes the leading cause of infertility in the western world, affecting mainly those women who postpone their first pregnancy beyond the age of 30 years. The genetic aetiology of EM is largely unknown in the majority of cases. We have undertaken a meta-analysis of genome-wide association studies (GWASs) in 3493 EM cases and 13 598 controls from 10 independent studies. No novel genetic variants were discovered, but the 17 variants previously associated with normal age at natural menopause as a quantitative trait (QT) were also associated with EM and primary ovarian insufficiency (POI). Thus, EM has a genetic aetiology which overlaps variation in normal age at menopause and is at least partly explained by the additive effects of the same polygenic variants. The combined effect of the common variants captured by the single nucleotide polymorphism arrays was estimated to account for ∼30% of the variance in EM. The association between the combined 17 variants and the risk of EM was greater than the best validated non-genetic risk factor, smoking.

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

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

Variants in MTNR1B influence fasting glucose levels.

To identify previously unknown genetic loci associated with fasting glucose concentrations, we examined the leading association signals in ten genome-wide association scans involving a total of 36,610 individuals of European descent. Variants in the gene encoding melatonin receptor 1B (MTNR1B) were consistently associated with fasting glucose across all ten studies. The strongest signal was observed at rs10830963, where each G allele (frequency 0.30 in HapMap CEU) was associated with an increase of 0.07 (95% CI = 0.06-0.08) mmol/l in fasting glucose levels (P = 3.2 x 10(-50)) and reduced beta-cell function as measured by homeostasis model assessment (HOMA-B, P = 1.1 x 10(-15)). The same allele was associated with an increased risk of type 2 diabetes (odds ratio = 1.09 (1.05-1.12), per G allele P = 3.3 x 10(-7)) in a meta-analysis of 13 case-control studies totaling 18,236 cases and 64,453 controls. Our analyses also confirm previous associations of fasting glucose with variants at the G6PC2 (rs560887, P = 1.1 x 10(-57)) and GCK (rs4607517, P = 1.0 x 10(-25)) loci.

Multivariate QST-FST comparisons: a neutrality test for the evolution of the g matrix in structured populations.

Neutrality tests in quantitative genetics provide a statistical framework for the detection of selection on polygenic traits in wild populations. However, the existing method based on comparisons of divergence at neutral markers and quantitative traits (Q(st)-F(st)) suffers from several limitations that hinder a clear interpretation of the results with typical empirical designs. In this article, we propose a multivariate extension of this neutrality test based on empirical estimates of the among-populations (D) and within-populations (G) covariance matrices by MANOVA. A simple pattern is expected under neutrality: D = 2F(st)/(1 - F(st))G, so that neutrality implies both proportionality of the two matrices and a specific value of the proportionality coefficient. This pattern is tested using Flury's framework for matrix comparison [common principal-component (CPC) analysis], a well-known tool in G matrix evolution studies. We show the importance of using a Bartlett adjustment of the test for the small sample sizes typically found in empirical studies. We propose a dual test: (i) that the proportionality coefficient is not different from its neutral expectation [2F(st)/(1 - F(st))] and (ii) that the MANOVA estimates of mean square matrices between and among populations are proportional. These two tests combined provide a more stringent test for neutrality than the classic Q(st)-F(st) comparison and avoid several statistical problems. Extensive simulations of realistic empirical designs suggest that these tests correctly detect the expected pattern under neutrality and have enough power to efficiently detect mild to strong selection (homogeneous, heterogeneous, or mixed) when it is occurring on a set of traits. This method also provides a rigorous and quantitative framework for disentangling the effects of different selection regimes and of drift on the evolution of the G matrix. We discuss practical requirements for the proper application of our test in empirical studies and potential extensions.

Genomic and proteomic approaches towards an understanding of sleep.

The basic functions of sleep are still unclear, however, recent advances in genomics and proteomics have begun to contribute to our understanding of both normal and pathological sleep. In this review, we focus primarily on normal sleep and wake that have been studied in model organisms such as mice. Mice have been especially valuable since many different inbred strains exist that differ in sleep-related traits, and genes can be altered by either mutagenesis or targeted approaches. Advances in QTL (Quantitative Trait Loci) analysis have also helped to identify important sleep related genes, and several other QTLs have been mapped as a first step toward finding the genes that underlie basic sleep traits. In addition to more traditional genetic approaches, the abundance of different mRNAs across sleep and wake can now be studied and compared in different brain regions much more thoroughly using microarray methods. Progress at the protein level has been more difficult, but a few studies have begun to investigate changes in proteins during sleep and wake, and we present some of our own preliminary data in this area. A knowledge of which genes and proteins control or respond to changes in sleep will not only help answer fundamental questions, but may also suggest novel drug targets for improving multiple aspects of sleep and wake.

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Although it has long been known that genetic factors play a major role in shaping the electroencephalogram (EEG), progress on identifying the underlying genes has, until recently, been limited. Using quantitative trait loci (QTL) analyses several genomic loci affecting the sleep EEG could be mapped in the mouse. For three of these QTLs the responsible genes were identified leading to the implication of novel signaling pathways affecting EEG traits. Moreover, the insight that in the mouse the sleep-wake dependent dynamics in the expression of EEG slow waves during sleep is under strong genetic control has paved the way for candidate gene studies in humans investigating the contribution of specific polymorphism to the trait-like inter-individual differences in the susceptibility to sleep loss. Candidate gene studies in the mouse were also instrumental in establishing an alternative, noncircadian function for clock genes in the homeostatic regulation of sleep and modulating rhythmic EEG activity of thalamocortical origin. Future efforts should combine system genetics approaches in the mouse and genome-wide association studies in humans to facilitate uncovering the molecular pathways that shape brain activity.

Evidence of new risk genetic factor to systemic lupus erythematosus: the UBASH3A gene

The ubiquitin associated and Src-homology 3 (SH3) domain containing A (UBASH3a) is a suppressor of T-cell receptor signaling, underscoring antigen presentation to T-cells as a critical shared mechanism of diseases pathogenesis. The aim of the present study was to determine whether the UBASH3a gene influence the susceptibility to systemic lupus erythematosus (SLE) in Caucasian populations. We evaluated five UBASH3a polymorphisms (rs2277798, rs2277800, rs9976767, rs13048049 and rs17114930), using TaqMan® allelic discrimination assays, in a discovery cohort that included 906 SLE patients and 1165 healthy controls from Spain. The SNPs that exhibit statistical significance difference were evaluated in a German replication cohort of 360 SLE patients and 379 healthy controls. The case-control analysis in the Spanish population showed a significant association between the rs9976767 and SLE (Pc = 9.9E-03 OR = 1.21 95%CI = 1.07-1.37) and a trend of association for the rs2277798 analysis (P = 0.09 OR = 0.9 95%CI = 0.79-1.02). The replication in a German cohort and the meta-analysis confirmed that the rs9976767 (Pc = 0.02; Pc = 2.4E-04, for German cohort and meta-analysis, respectively) and rs2277798 (Pc = 0.013; Pc = 4.7E-03, for German cohort and meta-analysis, respectively) UBASH3a variants are susceptibility factors for SLE. Finally, a conditional regression analysis suggested that the most likely genetic variation responsible for the association was the rs9976767 polymorphism. Our results suggest that UBASH3a gene plays a role in the susceptibility to SLE. Moreover, our study indicates that UBASH3a can be considered as a common genetic factor in autoimmune diseases.

Phylogenetic approach reveals that virus genotype largely determines HIV set-point viral load.

HIV virulence, i.e. the time of progression to AIDS, varies greatly among patients. As for other rapidly evolving pathogens of humans, it is difficult to know if this variance is controlled by the genotype of the host or that of the virus because the transmission chain is usually unknown. We apply the phylogenetic comparative approach (PCA) to estimate the heritability of a trait from one infection to the next, which indicates the control of the virus genotype over this trait. The idea is to use viral RNA sequences obtained from patients infected by HIV-1 subtype B to build a phylogeny, which approximately reflects the transmission chain. Heritability is measured statistically as the propensity for patients close in the phylogeny to exhibit similar infection trait values. The approach reveals that up to half of the variance in set-point viral load, a trait associated with virulence, can be heritable. Our estimate is significant and robust to noise in the phylogeny. We also check for the consistency of our approach by showing that a trait related to drug resistance is almost entirely heritable. Finally, we show the importance of taking into account the transmission chain when estimating correlations between infection traits. The fact that HIV virulence is, at least partially, heritable from one infection to the next has clinical and epidemiological implications. The difference between earlier studies and ours comes from the quality of our dataset and from the power of the PCA, which can be applied to large datasets and accounts for within-host evolution. The PCA opens new perspectives for approaches linking clinical data and evolutionary biology because it can be extended to study other traits or other infectious diseases.

Genetic and phenotypic architecture of metabolic syndrome-associated components in dyslipidemic and normolipidemic subjects: the GEMS Study.

Atherogenic dyslipidemia, manifest by low HDL-cholesterol and high TG levels, is an important component of ATP-III defined metabolic syndrome. Here, we dissected the phenotypic and genetic architecture of these traits by assessing their relationships with other metabolically relevant measures, including plasma adipo-cytokines, highly sensitive C-reactive protein (hsCRP) and LDL particle size, in a large family data set (n=2800) and in an independent set of dyslipidemic cases (n=716) and normolipidemic controls (n=1073). We explored the relationships among these phenotypes using variable clustering and then estimated their genetic heritabilities and cross-trait correlations. In families, four clusters explained 61% of the total variance, with one adiposity-related cluster (including hsCRP), one BP-related cluster, and two lipid-related clusters (HDL-C, TG, adiponectin and LDL particle size; apoB and non-HDL-C). A similar structure was observed in dyslipidemic cases and normolipidemic controls. The genetic correlations in the families largely paralleled the phenotype clustering results, suggesting that common genes having pleiotropic effects contributed to the correlations observed. In summary, our analyses support a model of metabolic syndrome with two major components, body fat and lipids, each with two subcomponents, and quantifies their degree of overlap with each other and with metabolic-syndrome related measures (adipokines, LDL particle size and hsCRP).

Natural genetic variation of root system architecture from Arabidopsis to Brachypodium: towards adaptive value.

Root system architecture is a trait that displays considerable plasticity because of its sensitivity to environmental stimuli. Nevertheless, to a significant degree it is genetically constrained as suggested by surveys of its natural genetic variation. A few regulators of root system architecture have been isolated as quantitative trait loci through the natural variation approach in the dicotyledon model, Arabidopsis. This provides proof of principle that allelic variation for root system architecture traits exists, is genetically tractable, and might be exploited for crop breeding. Beyond Arabidopsis, Brachypodium could serve as both a credible and experimentally accessible model for root system architecture variation in monocotyledons, as suggested by first glimpses of the different root morphologies of Brachypodium accessions. Whether a direct knowledge transfer gained from molecular model system studies will work in practice remains unclear however, because of a lack of comprehensive understanding of root system physiology in the native context. For instance, apart from a few notable exceptions, the adaptive value of genetic variation in root system modulators is unknown. Future studies should thus aim at comprehensive characterization of the role of genetic players in root system architecture variation by taking into account the native environmental conditions, in particular soil characteristics.

Genome-wide association study of co-occurring anxiety in major depression.

OBJECTIVES: Co-morbidity between depression and anxiety disorders is common. In this study we define a quantitative measure of anxiety by summating four anxiety items from the SCAN interview in a large collection of major depression (MDD) cases to identify genes contributing to this complex phenotype. METHODS: A total of 1522 MDD cases dichotomised according to those with at least one anxiety item scored (n = 1080) and those without anxiety (n = 442) were analysed, and also compared to 1588 healthy controls at a genome-wide level, to identify genes that may contribute to anxiety in MDD. RESULTS: For the quantitative trait, suggestive evidence of association was detected for two SNPs, and for the dichotomous anxiety present/absent ratings for three SNPs at genome-wide level. In the genome-wide analysis of MDD cases with co-morbid anxiety and healthy controls, two SNPs attained P values of < 5 × 10⁻⁶. Analysing candidate genes, P values ≤ 0.0005 were found with three SNPs for the quantitative trait and three SNPs for the dichotomous trait. CONCLUSIONS: This study provides an initial genome-wide assessment of possible genetic contribution to anxiety in MDD. Although suggestive evidence of association was found for several SNPs, our findings suggest that there are no common variants strongly associated with anxious depression.