Region-based association analysis of human quantitative traits in related individuals

Genetic and phenotypic links between obesity and extracellular vesicles

Obesity has a highly complex genetic architecture, making it difficult to understand the genetic mechanisms, despite the large number of discovered loci via genome-wide association studies (GWAS). Omics techniques have provided a better resolution to view this problem. As a proxy of cell-level biology, extracellular vesicles (EVs) are useful for studying cellular regulation of complex phenotypes such as obesity. Here, in a well-established Scottish cohort, we utilized a novel technology to detect surface proteins across millions of single EVs in each individual's plasma sample. Integrating the results with established obesity GWAS, we inferred 78 types of EVs carrying one or two of 12 surface proteins to be associated with adiposity-related traits such as waist circumference. We then verified that particular EVs' abundance is negatively correlated with body adiposity, while no association with lean body mass. We also revealed that genetic variants associated with protein-specific EVs capture 2-4-fold heritability enrichment for blood cholesterol levels. Our findings provide evidence that EVs with specific surface proteins have phenotypic and genetic links to obesity and blood lipids, respectively, guiding future EV biomarker research.

Genetic Dissection of Temperament Personality Traits in Italian Isolates

Human personality (i.e., temperament and character) is a complex trait related to mental health, influenced by genetic and environmental factors. Despite the efforts performed during the past decades, its genetic background is only just beginning to be identified. With the aim of dissecting the genetic basis of temperament, we performed a Genome-Wide Association Study (GWAS) on Cloninger’s Temperament and Character Inventory in 587 individuals belonging to different Italian genetic isolates. Data analysis led to the identification of four new genes associated with different temperament scales, such as Novelty Seeking (NS), Harm Avoidance (HA), and Reward Dependence (RD). In detail, we identified suggestive and significant associations between: MAGI2 (highest p-value = 9.14 × 10⁻⁸), a gene already associated with schizophrenia and depressive disorder, and the NS–Extravagance scale; CALCB (highest p-value = 4.34 × 10⁻⁶), a gene likely involved in the behavioral evolution from wild wolf to domestic dog, and the NS–Disorderliness scale; BTBD3 (highest p-value = 2.152 × 10⁻⁸), a gene already linked to obsessive–compulsive disorder, and the HA–Fatigability scale; PRKN (highest p-value = 8.27 × 10⁻⁹), a gene described for early onset Parkinson’s disease, and the RD scale. Our work provides new relevant insights into the genetics of temperament, helping to elucidate the molecular basis of psychiatric disorders.

Bipolar multiplex families have an increased burden of common risk variants for psychiatric disorders

Multiplex families with a high prevalence of a psychiatric disorder are often examined to identify rare genetic variants with large effect sizes. In the present study, we analysed whether the risk for bipolar disorder (BD) in BD multiplex families is influenced by common genetic variants. Furthermore, we investigated whether this risk is conferred mainly by BD-specific risk variants or by variants also associated with the susceptibility to schizophrenia or major depression. In total, 395 individuals from 33 Andalusian BD multiplex families (166 BD, 78 major depressive disorder, 151 unaffected) as well as 438 subjects from an independent, BD case/control cohort (161 unrelated BD, 277 unrelated controls) were analysed. Polygenic risk scores (PRS) for BD, schizophrenia (SCZ), and major depression were calculated and compared between the cohorts. Both the familial BD cases and unaffected family members had higher PRS for all three psychiatric disorders than the independent controls, with BD and SCZ being significant after correction for multiple testing, suggesting a high baseline risk for several psychiatric disorders in the families. Moreover, familial BD cases showed significantly higher BD PRS than unaffected family members and unrelated BD cases. A plausible hypothesis is that, in multiplex families with a general increase in risk for psychiatric disease, BD development is attributable to a high burden of common variants that confer a specific risk for BD. The present analyses demonstrated that common genetic risk variants for psychiatric disorders are likely to contribute to the high incidence of affective psychiatric disorders in the multiplex families. However, the PRS explained only part of the observed phenotypic variance, and rare variants might have also contributed to disease development.

Associations of autozygosity with a broad range of human phenotypes

In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (FROH) for >1.4 million individuals, we show that FROH is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of all environmental confounding.

A generalized model for combining dependent SNP-level summary statistics and its extensions to statistics of other levels

Here I propose a fundamentally new flexible model to reveal the association between a trait and a set of genetic variants in a genomic region/gene. This model was developed for the situation when original individual-level phenotype and genotype data are not available, but the researcher possesses the results of statistical analyses conducted on these data (namely, SNP-level summary Z score statistics and SNP-by-SNP correlations). The new model was analytically derived from the classical multiple linear regression model applied for the region-based association analysis of individual-level phenotype and genotype data by using the linear compression of data, where the SNP-by-SNP correlations are among the explanatory variables, and the summary Z score statistics are categorized as the response variables. I analytically show that the regional association analysis methods developed within the framework of the classical multiple linear regression model with additive effects of genetic variants can be reformulated in terms of the new model without the loss of information. The results obtained from the regional association analysis utilizing the classical model and those derived using the proposed model are identical when SNP-by-SNP correlations and SNP-level statistics are estimated from the same genetic data.

Exome sequencing in large, multiplex bipolar disorder families from Cuba

Bipolar disorder (BD) is a major psychiatric illness affecting around 1% of the global population. BD is characterized by recurrent manic and depressive episodes, and has an estimated heritability of around 70%. Research has identified the first BD susceptibility genes. However, the underlying pathways and regulatory networks remain largely unknown. Research suggests that the cumulative impact of common alleles with small effects explains only around 25-38% of the phenotypic variance for BD. A plausible hypothesis therefore is that rare, high penetrance variants may contribute to BD risk. The present study investigated the role of rare, nonsynonymous, and potentially functional variants via whole exome sequencing in 15 BD cases from two large, multiply affected families from Cuba. The high prevalence of BD in these pedigrees renders them promising in terms of the identification of genetic risk variants with large effect sizes. In addition, SNP array data were used to calculate polygenic risk scores for affected and unaffected family members. After correction for multiple testing, no significant increase in polygenic risk scores for common, BD-associated genetic variants was found in BD cases compared to healthy relatives. Exome sequencing identified a total of 17 rare and potentially damaging variants in 17 genes. The identified variants were shared by all investigated BD cases in the respective pedigree. The most promising variant was located in the gene SERPING1 (p.L349F), which has been reported previously as a genome-wide significant risk gene for schizophrenia. The present data suggest novel candidate genes for BD susceptibility, and may facilitate the discovery of disease-relevant pathways and regulatory networks.

Haplotype Heritability Mapping Method Uncovers Missing Heritability of Complex Traits

We propose a novel approach to analyze genomic data that incorporates haplotype information for detecting rare variants within a regional heritability mapping framework. The performance of our approach was tested in a simulation study based on human genotypes. The phenotypes were simulated by generating regional variance using either SNP(s) or haplotype(s). Regional genomic relationship matrices, constructed with either a SNP-based or a haplotype-based estimator, were employed to estimate the regional variance. The results from the study show that haplotype heritability mapping captures the regional effect, with its relative performance decreasing with increasing analysis window size. The SNP-based regional mapping approach often misses the effect of causal haplotype(s); however, it has a greater power to detect simulated SNP-based-variants. Heritability estimates suggest that the haplotype heritability mapping estimates the simulated regional heritability accurately for all phenotypes and analysis windows. However, the SNP-based analysis overestimates the regional heritability and performs less well than our haplotype-based approach for the simulated rare haplotype-based-variant. We conclude that haplotype heritability mapping is a useful tool to capture the effect of rare variants, and explain a proportion of the missing heritability.

On the impact of relatedness on SNP association analysis

BACKGROUND

When testing for SNP (single nucleotide polymorphism) associations in related individuals, observations are not independent. Simple linear regression assuming independent normally distributed residuals results in an increased type I error and the power of the test is also affected in a more complicate manner. Inflation of type I error is often successfully corrected by genomic control. However, this reduces the power of the test when relatedness is of concern. In the present paper, we derive explicit formulae to investigate how heritability and strength of relatedness contribute to variance inflation of the effect estimate of the linear model. Further, we study the consequences of variance inflation on hypothesis testing and compare the results with those of genomic control correction. We apply the developed theory to the publicly available HapMap trio data (N=129), the Sorbs (a self-contained population with N=977 characterised by a cryptic relatedness structure) and synthetic family studies with different sample sizes (ranging from N=129 to N=999) and different degrees of relatedness.

RESULTS

We derive explicit and easily to apply approximation formulae to estimate the impact of relatedness on the variance of the effect estimate of the linear regression model. Variance inflation increases with increasing heritability. Relatedness structure also impacts the degree of variance inflation as shown for example family structures. Variance inflation is smallest for HapMap trios, followed by a synthetic family study corresponding to the trio data but with larger sample size than HapMap. Next strongest inflation is observed for the Sorbs, and finally, for a synthetic family study with a more extreme relatedness structure but with similar sample size as the Sorbs. Type I error increases rapidly with increasing inflation. However, for smaller significance levels, power increases with increasing inflation while the opposite holds for larger significance levels. When genomic control is applied, type I error is preserved while power decreases rapidly with increasing variance inflation.

CONCLUSIONS

Stronger relatedness as well as higher heritability result in increased variance of the effect estimate of simple linear regression analysis. While type I error rates are generally inflated, the behaviour of power is more complex since power can be increased or reduced in dependence on relatedness and the heritability of the phenotype. Genomic control cannot be recommended to deal with inflation due to relatedness. Although it preserves type I error, the loss in power can be considerable. We provide a simple formula for estimating variance inflation given the relatedness structure and the heritability of a trait of interest. As a rule of thumb, variance inflation below 1.05 does not require correction and simple linear regression analysis is still appropriate.

Multivariate discovery and replication of five novel loci associated with Immunoglobulin G N-glycosylation

Joint modeling of a number of phenotypes using multivariate methods has often been neglected in genome-wide association studies and if used, replication has not been sought. Modern omics technologies allow characterization of functional phenomena using a large number of related phenotype measures, which can benefit from such joint analysis. Here, we report a multivariate genome-wide association studies of 23 immunoglobulin G (IgG) N-glycosylation phenotypes. In the discovery cohort, our multi-phenotype method uncovers ten genome-wide significant loci, of which five are novel (IGH, ELL2, HLA-B-C, AZI1, FUT6-FUT3). We convincingly replicate all novel loci via multivariate tests. We show that IgG N-glycosylation loci are strongly enriched for genes expressed in the immune system, in particular antibody-producing cells and B lymphocytes. We empirically demonstrate the efficacy of multivariate methods to discover novel, reproducible pleiotropic effects.Multivariate analysis methods can uncover the relationship between phenotypic measures characterised by modern omic techniques. Here the authors conduct a multivariate GWAS on IgG N-glycosylation phenotypes and identify 5 novel loci enriched in immune system genes.

Genetic Regulation of Transcriptional Variation in Natural Arabidopsis thaliana Accessions

An increased knowledge of the genetic regulation of expression in Arabidopsis thaliana is likely to provide important insights about the basis of the plant’s extensive phenotypic variation. Here, we reanalyzed two publicly available datasets with genome-wide data on genetic and transcript variation in large collections of natural A. thaliana accessions. Transcripts from more than half of all genes were detected in the leaves of all accessions, and from nearly all annotated genes in at least one accession. Thousands of genes had high transcript levels in some accessions, but no transcripts at all in others, and this pattern was correlated with the genome-wide genotype. In total, 2669 eQTL were mapped in the largest population, and 717 of them were replicated in the other population. A total of 646 cis-eQTL-regulated genes that lacked detectable transcripts in some accessions was found, and for 159 of these we identified one, or several, common structural variants in the populations that were shown to be likely contributors to the lack of detectable RNA transcripts for these genes. This study thus provides new insights into the overall genetic regulation of global gene expression diversity in the leaf of natural A. thaliana accessions. Further, it also shows that strong cis-acting polymorphisms, many of which are likely to be structural variations, make important contributions to the transcriptional variation in the worldwide A. thaliana population.

A Genome-Wide Association Study in isolated populations reveals new genes associated to common food likings

Food preferences are the first factor driving food choice and thus nutrition. They involve numerous different senses such as taste and olfaction as well as various other factors such as personal experiences and hedonistic aspects. Although it is clear that several of these have a genetic basis, up to now studies have focused mostly on the effects of polymorphisms of taste receptor genes. Therefore, we have carried out one of the first large scale (4611 individuals) GWAS on food likings assessed for 20 specific food likings belonging to 4 different categories (vegetables, fatty, dairy and bitter). A two-step meta-analysis using three different isolated populations from Italy for the discovery step and two populations from The Netherlands and Central Asia for replication, revealed 15 independent genome-wide significant loci (p < 5 × 10(-8)) for 12 different foods. None of the identified genes coded for either taste or olfactory receptors suggesting that genetics impacts in determining food likings in a much broader way than simple differences in taste perception. These results represent a further step in uncovering the genes that underlie liking of common foods that in the end will greatly help understanding the genetics of human nutrition in general.

The GenABEL Project for statistical genomics

Development of free/libre open source software is usually done by a community of people with an interest in the tool. For scientific software, however, this is less often the case. Most scientific software is written by only a few authors, often a student working on a thesis. Once the paper describing the tool has been published, the tool is no longer developed further and is left to its own device. Here we describe the broad, multidisciplinary community we formed around a set of tools for statistical genomics. The GenABEL project for statistical omics actively promotes open interdisciplinary development of statistical methodology and its implementation in efficient and user-friendly software under an open source licence. The software tools developed withing the project collectively make up the GenABEL suite, which currently consists of eleven tools. The open framework of the project actively encourages involvement of the community in all stages, from formulation of methodological ideas to application of software to specific data sets. A web forum is used to channel user questions and discussions, further promoting the use of the GenABEL suite. Developer discussions take place on a dedicated mailing list, and development is further supported by robust development practices including use of public version control, code review and continuous integration. Use of this open science model attracts contributions from users and developers outside the “core team”, facilitating agile statistical omics methodology development and fast dissemination.

FREGAT: an R package for region-based association analysis

Several approaches to the region-based association analysis of quantitative traits have recently been developed and successively applied. However, no software package has been developed that implements all of these approaches for either independent or structured samples. Here we introduce FREGAT (Family REGional Association Tests), an R package that can handle family and population samples and implements a wide range of region-based association methods including burden tests, functional linear models, and kernel machine-based regression. FREGAT can be used in genome/exome-wide region-based association studies of quantitative traits and candidate gene analysis. FREGAT offers many useful options to empower its users and increase the effectiveness and applicability of region-based association analysis.

AVAILABILITY AND IMPLEMENTATION

https://cran.r-project.org/web/packages/FREGAT/index.html

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics Online.

CONTACT

belon@bionet.nsc.ru.

Regional heritability mapping method helps explain missing heritability of blood lipid traits in isolated populations

Single single-nucleotide polymorphism (SNP) genome-wide association studies (SSGWAS) may fail to identify loci with modest effects on a trait. The recently developed regional heritability mapping (RHM) method can potentially identify such loci. In this study, RHM was compared with the SSGWAS for blood lipid traits (high-density lipoprotein (HDL), low-density lipoprotein (LDL), plasma concentrations of total cholesterol (TC) and triglycerides (TG)). Data comprised 2246 adults from isolated populations genotyped using ∼300 000 SNP arrays. The results were compared with large meta-analyses of these traits for validation. Using RHM, two significant regions affecting HDL on chromosomes 15 and 16 and one affecting LDL on chromosome 19 were identified. These regions covered the most significant SNPs associated with HDL and LDL from the meta-analysis. The chromosome 19 region was identified in our data despite the fact that the most significant SNP in the meta-analysis (or any SNP tagging it) was not genotyped in our SNP array. The SSGWAS identified one SNP associated with HDL on chromosome 16 (the top meta-analysis SNP) and one on chromosome 10 (not reported by RHM or in the meta-analysis and hence possibly a false positive association). The results further confirm that RHM can have better power than SSGWAS in detecting causal regions including regions containing crucial ungenotyped variants. This study suggests that RHM can be a useful tool to explain some of the ‘missing heritability' of complex trait variation.

The Multi-allelic Genetic Architecture of a Variance-Heterogeneity Locus for Molybdenum Concentration in Leaves Acts as a Source of Unexplained Additive Genetic Variance

Genome-wide association (GWA) analyses have generally been used to detect individual loci contributing to the phenotypic diversity in a population by the effects of these loci on the trait mean. More rarely, loci have also been detected based on variance differences between genotypes. Several hypotheses have been proposed to explain the possible genetic mechanisms leading to such variance signals. However, little is known about what causes these signals, or whether this genetic variance-heterogeneity reflects mechanisms of importance in natural populations. Previously, we identified a variance-heterogeneity GWA (vGWA) signal for leaf molybdenum concentrations in Arabidopsis thaliana. Here, fine-mapping of this association reveals that the vGWA emerges from the effects of three independent genetic polymorphisms that all are in strong LD with the markers displaying the genetic variance-heterogeneity. By revealing the genetic architecture underlying this vGWA signal, we uncovered the molecular source of a significant amount of hidden additive genetic variation or “missing heritability”. Two of the three polymorphisms underlying the genetic variance-heterogeneity are promoter variants for Molybdate transporter 1 (MOT1), and the third a variant located ~25 kb downstream of this gene. A fourth independent association was also detected ~600 kb upstream of MOT1. Use of a T-DNA knockout allele highlights Copper Transporter 6; COPT6 (AT2G26975) as a strong candidate gene for this association. Our results show that an extended LD across a complex locus including multiple functional alleles can lead to a variance-heterogeneity between genotypes in natural populations. Further, they provide novel insights into the genetic regulation of ion homeostasis in A. thaliana, and empirically confirm that variance-heterogeneity based GWA methods are a valuable tool to detect novel associations of biological importance in natural populations.

Most biological traits vary in natural populations, and understanding the genetic basis of this variation remains an important challenge. Genome-wide association (GWA) studies have emerged as a powerful tool to address this challenge by dissecting the genetic architecture of trait variation into the contribution of individual genes. This contribution has traditionally been measured as the difference in the phenotypic means between groups of individuals with alternative genotypes at one, or multiple loci. However, instead of altering the trait mean, certain loci alter the variability of the trait. Here, we describe the genetic dissection of one such variance-controlling locus that drives variation in leaf molybdenum concentrations amongst natural accessions of Arabidopsis thaliana. The variance-controlling locus was found to result from the contributions of multiple alleles at multiple loci that are closely linked on the chromosome and is a major contributor to the “missing heritability” for this trait identified in previous studies. This illustrates that multi-allelic genetic architectures can hide large amounts of additive genetic variation, and that it is possible to uncover this hidden variation using the appropriate experimental designs and statistical methods described here.

A Multi-Breed Genome-Wide Association Analysis for Canine Hypothyroidism Identifies a Shared Major Risk Locus on CFA12

Hypothyroidism is a complex clinical condition found in both humans and dogs, thought to be caused by a combination of genetic and environmental factors. In this study we present a multi-breed analysis of predisposing genetic risk factors for hypothyroidism in dogs using three high-risk breeds—the Gordon Setter, Hovawart and the Rhodesian Ridgeback. Using a genome-wide association approach and meta-analysis, we identified a major hypothyroidism risk locus shared by these breeds on chromosome 12 (p = 2.1x10^-11). Further characterisation of the candidate region revealed a shared ~167 kb risk haplotype (4,915,018–5,081,823 bp), tagged by two SNPs in almost complete linkage disequilibrium. This breed-shared risk haplotype includes three genes (LHFPL5, SRPK1 and SLC26A8) and does not extend to the dog leukocyte antigen (DLA) class II gene cluster located in the vicinity. These three genes have not been identified as candidate genes for hypothyroid disease previously, but have functions that could potentially contribute to the development of the disease. Our results implicate the potential involvement of novel genes and pathways for the development of canine hypothyroidism, raising new possibilities for screening, breeding programmes and treatments in dogs. This study may also contribute to our understanding of the genetic etiology of human hypothyroid disease, which is one of the most common endocrine disorders in humans.

Region-Based Association Test for Familial Data under Functional Linear Models

Region-based association analysis is a more powerful tool for gene mapping than testing of individual genetic variants, particularly for rare genetic variants. The most powerful methods for regional mapping are based on the functional data analysis approach, which assumes that the regional genome of an individual may be considered as a continuous stochastic function that contains information about both linkage and linkage disequilibrium. Here, we extend this powerful approach, earlier applied only to independent samples, to the samples of related individuals. To this end, we additionally include a random polygene effects in functional linear model used for testing association between quantitative traits and multiple genetic variants in the region. We compare the statistical power of different methods using Genetic Analysis Workshop 17 mini-exome family data and a wide range of simulation scenarios. Our method increases the power of regional association analysis of quantitative traits compared with burden-based and kernel-based methods for the majority of the scenarios. In addition, we estimate the statistical power of our method using regions with small number of genetic variants, and show that our method retains its advantage over burden-based and kernel-based methods in this case as well. The new method is implemented as the R-function 'famFLM' using two types of basis functions: the B-spline and Fourier bases. We compare the properties of the new method using models that differ from each other in the type of their function basis. The models based on the Fourier basis functions have an advantage in terms of speed and power over the models that use the B-spline basis functions and those that combine B-spline and Fourier basis functions. The 'famFLM' function is distributed under GPLv3 license and is freely available at http://mga.bionet.nsc.ru/soft/famFLM/.

Connecting Anxiety and Genomic Copy Number Variation: A Genome-Wide Analysis in CD-1 Mice

Genomic copy number variants (CNVs) have been implicated in multiple psychiatric disorders, but not much is known about their influence on anxiety disorders specifically. Using next-generation sequencing (NGS) and two additional array-based genotyping approaches, we detected CNVs in a mouse model consisting of two inbred mouse lines showing high (HAB) and low (LAB) anxiety-related behavior, respectively. An influence of CNVs on gene expression in the central (CeA) and basolateral (BLA) amygdala, paraventricular nucleus (PVN), and cingulate cortex (Cg) was shown by a two-proportion Z-test (p = 1.6 x 10^-31), with a positive correlation in the CeA (p = 0.0062), PVN (p = 0.0046) and Cg (p = 0.0114), indicating a contribution of CNVs to the genetic predisposition to trait anxiety in the specific context of HAB/LAB mice. In order to confirm anxiety-relevant CNVs and corresponding genes in a second mouse model, we further examined CD-1 outbred mice. We revealed the distribution of CNVs by genotyping 64 CD 1 individuals using a high-density genotyping array (Jackson Laboratory). 78 genes within those CNVs were identified to show nominally significant association (48 genes), or a statistical trend in their association (30 genes) with the time animals spent on the open arms of the elevated plus-maze (EPM). Fifteen of them were considered promising candidate genes of anxiety-related behavior as we could show a significant overlap (permutation test, p = 0.0051) with genes within HAB/LAB CNVs. Thus, here we provide what is to our knowledge the first extensive catalogue of CNVs in CD-1 mice and potential corresponding candidate genes linked to anxiety-related behavior in mice.

The Shepherds' Tale: A Genome-Wide Study across 9 Dog Breeds Implicates Two Loci in the Regulation of Fructosamine Serum Concentration in Belgian Shepherds

Diabetes mellitus is a serious health problem in both dogs and humans. Certain dog breeds show high prevalence of the disease, whereas other breeds are at low risk. Fructosamine and glycated haemoglobin (HbA1c) are two major biomarkers of glycaemia, where serum concentrations reflect glucose turnover over the past few weeks to months. In this study, we searched for genetic factors influencing variation in serum fructosamine concentration in healthy dogs using data from nine dog breeds. Considering all breeds together, we did not find any genome-wide significant associations to fructosamine serum concentration. However, by performing breed-specific analyses we revealed an association on chromosome 3 (pcorrected ≈ 1:68 × 10-6) in Belgian shepherd dogs of the Malinois subtype. The associated region and its close neighbourhood harbours interesting candidate genes such as LETM1 and GAPDH that are important in glucose metabolism and have previously been implicated in the aetiology of diabetes mellitus. To further explore the genetics of this breed specificity, we screened the genome for reduced heterozygosity stretches private to the Belgian shepherd breed. This revealed a region with reduced heterozygosity that shows a statistically significant interaction (p = 0.025) with the association region on chromosome 3. This region also harbours some interesting candidate genes and regulatory regions but the exact mechanisms underlying the interaction are still unknown. Nevertheless, this finding provides a plausible explanation for breed-specific genetic effects for complex traits in dogs. Shepherd breeds are at low risk of developing diabetes mellitus. The findings in Belgian shepherds could be connected to a protective mechanism against the disease. Further insight into the regulation of glucose metabolism could improve diagnostic and therapeutic methods for diabetes mellitus.

Genome-wide association analysis on five isolated populations identifies variants of the HLA-DOA gene associated with white wine liking

Wine is the most popular alcoholic beverage around the world and because of its importance in society has been widely studied. Understanding what drives its flavor has been a quest for decades but much is still unknown and will be determined at least in part by individual taste preferences. Recently studies in the genetics of taste have uncovered the role of different genes in the determination of food preferences giving new insight on its physiology. In this context we have performed a genome-wide association study on red and white wine liking using three isolated populations collected in Italy, and replicated our results on two additional populations coming from the Netherland and Central Asia for a total of 3885 samples. We have found a significant association (P=2.1 × 10(-8)) between white wine liking and rs9276975:C>T a polymorphism in the HLA-DOA gene encoding a non-canonical MHC II molecule, which regulates other MHC II molecules. The same association was also found with red wine liking (P=8.3 × 10(-6)). Sex-separated analysis have also revealed that the effect of HLA-DOA is twice as large in women as compared to men suggesting an interaction between this polymorphism and gender. Our results are one of the first examples of genome-wide association between liking of a commonly consumed food and gene variants. Moreover, our results suggest a role of the MHC system in the determination of food preferences opening new insight in this field in general.

A family-based joint test for mean and variance heterogeneity for quantitative traits

Traditional quantitative trait locus (QTL) analysis focuses on identifying loci associated with mean heterogeneity. Recent research has discovered loci associated with phenotype variance heterogeneity (vQTL), which is important in studying genetic association with complex traits, especially for identifying gene-gene and gene-environment interactions. While several tests have been proposed to detect vQTL for unrelated individuals, there are no tests for related individuals, commonly seen in family-based genetic studies. Here we introduce a likelihood ratio test (LRT) for identifying mean and variance heterogeneity simultaneously or for either effect alone, adjusting for covariates and family relatedness using a linear mixed effect model approach. The LRT test statistic for normally distributed quantitative traits approximately follows χ(2)-distributions. To correct for inflated Type I error for non-normally distributed quantitative traits, we propose a parametric bootstrap-based LRT that removes the best linear unbiased prediction (BLUP) of family random effect. Simulation studies show that our family-based test controls Type I error and has good power, while Type I error inflation is observed when family relatedness is ignored. We demonstrate the utility and efficiency gains of the proposed method using data from the Framingham Heart Study to detect loci associated with body mass index (BMI) variability.

Fine-mapping QTLs in advanced intercross lines and other outbred populations

Quantitative genetic studies in model organisms, particularly in mice, have been extremely successful in identifying chromosomal regions that are associated with a wide variety of behavioral and other traits. However, it is now widely understood that identification of the underlying genes will be far more challenging. In the last few years, a variety of populations have been utilized in an effort to more finely map these chromosomal regions with the goal of identifying specific genes. The common property of these newer populations is that linkage disequilibrium spans relatively short distances, which permits fine-scale mapping resolution. This review focuses on advanced intercross lines (AILs) which are the simplest such population. As originally proposed in 1995 by Darvasi and Soller, an AIL is the product of intercrossing two inbred strains beyond the F2 generation. Unlike recombinant inbred strains, AILs are maintained as outbred populations; brother-sister matings are specifically avoided. Each generation of intercrossing beyond the F2 further degrades linkage disequilibrium between adjacent makers, which allows for fine-scale mapping of quantitative trait loci (QTLs). Advances in genotyping technology and techniques for the statistical analysis of AILs have permitted rapid advances in the application of AILs. We review some of the analytical issues and available software, including QTLRel, EMMA, EMMAX, GEMMA, TASSEL, GRAMMAR, WOMBAT, Mendel, and others.

FFBSKAT: fast family-based sequence kernel association test

The kernel machine-based regression is an efficient approach to region-based association analysis aimed at identification of rare genetic variants. However, this method is computationally complex. The running time of kernel-based association analysis becomes especially long for samples with genetic (sub) structures, thus increasing the need to develop new and effective methods, algorithms, and software packages. We have developed a new R-package called fast family-based sequence kernel association test (FFBSKAT) for analysis of quantitative traits in samples of related individuals. This software implements a score-based variance component test to assess the association of a given set of single nucleotide polymorphisms with a continuous phenotype. We compared the performance of our software with that of two existing software for family-based sequence kernel association testing, namely, ASKAT and famSKAT, using the Genetic Analysis Workshop 17 family sample. Results demonstrate that FFBSKAT is several times faster than other available programs. In addition, the calculations of the three-compared software were similarly accurate. With respect to the available analysis modes, we combined the advantages of both ASKAT and famSKAT and added new options to empower FFBSKAT users. The FFBSKAT package is fast, user-friendly, and provides an easy-to-use method to perform whole-exome kernel machine-based regression association analysis of quantitative traits in samples of related individuals. The FFBSKAT package, along with its manual, is available for free download at http://mga.bionet.nsc.ru/soft/FFBSKAT/.

Association analysis of bitter receptor genes in five isolated populations identifies a significant correlation between TAS2R43 variants and coffee liking

Coffee, one of the most popular beverages in the world, contains many different physiologically active compounds with a potential impact on people’s health. Despite the recent attention given to the genetic basis of its consumption, very little has been done in understanding genes influencing coffee preference among different individuals. Given its markedly bitter taste, we decided to verify if bitter receptor genes (TAS2Rs) variants affect coffee liking. In this light, 4066 people from different parts of Europe and Central Asia filled in a field questionnaire on coffee liking. They have been consequently recruited and included in the study. Eighty-eight SNPs covering the 25 TAS2R genes were selected from the available imputed ones and used to run association analysis for coffee liking. A significant association was detected with three SNP: one synonymous and two functional variants (W35S and H212R) on the TAS2R43 gene. Both variants have been shown to greatly reduce in vitro protein activity. Surprisingly the wild type allele, which corresponds to the functional form of the protein, is associated to higher liking of coffee. Since the hTAS2R43 receptor is sensible to caffeine, we verified if the detected variants produced differences in caffeine bitter perception on a subsample of people coming from the FVG cohort. We found a significant association between differences in caffeine perception and the H212R variant but not with the W35S, which suggests that the effect of the TAS2R43 gene on coffee liking is mediated by caffeine and in particular by the H212R variant.

No other significant association was found with other TAS2R genes. In conclusion, the present study opens new perspectives in the understanding of coffee liking. Further studies are needed to clarify the role of the TAS2R43 gene in coffee hedonics and to identify which other genes and pathways are involved in its genetics.