Absence epilepsy (AE) is a complex, heritable disease characterized by a brief disruption of normal behavior and accompanying spike wave discharges (SWD) on the electroencephalogram. Only a handful of genes has been definitively associated with AE in humans and rodent models. Most studies suggest that genetic interactions play a large role in the etiology and severity of AE, but mapping and understanding their architecture remains a challenge, requiring new computational approaches. Here we use Combined Analysis of Pleiotropy and Epistasis (CAPE) to detect and interpret genetic interactions in a meta-population derived from three C3H x B6 strain crosses, each of which is fixed for a different SWD-causing mutation. Although each mutation causes SWD through a different molecular mechanism, the phenotypes caused by each mutation are exacerbated on the C3H genetic background compared with B6, suggesting common modifiers. By combining information across two phenotypic measures – SWD duration and frequency – CAPE revealed a large, directed genetic network consisting of suppressive and enhancing interactions between loci on 10 chromosomes. These results illustrate the power of CAPE in identifying novel modifier loci and interactions in a complex neurological disease, towards a more comprehensive view of its underlying genetic architecture. Absence epilepsy (AE) is a form of idiopathic generalized epilepsy characterized by brief disruption of normal behavior concurrent with spike-wave discharges (SWD) on an electroencephalogram (EEG). In humans AE has a strong genetic component , and oveDespite modest progress, the genetics of AE is still largely unknown. Human  and rodeGria4, Scn8a, or Gabrg2. Gria4 encodes one of four amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor subunits  also causes SWD in mice (Gabrg2 encodes the gamma-aminobutyric acid (GABA) A receptor, subunit γ 2, and has been associated with AE and FS in humans  . We combsubunits . Spontansubunits . Mutatio in mice . Finallyn humans  and micen humans .Mutations in each of these genes cause SWD with strain-dependent severity, suggesting multigenic modification of the phenotype. Specifically, each mutation causes more severe SWD in the C3H background than the B6J background ;. These Interval mapping focusing on the individual mutations has yielded significant quantitative trait loci (QTL), but no common modifiers across the three mutations, and no significant interactions  tm1DgenGria4  and 8JScn8a  mutant mice were described previously F1 hybrid to B6J to generate an N2 population comprising of 26 females and 15 males. For Gabrg2, (B6J.129-tm1Spet(R43Q)Gabrg2 x FeJ)F1 hybrids were intercrossed, generating an F2 population comprising 9 females and 51 males. For Gria4, FeJ mice with the spkw1Gria4 allele introduced from the C3HeB/HeJ substrain (FeJ.HeJ-spkw1Gria4F1 hybrids) were crossed to B6J mice with the Gria4 insertion null allele (B6J-tm1DgenGria4) to generate an N2 population comprising 47 females and 42 males. None of these mice had the wild type Gria4 allele, although, due to the design, approximately half were compound heterozygotes and half tm1Dgen/tm1DgenGria4. The C3HeB/FeJ substrain does not naturally contain the spontaneous Gria4 mutation of the HeJ strain, and therefore does not experience the frequent SWD of the C3H strain (C57BL/6J (B6J) and C3HeB/FeJ (FeJ) inbred strains were obtained from The Jackson Laboratory production facility. The origins of eviously . B6J.129brg2R43Q ) mice webrg2R43Q . Three gH strain . For theTail-tip excision genomic DNA was prepared from each backcross or intercross animal, and single nucleotide polymorphism (SNP) genotyping at a marker density of approximately 15 Mb was provided by Kbioscience , using a custom panel. The final set of markers used in CAPE analysis is provided in Adult mice aged between 6 and 9 weeks were anesthetized with tribromoethanol (400 mg/kg i.p.) Small burr holes were drilled (1 mm anterior to the bregma and 2 mm posterior to the bregma) on both sides of the skull 2 mm lateral to the midline. Four teflon-coated silver wires were soldered onto the pins of a microconnector . The wires were placed between the dura and the brain and an inorganic-based dental cap was then applied. The mice were given a post-operative analgesic of carprofen (5 mg/kg subcutaneous) and allowed a minimum 48 h recovery period before recordings were made. The mice were connected to the EEG Stellate Lamont Pro-36 programmable amplifier  using a flexible cable with free access to all corners of the cage, including food, for 2 hr periods on 2 consecutive days , during the middle of the light cycle . EEG data were collected with the software program Stellate Harmonie . SWD episodes were scored visually using the following criteria: the EEG recording showed a SWD discharge that lasted for at least 0.5 seconds, with amplitude at least 2-fold higher than background, and observed concurrently in the majority of the 6 recording channels.Scn8a and Gria4 mutations), were backcrosses. Second, very few loci in the intercross were homozygous for C3H (SWD incidence (SWD/hr) and length (s) were measured on two different days, and the average of the two days was used in this analysis. Each phenotype was mean-centered and adjusted with Z-transform normalization within each cross separately, and the crosses were combined into one data set containing 190 individuals. The same 191 genetic markers were measured across the genome in each of the three crosses. We recoded the entire meta-cross as a backcross for two main reasons. First, two of the three contributing crosses  into their principal components, called eigentraits (ETs). This step de-correlates the phenotypes, reorganizing phenotypic signals into orthogonal, composite phenotypes. This procedure potentially concentrates genetic associations, in that variants with weak associations to multiple original phenotypes often exhibit strong association to one ET. Although the final CAPE-derived model will be recast in terms of the original phenotypes, this provides enhanced detection of candidate loci for interaction analysis.We filtered marker pairs based on linkage. High Pearson correlation between markers indicates that the individual markers supply redundant information and can lead to false-positive interactions. We included only those marker pairs with a Pearson correlation r ≤ 0.75.U represents ETs, and ε is an error term. The index i runs from 1 to the number of individuals, and j runs from 1 to 2 (the number of ETs.) ix is the probability of the presence of the C3H allele for individual i at locus j. For all regression models, the coefficient of determination was computed as regSS is the sum of squares of the regression and totSS is the sum of squares of the dependent variable.We then performed a pairwise linear regression on all pairs of filtered markers 1 and 2: δ1 and δ2) are defined in terms of the interaction coeffici1`-ents from the pairwise regression. δ1 can be thought of as the additional genetic activity of the variant near marker 1 when the variant near marker 2 is present. The δ terms are independent of phenotype and together completely describe the interaction term. They can be interpreted as the extent to which each marker influences the effect of the other on the phenotypes. For example, a negative δ2 indicates that the presence of variant 2 suppresses the effect of variant 1 on the ETs. The δ terms are related to the main effects and interaction effects as follows:The coefficients from the pairwise regression are then reparametrized to give direct influence coefficients, describing how each marker either enhances or suppresses the activity of each other marker. First two new parameters  of 0.05. Finally, all variant-to-eigentrait effects were multiplied by the singular value matrices from the original SVD to yield main effects and standard error estimates of the variants on the original phenotypes.r ≤ 0.5. This marker then became the first marker in the next linkage block, and the process was repeated until all makers were assigned to a linkage block. The value 0.5 was chosen to give a conservative estimate of linkage blocks. A block was considered to have a significant effect on a phenotype if one or more of the resident markers had a significant effect. After this point, we refer to linkage blocks with significant associations as QTL or loci. The main effect traces of all markers as determined by CAPE can be seen with the linkage block delineations in To define distinct QTL regions in the interaction network, adjacent markers were combined into linkage blocks. The first marker on a chromosome was assigned to the first linkage block. We then stepped along the chromosome comparing each subsequent marker to the first marker until we found one with a Pearson correlation Gria4 and Scn8a mutations were fully backcrossed to the C3H strain compared with B6J  and to the C57BL/6J (B6J) inbred strains and examined SWD incidence and average length from EEG recordings for each respective congenic strain. As expected from previous studies with partially backcrossed mice, SWD measures were significantly enhanced after the with B6J . Interesirection . The SWDirection .Gabrg2 cross was low relative to the other two crosses  with SWD incidence  to decompose the two SWD measures in the meta-cross into two orthogonal eigentraits (ETs). The first ET represented the common features between SWD length and incidence while the second ET represented features that are disparate between the two phenotypes. Both ETs were used in the analysis.p < 0.05) indicating it influences both SWD incidence and length , compared to median values from the corresponding non-interacting pair-wise models (R2 = 0.036 for SWD length and R2 = 0.088 for SWD incidence). Although the interacting models invariably fit more variance, an analogous comparison of non-significant models yields only modest improvement in fit for the interacting model .We estimated the phenotypic variance explained by each model to assess the impact of the interactions. These calculations can be directly computed from the regression fits, which are unchanged by our model reparametrization. The interacting models had high coefficients of determination  has been an obstacle in elucidating its molecular basis. A few causal genes have been identified in both humans and rodent models, but evidence suggests that further progress requires direct assessment of genetic interactions modifying AE phenotypes. Here we examine genetic interactions across three mouse models of AE using CAPE, a novel approach for detecting and interpreting epistasis. With this approach we demonstrated increased power to detect genetic interactions and main effects compared with regression-based techniques. Furthermore, the combination of molecular mechanisms in the population, provided the opportunity to identify broadly relevant genetic modifiers of AE phenotypes.Gria4 mutation, found a significant main effect on Chr 15  were constructed in the 129 strain before backcrossing began, potentially substituting B6 alleles with 129, which might be functionally shared with C3H. Backcrossing to congenicity does not completely isolate the mutation of interest, and a segment of donor strain genetic material – so called “passenger” - usually remains after backcrossing is completed. However, in each subpopulation, backcrossing was performed for at least 10 generations. Theoretical work suggests that after 10 generations of backcrossing, the introgressed region is likely less than 10 cM  does not distinguish between B6 and 129, so we cannot determine the passenger region within 10 Mb. However, the backcross for this mutation was performed for 10 generations to minimize the size of the introgressed region as much as practically possible. In the Gabrg2 mutants, the nearest marker to the mutation (5 Mb away) was B6 for the population, indicating that the introgressed region was small. Finally, the Scn8a mutation was originally in the B6 background and should not contain a passenger segment. Furthermore, this region was not included in the interaction network and is thus of minimal concern. So although it is possible that passenger genes near the mutations of interest modify the phenotypes studied here, reasonable practical measures were taken to minimize passenger regions surrounding the mutations. Moreover, our goal is to prioritize candidate regions for further research and mapping, rather than to identify genes, so we assert that possible contamination is of minimal concern.In our data, passenger material from 129 is likely to exist in the Scn8a and Gabrg2 mutant mice are by definition all heterozygous for closely linked genes). Detecting closely linked modifiers is an inherent problem in any experimental cross. However, from genomic sequence from B6, 129 and C3H strains, the mutant genes themselves do not appear to harbor nonsynonymous coding, splice site or structural variant polymorphisms.Second, congenic passenger segments aside, epistatic modifiers that happen to be closely linked to a sensitizing mutation, even when detected in other crosses, are impossible to detect in the ‘cis’ cross, because of transmission bias  suggests that this phenotype is more genetically complex than SWD incidence, such that candidate loci are more difficult to detect unless interactions are included in the model. Although it is tempting to estimate a total variance by summing all models, all interaction models were independently fit. Therefore each pair-wise model includes main effects from two loci (in addition to the interaction) that would be repeatedly included in the sum. Furthermore, a number of interactions may encode redundant information of pathway structure  frequencies in the combined meta-cross. The homozygous C3H genotype (purple) is rare compared to the homozygous B6J genotype (blue) and heterozygous B6J/C3H genotype (green). For this reason we coded the genotypes assuming C3H dominance with homozygous B6J coded as 0 and both heterozygous and homozygous C3H were coded as 1. In the cross containing the Gria4 mutation there are two null Gria4 alleles present near the most proximal marker on Chr 9. One is the Gria4spkw1 allele and the Gria4tm1Dgen allele. Because these alleles are both null alleles that exhibit the same phenotype, they were coded identically.Figure 2. Main Effects Determined by CAPE with Linkage Blocks Superimposed. The main effects for each genotyped marker determined by CAPE. Gray and white polygons indicate different chromosomes. Vertical gray lines indicate boundaries of the linkage blocks. Horizontal lines mark p values adjusted for multiple tests.Figure 3. Effects of Individual Loci on SWD Incidence and Length. The standardized effect size (β/σ) shows the direction of effect of each locus on SWD length and incidence. Chromosomes are indicated by alternating gray and white bars, and chromosome numbers are marked along the x-axis.Figure 4. Interaction Plots Representing All Interactions Between Chromosomal Segments (a–b) Each panel shows a representative interaction between two chromosomal regions. All significant interactions are plotted. The left-hand section of each figure shows a cartoon depiction of the interaction. Green arrows between chromosomal regions indicate enhancing interactions, and red arrows indicate suppressive interactions. Green arrows to phenotypes indicate positive main effects, and red arrows indicate negative main effects. The right-hand section of each panel shows the interaction plot for the interaction depicted in the cartoon. The normalized phenotype of each genotype group is shown with error bars showing standard error.Supp TableS1A comma-separated text file listing all single nucleotide polymorphisms (SNPs) genotyped for this analysis.Supp TableS2A comma-separated text file listing the approximate locations in Mb of each linkage block as defined in the methods. Each region is defined by the first and last markers in the block. Because we do not use interval mapping to identify the locations of the interactions, these regions should be taken only as marker groups and not necessarily regions containing the QTL.Supp TableS3A comma-separated text file listing all directed genetic influences obtained from CAPE. The individual SNPs listed here were compressed by linkage to generate the network in Supp TableS4A comma-separated text file formatted for use in CAPE. This file contains the genotype and phenotype data for the three crosses. The phenotypes have been mean-centered and adjusted with Z-transform normalization within each cross separately. The genotypes have been coded assuming dominance of the C3H allele. The genotype A indicates homozygous B6, and H indicates either heterozygous or homozygous C3H.


Fear/anxiety and anger/aggression greatly influence health, quality of life and social interactions. They are a huge burden to wellbeing, and personal and public economics. However, while much is known about the physiology and neuroanatomy of such emotions, little is known about their genetics – most importantly, why some individuals are more susceptible to pathology under stress.IGF1 and HMGA2 loci variants for small body size are associated with separation anxiety, touch-sensitivity, owner directed aggression and dog rivalry; and ii) two loci, between GNAT3 and CD36 on chr18, and near IGSF1 on chrX, are associated with several traits, including touch-sensitivity, non-social fear, and fear and aggression that are directed toward unfamiliar dogs and humans. All four genome loci are among the most highly evolutionarily-selected in dogs, and each of those was previously shown to be associated with morphological traits. We propose that the IGF1 and HMGA2 loci are candidates for identical variation being associated with both behavior and morphology. In contrast, we show that the GNAT3-CD36 locus has distinct variants for behavior and morphology. The chrX region is a special case due to its extensive linkage disequilibrium (LD). Our evidence strongly suggests that sociability (which we propose is associated with HS6ST2) and fear/aggression are two distinct GWA loci within this LD block on chrX, but there is almost perfect LD between the peaks for fear/aggression and animal size.We conducted genomewide association (GWA) mapping of breed stereotypes for many fear and aggression traits across several hundred dogs from diverse breeds. We confirmed those findings using GWA in a second cohort of partially overlapping breeds. Lastly, we used the validated loci to create a model that effectively predicted fear and aggression stereotypes in a third group of dog breeds that were not involved in the mapping studies. We found that i) known GNAT3-CD36 and IGSF1 loci. CD36 is widely expressed, but areas of the amygdala and hypothalamus are among the brain regions with highest enrichment; and CD36-knockout mice are known to have significantly increased anxiety and aggression. Both of the other genes have very high tissue-specificity and are very abundantly expressed in brain regions that comprise the core anatomy of fear and aggression – the amygdala to hypothalamic-pituitary-adrenal (HPA) axis. We propose that reduced-fear variants at these loci may have been involved in the domestication process.We have mapped many canine fear and aggression traits to single haplotypes at the The online version of this article (doi:10.1186/s12864-016-2936-3) contains supplementary material, which is available to authorized users. It is difficult to perform genomewide genetic association studies (GWAS) of human behavior. This is due to heterogeneity, biological complexity, ambiguous phenotype classifications and the challenges of phenotyping large numbers of individuals. As a result, very little is known about human behavioral genetics. Most of the progress has been driven by the availability of epidemiological data of medical relevance: smoking behavior , coffee Largely due to heterogeneity, most complex traits are difficult to map in humans. Dozens to thousands of variants can each contribute minute amounts to heritable risk, and this can differ dramatically in different ethnicities and their subgroups. Many major breakthroughs in human genetics have resulted from studying isolated populations and multigenerational families. The advantages of those latter approaches, and others, are dramatically exaggerated in dogs: (i) There are approximately 400 dog breeds, each on the order of 100-fold less genetically-complex than the full population. Thus, compared to humans and their major ethnic groups, dogs are much more similar within breeds and much more different across breeds. (ii) Dogs are often part of a family or working environment and receive high levels of health care. Lastly, iii) dogs have more phenotypic variation than any other land mammal; and much of that variation is the result of “evolutionary” selection under domestication. The strengths of dog models of complex genetics have been exploited mainly in the area of cancer , but recIGF1, IGF1 receptor, growth hormone receptor, HMGA2 and SMAD2) and others indicate opportunities for discovery . A landmark GWAS from 2008 reported many loci and five candidate genes associated with the following behavioral traits: herding, boldness, excitability, pointing, and trainability . Both othalamus . Furtherman size . Importaactivity . Rat stu stimuli ; and IGS stimuli . Thus boBecause our model of the chr18 and X variation was successful in predicting the relevant fear and aggression behaviors in a third group of non-overlapping breeds, we believe these markers can be used to predict and, in part, explain such behavior across many dog breeds. However, the behavioral stereotypes of some breeds were not explained by our predictive model, and many breeds have not been tested. It seems likely that many breeds have epistatic variation with chr18 and X variants or have other variants that are less commonly associated with these traits across dog breeds. All of these issues can be addressed by studies of individual breeds. Since we have only found the common co-occurrence of the increased-fear/aggression variation on chr18 and X in small and medium dogs, it will be interesting to see if this is also present in large dogs bred for aggression or fighting.Dogs were the first animals to be domesticated by humans. New studies indicate that dog domestication has a single origin in southern East Asia ~33,000 years ago, followed by migration to the Middle East, Africa and Europe ~15,000 years ago . Canine IGF1/chr15, HMGA2/chr10 and IGF1R/chr3 small-size variants further establishes that small size is not the predominant cause. However, it is also notable that a subset of large breeds carries the chrX reduced-fear/aggression and increased-size variants in perfect LD.A concrete understanding of dog domestication may soon emerge, as there is a major effort underway to sequence ancient dog genomes . Our preGNAT3-CD36 lies in one of the sweep regions of European (but not Asian) pigs [GNAT3-CD36 variation is directly associated with that domestication event. Our findings that loci spanning GNAT3-CD36  and IGSF1  are associated with canine fear and aggression are thus consistent with a very large body of work implicating the HPA axis.The biochemistry and neuroanatomy of the emotions of fear and aggression are highly conserved in vertebrates, and some argue this is true across the animal kingdom . Across an) pigs ; but it It is clear from animal and human studies that fear and aggression are often associated, but it is not always in the same direction . Based oWe have identified common variants associated with fear and aggression across dog breeds. These can now be biologically dissected at the levels of development, epigenetics, neuroanatomy, physiology and behavior  or in genetically-modified rodent models. Among the areas of research on fear and aggression  that areSince the analysis of dog breed data is highly vulnerable to population structure issues and false positive detection, we designed our analysis in two phases: first, a discovery phase, where available SNP data was used to map aggression and fear behavioral traits. We designed this phase to analyze two independent SNP datasets using nine independent behavioral phenotypes for aggression and fear. Significant hits were taken then into a second phase for validation. To validate our findings we evaluated the performance of behavioral values predicted for breeds not included in the discovery phase. The expectation was to be able to predict behavioral traits from a few markers. Since this data uses publicly available and previously published data, no additional ethics committee approvals were required.C-BARQ phenotype values and distributions for aggression and fear variables were published for the top 30 most popular breeds of the AKC . The pheTo validate the hits mapped using the discovery data, we inferred predictions based on the markers detected for the breeds from the Vaysse dataset  that werTwo previously published SNP datasets were used in this study. The first dataset contained ~175,000 SNPs on the Illumina CanineHD array; we refer to this dataset as the “Vaysse dataset” . The secP-value equal or less than 1 × 10−8; for the Boyko dataset, genomewide significance was declared for a P-value equal or less than 1 × 10−5. GEMMA was run on the Ohio Supercomputer Center’s Oakley cluster (www.osc.edu) for faster processing. To avoid irreproducibility issues, no dataset trimming or LD clustering were performed on the SNP data. All Manhattan plots were generated by SAS v.9.3 from GEMMA outputs. Genomewide significant hits were mapped on the UCSC Genome Browser [The preparation of datasets and subject removal were carried out in PLINK v1.07 . Princip Browser  but coor Browser  since thThe full description of the methods and results of the prediction modeling are given in Additional file LD blocks of adjacent top significant hits and haplotype determination were evaluated by Haploview v.4.2 . Only toWe used the top GWA marker for each trait (or for arbitrarily chosen control regions) to segregate individuals within each breed by their carrier status: heterozygous and homozygous. Continuing the analysis within breeds, the homozygotes for alleles A and B were analyzed further. To construct the largest common phased haplotype within a breed, we defined the boundaries by evaluating each SNP upstream and downstream, and keeping only SNP markers that have an allele frequency of at least 0.95. The largest such interval for each allele at the peak SNP was called a phased-haplotype block. In this work we only include within-breed phased-haplotype blocks if they were present in four or more dogs. Allele frequency calculations and data analyses were performed in PLINK v.1.07 .Si and Di data previously published by Vaysse et al. using the same genotype data [http://dogs.genouest.org/SWEEP.dir/Supplemental.html. We converted all SNP positions from the original CanFam2 assembly to CanFam3.1 to take advantage of the Broad Institute’s CanFam3 Improved Annotation Data V.1 [We used ype data . This inData V.1 . The imp


The cost of next-generation sequencing is now approaching that of the first generation of genome-wide single-nucleotide genotyping panels, but this is still out of reach for large-scale epidemiologic studies with tens of thousands of subjects. Furthermore, the anticipated yield of millions of rare variants poses serious challenges for distinguishing causal from noncausal variants for disease. We explore the merits of using family-based designs for sequencing substudies to identify novel variants and prioritize them for their likelihood of causality. While the sharing of variants within families means that family-based designs may be less efficient for discovery than sequencing of a comparable number of unrelated individuals, the ability to exploit cosegregation of variants with disease within families helps distinguish causal from noncausal ones. We introduce a score test criterion for prioritizing discovered variants in terms of their likelihood of being functional. We compare the relative statistical efficiency of 2-stage versus1-stage family-based designs by application to the Genetic Analysis Workshop 18 simulated sequence data. Most genome-wide association studies for discovering common variants associated with disease traits have been conducted using a case-control design with unrelated controls. Not only are unrelated individuals easier to identify and enroll than are entire families (particularly multiple-case families), but the statistical efficiency per subject genotyped is typically higher using unrelated controls than using unaffected siblings or other relatives . HoweverWe consider a 2-stage design using individuals from a family study for discovery of novel variants and screening, followed by association testing in an independent data set using the linear regression of the phenotype on the genotype. A subset of members is selected for sequencing, preferentially sampling those with the most extreme phenotypes. We rank all the variants identified in these individuals using a novel score test and select those most likely to be causal for genotyping and association testing in the replication data set. These designs are compared with a single-stage family-based design in which all available members are sequenced .Bayes factor criterion. Following the principles described in Petersen et al N = 157).We arbitrarily fixed the total number of variants to be prioritized at 100, but the general principles for design of 2-stage designs -24 couldrelative performance of the 1-stage and 2-stage designs). Because treatment is itself related to blood pressure, it is both a confounder and an intermediate variable on a causal pathway, so neither ignoring it nor covariate adjustment is appropriate. This problem was extensively discussed at Genetic Analysis Workshop 13 [It is also possible that more appropriate adjustment for time-dependent treatment data would improve power for all these analyses (although it's unlikely to affect the kshop 13 ; see refkshop 13 -29 for dThe authors declare that they have no competing interests.ZY participated in statistical analysis. DCT developed the statistical method. Both authors participated in drafting and revising the manuscript. Both authors read and approved the final manuscript.


Motivation: Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem.Results: Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype–phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores , and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis.Conclusion: MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype–phenotype models.Availability and implementation: MGAS is freely available in KGG v3.0 (http://statgenpro.psychiatry.hku.hk/limx/kgg/download.php). Access to the metabolic dataset can be requested at dbGaP (https://dbgap.ncbi.nlm.nih.gov/). The R-simulation code is available from http://ctglab.nl/people/sophie_van_der_sluis.Contact: mxli@hku.hkSupplementary information: Supplementary data are available at Bioinformatics online. This analysis is limited in two important ways. First, genes, not SNPs, are the functional unit in the genome (Standard genome-wide association studies (GWAS) involve the univariate regression of one trait on a large number of genetic variants , while adapting the nominal α criterion level for the extensive multiple testing . Similarly, diagnostic interviews and clinical questionnaires cover a collection of symptoms to deal with the symptomatic heterogeneity. In the GWAS context, this multivariate information is generally reduced to a univariate score, i.e. a univariate full-scale IQ score, or a binary case–control index. Reducing multivariate to univariate data nearly always results in loss of information. Specifically, whether univariate composite scores exhaustively summarize all information in the multivariate data (i.e. are sufficient statistics) depends on the true trait-generating genotype–phenotype model, i.e. the model that describes how the multiple phenotypes and genes jointly generate the observed trait . SpecifA. In allhttp://ctglab.nl/software/]. However, to date, only two recently published methods , however, is known to be powerful specifically if the genetic variant affects only one or a few of multiple correlated phenotypes, but power decreases when the variant affects all or many of the phenotypes , is freely available (http://statgenpro.psychiatry.hku.hk/limx/kgg/download.php), and has a user-friendly graphical interface for loading P-value files and genetic and phenotypic correlational information, and for visualizing results and annotating sequence variants and interesting genes.Gene-based methods for the univariate trait setting are available . MGAS subsequently combines the resulting, ascendingly ordered, P-values p1 … mp × n to obtain one overall, multivariate gene-based P-value PMGAS as follows:Suppose eq denotes the effective number of independent P-values within a gene. Whereas the total number of P-values equals m × n, the effective number is corrected for the fact that the P-values are dependent, i.e. correlated due to both the correlations between the phenotypes and the correlations between the SNPs. Parameter ejq denotes the effective number of P-values among the top j P-values, where j runs from 1 to m × n, and jp denotes the jth P-value in the list of ordered P-values. PMGAS is thus the smallest weighted P-value associated with the null hypothesis that there are no associations between the m phenotypes and the n SNPs within the gene, and the alternative hypothesis that at least one of the m phenotypes is associated to at least one of the n SNPs in the gene.Here, p1 and p2 denote the P-values associated with the test of the association between phenotype m1 and SNP n1, and between phenotype m2 and SNP n2. The correlation between p1 and p2 depends on the observed correlations between the SNPs nr1n2, and the observed correlation between the phenotypes mr1m2. An estimate of the effective number of P-values among the top j P-values, ejq, is obtained through eigenvalue decomposition of the correlation matrix Φ among the m × n ascendingly ordered P-values. This correlation matrix is not observed, but can be accurately approximated from the n × n SNP correlation matrix, Ω, and the m × m phenotypic correlation matrix, Σ (see Section 2.2 later). Specifically, ejq is calculated as:Let j denotes the number of top j P-values, iλ denotes the ith eigenvalue, and I(iλ) is an indicator function, which takes on value 0 if iλ ≤ 1 and value 1 if iλ > 1:where P-values ejq is thus calculated as the observed number of P-values j minus the sum of the difference between iλ and 1 for those eigenvalues > 1. If all j P-values are uncorrelated , then all j eigenvalues equal 1, and ejq = j − 0 = j. Conversely, if the j P-values are perfectly correlated (i.e. if the phenotypes m and the SNPs n are perfectly correlated), then the first eigenvalue equals j, all other eigenvalues equal 0, and ejq = j − (j − 1) = 1 (i.e. testing the association of perfectly correlated phenotypes with perfectly correlated SNPs yields only one unique unit of information). In practice, we expect that ejq will generally be smaller than j but larger than 1, since the phenotypic correlations and the SNP correlations likely take on values between 0 and |1|. Finally, eq is a special case of ejq, i.e. eq = ejq when j = m × n, i.e. when the selection of P-values j covers all m × n P-values.The effective number of eq is by definition ≥ejq, the weighted jp is always ≥ the unweighted jp. This weighting procedure has three consequences. First, when all P-values are large (i.e. non-significant tests), the originally largest P-value can sometimes be selected by the MGAS procedure as PMGAS, i.e. as the smallest weighted P-value that is selected as the multivariate gene-based P-value. Because for the largest P-value the weight eq/ejq = 1, and eq/ejq > 1 for all other P-values, the largest P-value before weighting can become the smallest P-value after weighting when all P-values were high to begin with. Second, because for most P-values eq/ejq > 1, weighted P-values can become >1, especially when the unweighted P-values are large to begin with. However, PMGAS cannot exceed 1. Third, the search for the smallest weighted P-value can stop once epjq/ejq is ≤ jp + 1: as the weight eq/ejq is always ≥1, no subsequent weighted P-values will be ≤ epqj/ejq. This rule saves computation time for calculating the effective number of independent tests.It is important to note that as m × n correlation matrix between the P-values is not observed in practice. Following P-values Φ on the other, under the assumption of no genetic association (i.e. under the null hypothesis). Assuming two correlated bi-allelic SNPs and two correlated phenotypes, genotype and phenotype data were simulated for N = 4000 subjects for every combination of allele frequencies (assuming Hardy–Weinberg equilibrium), LD coefficient r, and phenotypic correlation ρ. For each LD coefficient r, the SNP haplotypes were randomly generated by reference to a categorical distribution. For quantitative phenotypes, phenotypic scores were randomly drawn from a bivariate normal distribution 2 test) was used to evaluate the association between each phenotype and each SNP. Allele frequencies, r, and ρ were increased from the smallest values to the largest values with steps of 0.05 to generate a series of data points. For each setting, 10 000 datasets were generated, yielding 10 000 sets of P-values from which the correlation between P-values could be estimated. Our simulations then showed that the correlation matrix of the m × nP-values Φ could be accurately approximated using a sixth-order polynomial function featuring the Kronecker multiplication of the observed SNP-correlation matrix Ω and observed phenotypic correlation matrix Σ, with coefficient of determination R2 = 0.995:The Supplementary Fig. S1).Neither sample size nor allele frequencies affected the accuracy of this approximation (see http://hapmap.ncbi.nlm.nih.gov) or the 1000 Genomes Project (http://www.1000genomes.org/). KGG v3.0 facilitates the building of Ω from both observed and reference data.MGAS as implemented in KGG v3.0 calculates the expected matrix Φ using the user-provided correlation matrices Σ and Ω. The first can be calculated from the raw phenotype data, while the latter can either be calculated from the actual genotype data or can be obtained from reference genotype data in HapMap  × (20 × 200)]. Depending on the number of phenotypes and SNPs per gene, building the genomic correlation matrix and running the MGAS analysis takes 15 min  to 2 hr (20 phenotypes and 2.4 million SNPs) on an ordinary desktop computer with Intel(R) Core(TM) i7-3770, CPU 3.40 GhZ, RAM 8.00 GB and 64-bit Windows 7.0 Enterprise 2009.To speed up computation of Supplementary Information for details). Phenotype data included 20 standard normally distributed phenotypes. The covariance structure of the phenotypic data was either a unidimensional common factor model .Genetic data included a small gene (10 SNPs) or a larger gene  with varying numbers of disease-susceptibility loci , and LD blocks. r was set to 0.9 and 0, respectively. The simulated regression weights of DSL were chosen to account for 0.5% of the phenotypic variance in small genes and 1% in large genes (see Section 2.5 later). Note that in the univariate regression of one phenotype on a DSL, the effect of the DSL can be larger than simulated when the DSL is in high LD with other DSL (i.e. the effect is augmented because of the correlation of the DSL to other DSL in the same LD block: see Supplementary Information Simulations). Hence, power comparisons should only be made within each scenario, not between scenarios.In all simulations, the minor allele frequency of all SNPs was 0.2. Within and between LD blocks, LD measure P-value. First, the sum score, calculated across all 20 phenotypes, was regressed on all SNPs within a gene, and the P-values from these univariate tests were submitted to the GATES-sum procedure. Second, this sum score was submitted to a multiple regression model, in which all SNPs within a gene were included as predictors. Third, MANOVA was conducted with all 20 phenotypes as dependent variables, and all SNPs within a gene as predictors , bar a small tendency toward inflation (∼0.063) for MGAS over multiple simulation runs (not shown) when analysis concerned a large gene in the clustered network scenario , and performed 10 whole-genome multivariate gene-based association analyses using MGAS. These analyses showed that both small (≤10 SNPs) and larger (>10 SNPs) genes had empirical Type I error rates very close to the nominal alpha bar some stochastic fluctuation .Type I error rates were correct for all methods in all scenarios , it is not directly sensitive to increased numbers of DSL within one gene. We note, however, that ‘indirectly’, MGAS does favor genes with multiple DSL because the weights eq/ejq [see P-value < 0.01/88.44 = 0.00011, while to implicate a gene harboring four DSL, the P-value of the DSL with the weakest signal only needs to be <0.01/23.70 = 0.0004, i.e. can be approximately four times higher.Since MGAS revolves about selection of the best SNP  in low LD. For instance, while the first four weights eq/ejq are 35.66, 19.68, 13.59 and 10.38 when 10 phenotypes correlate 0.53 and 10 SNPs correlate 0.9, they equal 88.44, 44.94, 31.04 and 23.70 when the 10 SNPs correlate only 0.3. Assuming the same number of SNPs representing a gene and DSL with the same effect size, MGAS thus more easily detects genes with DSL that are part of an LD block. This is also illustrated by the relatively high power of MGAS in scenarios V and VI, compared with IV, in j in eq/ejq increase, thereby decreasing the probability that weighted P-values will be below a chosen criterion level α. For instance, assuming 10 phenotypes correlating 0.53 and a gene with 60 SNPs divided over six LD blocks , the first four weights equal 213.94, 115.61, 79.20 and 60.24. If one was to select one SNP per block such that the gene is represented by six unrelated SNPs, the weights would drop to 49.85, 27.51, 19.00 and 14.51, respectively. That is: when adding highly correlated SNPs to the analysis, power only increases if these SNPs are actually DSL that convey an independent association signal. In practice, however, we do not know which SNPs are such DSL. Therefore, while imputation and sequencing yield dense genotype files, the power of MGAS (as of other SNP-based and gene-based tests) to detect associated genes will generally improve if one uses LD-based pruned data in the analysis since most SNPs are not DSL [a similar conclusion was reached for univariate gene-based tests by P-value based pruning is suited for procedures like MGAS that are based on P-value selection, or maybe introduces bias through capitalization on chance, requires further study.At the same time, because the weights N = 4763; http://statgenpro.psychiatry.hku.hk/limx/snptracker). To run MGAS, KGG v3.0 requires a phenotypic correlation matrix and a file containing the P-values from the statistical association test between each SNP and each of the nine phenotypes. Phenotypic correlations, corrected for the covariates used in the original genome-wide analysis, were calculated from the original phenotype data downloaded from dbGaP (Supplementary Table S8). To follow the original analyses and results as closely as possible, we used the original P-value files provided by the authors and available in dbGaP . Combining these nine P-value files, we had information on 328 836 SNPs, 180 520 of which were located within genes, covering 21 153 genes in total. In KGG v3.0, SNPs were mapped onto genes defined by the RefGene database with 5 kb boundary extensions on both sides. When an SNP was located in overlapping regions of multiple genes, the SNP was assigned to all involved genes. The LD structure within each gene was determined in KGG v3.0 by reference to the HapMap LD data (http://hapmap.ncbi.nlm.nih.gov/downloads/ld_data/latest/).To illustrate the usefulness of MGAS, we re-analysed nine quantitative metabolic traits measured in the population-based NFBC1966 . The NFBC1966 data do not contain univariate metabolic syndrome case/control status scores. Therefore, to conduct the univariate gene-based method GATES, we created a univariate composite score reflecting the number of endorsed metabolic risk factors using five standard clinical diagnostic criteria to identify metabolic syndrome (see http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1880831/). These criteria are: TG ≥ 1.70; HDL males < 1.04, HDL females HDL < 1.30; GLU ≥ 6.1, BMI ≥ 25 and blood pressure ≥ 130/85. For each participant, we established whether they met the criteria  and then calculated the sum of endorsed criteria. This sum, ranging from 0 to 5 , was then treated as a continuous dependent variable in a genome-wide analysis that we conducted in PLINK . Of these 44 regions, 30 were not reported in the original analysis, but 39 have been identified before in unrelated studies on the separate metabolic traits according to the Catalog of Published Genome-wide Association Studies . Inspection of the univariate SNP-based P-values underlying the multivariate MGAS P-values showed that while the top regions . Three of the five regions newly identified by MGAS were previously associated to disorders of which treatment/prognosis is known to be related to metabolism (see Supplementary Table S10). For instance, the region harboring GLT8D1, GNL3, SNORD19B and SNORD69 (here associated to GLU and LDL) has been associated to major psychiatric mood disorders  and the univariate gene-based method GATES conducted on the sum of endorsed risk factors (Supplementary Table S12). GATES analyses based on the nine individual continuous metabolic phenotypes (Supplementary Table S13), however, yielded results largely similar to those obtained with MGAS (comparison in Supplementary Table S14), except that the MGAS P-values are properly corrected for the phenotypic correlations and multiple testing.MGAS identified more genes compared with the multivariate SNP-based method TATES  is unproblematic. Second, standard GWAS software deals with quality control in a genome-wide setting, covariates and population structure or stratification. MGAS, subsequently using the P-values resulting from these specialized packages, benefits from the strengths of such software. Third, MGAS is relatively fast as the method does not require permutation. Fourth, for the simulated genotype–phenotype models, MGAS often proved the most powerful gene-based methods, especially for the detection of pleiotropic genes . Although contrasting effects of SNPs in high LD, located within one gene, may indeed exist, e.g. as a balancing mechanism, the scope of this scenario, and thus of this shortcoming, is probably limited. Yet, multivariate gene-based methods that can detect such contrasting effects are currently lacking.It is important to note that, like all other multivariate gene-based methods included in our simulations, MGAS has difficulty detecting genes that harbor multiple DSL in high LD, with contrasting effects  because the resulting P-values will not show the expected correlations resulting from both the phenotypic correlations and the LD structure in the genome. Also, those P-values might be based on samples of different size, obtained using variable recruitment strategies, and might originate diverging analytic choices with respect to data cleaning and inclusions of covariates. Second, KGG v3.0 facilitates estimation of the genetic correlation matrix from a reference population like HapMap: actual genotypes of study samples do not need to be available. However, when the LD structure of the reference population differs from the LD in the actual study sample, MGAS can be liberal or conservative: we refer to We close with some final remarks concerning the practical use of MGAS. First, MGAS, like TATES, cannot be used to analyse data collected in multiple, non-overlapping samples (e.g. trait Traits that have to date yielded few results in GWAS are often believed to be genetically complex, i.e. they involve many genes of small effect. However, genome-wide studies of complex traits have so far almost invariably relied on composite scores like sum scores or case–control dichotomies, thereby assuming the true trait-generating genotype–phenotype model to be a 1-factor model with the gene-effects on the factor A. Our pr


Summary: High-throughput sequencing technologies survey genetic variation at genome scale and are increasingly used to study the contribution of rare and low-frequency genetic variants to human traits. As part of the Cohorts arm of the UK10K project, genetic variants called from low-read depth (average 7×) whole genome sequencing of 3621 cohort individuals were analysed for statistical associations with 64 different phenotypic traits of biomedical importance. Here, we describe a novel genome browser based on the Biodalliance platform developed to provide interactive access to the association results of the project.Availability and implementation: The browser is available at http://www.uk10k.org/dalliance.html. Source code for the Biodalliance platform is available under a BSD license from http://github.com/dasmoth/dalliance, and for the LD-display plugin and backend from http://github.com/dasmoth/ldserv.Contact: ns6@sanger.ac.uk or thomas@biodalliance.orgSupplementary information: Supplementary data are available at Bioinformatics online. Rare and low-frequency genetic variants play an important role in determining population variance of complex traits and disease; however, until recently their systematic evaluation has been beyond the reach of empirical population-based genetic studies.The UK10K project was designed to characterize rare and low-frequency variation in the UK genome wide, and study its contribution to a broad spectrum of biomedically relevant quantitative traits and diseases with different predicted genetic architectures. The data generated by the different arms of the UK10K project and their use are described elsewhere . Here, wBiodalliance is a pure JavaScript genome browser, using HTML5 canvas for displays. Biodalliance’s preferred approach for fetching data is to use indexed binary files such as bigWig or bigBed files . Secondly, our desire to present LD data in a flexible, interactive manner does not fit well with the usual indexed-file approach of retrieving a single block of data corresponding to some range of genomic coordinates.However, creating a display showing genetic association data posed additional challenges. Firstly, there is no perfect file format for representing the association results themselves  for each position in the genome where a variant was tested. A bigBed file containing the variant ID and other information such as SNP consequence predictions supplements this. We added support in the core Biodalliance code for merging multiple data sources into a single track. The Biodalliance renderer and stylesheet system runs on this merged dataset, so in our association tracks we can show association score (from a bigWig file) as the y-axis position of the feature, but choose a point style based on the SNP consequence (from the bigBed file). The y-axis encoding had the added benefit of enabling a feature in the browser to search for the next variant in the genome above an association score threshold (exploiting information encoded in the reduced resolution views of bigWig files) or by identifier (using a name index in the bigBed file).To represent the association results, we used a pair of binary files. Firstly, a bigWig file contains the association test result  reference variant(s), allowing the genotype data itself to remain securely on the server. We also developed Biodalliance plug-ins for selecting reference variants, and for communicating with the LD server. Once again, the capability to merge results from multiple data sources proved useful, and the LD scores simply get merged into the rest of the feature data before it is passed to the Biodalliance renderer.P-values were prepared for each combination of the traits in Supplementary Appendix S1, and the statistical tests in Supplementary Appendix S2. A description of the statistical tests implemented is given in Supplementary Appendix 3. The large number of resulting files were organized in a track-hub, which the UK10K Genome Browser was configured to use by default. The browser functionality is described in Supplementary Appendix 4. A reference summary of test statistics, and key navigation functions, is given in Supplementary Appendix 5. Finally, a step-by-step user tutorial to the browser is also provided (Supplementary Appendix 6).Having extended the Biodalliance code in this way, bigWig and files containing r2) for inferring statistical independence between local association signals and one or more SNP(s) of interest. Calculations are performed in real time using the UK10K-cohorts sequence data. Second, it allows exporting of local association results into high-quality images  that can be used in publications. Finally, it provides dynamic annotation of each SNP, including information on minor allele frequency, SNP quality metrics, as well as links to SNP annotation resources such as dbSNP and functional genome annotation track-hubs such as those generated by the ENCODE (The UK10K Genome Browser exploits the key basic features of the Biodalliance Genome Browser. A number of custom features were specifically designed to aid navigation and interpretation of the UK10K-cohorts data. First, the genome browser supports dynamic estimation of local LD statistics (i.e. the metric In summary, the UK10K browser provides an intuitive and efficient platform to access association statistics for common, low-frequency and rare variants against a large number of human phenotypic traits. As efforts progress to systematically map the contribution of human genetic variation to healthy and disease phenotypes, and to integrate it with genome functional resources, the development of platforms like ours will become essential enabling instruments for the integration and cross-validation of genetic discoveries within the scientific community.


Pleiotropy arises when a locus influences multiple traits. Rich GWAS findings of various traits in the past decade reveal many examples of this phenomenon, suggesting the wide existence of pleiotropic effects. What underlies this phenomenon is the biological connection among seemingly unrelated traits/diseases. Characterizing the molecular mechanisms of pleiotropy not only helps to explain the relationship between diseases, but may also contribute to novel insights concerning the pathological mechanism of each specific disease, leading to better disease prevention, diagnosis and treatment. However, most pleiotropic effects remain elusive because their functional roles have not been systematically examined. A systematic investigation requires availability of qualified measurements at multilayered biological processes . The rise of Big Data in biomedicine, such as high-quality multi-omics data, biomedical imaging data and electronic medical records of patients, offers us an unprecedented opportunity to investigate pleiotropy. There will be a great need of computationally efficient and statistically rigorous methods for integrative analysis of these Big Data in biomedicine. In this review, we outline many opportunities and challenges in methodology developments for systematic analysis of pleiotropy, and highlight its implications on disease prevention, diagnosis and treatment. According to this definition, this locus is said to have pleiotropic effect if both u(1) and u(2) are nonzero. Clearly, this is a local measure of pleiotropy as it only refers to a specific locus. In the genomics era, the local measure has been extended to a global one which is defined as the correlation between the effect sizes of all genetic variants on the two phenotypes  arise as a powerful tool to address this challenge ( ∈ ℝnk × 1 and Gk) for the k-th phenotype, βk)m and u(2)m be the m-th element of u(1) and u(2), respectively. Then they are assumed to jointly follow the bivariate Gaussian distribution asku is the standard deviation of the effect sizes on the k-th phenotype, and ρu ∈  is the genetic correlation to capture global PE. In such a way, these random effects uk) can be estimated using maximum likelihood (ML) or restricted maximum likelihood (REML)  may lead to many false positives (Type I error), while a stringent threshold  may produce too many false negatives (type II errors). Therefore, it may be problematic to examine pleiotropy based on the intersection of identified GWAS loci after a simple thresholding. Second, spurious pleiotropy may appear due to the artifact in experimental design and the linkage disequilibrium (LD) among genetic variants. On one hand, ascertainment bias can introduce spurious PE when sample recruitment based on the first phenotype changes the prevalence of the second phenotypes  project  and the cellular phenotype  may lead to even more fruitful discoveries as the genetic variants often have much larger effect sizes on cellular phenotypes (Battle et al., The advent of big data has revolutionized biomedical research. We are able to comprehensively characterize the health condition of a human subject with quantitative measurements generated at both cellular and organismal levels. From these data, we may find direct or indirect evidence to resolve long-standing problems and motivate advanced scientific hypotheses. Characterization of PE based on integration of various data types is such an exciting process, while the risk of identification of spurious PE may be largely increased due to the enlarged search space. We believe that statistically rigorous methods which effectively account for uncertainties in data integration will continue to play a critical role in improving statistical power and decreasing false positive findings.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Approximate Bayesian computation (ABC), a novel and powerful computational procedure, allows the incorporation of CGMs directly into the estimation of whole genome marker effects in WGP. Here we provide a proof of concept study for this novel approach and demonstrate its use with synthetic data sets. We show that this novel approach can be considerably more accurate than the benchmark WGP method GBLUP in predicting performance in environments represented in the estimation set as well as in previously unobserved environments for traits determined by non-additive gene effects. We conclude that this proof of concept demonstrates that using ABC for incorporating biological knowledge in the form of CGMs into WGP is a very promising and novel approach to improving prediction accuracy for some of the most challenging scenarios in plant breeding and applied genetics.Genomic selection, enabled by whole genome prediction (WGP) methods, is revolutionizing plant breeding. Existing WGP methods have been shown to deliver accurate predictions in the most common settings, such as prediction of across environment performance for traits with additive gene effects. However, prediction of traits with non-additive gene effects and prediction of genotype by environment interaction (G×E), continues to be challenging. Previous attempts to increase prediction accuracy for these particularly difficult tasks employed prediction methods that are purely statistical in nature. Augmenting the statistical methods with biological knowledge has been largely overlooked thus far. Crop growth models (CGMs) attempt to represent the impact of functional relationships between plant physiology and the environment in the formation of yield and similar output traits of interest. Thus, they can explain the impact of G×E and certain types of non-additive gene effects on the expressed phenotype.  Genomic selection , enabledIn-silico phenotypic prediction, enabled by dynamic crop growth models (CGMs), dates back to the late 1960’s  and it hEarly attempts to extend the use of CGMs to enable genetic prediction have focused on developing genetic models for parameters of main process equations within the CGM , 28, 29.There is ample evidence for the importance of epistasis in crops, including for economically important traits such as grain yield in maize –33. YielThe target population of environments for plant breeding programs is subject to continuous re-evaluation . To seleWhile these previous attempts are promising, they are purely statistical in nature and do not leverage the substantial biological insights into the mechanisms determining performance in specific environments. CGMs are an embodiment of this biological knowledge and might serve as a key component in novel WGP models for predicting G×E interactions. In fact, Heslot et al.  recognizapproximate Bayesian computation (ABC) methods, which replace the calculation of a likelihood function with a simulation step, and thereby facilitate analysis when calculation of a likelihood function is impossible or computationally prohibitive. ABC methods were developed in population genetics, where they helped solve otherwise intractable problems  ,  ,  [55] and  [56–58], respectively, with average values at the midpoints of the intervals.We used the maize CGM developed by Muchow et al. , which mwww.sws.uiuc.edu/warm). The sowing date in 2012 was April 15th and in 2013 it was May 15th. We modified the original CGM of Muchow et al. .The weather and management data Ωθ. The prior for μTLN was mTLN from a Uniform distribution over the interval mTLN and 2. The prior means of AM, SRE and MTU were obtained accordingly and the prior variances 2, 0.32 and 2252, respectively.We used independent Normal distribution priors for all components of uTLN was BayesC prior , where M is the number of markers and var(TLN) the observed population variance of TLN. The prior variances of marker effects of the other traits were obtained accordingly.The prior for the marker effects sC prior . In Bayeve,1.2 ⋅ ve], where ve is the residual variance component of the phenotypic grain yield values used to fit the model.The value of ϵ for the distance between the simulated and observed data was tuned in a preliminary run of the algorithm to result in an acceptance rate of approximately 1 ⋅ 10−6. The number of posterior samples drawn was 100. We will refer to this ABC based WGP method that incorporates the CGM as CGM-WGP. The CGM-WGP algorithm was implemented as a C routine integrated with the R software environment [Algorithm 1 in ironment .To test the performance of CGM-WGP, we created a biparental population of 1,550 doubled haploid (DH) inbred lines in silico. The genome consisted of a single chromosome of 1.5 Morgan length. The genotypes of the DH lines were generated by simulating meiosis events with the software package hypred  accordineik is a Gaussian noise variable with mean zero and variance ve. The value of ve was chosen such that the within-environment heritability of yik was equal to 0.85. We generated 50 synthetic data sets by repeating the whole process. An example synthetic data set is available as supplemental material . Predictions for an environment from which no data were used in fitting the model will be referred to as new environment predictions .The models were fitted using F(⋅)ik over the accepted θ samples, using the weather and management data Ωk pertaining to that environment.As a point estimate for predicted grain yield performance in a specific environment, we used the mean of the posterior predictive distribution for the DH line in question. The posterior predictive distribution was obtained by evaluating F(⋅)ik with the true values of the physiological traits.Prediction accuracy was computed as the Pearson correlation between predicted and true performance in the environment for which the prediction was made. The true grain yield performance was obtained by computing β0 is the intercept, u the vector of marker effects and ei a residual. As before, zi denotes the marker genotype vector. The GBLUP model was fitted with the R package rrBLUP [β0+ziu. Note that because the conventional GBLUP model does not utilize information about the environment for which predictions are made, observed and new environment predictions are identical.As a performance benchmark we used genomic best linear unbiased prediction (GBLUP ). The moe rrBLUP . GBLUP aThe accuracy of observed environment predictions achieved by CGM-WGP was considerably larger than that of the benchmark method GBLUP in both environments , S2 Fig.t) during the maize growth cycle, dry matter growth (DMg) results from the interception of solar radiation (SR) and its conversion into mass with efficiency SRE. Light interception in turn depends on the size of the canopy, which is determined by the leaf area per plant (LAPP) and the plant population (PPOP), and the distribution of light within the canopy, which is modeled using a coefficient of light extinction (k). The relationship between DMg, SR, SRE, LAPP and PPOP is non-linear [g increases with increasing AM but only up to a point when canopy size maximizes light interception. Because grain yield is a fraction of the integral of DMg over the growing season, there is a non-linear relationship between AM and grain yield, which can often be detected as a weak correlation  are purely additive [The scenario we studied is an example of a particular case of epistasis, which might be called additive . We accoadditive –66. WhilNew environment prediction accuracy was considerably lower than observed environment prediction accuracy, for both prediction methods , S2 Fig.The genetic rank correlation between true performance in 2012 and 2013 was only 0.54 (averaged over 50 synthetic data sets), which indicated the presence of considerable G×E interactions, including changes in rank . A genetThe interaction between the environment and TLN again explains the occurrence of G×E to a large degree. In the shorter 2012 season, the late maturing genotypes cannot realize their growth and yield potential and are outperformed by the genotypes with early and intermediate maturity Figs . In the That new environment prediction under the presence of G×E interaction is considerably less accurate than observed environment prediction was expected and already observed in other studies , 70. It Predicting G×E interactions in new environments for which no yield data are available, requires WGP models that link genetic effects  with information that characterizes the environments. Jarquín et al.  accompliNonetheless, while novel prediction methods might succeed in narrowing the gap between new and observed environment prediction, the former should always be expected to be less accurate than the latter. Field testing should therefore be performed in environments of particular importance for a breeding program to achieve the maximum attainable prediction accuracy for these. The same applies for target environments in which G×E interaction effects are expected to be particularly strong. CGMs can help to identify such environments and to inform experimental design and utilization of managed environments , 29. HowWith continual technology improvements for phenotyping traits it is becoming increasingly feasible to assay phenotypic variation for many of the physiological traits underlying the CGM . There wOne alternative that can be considered is a two-step procedure, in which (1) physiological traits are predicted based on QTL identified in dedicated mapping experiments and (2) the so obtained physiological trait values are used to parametrize CGMs and predict the expected yield performance of novel genotypes in the same or different environments –73. UsinOne shortcoming of this approach is that all relevant physiological traits have to be measured for all genotypes in the estimation set. This may prove to be unfeasible in practice, particularly when done on an industrial scale . The situation is exacerbated when more sophisticated models like APSIM , which cA key novelty of CGM-WGP is that it can accommodate partially or fully unobserved physiological traits by treating them as hidden variables. It could thus facilitate incorporating a CGM in WGP even when phenotyping all relevant physiological traits is not feasible.However, CGM-WGP and the described two-step approach have a common objective, which is to apply a suitable CGM to capture non-linear relationships among traits and the environment to succeed in the crucial but challenging task of predicting yield in future environments. At this stage it is premature to suggest one approach ahead of these or other possibilities. However, the results of the present study indicate that there are opportunities to improve predictions for quantitative traits influenced by epistasis and G × E interaction, through effective integration of appropriate CGMs into the genetic prediction methodology.APSIM[For this first proof of concept study, we assumed that the CGM used in the estimation process fully represented the systematic component of the data generating process, besides the random noise. This was clearly a ‘best case scenario’. However, decades of crop growth modeling research have provided the know-how necessary to approximate real crop development to a high degree of accuracy , 30, 77.APSIM, for exaWith multiple possible CGMs to choose from, model selection becomes an issue. The ABC algorithm underlying CGM-WGP could in principle be used to perform model selection simultaneously with parameter estimation , 51, 78.ϵ goes to zero [There are examples of the use of fully deterministic model operators in ABC , 79. How to zero  and inst to zero , who con to zero , 17, the to zero . In addi to zero , stochas to zero .For this proof of concept study we used the basic ABC rejection sampling algorithm , 45. ConMCMC-ABC, which incorporates ABC with the Metropolis-Hastings algorithm [Another interesting development is lgorithm . MCMC-ABThe greatest computational advantage of the original ABC rejection algorithm over Monte Carlo based ABC methods is that it generates independent samples and therefore readily lends itself to ‘embarrassingly’ parallel computation . The comWe used mildly informative prior distributions, the parameters of which were derived from the population means and variances of the physiological traits. In practice, the required prior information must be obtained from extraneous sources, such as past experiments or from the literature . Such ina priori independent and note that this does not preclude posterior correlation if CGM-WGP in its current implementation is applied to a scenario with correlated component traits. However, it is possible to model whole genome marker effects as a priori correlated. This was explored for the purpose of fitting WGP models with breed specific but correlated marker effects in animal breeding [In the synthetic data sets we generated, the component traits were controlled by QTL with independent effects and thus uncorrelated. In practice, however, the traits might be correlated, because of pleiotropic QTL, for example. In this situation, marker effects are correlated too. We modeled the marker effects as breeding . Modelinπ’ [Lastly, CGM-WGP can be modified to use ‘BayesB’  or ‘Bayeπ’  as priorWe applied CGM-WGP to a biparental population, which is by far the most common population type in commercial plant breeding programs . PreviouIn contrast to the complex trait of interest, component physiological traits may be realistically modeled based on a relatively simple genetic architecture, and for such traits, QTL explaining a sizable proportion of genetic variance can be mapped and characterized , 93–96. It is possible that the CGM generates the same yield for two or more sets of component trait values. There are often several possible biological strategies with equivalent outcome, so this does not necessarily indicate model missspecification. In this situation, however, it is not possible to identify from the observed yield data alone which set of trait values is more appropriate. By extension, the same applies to the sets of marker effects of which the component trait values are linear functions. This is referred to as likelihood nonidentifiability (short ‘nonidentifiability’) and is a known problem in biological modeling with hidden variables . When anIn addition to using informative prior distributions, increasing the informativeness of the data with respect to the parameters is a direct way to improve their identifiability. In our case, this can be achieved by using additional response variables next to final grain yield. One possible choice is to use grain yield measurements from multiple environments, because several sets of component trait values might generate the same grain yield in one environment, but not in the others. Other possible choices are intermediate traits generated by the CGM, such as early biomass development or leaf area index, which can be measured non-destructively and with high-throughput –107.Actually measuring the underlying physiological traits obviates the need to treat them as latent, hidden variables. This would obviously guarantee identifiability. As mentioned before, it might be possible to measure at least some of the traits. If these are key traits in the development of grain yield, observing them would identify the unobserved traits, too. One way of exploiting the information from observed physiological traits in CGM-WGP is to treat them as constants in the estimation procedure. In this framework, physiological trait values of new genotypes have to be predicted from conventional QTL or WGP models, as described above. However, CGM-WGP could also be extended to estimate marker effects for observed and hidden physiological traits simultaneously.The idea of incorporating biological insights into WGP models is not limited to CGMs. Plant metabolites are chemical compounds produced as intermediate or end products of biochemical pathways. They are seen as potential bridges between genotypes and phenotypes of plants  and are Despite ever increasing sample sizes and marker densities, most of the genetic variance of complex traits remains unaccounted for in genome-wide association studies . MarjoraS1 Table(PDF)Click here for additional data file.S1 DatasetThe data set is a representative example of the 50 synthetic data sets used in the study.(CSV)Click here for additional data file.S1 FigThe early, intermediate and late maturing genotypes had a total leaf number (TLN) of 6, 14.5 and 23, respectively. The values for the other three traits were 750 for AM, 1.6 for SRE and 1150 for MTU and in common for all genotypes. The full and dotted vertical lines indicate the end of the 2012 and 2013 growing season, respectively.(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 FigData shown are a random sample of 1000 genotypes from a representative example replication.(PDF)Click here for additional data file.S4 FigThe grey lines indicate the performance of specific genotypes in both environments. Data shown is from a representative example replication.(PDF)Click here for additional data file.


The hormone cortisol is likely to be a key mediator of the stress response that influences multiple physiologic systems that are involved in common chronic disease, including the cardiovascular system, the immune system, and metabolism. In this paper, a candidate gene approach was used to investigate genetic contributions to variability in multiple correlated features of the daily cortisol profile in a sample of European Americans, African Americans, and Hispanic Americans from the Multi-Ethnic Study of Atherosclerosis (MESA). We proposed and applied a new gene-level multiple-phenotype analysis and carried out a meta-analysis to combine the ethnicity specific results. This new analysis, instead of a more routine single marker-single phenotype approach identified a significant association between one gene (ADRB2) and cortisol features , which was not identified by three other commonly used existing analytic strategies: 1. Single marker association tests involving each single cortisol feature separately; 2. Single marker association tests jointly testing for multiple cortisol features; 3. Gene-level association tests separately carried out for each single cortisol feature. The analytic strategies presented consider different hypotheses regarding genotype-phenotype association and imply different costs of multiple testing. The proposed gene-level analysis integrating multiple cortisol features across multiple ethnic groups provides new insights into the gene-cortisol association. Cortisol concentrations follow a strong daily pattern. They are high upon awakening, reach a maximum concentration approximately half an hour later, and slowly decrease throughout the rest of the day . AdditioThe increase in cortisol concentrations in response to a stressor occurs through the activation of the hypothalamic-pituitary-adrenal (HPA) axis, which has effects on the cardiovascular system, the immune system, and metabolism . The corIn this study we investigate how variation in these six genes is related to seven diurnal cortisol features within and across ethnic groups. In an attempt to better characterize the biological processes involved, the seven cortisol features are defined as mathematical functions of measurements collected six times per day over three consecutive weekdays. This is an advance over prior work that has focused on single measurements of cortisol, which are likely to be imperfect markers of the real phenotype of interest  . MoreoveRecent studies showed the advantages of gene-level analysis over individual single nucleotide polymorphism (SNP) analyses , 22. WheWe contrasted and compared four different analytic strategies with different hypotheses, all targeted towards gene discovery, resulting in varying number of tests: 1. Standard strategy which tests association of single SNPs (approximately 500 markers located on the six candidate genes), one at a time with each cortisol feature (~500 x 7 = 3500 tests in our study); 2. Multivariate association tests of single SNPs with multiple cortisol features (~ 500 x 1 = 500 tests); 3. Gene-level  window up- and downstream of each gene) SNP-set analysis for a single cortisol feature (6 x 7 = 42 tests); 4. A new gene-level multiple-phenotype analysis (only 6 tests) we proposed for summarizing the gene-level results of the seven cortisol features. In each approach we test for different hypotheses, for example, marginal association of a single SNP with a single outcome, joint association of multiple outcomes with a SNP, joint association of a SNP-set with a single outcome, joint association of a SNP-set with multiple outcomes. Bonferroni correction applied to any of these set of tests controls for the family-wise error rate or the probability of rejecting at least one true null hypothesis when the global null hypotheses of no association holds. However, one has to remember that the family of tests being performed in each method and the inferential focus as stated in terms of the respective null hypotheses is different, that in turn, leads to different number of tests and different cost of multiple testing. For each analysis we carried out meta-analysis to combine the ethnicity specific results. Among the six stress response genes investigated, the new gene-level multiple-phenotype analysis identified a significant association between one gene (ADRB2) and cortisol features , while none of the meta-analysis results of the other three strategies are significant after Bonferroni correction.We analyzed data from the MESA Stress Study, which is an ancillary study to the original MESA study, a longitudinal cohort study focused on investigating the early stages of atherosclerosis. The MESA Stress Study was approved by the Institutional Review Boards at UCLA and Columbia University. This research was also approved by the Health Sciences and Behavioral Sciences Institutional Review Board at the University of Michigan. The data was anonymized and de-identified prior to analysis. All procedures were carried out with the adequate understanding and written consent of the subjects. Eligible participants were 45–84 years of age and free from history of cardiovascular disease at the baseline examination (2000–2002) . The MESEach MESA Stress Study participant was asked to collect six saliva samples per day at pre-specified times over three consecutive weekdays, for a maximum of 18 samples, using Salivette collection tubes. The samples were collected using the following schedule: Sample 1) upon waking and before getting out of bed; 2) 30 minutes later; 3) Around 10:00am; 4) Around 12:00 noon or before lunch, whichever came first; 5) Around 6:00pm or before dinner, whichever came first; 6) just before bed. Because earlier work has shown that the use of a time tracking device improves sample collection compliance , each coWe explored seven features of the diurnal cortisol cycle averaged across the three days for each individual : 1. CortGenotyping data included both measured and imputed SNPs available through participation in the MESA SHARe  project. Under the SHARe project, genome-wide genotyping was obtained using the Affymetrix Genome-Wide Human SNP Array 6.0 platform. Imputation to HapMap was completed at the MESA Genetics Centers using the IMPUTE2  program We defined the six stress response gene regions as the entire gene, plus a window ±5kb around each gene. Base pair start and end positions for each gene were assigned based on annotation from the UCSC Genome Browser , and theWe conducted ethnicity specific analysis suggested by the evaluation of inter-ethnic variability of cortisol features using ANOVA by which race has an association with cortisol features  corresponding to subject i; αk0 is an intercept term; Xi is a vector of non-genetic covariates; gi is a minor allele count of single SNP in the regions  and measurement error εik follows any distribution with mean zero and variance αk is a vector of regression coefficients for the covariates, and βk is a regression coefficient for the SNP regressed on the k-th cortisol feature. Standard Wald test is used to test the null hypothesis H0:βk = 0.For each ethnic group, we applied multivariable linear regression to investigate associations between each cortisol feature and each SNP separately. Given the seven cortisol features and approximately 500 SNPs in total , this strategy results in 7×500 = 3500 tests. The specific model is as follows:m  is a minor allele count of SNPgi; Yik is the k-th cortisol feature corresponding to subject i; αgm0 is an intercept term; Xi is a vector of non-genetic covariates. αgm is a vector of regression coefficients for the covariates, and βgk is a regression coefficient for the k-th cortisol feature. Then it uses a likelihood ratio test with K degrees of freedom to test the global null hypothesis H0:βg1 = … = βgk = 0. Since MultiPhen cannot be directly applied to dosage data, we use best call genotype data for this analysis.We then applied the MultiPhen method to test for the joint association of the multiple cortisol phenotypes with a given SNP . This stG|Y,X the method is only used to derive joint association test for the global null hypotheses that none of the multiple outcomes are related to the single genetic marker.Note that though MultiPhen can aggregate the information across multiple phenotypes, the statistical justification for covariate adjustment in such a reverse regression setting with the proportional odds link function remains unclear. The method only tests for conditional association of genes and outcomes  and is not designed to unbiasedly estimate the beta coefficients or arrive at a causal interpretation. We also note that MESA is a cohort study and modeling the genes as outcomes is not interpretable and the reverse regression setting is a mere statistical artifact to construct the multivariate tests for association. One cannot interpret the beta-coefficients obtained from this model of Yik is the k-th cortisol feature corresponding to subject i; αk0 is an intercept term; Xi is a vector of non-genetic covariates; Gi a vector of genotypes and measurement error εik follows any distribution with mean zero and variance αk is a vector of regression coefficients for the covariates, and βk = ' is a vector of regression coefficients for the genotypes corresponding to the p markers on a gene. In SKAT, one assumes that each of the βjk, j = 1,…,p, independently follows an arbitrary distribution with mean zero and variance wjk is a weighting parameter to up-weight rarer variants. Testing H0:βk = 0, i.e., H0:βjk = 0 for j = 1,…,p. We used wjk = 1 for all SNPs since the weighting scheme is developed for rare variant association tests and hence not necessary for testing for common variant associations. The asymptotic null distribution of the resultant score statistic is a mixture of chi-squares [To assess the impact of the six stress response genes on cortisol features, for each ethnic group, we performed gene-level analysis using the sequence kernel association test (SKAT) separately for each pair of cortisol feature and gene region . This st-squares .We proposed and applied a new multiple phenotype analysis to integrate the information contained in the seven cortisol features but still using a gene as the unit of analysis. This strategy results in only 6 tests (number of genes). The method appropriately combines p-values of the gene-level tests corresponding to each individual cortisol feature obtained in Method 3 and provides a combined p-value using Fisher’s combined probability test . In ordeStep 1: Randomly permute the subject ID linked to the genotype data to reflect the null hypothesis of no association between the cortisol features and gene regions.Step 2: Apply SKAT (Method 3) separately for each pair of cortisol feature and gene region, adjusting for the confounders. The model with permuted genotype data is given by:Gi* is now a vector of the genotypes corresponding to another randomly chosen subject i*.Step 3: Use Fisher’s combined probability test to combine the test for each cortisol feature and compute a combined p-value.Step 4: Repeat Step 1–Step 3 for 10000 times and get the distribution of the combined p-values under the null hypothesis.Step 5: Adjust the observed p-value and determine significance using the null distribution of the combined p-value generated by the permutation procedure.Because the permutation only destroys the association between cortisol features and gene regions but retains the correlation structure among cortisol features, this procedure can account for the correlation structure of multiple phenotypes to take advantage of the rich information contained in the seven cortisol features and leading to a single multivariate association test P-value capturing gene-level association.A limitation of the above permutation approach is that it cannot account for the possible correlation between genotypes and covariates. Genotype-covariate association may be expected, for example when principal components are used as covariates to adjust for population stratification. As a result, such a permutation procedure may not provide entirely accurate Type I error rates in some scenarios .Given that we have a multi-ethnic sample and the association may vary across groups, we employed meta-analysis for both gene-level analysis and single SNP analysis using three different tests: 1. Weighted Z-score test ; 2. FishBasic demographic and health status information on the 950 Stress Study participants at baseline is provided in −5 for single cortisol analysis, 0.0001 for MultiPhen) due to the burden of multiple comparisons and the limited sample size . These results are used as supportive information to further evaluate the association via other methods.The most significant SNP in each gene identified by the single-SNP analysis (Methods 1 and 2) are summarized in Table D in . We noteADRB2 exhibited significant association with cortisol features by the multi-phenotype meta-analysis using Fisher’s combined probability test . The association was most significant in European Americans  less significant in African Americans , and not significant in Hispanic Americans . We note that the p-values of the weighted tests are larger because the weighted tests assign a higher weight on Hispanic Americans. There were four instances where the association p-value was less than 0.05 among the cortisol features by meta-analysis: CAR , Bedtime , AUC  and ODSlope . The association pattern is not uniformly consistent among ethnic groups and cortisol features: ADRB2 is associated with CAR, Bedtime and LDSlope in European Americans, but only associated with AUC and ODSlope in African Americans and not significant in Hispanic Americans.The results for the gene-level assessment of gene-by-cortisol associations by Method 3 are presented in ADRB2 and multiple cortisol features at a SNP level, we examined in more detail the individual SNP-by-cortisol joint association in this region as produced by Method 2. Results for this association using meta-analysis of MultiPhen p-values are shown using LocusZoom in To further investigate the association between This candidate gene association study investigated the associations between a’priori selected stress response genes and cortisol features in a multi-ethnic population by utilizing a gene-level multi-phenotype analysis approach. We found statistical evidence that variation in established stress response gene regions is related to features of the diurnal cortisol curve. One of the six stress response gene regions, ADRB2 had a significant association in a joint meta-analysis of multiple cortisol features. There were significant associations in European Americans, suggestive associations in African Americans, but no associations in Hispanic Americans. This gene encodes beta-2-adrenergic receptor which is in the G protein-coupled receptor superfamily . It playThe SKAT methodology used for this gene-level analysis has several advantages as a set-based variance component test. First, SKAT is a powerful method that can integrate all SNPs in the region and can reduce the test degree of freedom when the SNPs are correlated due to linkage disequilibrium, which is of particular importance given the small ethnic group specific sample sizes for these analyses. Second, SKAT allows for the individual variant effects to vary from mean zero in either direction, and does not lose power when variants have different direction of effect unlike the Cohort Allelic Sum Test (CAST)  or WeighThe Fisher method is a simple and powerful approach to combine p-values to increase association signals. We used the Fisher method to combine gene based association test p-values across seven cortisol features and obtained more significant results than the individual phenotype tests. Permutation was used to obtain p-values of the Fisher method, which makes it computationally expensive. An alternative approach is to extend the SKAT method to multiple phenotypes tests . We consThe permutation procedure we used for the Fisher method can preserve the correlation among cortisol features and the correlation between cortisol features and confounders. This strategy has been used in genetics and other areas of biological sciences and provided a reasonable performance for type I error correction . HoweverADRB2) are more involved in sympathetic activity than HPA activity per se; however both systems are interrelated. In this sense our analyses serve to illustrate a useful methodologic approach to examine genetic predictors of cortisol than to identify the most relevant genes. Future analyses should include a broader set of potentially relevant genes.There are three main limitations to this work. The first is a design limitation due the use of HapMap imputed variants, which are not functional SNPs. However, as the HapMap tagging SNPs may be in linkage disequilibrium with causal SNPs they are still useful for identifying genomic regions of potential interest. Second, compliance with cortisol sampling protocols is necessary for estimating reliable cortisol features , 45. ComDespite the limitations, this work is novel in the ability to integrate the information of multiple SNPs in the region, multiple cortisol features and multiple ethnic groups, which was possible through the use of the innovative SKAT and multi-phenotype methodologies as well as the unique, highly detailed cortisol phenotype information. The gene-level multi-phenotype approach allows us to address the concern that individual SNPs may not replicate across ethnic groups as well as cortisol features due to differences in underlying patterns of linkage disequilibrium, differences in allele frequencies , 47, andS1 FigADRB2 gene region was analyzed by MultiPhen . The p-values were plotted using LocusZoom .Each SNP in the (TIFF)Click here for additional data file.S2 FigADRB2 gene region was analyzed by MultiPhen . The p-values were plotted using LocusZoom .Each SNP in the (TIFF)Click here for additional data file.S3 FigADRB2 gene region was analyzed by MultiPhen . The p-values were plotted using LocusZoom .Each SNP in the (TIFF)Click here for additional data file.S1 Tables(DOCX)Click here for additional data file.