Multivariate association test using haplotype trend regression

Bivariate quantitative Bayesian LASSO for detecting association of rare haplotypes with two correlated continuous phenotypes

In genetic association studies, the multivariate analysis of correlated phenotypes offers statistical and biological advantages compared to analyzing one phenotype at a time. The joint analysis utilizes additional information contained in the correlation and avoids multiple testing. It also provides an opportunity to investigate and understand shared genetic mechanisms of multiple phenotypes. Bivariate logistic Bayesian LASSO (LBL) was proposed earlier to detect rare haplotypes associated with two binary phenotypes or one binary and one continuous phenotype jointly. There is currently no haplotype association test available that can handle multiple continuous phenotypes. In this study, by employing the framework of bivariate LBL, we propose bivariate quantitative Bayesian LASSO (QBL) to detect rare haplotypes associated with two continuous phenotypes. Bivariate QBL removes unassociated haplotypes by regularizing the regression coefficients and utilizing a latent variable to model correlation between two phenotypes. We carry out extensive simulations to investigate the performance of bivariate QBL and compare it with that of a standard (univariate) haplotype association test, Haplo.score (applied twice to two phenotypes individually). Bivariate QBL performs better than Haplo.score in all simulations with varying degrees of power gain. We analyze Genetic Analysis Workshop 19 exome sequencing data on systolic and diastolic blood pressures and detect several rare haplotypes associated with the two phenotypes.

Genome-Wide Association Analyses Track Genomic Regions for Resistance to Ascochyta rabiei in Australian Chickpea Breeding Germplasm

Ascochyta blight (AB), caused by a necrotrophic fungus, Ascochyta rabiei (syn. Phoma rabiei) has the potential to destroy the chickpea industry worldwide, due to limited sources of genetic resistance in the cultivated gene pool, high evolutionary potential of the pathogen and challenges with integrated disease management. Therefore, the deployment of stable genetic resistance in new cultivars could provide an effective disease control strategy. To investigate the genetic basis of AB resistance, genotyping-by-sequencing based DArTseq-single nucleotide polymorphism (SNP) marker data along with phenotypic data of 251 advanced breeding lines and chickpea cultivars were used to perform genome-wide association (GWAS) analysis. Host resistance was evaluated seven weeks after sowing using two highly aggressive single spore isolates (F17191-1 and TR9571) of A. rabiei. GWAS analyses based on single-locus and multi-locus mixed models and haplotyping trend regression identified twenty-six genomic regions on Ca1, Ca4, and Ca6 that showed significant association with resistance to AB. Two haplotype blocks (HB) on chromosome Ca1; HB5 (992178-1108145 bp), and HB8 (1886221-1976301 bp) were associated with resistance against both isolates. Nine HB on the chromosome, Ca4, spanning a large genomic region (14.9-56.6 Mbp) were also associated with resistance, confirming the role of this chromosome in providing resistance to AB. Furthermore, trait-marker associations in two F₃ derived populations for resistance to TR9571 isolate at the seedling stage under glasshouse conditions were also validated. Eighty-nine significantly associated SNPs were located within candidate genes, including genes encoding for serine/threonine-protein kinase, Myb protein, quinone oxidoreductase, and calmodulin-binding protein all of which are implicated in disease resistance. Taken together, this study identifies valuable sources of genetic resistance, SNP markers and candidate genes underlying genomic regions associated with AB resistance which may enable chickpea breeding programs to make genetic gains via marker-assisted/genomic selection strategies.

Detecting rare haplotype association with two correlated phenotypes of binary and continuous types

Multiple correlated traits/phenotypes are often collected in genetic association studies and they may share a common genetic mechanism. Joint analysis of correlated phenotypes has well-known advantages over one-at-a-time analysis including gain in power and better understanding of genetic etiology. However, when the phenotypes are of discordant types such as binary and continuous, the joint modeling is more challenging. Another research area of current interest is discovery of rare genetic variants. Currently there is no method available for detecting association of rare (or common) haplotypes with multiple discordant phenotypes jointly. Our goal is to fill this gap specifically for two discordant phenotypes. We consider a rare haplotype association method for a binary phenotype, logistic Bayesian LASSO (univariate LBL) and its extension for two correlated binary phenotypes (bivariate LBL-2B). Under this framework, we propose a haplotype association test with binary and continuous phenotypes jointly (bivariate LBL-BC). Specifically, we use a latent variable to induce correlation between the two phenotypes. We carry out extensive simulations to investigate bivariate LBL-BC and compare it with univariate LBL and bivariate LBL-2B. In most settings, bivariate LBL-BC performs the best. In only two situations, bivariate LBL-BC has similar performance-when the two phenotypes are (1) weakly or not correlated and the target haplotype affects the binary phenotype only and (2) strongly positively correlated and the target haplotype affects both phenotypes in positive direction. Finally, we apply the method to a data set on lung cancer and nicotine dependence and detect several haplotypes including a rare one.

Bivariate logistic Bayesian LASSO for detecting rare haplotype association with two correlated phenotypes

In genetic association studies, joint modeling of related traits/phenotypes can utilize the correlation between them and thereby provide more power and uncover additional information about genetic etiology. Moreover, detecting rare genetic variants are of current scientific interest as a key to missing heritability. Logistic Bayesian LASSO (LBL) has been proposed recently to detect rare haplotype variants using case-control data, that is, a single binary phenotype. As there is currently no haplotype association method that can handle multiple binary phenotypes, we extend LBL to fill this gap. We develop a bivariate model by using a latent variable to induce correlation between the two outcomes. We carry out extensive simulations to investigate the bivariate LBL and compare with the univariate LBL. The bivariate LBL performs better or similar to the univariate LBL in most settings. It has the highest gain in power when a haplotype is associated with both traits and it affects at least one trait in a direction opposite to the direction of the correlation between the traits. We analyze two data sets-Genetic Analysis Workshop 19 sequence data on systolic and diastolic blood pressures and a genome-wide association data set on lung cancer and smoking and detect several associated rare haplotypes.

Genome-wide association for milk production traits and somatic cell score in different lactation stages of Ayrshire, Holstein, and Jersey dairy cattle

We performed genome-wide association analyses for milk, fat, and protein yields and somatic cell score based on lactation stages in the first 3 parities of Canadian Ayrshire, Holstein, and Jersey cattle. The genome-wide association analyses were performed considering 3 different lactation stages for each trait and parity: from 5 to 95, from 96 to 215, and from 216 to 305 d in milk. Effects of single nucleotide polymorphisms (SNP) for each lactation stage, trait, parity, and breed were estimated by back-solving the direct breeding values estimated using the genomic best linear unbiased predictor and single-trait random regression test-day models containing only the fixed population average curve and the random genomic curves. To identify important genomic regions related to the analyzed lactation stages, traits, parities and breeds, moving windows (SNP-by-SNP) of 20 adjacent SNP explaining more than 0.30% of total genetic variance were selected for further analyses of candidate genes. A lower number of genomic windows with a relatively higher proportion of the explained genetic variance was found in the Holstein breed compared with the Ayrshire and Jersey breeds. Genomic regions associated with the analyzed traits were located on 12, 8, and 15 chromosomes for the Ayrshire, Holstein, and Jersey breeds, respectively. Especially for the Holstein breed, many of the identified candidate genes supported previous reports in the literature. However, well-known genes with major effects on milk production traits (e.g., diacylglycerol O-acyltransferase 1) showed contrasting results among lactation stages, traits, and parities of different breeds. Therefore, our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the analyzed traits across breeds, parities, and lactation stages. Further functional studies are needed to validate our findings in independent populations.

Bivariate genome-wide association analyses identified genetic pleiotropic effects for bone mineral density and alcohol drinking in Caucasians

Several studies indicated bone mineral density (BMD) and alcohol intake might share common genetic factors. The study aimed to explore potential SNPs/genes related to both phenotypes in US Caucasians at the genome-wide level. A bivariate genome-wide association study (GWAS) was performed in 2069 unrelated participants. Regular drinking was graded as 1, 2, 3, 4, 5, or 6, representing drinking alcohol never, less than once, once or twice, three to six times, seven to ten times, or more than ten times per week respectively. Hip, spine, and whole body BMDs were measured. The bivariate GWAS was conducted on the basis of a bivariate linear regression model. Sex-stratified association analyses were performed in the male and female subgroups. In males, the most significant association signal was detected in SNP rs685395 in DYNC2H1 with bivariate spine BMD and alcohol drinking (P = 1.94 × 10-8). SNP rs685395 and five other SNPs, rs657752, rs614902, rs682851, rs626330, and rs689295, located in the same haplotype block in DYNC2H1 were the top ten most significant SNPs in the bivariate GWAS in males. Additionally, two SNPs in GRIK4 in males and three SNPs in OPRM1 in females were suggestively associated with BMDs (of the hip, spine, and whole body) and alcohol drinking. Nine SNPs in IL1RN were only suggestively associated with female whole body BMD and alcohol drinking. Our study indicated that DYNC2H1 may contribute to the genetic mechanisms of both spine BMD and alcohol drinking in male Caucasians. Moreover, our study suggested potential pleiotropic roles of OPRM1 and IL1RN in females and GRIK4 in males underlying variation of both BMD and alcohol drinking.

A post-GWAS replication study confirming the PTK2 gene associated with milk production traits in Chinese Holstein

Our initial genome-wide association study (GWAS) demonstrated that two SNPs (ARS-BFGL-NGS-33248, UA-IFASA-9288) within the protein tyrosine kinase 2 (PTK2) gene were significantly associated with milk production traits in Chinese Holstein dairy cattle. To further validate if the statistical evidence provided in GWAS were true-positive findings, a replication study was performed herein through genotype-phenotype associations. The two tested SNPs were found to show significant associations with milk production traits, which confirmed the associations observed in the original study. Specifically, SNPs lying in the PTK2 gene were also detected by sequencing 14 unrelated sires in Chinese Holsteins and a total of thirty-three novel SNPs were identified. Thirteen out of these identified SNPs were genotyped and tested for association with milk production traits in an independent resource population. After Bonferroni correction for multiple testing, twelve SNPs were statistically significant for more than two milk production traits. Analyses of pairwise D' measures of linkage disequilibrium (LD) between all SNPs were also explored. Two haplotype blocks were inferred and the association study at haplotype level revealed similar effects on milk production traits. In addition, the RNA expression analyses revealed that a non-synonymous coding SNP (g.4061098T>G) was involved in the regulation of gene expression. Thus the findings presented here provide strong evidence for associations of PTK2 variants with dairy production traits and may be applied in Chinese Holstein breeding program.

Multivariate generalized multifactor dimensionality reduction to detect gene-gene interactions

Background

Recently, one of the greatest challenges in genome-wide association studies is to detect gene-gene and/or gene-environment interactions for common complex human diseases. Ritchie et al. (2001) proposed multifactor dimensionality reduction (MDR) method for interaction analysis. MDR is a combinatorial approach to reduce multi-locus genotypes into high-risk and low-risk groups. Although MDR has been widely used for case-control studies with binary phenotypes, several extensions have been proposed. One of these methods, a generalized MDR (GMDR) proposed by Lou et al. (2007), allows adjusting for covariates and applying to both dichotomous and continuous phenotypes. GMDR uses the residual score of a generalized linear model of phenotypes to assign either high-risk or low-risk group, while MDR uses the ratio of cases to controls.

Methods

In this study, we propose multivariate GMDR, an extension of GMDR for multivariate phenotypes. Jointly analysing correlated multivariate phenotypes may have more power to detect susceptible genes and gene-gene interactions. We construct generalized estimating equations (GEE) with multivariate phenotypes to extend generalized linear models. Using the score vectors from GEE we discriminate high-risk from low-risk groups. We applied the multivariate GMDR method to the blood pressure data of the 7,546 subjects from the Korean Association Resource study: systolic blood pressure (SBP) and diastolic blood pressure (DBP). We compare the results of multivariate GMDR for SBP and DBP to the results from separate univariate GMDR for SBP and DBP, respectively. We also applied the multivariate GMDR method to the repeatedly measured hypertension status from 5,466 subjects and compared its result with those of univariate GMDR at each time point.

Results

Results from the univariate GMDR and multivariate GMDR in two-locus model with both blood pressures and hypertension phenotypes indicate best combinations of SNPs whose interaction has significant association with risk for high blood pressures or hypertension. Although the test balanced accuracy (BA) of multivariate analysis was not always greater than that of univariate analysis, the multivariate BAs were more stable with smaller standard deviations.

Conclusions

In this study, we have developed multivariate GMDR method using GEE approach. It is useful to use multivariate GMDR with correlated multiple phenotypes of interests.

Suggestion of GLYAT gene underlying variation of bone size and body lean mass as revealed by a bivariate genome-wide association study

Bone and muscle, two major tissue types of musculoskeletal system, have strong genetic determination. Abnormality in bone and/or muscle may cause musculoskeletal diseases such as osteoporosis and sarcopenia. Bone size phenotypes (BSPs), such as hip bone size (HBS), appendicular bone size (ABS), are genetically correlated with body lean mass (mainly muscle mass). However, the specific genes shared by these phenotypes are largely unknown. In this study, we aimed to identify the specific genes with pleiotropic effects on BSPs and appendicular lean mass (ALM). We performed a bivariate genome-wide association study (GWAS) by analyzing ~690,000 SNPs in 1,627 unrelated Han Chinese adults (802 males and 825 females) followed by a replication study in 2,286 unrelated US Caucasians (558 males and 1,728 females). We identified 14 interesting single nucleotide polymorphisms (SNPs) that may contribute to variation of both BSPs and ALM, with p values <10(-6) in discovery stage. Among them, the association of three SNPs (rs2507838, rs7116722, and rs11826261) in/near GLYAT (glycine-N-acyltransferase) gene was replicated in US Caucasians, with p values ranging from 1.89 × 10(-3) to 3.71 × 10(-4) for ALM-ABS, from 5.14 × 10(-3) to 1.11 × 10(-2) for ALM-HBS, respectively. Meta-analyses yielded stronger association signals for rs2507838, rs7116722, and rs11826261, with pooled p values of 1.68 × 10(-8), 7.94 × 10(-8), 6.80 × 10(-8) for ALB-ABS and 1.22 × 10(-4), 9.85 × 10(-5), 3.96 × 10(-4) for ALM-HBS, respectively. Haplotype allele ATA based on these three SNPs was also associated with ALM-HBS and ALM-ABS in both discovery and replication samples. Interestingly, GLYAT was previously found to be essential to glucose metabolism and energy metabolism, suggesting the gene's dual role in both bone development and muscle growth. Our findings, together with the prior biological evidence, suggest the importance of GLYAT gene in co-regulation of bone phenotypes and body lean mass.

Bivariate association analysis in selected samples: application to a GWAS of two bone mineral density phenotypes in males with high or low BMD

Our specific aims were to evaluate the power of bivariate analysis and to compare its performance with traditional univariate analysis in samples of unrelated subjects under varying sampling selection designs. Bivariate association analysis was based on the seemingly unrelated regression (SUR) model that allows different genetic models for different traits. We conducted extensive simulations for the case of two correlated quantitative phenotypes, with the quantitative trait locus making equal or unequal contributions to each phenotype. Our simulation results confirmed that the power of bivariate analysis is affected by the size, direction and source of the phenotypic correlations between traits. They also showed that the optimal sampling scheme depends on the size and direction of the induced genetic correlation. In addition, we demonstrated the efficacy of SUR-based bivariate test by applying it to a real Genome-Wide Association Study (GWAS) of Bone Mineral Density (BMD) values measured at the lumbar spine (LS) and at the femoral neck (FN) in a sample of unrelated males with low BMD (LS Z-scores ≤ -2) and with high BMD (LS and FN Z-scores >0.5). A substantial amount of top hits in bivariate analysis did not reach nominal significance in any of the two single-trait analyses. Altogether, our studies suggest that bivariate analysis is of practical significance for GWAS of correlated phenotypes.

Bivariate association analysis for quantitative traits using generalized estimation equation

Quantitative traits often underlie risk for complex diseases. Many studies collect multiple correlated quantitative phenotypes and perform univariate analyses on each of them respectively. However, this strategy may not be powerful and has limitations to detect pleiotropic genes that may underlie correlated quantitative traits. In addition, testing multiple traits individually will exacerbate perplexing problem of multiple testing. In this study, generalized estimating equation 2 (GEE2) is applied to association mapping of two correlated quantitative traits. We suppose that a quantitative trait locus is located in a chromosome region that exerts pleiotropic effects on multiple quantitative traits. In that region, multiple SNPs are genotyped. Genotypes of these SNPs and the two quantitative traits affected by a causal SNP were simulated under various parameter values: residual correlation coefficient between two traits, causal SNP heritability, minor allele frequency of the causal SNP, extent of linkage disequilibrium with the causal SNP, and the test sample size. By power analytical analyses, it is showed that the bivariate method is generally more powerful than the univariate method. This method is robust and yields false-positive rates close to the pre-set nominal significance level. Our real data analyses attested to the usefulness of the method.

Powerful bivariate genome-wide association analyses suggest the SOX6 gene influencing both obesity and osteoporosis phenotypes in males

Background

Current genome-wide association studies (GWAS) are normally implemented in a univariate framework and analyze different phenotypes in isolation. This univariate approach ignores the potential genetic correlation between important disease traits. Hence this approach is difficult to detect pleiotropic genes, which may exist for obesity and osteoporosis, two common diseases of major public health importance that are closely correlated genetically.

Principal Findings

To identify such pleiotropic genes and the key mechanistic links between the two diseases, we here performed the first bivariate GWAS of obesity and osteoporosis. We searched for genes underlying co-variation of the obesity phenotype, body mass index (BMI), with the osteoporosis risk phenotype, hip bone mineral density (BMD), scanning ∼380,000 SNPs in 1,000 unrelated homogeneous Caucasians, including 499 males and 501 females. We identified in the male subjects two SNPs in intron 1 of the SOX6 (SRY-box 6) gene, rs297325 and rs4756846, which were bivariately associated with both BMI and hip BMD, achieving p values of 6.82×10⁻⁷ and 1.47×10⁻⁶, respectively. The two SNPs ranked at the top in significance for bivariate association with BMI and hip BMD in the male subjects among all the ∼380,000 SNPs examined genome-wide. The two SNPs were replicated in a Framingham Heart Study (FHS) cohort containing 3,355 Caucasians (1,370 males and 1,985 females) from 975 families. In the FHS male subjects, the two SNPs achieved p values of 0.03 and 0.02, respectively, for bivariate association with BMI and femoral neck BMD. Interestingly, SOX6 was previously found to be essential to both cartilage formation/chondrogenesis and obesity-related insulin resistance, suggesting the gene's dual role in both bone and fat.

Conclusions

Our findings, together with the prior biological evidence, suggest the SOX6 gene's importance in co-regulation of obesity and osteoporosis.