Increasing empirical evidence suggests that many genetic variants influence multiple distinct phenotypes. When cross-phenotype effects exist, multivariate association methods that consider pleiotropy are often more powerful than univariate methods that model each phenotype separately. Although several statistical approaches exist for testing cross-phenotype effects for common variants, there is a lack of similar tests for gene-based analysis of rare variants. In order to fill this important gap, we introduce a statistical method for cross-phenotype analysis of rare variants using a nonparametric distance-covariance approach that compares similarity in multivariate phenotypes to similarity in rare-variant genotypes across a gene. The approach can accommodate both binary and continuous phenotypes and further can adjust for covariates. Our approach yields a closed-form test whose significance can be evaluated analytically, thereby improving computational efficiency and permitting application on a genome-wide scale. We use simulated data to demonstrate that our method, which we refer to as the Gene Association with Multiple Traits (GAMuT) test, provides increased power over competing approaches. We also illustrate our approach using exome-chip data from the Genetic Epidemiology Network of Arteriopathy.

A majority of girls with classic galactosemia demonstrate evidence of diminished ovarian reserve by 3 months of age, and predicted cryptic residual GALT activity is a modifier of ovarian function in galactosemic girls and women.

Abstract Integration of genome-wide association study (GWAS) signals with expression quantitative trait loci (eQTL) studies enables identification of candidate genes. However, evaluating whether nearby signals may share causal variants, termed colocalization, is affected by the presence of allelic heterogeneity, different variants at the same locus impacting the same phenotype. We previously identified eQTL in subcutaneous adipose tissue from 770 participants in the Metabolic Syndrome in Men (METSIM) study and detected 15 eQTL signals that colocalized with GWAS signals for waist–hip ratio adjusted for body mass index (WHRadjBMI) from the Genetic Investigation of Anthropometric Traits consortium. Here, we reevaluated evidence of colocalization using two approaches, conditional analysis and the Bayesian test COLOC, and show that providing COLOC with approximate conditional summary statistics at multi-signal GWAS loci can reconcile disagreements in colocalization classification between the two tests. Next, we performed conditional analysis on the METSIM subcutaneous adipose tissue data to identify conditionally distinct or secondary eQTL signals. We used the two approaches to test for colocalization with WHRadjBMI GWAS signals and evaluated the differences in colocalization classification between the two tests. Through these analyses, we identified four GWAS signals colocalized with secondary eQTL signals for FAM13A, SSR3, GRB14 and FMO1. Thus, at loci with multiple eQTL and/or GWAS signals, analyzing each signal independently enabled additional candidate genes to be identified.

Expression quantitative trait locus (eQTL) studies in human liver are crucial for elucidating how genetic variation influences variability in disease risk and therapeutic outcomes and may help guide strategies to obtain maximal efficacy and safety of clinical interventions. Associations between expression microarray and genome-wide genotype data from four human liver eQTL studies (n = 1,183) were analyzed. More than 2.3 million cis-eQTLs for 15,668 genes were *

BackgroundAlthough more than 100 non-HLA variants have been tested for associations with juvenile idiopathic arthritis (JIA) in candidate gene studies, only a few have been replicated. We sought to replicate reported associations of single nucleotide polymorphisms (SNPs) in the PTPN22, TNFA and MIF genes in a well-characterized cohort of children with JIA.MethodsWe genotyped and analyzed 4 SNPs in 3 genes: PTPN22 C1858T (rs2476601), TNFA G-308A, G-238A (rs1800629, rs361525) and MIF G-173C (rs755622) in 647 JIA cases and 751 healthy controls. We tested for association between each variant and JIA as well as JIA subtypes. We adjusted for multiple testing using permutation procedures. We also performed a meta-analysis that combined our results with published results from JIA association studies.ResultsWhile the PTPN22 variant showed only modest association with JIA (OR = 1.29, p = 0.0309), it demonstrated a stronger association with the RF-positive polyarticular JIA subtype (OR = 2.12, p = 0.0041). The MIF variant was not associated with the JIA as a whole or with any subtype. The TNFA-238A variant was associated with JIA as a whole (OR 0.66, p = 0.0265), and demonstrated a stronger association with oligoarticular JIA (OR 0.33, p = 0.0006) that was significant after correction for multiple testing. TNFA-308A was not associated with JIA, but was nominally associated with systemic JIA (OR = 0.33, p = 0.0089) and enthesitis-related JIA (OR = 0.40, p = 0.0144). Meta-analyses confirmed significant associations between JIA and PTPN22 (OR 1.44, p <0.0001) and TNFA-238A (OR 0.69, p < 0.0086) variants. Subtype meta-analyses of the PTPN22 variant revealed associations between RF-positive, RF-negative, and oligoarticular JIA, that remained significant after multiple hypothesis correction (p < 0.0005, p = 0.0007, and p < 0.0005, respectively).ConclusionsWe have confirmed associations between JIA and PTPN22 and TNFA G-308A. By performing subtype analyses, we discovered a statistically-significant association between the TNFA-238A variant and oligoarticular JIA. Our meta-analyses confirm the associations between TNFA-238A and JIA, and show that PTPN22 C1858T is associated with JIA as well as with RF-positive, RF-negative and oligoarticular JIA.

Objectives Juvenile idiopathic arthritis (JIA) affects children of all races. Prior studies suggest that phenotypic features of JIA in African American (AA) children differ from those of Non-Hispanic White (NHW) children. We evaluated the phenotypic differences at presentation between AA and NHW children enrolled in the CARRA Registry, and replicated the findings in a JIA cohort from a large center in South Eastern USA. Methods Children with JIA enrolled in the multi-center CARRA Registry and from Emory University comprised the study and replication cohorts. Phenotypic data on Non-Hispanic AA children were compared with NHW children with JIA using Chi-square, Fisher's exact and Wilcoxon rank sum tests. Results In all, 4177 NHW and 292 AA JIA cases from the CARRA Registry, and 212 NHW and 71 AA cases from Emory were analyzed. AA subjects more often had RF-positive polyarthritis in both CARRA (13.4% vs. 4.7%, p=5.3×10-7) and Emory (26.8% vs. 6.1%, p =1.1×10-5) cohorts. AA children had positive tests for RF and CCP more frequently, but oligoarticular or early onset ANA-positive JIA less frequently in both cohorts. AA children were older at onset in both cohorts and this difference persisted after excluding RF-positive polyarthritis in the CARRA Registry (median age 8.5 vs. 5.0 years; p =1.4×10-8). Conclusions Compared to NHW children, AA children with JIA are more likely to have RF/CCP positive polyarthritis, and are older at disease onset, and less likely to have oligoarticular or ANA-positive early onset JIA, suggesting that the JIA phenotype is different in African American children.

Identifying the molecular mechanisms by which genome-wide association study (GWAS) loci influence traits remains challenging. Chromatin accessibility quantitative trait loci (caQTLs) help identify GWAS loci that may alter GWAS traits by modulating chromatin structure, but caQTLs have been identified in a limited set of human tissues. Here we mapped caQTLs in human liver tissue in 20 liver samples and identified 3,123 caQTLs. The caQTL variants are enriched in liver tissue promoter and enhancer states and frequently disrupt binding motifs of transcription factors expressed in liver. We predicted target genes for 861 caQTL peaks using proximity, chromatin interactions, correlation with promoter accessibility or gene expression, and colocalization with expression QTLs. Using GWAS signals for 19 liver function and/or cardiometabolic traits, we identified 110 colocalized caQTLs and GWAS signals, 56 of which contained a predicted caPeak target gene. At the LITAF LDL-cholesterol GWAS locus, we validated that a caQTL variant showed allelic differences in protein binding and transcriptional activity. These caQTLs contribute to the epigenomic characterization of human liver and help identify molecular mechanisms and genes at GWAS loci.

SUMMARY Proper control of confounding due to population stratification is crucial for valid analysis of case-control association studies. Fine matching of cases and controls based on genetic ancestry is an increasingly popular strategy to correct for such confounding, both in genome-wide association studies (GWAS) as well as studies that employ next-generation sequencing, where matching can be used when selecting a subset of participants from a GWAS for rare-variant analysis. Existing matching methods match on measures of genetic ancestry that combine multiple components of ancestry into a scalar quantity. However, we show that including non-confounding ancestry components in a matching criterion can lead to inaccurate matches, and hence to an improper control of confounding. To resolve this issue, we propose a novel method that assigns cases and controls to matched strata based on the stratification score (Epstein et al., 2007, AJHG: 80: 921–930), which is the probability of disease given genomic variables. Matching on the stratification score leads to more accurate matches because case participants are matched to control participants who have a similar risk of disease given ancestry information. We illustrate our matching method using the African-American arm of the GAIN GWAS of schizophrenia. In this study, we observe that confounding due to stratification that can be resolved by our matching approach but not by other existing matching procedures. We also use simulated data to show our novel matching approach can provide a more appropriate correction for population stratification than existing matching approaches.