Yunxuan Jiang

Dissecting Vancomycin-Intermediate Resistance in Staphylococcus aureus Using Genome-Wide Association

Vancomycin-intermediate Staphylococcus aureus (VISA) is currently defined as having minimal inhibitory concentration (MIC) of 4–8 µg/ml. VISA evolves through changes in multiple genetic loci with at least 16 candidate genes identified in clinical and in vitro-selected VISA strains. We report a whole-genome comparative analysis of 49 vancomycin-sensitive S. aureus and 26 VISA strains. Resistance to vancomycin was determined by broth microdilution, Etest, and population analysis profile-area under the curve (PAP-AUC). Genome-wide association studies (GWAS) of 55,977 single-nucleotide polymorphisms identified in one or more strains found one highly significant association (P = 8.78E-08) between a nonsynonymous mutation at codon 481 (H481) of the rpoB gene and increased vancomycin MIC. Additionally, we used a database of public S. aureus genome sequences to identify rare mutations in candidate genes associated with VISA. On the basis of these data, we proposed a preliminary model called ECM+RMCG for the VISA phenotype as a benchmark for future efforts. The model predicted VISA based on the presence of a rare mutation in a set of candidate genes (walKR, vraSR, graSR, and agrA) and/or three previously experimentally verified mutations (including the rpoB H481 locus) with an accuracy of 81% and a sensitivity of 73%. Further, the level of resistance measured by both Etest and PAP-AUC regressed positively with the number of mutations present in a strain. This study demonstrated 1) the power of GWAS for identifying common genetic variants associated with antibiotic resistance in bacteria and 2) that rare mutations in candidate gene, identified using large genomic data sets, can also be associated with resistance phenotypes.

A Permutation Procedure to Correct for Confounders in Case-Control Studies, Including Tests of Rare Variation

Many case-control tests of rare variation are implemented in statistical frameworks that make correction for confounders like population stratification difficult. Simple permutation of disease status is unacceptable for resolving this issue because the replicate data sets do not have the same confounding as the original data set. These limitations make it difficult to apply rare-variant tests to samples in which confounding most likely exists, e.g., samples collected from admixed populations. To enable the use of such rare-variant methods in structured samples, as well as to facilitate permutation tests for any situation in which case-control tests require adjustment for confounding covariates, we propose to establish the significance of a rare-variant test via a modified permutation procedure. Our procedure uses Fishers noncentral hypergeometric distribution to generate permuted data sets with the same structure present in the actual data set such that inference is valid in the presence of confounding factors. We use simulated sequence data based on coalescent models to show that our permutation strategy corrects for confounding due to population stratification that, if ignored, would otherwise inflate the size of a rare-variant test. We further illustrate the approach by using sequence data from the Dallas Heart Study of energy metabolism traits. Researchers can implement our permutation approach by using the R package BiasedUrn.

Hierarchy of risk of childhood onset rheumatoid arthritis conferred by HLA-DRB1 alleles encoding the shared epitope

OBJECTIVE Associations between shared epitope (SE)-encoding HLA-DRB1 alleles and rheumatoid arthritis (RA) are well established. However, only a limited number of studies have investigated these alleles in patients with childhood-onset RA, which is defined as rheumatoid factor- and/or anti-citrullinated protein antibody-positive juvenile idiopathic arthritis. The aims of this study were to investigate the largest cohort of patients with childhood-onset RA for association with SE alleles and to determine whether there is a hierarchy of risk based on the amino acid sequence of the SE. METHODS High-resolution HLA-DRB1 genotypes were obtained for 204 patients with childhood-onset RA and 373 healthy control subjects. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated for different SE-encoding HLA-DRB1 alleles. In addition, genotype ORs were calculated for combinations of SE alleles classified into S(2) , S(3P) , or L alleles, based on amino acid sequences in position 70-74 of the DRβ1 chain, as proposed by Tezenas du Montcel et al. RESULTS We confirmed associations between HLA-DRB1 SE alleles and childhood-onset RA (76% of patients carried 1 or 2 SE alleles compared with 46% of control subjects; OR 3.81, 95% CI 2.4-6.0, P < 1 × 10(-7) ). We also observed associations between individual SE alleles (HLA-DRB1*0101, *0401, *0404, *0405, *0408, and *1001) and childhood-onset RA. Genotype-specific risk estimates suggested a hierarchy of risk, with the highest risk among individuals heterozygous for S(2) /S(3P) (OR 22.3, 95% CI 9.9-50.5, P < 0.0001). CONCLUSION We confirm the association between SE-encoding HLA-DRB1 alleles and susceptibility to childhood-onset RA. The excess risk conferred by carriage of the combination of S(2) and S(3P) risk alleles suggests that children with DRβ1 chains containing the KRAA and QRRAA or RRRAA sequences are especially susceptible to RA.

Assessing the Impact of Population Stratification on Association Studies of Rare Variation

Aims: The study of rare variants, which can potentially explain a great proportion of heritability, has emerged as an important topic in human gene mapping of complex diseases. Although several statistical methods have been developed to increase the power to detect disease-related rare variants, none of these methods address an important issue that often arises in genetic studies: false positives due to population stratification. Using simulations, we investigated the impact of population stratification on false-positive rates of rare-variant association tests. Methods: We simulated a series of case-control studies assuming various sample sizes and levels of population structure. Using such data, we examined the impact of population stratification on rare-variant collapsing and burden tests of rare variation. We further evaluated the ability of 2 existing methods (principal component analysis and genomic control) to correct for stratification in such rare-variant studies. Results: We found that population stratification can have a significant influence on studies of rare variants especially when the sample size is large and the population is severely stratified. Our results showed that principal component analysis performed quite well in most situations, while genomic control often yielded conservative results. Conclusions: Our results imply that researchers need to carefully match cases and controls on ancestry in order to avoid false positives caused by population structure in studies of rare variants, particularly if genome-wide data are not available.

Susceptibility to childhood-onset rheumatoid arthritis: Investigation of a weighted genetic risk score that integrates cumulative effects of variants at five genetic loci

OBJECTIVE Children with childhood-onset rheumatoid arthritis (RA) include those with rheumatoid factor or anti-citrullinated protein antibody-positive juvenile idiopathic arthritis. To test the hypothesis that adult-onset RA-associated variants are also associated with childhood-onset RA, we investigated RA-associated variants at 5 loci in a cohort of patients with childhood-onset RA. We also assessed the cumulative association of these variants in susceptibility to childhood-onset RA using a weighted genetic risk score (wGRS). METHODS A total of 155 children with childhood-onset RA and 684 healthy controls were genotyped for 5 variants in the PTPN22, TRAF1/C5, STAT4, and TNFAIP3 loci. High-resolution HLA-DRB1 genotypes were available for 149 cases and 373 controls. We tested each locus for association with childhood-onset RA via logistic regression. We also computed a wGRS for each subject, with weights based on the natural log of the published odds ratios (ORs) for the alleles investigated, and used logistic regression to test the wGRS for association with childhood-onset RA. RESULTS Childhood-onset RA was associated with TNFAIP3 rs10499194 (OR 0.60 [95% confidence interval 0.44-0.83]), PTPN22 rs2476601 (OR 1.61 [95% confidence interval 1.11-2.31]), and STAT4 rs7574865 (OR 1.41 [95% confidence interval 1.06-1.87]) variants. The wGRS was significantly different between cases and controls (P < 2 × 10(-16) ). Individuals in the third to fifth quintiles of wGRS had a significantly increased disease risk compared to baseline (individuals in the first quintile). Higher wGRS was associated with increased risk of childhood-onset RA, especially among males. CONCLUSION The magnitude and direction of the association between TNFAIP3, STAT4, and PTPN22 variants and childhood-onset RA are similar to those observed in RA, suggesting that adult-onset RA and childhood-onset RA share common genetic risk factors. Using a wGRS, we have demonstrated the cumulative association of RA-associated variants with susceptibility to childhood-onset RA.

Flexible and Robust Methods for Rare‐Variant Testing of Quantitative Traits in Trios and Nuclear Families

Most rare‐variant association tests for complex traits are applicable only to population‐based or case‐control resequencing studies. There are fewer rare‐variant association tests for family‐based resequencing studies, which is unfortunate because pedigrees possess many attractive characteristics for such analyses. Family‐based studies can be more powerful than their population‐based counterparts due to increased genetic load and further enable the implementation of rare‐variant association tests that, by design, are robust to confounding due to population stratification. With this in mind, we propose a rare‐variant association test for quantitative traits in families; this test integrates the QTDT approach of Abecasis et al. [Abecasis et al., ] into the kernel‐based SNP association test KMFAM of Schifano et al. [Schifano et al., ]. The resulting within‐family test enjoys the many benefits of the kernel framework for rare‐variant association testing, including rapid evaluation of P‐values and preservation of power when a region harbors rare causal variation that acts in different directions on phenotype. Additionally, by design, this within‐family test is robust to confounding due to population stratification. Although within‐family association tests are generally less powerful than their counterparts that use all genetic information, we show that we can recover much of this power (although still ensuring robustness to population stratification) using a straightforward screening procedure. Our method accommodates covariates and allows for missing parental genotype data, and we have written software implementing the approach in R for public use.

Robust Rare-Variant Association Tests for Quantitative Traits in General Pedigrees

Next-generation sequencing technology has propelled the development of statistical methods to identify rare polygenetic variation associated with complex traits. The majority of these statistical methods are designed for case–control or population-based studies, with few methods that are applicable to family-based studies. Moreover, existing methods for family-based studies mainly focus on trios or nuclear families; there are far fewer existing methods available for analyzing larger pedigrees of arbitrary size and structure. To fill this gap, we propose a method for rare-variant analysis in large pedigree studies that can utilize information from all available relatives. Our approach is based on a kernel machine regression (KMR) framework, which has the advantages of high power, as well as fast and easy calculation of p-values using the asymptotic distribution. Our method is also robust to population stratification due to integration of a QTDT framework (Abecasis et al., Eur J Hum Genet 8(7):545–551, 2000b) with the KMR framework. In our method, we first calculate the expected genotype (between-family component) of a non-founder using all founders’ information and then calculate the deviates (within-family component) of observed genotype from the expectation, where the deviates are robust to population stratification by design. The test statistic, which is constructed using within-family component, is thus robust to population stratification. We illustrate and evaluate our method using simulated data and sequence data from Genetic Analysis Workshop 18.

Powerful and robust cross-phenotype association test for case-parent trios

There has been increasing interest in identifying genes within the human genome that influence multiple diverse phenotypes. In the presence of pleiotropy, joint testing of these phenotypes is not only biologically meaningful but also statistically more powerful than univariate analysis of each separate phenotype accounting for multiple testing. Although many cross‐phenotype association tests exist, the majority of such methods assume samples composed of unrelated subjects and therefore are not applicable to family‐based designs, including the valuable case‐parent trio design. In this paper, we describe a robust gene‐based association test of multiple phenotypes collected in a case‐parent trio study. Our method is based on the kernel distance covariance (KDC) method, where we first construct a similarity matrix for multiple phenotypes and a similarity matrix for genetic variants in a gene; we then test the dependency between the two similarity matrices. The method is applicable to either common variants or rare variants in a gene, and resulting tests from the method are by design robust to confounding due to population stratification. We evaluated our method through simulation studies and observed that the method is substantially more powerful than standard univariate testing of each separate phenotype. We also applied our method to phenotypic and genotypic data collected in case‐parent trios as part of the Genetics of Kidneys in Diabetes (GoKinD) study and identified a genome‐wide significant gene demonstrating cross‐phenotype effects that was not identified using standard univariate approaches.

Susceptibility to childhood-onset rheumatoid arthritis

OBJECTIVE Children with childhood-onset rheumatoid arthritis (RA) include those with rheumatoid factor or anti-citrullinated protein antibody-positive juvenile idiopathic arthritis. To test the hypothesis that adult-onset RA-associated variants are also associated with childhood-onset RA, we investigated RA-associated variants at 5 loci in a cohort of patients with childhood-onset RA. We also assessed the cumulative association of these variants in susceptibility to childhood-onset RA using a weighted genetic risk score (wGRS). METHODS A total of 155 children with childhood-onset RA and 684 healthy controls were genotyped for 5 variants in the PTPN22, TRAF1/C5, STAT4, and TNFAIP3 loci. High-resolution HLA-DRB1 genotypes were available for 149 cases and 373 controls. We tested each locus for association with childhood-onset RA via logistic regression. We also computed a wGRS for each subject, with weights based on the natural log of the published odds ratios (ORs) for the alleles investigated, and used logistic regression to test the wGRS for association with childhood-onset RA. RESULTS Childhood-onset RA was associated with TNFAIP3 rs10499194 (OR 0.60 [95% confidence interval 0.44-0.83]), PTPN22 rs2476601 (OR 1.61 [95% confidence interval 1.11-2.31]), and STAT4 rs7574865 (OR 1.41 [95% confidence interval 1.06-1.87]) variants. The wGRS was significantly different between cases and controls (P < 2 × 10(-16) ). Individuals in the third to fifth quintiles of wGRS had a significantly increased disease risk compared to baseline (individuals in the first quintile). Higher wGRS was associated with increased risk of childhood-onset RA, especially among males. CONCLUSION The magnitude and direction of the association between TNFAIP3, STAT4, and PTPN22 variants and childhood-onset RA are similar to those observed in RA, suggesting that adult-onset RA and childhood-onset RA share common genetic risk factors. Using a wGRS, we have demonstrated the cumulative association of RA-associated variants with susceptibility to childhood-onset RA.

Brief Report: Susceptibility to Childhood‐Onset Rheumatoid Arthritis: Investigation of a Weighted Genetic Risk Score That Integrates Cumulative Effects of Variants at Five Genetic Loci

OBJECTIVE Children with childhood-onset rheumatoid arthritis (RA) include those with rheumatoid factor or anti-citrullinated protein antibody-positive juvenile idiopathic arthritis. To test the hypothesis that adult-onset RA-associated variants are also associated with childhood-onset RA, we investigated RA-associated variants at 5 loci in a cohort of patients with childhood-onset RA. We also assessed the cumulative association of these variants in susceptibility to childhood-onset RA using a weighted genetic risk score (wGRS). METHODS A total of 155 children with childhood-onset RA and 684 healthy controls were genotyped for 5 variants in the PTPN22, TRAF1/C5, STAT4, and TNFAIP3 loci. High-resolution HLA-DRB1 genotypes were available for 149 cases and 373 controls. We tested each locus for association with childhood-onset RA via logistic regression. We also computed a wGRS for each subject, with weights based on the natural log of the published odds ratios (ORs) for the alleles investigated, and used logistic regression to test the wGRS for association with childhood-onset RA. RESULTS Childhood-onset RA was associated with TNFAIP3 rs10499194 (OR 0.60 [95% confidence interval 0.44-0.83]), PTPN22 rs2476601 (OR 1.61 [95% confidence interval 1.11-2.31]), and STAT4 rs7574865 (OR 1.41 [95% confidence interval 1.06-1.87]) variants. The wGRS was significantly different between cases and controls (P < 2 × 10(-16) ). Individuals in the third to fifth quintiles of wGRS had a significantly increased disease risk compared to baseline (individuals in the first quintile). Higher wGRS was associated with increased risk of childhood-onset RA, especially among males. CONCLUSION The magnitude and direction of the association between TNFAIP3, STAT4, and PTPN22 variants and childhood-onset RA are similar to those observed in RA, suggesting that adult-onset RA and childhood-onset RA share common genetic risk factors. Using a wGRS, we have demonstrated the cumulative association of RA-associated variants with susceptibility to childhood-onset RA.