Association of air pollution with postmenopausal breast cancer risk in UK Biobank.

BACKGROUND: We investigated the association of several air pollution measures with postmenopausal breast cancer (BCa) risk. METHODS: This study included 155,235 postmenopausal women (of which 6146 with BCa) from UK Biobank. Cancer diagnoses were ascertained through the linkage to the UK National Health Service Central Registers. Annual exposure averages were available from 2005, 2006, 2007, and 2010 for NO2, from 2007 and 2010 for PM10, and from 2010 for PM2.5, NOX, PM2.5-10 and PM2.5 absorbance. Information on BCa risk factors was collected at baseline. Cox proportional hazards regression was used to evaluate the associations of year-specific and cumulative average exposures with BCa risk, overall and with 2-year exposure lag, while adjusting for BCa risk factors. RESULTS: PM10 in 2007 and cumulative average PM10 were positively associated with BCa risk (2007 PM10: Hazard ratio [HR] per 10 µg/m3 = 1.18, 95% CI 1.08, 1.29; cumulative average PM10: HR per 10 µg/m3 = 1.99, 95% CI 1.75, 2.27). Compared to women with low exposure, women with higher 2007 PM10 and cumulative average PM10 had greater BCa risk (4th vs. 1st quartile HR = 1.15, 95% CI 1.07, 1.24, p-trend = 0.001 and HR = 1.35, 95% CI 1.25, 1.44, p-trend < 0.0001, respectively). No significant associations were found for any other exposure measures. In the analysis with 2-year exposure lag, both 2007 PM 10 and cumulative average PM10 were positively associated with BCa risk (4th vs. 1st quartile HR = 1.19, 95% CI 1.10, 1.28 and HR = 1.29, 95% CI 1.19, 1.39, respectively). CONCLUSION: Our findings suggest a positive association of 2007 PM10 and cumulative average PM10 with postmenopausal BCa risk.

Sex-dimorphic gene effects on survival outcomes in people with coronary artery disease.

Background: Ischemic coronary heart disease (IHD) is the leading cause of death worldwide. Genetic variation is presumed to be a major factor underlying sex differences for IHD events, including mortality. The purpose of this study was to identify sex-specific candidate genes associated with all-cause mortality among people diagnosed with coronary artery disease (CAD). Methods: We performed a sex-stratified, exploratory genome-wide association (GWAS) screen using existing data from CAD-diagnosed males (n = 510) and females (n = 174) who reported European ancestry from the Duke Catheterization Genetics biorepository. Extant genotype data for 785,945 autosomal SNPs generated with the Human Omni1-Quad BeadChip (Illumina, CA, USA) were analyzed using an additive inheritance model. We estimated instantaneous risk of all-cause mortality by genotype groups across the 11-year follow-up using Cox multivariate regression, covarying for age and genomic ancestry. Results: The top GWAS hits associated with all-cause mortality among people with CAD included 8 SNPs among males and 15 among females (p = 1 × 10-6 or 10-7), adjusted for covariates. Cross-sex comparisons revealed distinct candidate genes. Biologically relevant candidates included rs9932462 (EMP2/TEKT5) and rs2835913 (KCNJ6) among males and rs7217169 (RAP1GAP2), rs8021816 (PRKD1), rs8133010 (PDE9A), and rs12145981 (LPGAT1) among females. Conclusions: We report 20 sex-specific candidate genes having suggestive association with all-cause mortality among CAD-diagnosed subjects. Findings demonstrate proof of principle for identifying sex-associated genetic factors that may help explain differential mortality risk in people with CAD. Replication and meta-analyses in larger studies with more diverse samples will strengthen future work in this area.

PM2.5 Constituents and Onset of Gestational Diabetes Mellitus: Identifying Susceptible Exposure Windows.

Fine particulate matter (PM2.5) has been linked to gestational diabetes mellitus (GDM). However, PM2.5 is a complex mixture with large spatiotemporal heterogeneities, and women with early-onset GDM (i.e., diagnosed before 24th gestation week) have distinct maternal characteristics and a higher risk of worse health outcomes compared with those with late-onset GDM (i.e., diagnosed in or after 24th gestation week). We aimed to examine differential impacts of PM2.5 and its constituents on early- vs. late-onset GDM, and to identify corresponding susceptible exposure windows. We leveraged statewide linked electronic health records and birth records data in Florida in 2012-2017. Exposures to PM2.5 and its constituents (i.e., sulfate [SO4 2-], ammonium [NH4 +], nitrate [NO3 -], organic matter [OM], black carbon [BC], mineral dust [DUST], and sea-salt [SS]) were spatiotemporally linked to pregnant women based on their residential histories. Cox proportional hazards models and multinomial logistic regression were used to examine the associations of PM2.5 and its constituents with GDM and its onsets. Distributed non-linear lag models were implemented to identify susceptible exposure windows. Exposures to PM2.5, SO4 2-, NH4 +, and BC were statistically significantly associated with higher hazards of GDM. Exposures to PM2.5 during weeks 1-12 of gestation were positively associated with GDM. Associations of early-onset GDM with PM2.5 in the 1st and 2nd trimesters, SO4 2- in the 1st and 2nd trimesters, and NO3 - in the preconception and 1st trimester were considerably stronger than observations for late-onset GDM. Our findings suggest there are differential associations of PM2.5 and its constituents with early- vs. late-onset GDM, with different susceptible exposure windows. This study helps better understand the impacts of air pollution on GDM accounting for its physiological heterogeneity.

High-Order Association Mapping for Expression Quantitative Trait Loci.

Mapping expression quantitative trait loci (eQTLs) is an important avenue to identify putative genetic variants in regulatory regions. Famed eQTL mapping methods exploit the mean effects of locus-wise genetic variants on expression quantitative traits. Despite their successes, such methods are suboptimal because they neglect high-order heterogeneity inherent in genetic variants and covariates. High-order effects of observed loci are common due to their connections to various latent factors, i.e., latent interactions among genes and environmental factors. In this chapter, we introduce a new scheme to harmoniously integrate mean and high-order effects of genetic variants on expression quantitative trait. We rigorously evaluate its validity and utility of signal augmentation.

Identifying Rare Variant Associations in Admixed Populations.

An admixed population and its ancestral populations bear different burdens of a complex disease. The ancestral populations may have different haplotypes of deleterious alleles and thus ancestry-gene interaction can influence disease risk in the admixed population. Among admixed individuals, deleterious haplotypes and their ancestries are dependent and can provide non-redundant association information. Herein we propose a local ancestry boosted sum test (LABST) for identifying chromosomal blocks that harbor rare variants but have no ancestry switches. For such a stable ancestral block, our LABST exploits ancestry-gene interaction and the number of rare alleles therein. Under the null of no genetic association, the test statistic asymptotically follows a chi-square distribution with one degree of freedom (1-df). Our LABST properly controlled type I error rates under extensive simulations, suggesting that the asymptotic approximation was accurate for the null distribution of the test statistic. In terms of power for identifying rare variant associations, our LABST uniformly outperformed several famed methods under four important modes of disease genetics over a large range of relative risks. In conclusion, exploiting ancestry-gene interaction can boost statistical power for rare variant association mapping in admixed populations.

Identifying Multi-Omics Causers and Causal Pathways for Complex Traits.

The central dogma of molecular biology delineates a unidirectional causal flow, i.e., DNA → RNA → protein → trait. Genome-wide association studies, next-generation sequencing association studies, and their meta-analyses have successfully identified ~12,000 susceptibility genetic variants that are associated with a broad array of human physiological traits. However, such conventional association studies ignore the mediate causers (i.e., RNA, protein) and the unidirectional causal pathway. Such studies may not be ideally powerful; and the genetic variants identified may not necessarily be genuine causal variants. In this article, we model the central dogma by a mediate causal model and analytically prove that the more remote an omics level is from a physiological trait, the smaller the magnitude of their correlation is. Under both random and extreme sampling schemes, we numerically demonstrate that the proteome-trait correlation test is more powerful than the transcriptome-trait correlation test, which in turn is more powerful than the genotype-trait association test. In conclusion, integrating RNA and protein expressions with DNA data and causal inference are necessary to gain a full understanding of how genetic causal variants contribute to phenotype variations.

Calibrating Population Stratification in Association Analysis.

In genetic association studies, it is necessary to correct for population structure to avoid inference bias. During the past decade, prevailing corrections often only involved adjustments of global ancestry differences between sampled individuals. Nevertheless, population structure may vary across local genomic regions due to the variability of local ancestries associated with natural selection, migration, or random genetic drift. Adjusting for global ancestry alone may be inadequate when local population structure is an important confounding factor. In contrast, adjusting for local ancestry can more effectively prevent false positives due to local population structure. To more accurately locate disease genes, we recommend adjusting for local ancestries by interrogating local structure. In practice, locus-specific ancestries are usually unknown and must be inferred. For recently admixed populations with known reference ancestral populations, locus-specific ancestries can be inferred accurately using some hidden Markov model-based methods. However, SNP-wise ancestries cannot be accurately inferred when ancestral population information is not available. For such scenarios, we propose employing local principal components (PCs) to present local ancestries and adjusting for local PCs when testing for gene-phenotype association.

Detecting Multiethnic Rare Variants.

Genome-wide association studies have identified many common genetic variants which are associated with certain diseases. The identified common variants, however, explain only a small portion of the heritability of a complex disease phenotype. The missing heritability motivated researchers to test the hypothesis that rare variants influence common diseases. Next-generation sequencing technologies have made the studies of rare variants practicable. Quite a few statistical tests have been developed for exploiting the cumulative effect of a set of rare variants on a phenotype. The best-known sequence kernel association tests (SKATs) were developed for rare variants analysis of homogeneous genomes. In this chapter, we illustrate applications of the SKATs and offer several caveats regarding them. In particular, we address how to modify the SKATs to integrate local allele ancestries and calibrate the cryptic relatedness and population structure of admixed genomes.

A comparison of three induction therapies on patients with delayed graft function after kidney transplantation.

We compare the outcomes of induction therapies with either methylprednisolone (group 1, n = 58), basiliximab (group 2, n = 56) or alemtuzumab (group 3, n = 98) in primary deceased donor kidney transplants with delayed graft function (DGF). Protocol biopsies were performed. Maintenance was tacrolimus and mycophenolate with steroid (group 1 and 2) or without steroid (group 3). One-year biopsy-confirmed acute rejection (AR) rates were 27.6, 19.6 and 10.2 % in group 1, 2 and 3 (p = 0.007). AR was significantly lower in group 3 (p = 0.002) and group 2 (p = 0.03) than in group 1. One-year graft survival rates were 90, 96 and 100 % in group 1, 2 and 3 (log rank p = 0.006). Group 1 had inferior graft survival than group 2 (p = 0.03) and group 3 (p = 0.002). The patient survival rates were not different (96.6, 98.2 and 100 %, log rank p = 0.81). Multivariable analysis using methylprednisolone induction as control indicated that alemtuzumab (OR 0.31, 95 % CI 0.11-0.82; p = 0.03) and basiliximab (OR 0.60, 95 % CI 0.23-0.98; p = 0.018) were associated with lower risk of AR. Therefore, alemtuzumab or basiliximab induction decreases AR and improves graft survival than methylprednisolone alone in patients with DGF. Alemtuzumab induction might also allow patients with DGF to be maintained with contemporary steroid-withdrawal protocol.

Integrating mean and variance heterogeneities to identify differentially expressed genes.

BACKGROUND: In functional genomics studies, tests on mean heterogeneity have been widely employed to identify differentially expressed genes with distinct mean expression levels under different experimental conditions. Variance heterogeneity (aka, the difference between condition-specific variances) of gene expression levels is simply neglected or calibrated for as an impediment. The mean heterogeneity in the expression level of a gene reflects one aspect of its distribution alteration; and variance heterogeneity induced by condition change may reflect another aspect. Change in condition may alter both mean and some higher-order characteristics of the distributions of expression levels of susceptible genes. RESULTS: In this report, we put forth a conception of mean-variance differentially expressed (MVDE) genes, whose expression means and variances are sensitive to the change in experimental condition. We mathematically proved the null independence of existent mean heterogeneity tests and variance heterogeneity tests. Based on the independence, we proposed an integrative mean-variance test (IMVT) to combine gene-wise mean heterogeneity and variance heterogeneity induced by condition change. The IMVT outperformed its competitors under comprehensive simulations of normality and Laplace settings. For moderate samples, the IMVT well controlled type I error rates, and so did existent mean heterogeneity test (i.e., the Welch t test (WT), the moderated Welch t test (MWT)) and the procedure of separate tests on mean and variance heterogeneities (SMVT), but the likelihood ratio test (LRT) severely inflated type I error rates. In presence of variance heterogeneity, the IMVT appeared noticeably more powerful than all the valid mean heterogeneity tests. Application to the gene profiles of peripheral circulating B raised solid evidence of informative variance heterogeneity. After adjusting for background data structure, the IMVT replicated previous discoveries and identified novel experiment-wide significant MVDE genes. CONCLUSIONS: Our results indicate tremendous potential gain of integrating informative variance heterogeneity after adjusting for global confounders and background data structure. The proposed informative integration test better summarizes the impacts of condition change on expression distributions of susceptible genes than do the existent competitors. Therefore, particular attention should be paid to explicitly exploit the variance heterogeneity induced by condition change in functional genomics analysis.

Unified tests for fine-scale mapping and identifying sparse high-dimensional sequence associations.

MOTIVATION: In searching for genetic variants for complex diseases with deep sequencing data, genomic marker sets of high-dimensional genotypic data and sparse functional variants are quite common. Existing sequence association tests are incapable of identifying such marker sets or individual causal loci, although they appeared powerful to identify small marker sets with dense functional variants. In sequence association studies of admixed individuals, cryptic relatedness and population structure are known to confound the association analyses. METHOD: We here propose a unified marker wise test (uFineMap) to accurately localize causal loci and a unified high-dimensional set based test (uHDSet) to identify high-dimensional sparse associations in deep sequencing genomic data of multi-ethnic individuals with random relatedness. These two novel tests are based on scaled sparse linear mixed regressions with Lp (0 < p < 1) norm regularization. They jointly adjust for cryptic relatedness, population structure and other confounders to prevent false discoveries and improve statistical power for identifying promising individual markers and marker sets that harbor functional genetic variants of a complex trait. RESULTS: With large scale simulation data and real data analyses, the proposed tests appropriately controlled Type I error rates and appeared to be more powerful than several prominent methods. We illustrated their practical utilities by the applications to DNA sequence data of Framingham Heart Study for osteoporosis. The proposed tests identified 11 novel significant genes that were missed by the prominent famSKAT and GEMMA. In particular, four out of six most significant pathways identified by the uHDSet but missed by famSKAT have been reported to be related to BMD or osteoporosis in the literature. AVAILABILITY AND IMPLEMENTATION: The computational toolkit is available for academic use: https://sites.google.com/site/shaolongscode/home/uhdset CONTACT: wyp@tulane.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Generalized Sequential Bonferroni Procedure for GWAS in Admixed Populations Incorporating Admixture Mapping Information into Association Tests.

OBJECTIVE: To develop effective methods for GWAS in admixed populations such as African Americans. METHODS: We show that, when testing the null hypothesis that the test SNP is not in background linkage disequilibrium with the causal variants, several existing methods cannot control well the family-wise error rate (FWER) in the strong sense in GWAS. These existing methods include association tests adjusting for global ancestry and joint association tests that combine statistics from admixture mapping tests and association tests that correct for local ancestry. Furthermore, we describe a generalized sequential Bonferroni (smooth-GSB) procedure for GWAS that incorporates smoothed weights calculated from admixture mapping tests into association tests that correct for local ancestry. We have applied the smooth-GSB procedure to analyses of GWAS data on American Africans from the Atherosclerosis Risk in Communities (ARIC) Study. RESULTS: Our simulation studies indicate that the smooth-GSB procedure not only control the FWER, but also improves statistical power compared with association tests correcting for local ancestry. CONCLUSION: The smooth-GSB procedure can result in a better performance than several existing methods for GWAS in admixed populations.

Long-term outcome of ketoconazole and tacrolimus co-administration in kidney transplant patients.

AIM: To study the long-term outcome of ketoconazole and tacrolimus combination in kidney transplant recipients. METHODS: From 2006 to 2010, ketoconazole was given in 199 patients and was continued for at least 1 year or until graft failure (Group 1), while 149 patients did not receive any ketoconazole (Group 2). A combination of tacrolimus, mycophenolate and steroid was used as maintenance therapy. High risk patients received basiliximab induction. RESULTS: Basic demographic data was similar between the 2 groups. The 5-year cumulative incidence of biopsy-confirmed and clinically-treated acute rejection was significantly higher in Group 1 than in Group 2 (34% vs 18%, P = 0.01). The 5-year Kaplan-Meier estimated graft survival (74.3% vs 76.4%, P = 0.58) and patient survival (87.8% vs 87.5%, P = 0.93) were not different between the 2 groups. Multivariable analyses identified ketoconazole usage as an independent risk of acute rejection (HR = 2.33, 95%CI: 1.33-4.07; P = 0.003) while tacrolimus dose in the 2(nd) month was protective (HR = 0.89, 95%CI: 0.75-0.96; P = 0.041). CONCLUSION: Co-administration of ketoconazole and tacrolimus is associated with significantly higher incidence of acute rejection in kidney transplant recipients.

A unified sparse representation for sequence variant identification for complex traits.

Joint adjustment of cryptic relatedness and population structure is necessary to reduce bias in DNA sequence analysis; however, existent sparse regression methods model these two confounders separately. Incorporating prior biological information has great potential to enhance statistical power but such information is often overlooked in many existent sparse regression models. We developed a unified sparse regression (USR) to incorporate prior information and jointly adjust for cryptic relatedness, population structure, and other environmental covariates. Our USR models cryptic relatedness as a random effect and population structure as fixed effect, and utilize the weighted penalties to incorporate prior knowledge. As demonstrated by extensive simulations, our USR algorithm can discover more true causal variants and maintain a lower false discovery rate than do several commonly used feature selection methods. It can handle both rare and common variants simultaneously. Applying our USR algorithm to DNA sequence data of Mexican Americans from GAW18, we replicated three hypertension pathways, demonstrating the effectiveness in identifying susceptibility genetic variants.

Power comparison of admixture mapping and direct association analysis in genome-wide association studies.

When dense markers are available, one can interrogate almost every common variant across the genome via imputation and single nucleotide polymorphism (SNP) test, which has become a routine in current genome-wide association studies (GWASs). As a complement, admixture mapping exploits the long-range linkage disequilibrium (LD) generated by admixture between genetically distinct ancestral populations. It is then questionable whether admixture mapping analysis is still necessary in detecting the disease associated variants in admixed populations. We argue that admixture mapping is able to reduce the burden of massive comparisons in GWASs; it therefore can be a powerful tool to locate the disease variants with substantial allele frequency differences between ancestral populations. In this report we studied a two-stage approach, where candidate regions are defined by conducting admixture mapping at stage 1, and single SNP association tests are followed at stage 2 within the candidate regions defined at stage 1. We first established the genome-wide significance levels corresponding to the criteria to define the candidate regions at stage 1 by simulations. We next compared the power of the two-stage approach with direct association analysis. Our simulations suggest that the two-stage approach can be more powerful than the standard genome-wide association analysis when the allele frequency difference of a causal variant in ancestral populations, is larger than 0.4. Our conclusion is consistent with a theoretical prediction by Risch and Tang ([2006] Am J Hum Genet 79:S254). Surprisingly, our study also suggests that power can be improved when we use less strict criteria to define the candidate regions at stage 1.

Allowing for population stratification in association analysis.

In genetic association studies, it is necessary to correct for population structure to avoid inference bias. During the past decade, prevailing corrections often only involved adjustments of global ancestry differences between sampled individuals. Nevertheless, population structure may vary across local genomic regions due to the variability of local ancestries associated with natural selection, migration, or random genetic drift. Adjusting for global ancestry alone may be inadequate when local population structure is an important confounding factor. In contrast, adjusting for local ancestry can more effectively prevent false-positives due to local population structure. To more accurately locate disease genes, we recommend adjusting for local ancestries by interrogating local structure. In practice, locus-specific ancestries are usually unknown and cannot be accurately inferred when ancestral population information is not available. For such scenarios, we propose employing local principal components (PC) to represent local ancestries and adjusting for local PCs when testing for genotype-phenotype association. With an acceptable computation burden, the proposed algorithm successfully eliminates the known spurious association between SNPs in the LCT gene and height due to the population structure in European Americans.

Genetic background of patients from a university medical center in Manhattan: implications for personalized medicine.

BACKGROUND: The rapid progress currently being made in genomic science has created interest in potential clinical applications; however, formal translational research has been limited thus far. Studies of population genetics have demonstrated substantial variation in allele frequencies and haplotype structure at loci of medical relevance and the genetic background of patient cohorts may often be complex. METHODS AND FINDINGS: To describe the heterogeneity in an unselected clinical sample we used the Affymetrix 6.0 gene array chip to genotype self-identified European Americans (N = 326), African Americans (N = 324) and Hispanics (N = 327) from the medical practice of Mount Sinai Medical Center in Manhattan, NY. Additional data from US minority groups and Brazil were used for external comparison. Substantial variation in ancestral origin was observed for both African Americans and Hispanics; data from the latter group overlapped with both Mexican Americans and Brazilians in the external data sets. A pooled analysis of the African Americans and Hispanics from NY demonstrated a broad continuum of ancestral origin making classification by race/ethnicity uninformative. Selected loci harboring variants associated with medical traits and drug response confirmed substantial within- and between-group heterogeneity. CONCLUSION: As a consequence of these complementary levels of heterogeneity group labels offered no guidance at the individual level. These findings demonstrate the complexity involved in clinical translation of the results from genome-wide association studies and suggest that in the genomic era conventional racial/ethnic labels are of little value.

Estimating heritability using family and unrelated individuals data.

For the family data from Genetic Analysis Workshop 17, we obtained heritability estimates of quantitative traits Q1 and Q4 using the ASSOC program in the S.A.G.E. software package. ASSOC is a family-based method that estimates heritability through the estimation of variance components. The covariate-adjusted mean heritability was 0.650 for Q1 and 0.745 for Q4. For the unrelated individuals data, we estimated the heritability of Q1 as the proportion of total variance that can be accounted for by all single-nucleotide polymorphisms under an additive model. We examined a novel ordinary least-squares method, a naïve restricted maximum-likelihood method, and a calibrated restricted maximum-likelihood method. We applied the different methods to all 200 replicates for Q1. We observed that the ordinary least-squares method yielded many estimates outside the interval [0, 1]. The restricted maximum-likelihood estimates were more stable than the ordinary least-squares estimates. The naïve restricted maximum-likelihood method yielded an average estimate of 0.462 ± 0.1, and the calibrated restricted maximum-likelihood method yielded an average of 0.535 ± 0.121. Our results demonstrate discrepancies in heritability estimates using the family data and the unrelated individuals data.

Interrogating population structure and its impact on association tests.

We found from our analysis of the Genetic Analysis Workshop 17 data that the population structure of the 697 unrelated individuals was an important confounding factor for association studies, even if it was not explicitly considered when simulating the phenotypes. We uncovered structures beyond the reported ethnicities and found ample evidence of phenotype-population structure associations. The first 10 principal components of the genotype data of the 697 individuals demonstrated much stronger associations with Q1, Q2, and the disease than did the individuals' ethnicities. In addition, we observed that population structure was a confounding factor for the Q1-gene association when identifying the significant genes both with and without adjusting for the causal single-nucleotide polymorphisms, the ethnicities, and the principal components. Many false discoveries remained after adjusting for the causal single-nucleotide polymorphisms. Adjusting for the principal components appeared more effective than did adjusting for ethnicity in terms of preventing false discoveries. This analysis was performed with knowledge of the causal loci.