Dietary inflammatory index and risk of colorectal adenoma: effect measure modification by race, nonsteroidal anti-inflammatory drugs, cigarette smoking and body mass index?

PURPOSE: To investigate if the association between dietary inflammatory potential and colorectal adenoma (CRA) is modified by race and factors known to modulate inflammation. METHODS: We examined effect measure modification of race, nonsteroidal anti-inflammatory drugs (NSAIDs), cigarette smoking and body mass index (BMI) on the diet-CRA association by employing energy-adjusted dietary inflammatory index (E-DII™) to characterize dietary inflammatory potential among 587 cases and 1,313 controls participating in a colonoscopy screening-based cross-sectional study of CRA. Participants completed a food frequency questionnaire from which E-DII score was derived. E-DII score was calculated from 34 food parameters (constituents), utilizing an energy-adjusted global comparative database to compute z scores from which centered proportions were summed to create the score. CRA cases were defined as individuals whose colonoscopy detected at least one pathologically confirmed adenomatous polyp. Unconditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: A pro-inflammatory diet was not statistically significantly associated with elevated CRA risk (OR 1.07; 95% CI 0.97-1.19; p value = 0.18) in the multivariate regression model. NSAIDs use (ORnever-users 1.19; 95% CI 1.03-1.38; ORever-users 0.96; 95% CI 0.83-1.12; Pinteraction = 0.04) and race (ORAfrican Americans 1.22; 95% CI 1.03-1.44; OREuropean Americans 0.99; 95% CI 0.86-1.14; Pinteraction = 0.14) appeared to modify the association, whereas cigarette smoking and BMI did not (Pinteraction = 0.40 and 0.78, respectively). CONCLUSION: NSAIDs use and race may modify the diet-CRA association. Further investigation in prospective cohort studies is warranted to confirm these findings.

What is the significance of difference in phenotypic variability across SNP genotypes?

We studied the general problem of interpreting and detecting differences in phenotypic variability among the genotypes at a locus, from both a biological and a statistical point of view. The scales on which we measure interval-scale quantitative traits are man-made and have little intrinsic biological relevance. Before claiming a biological interpretation for genotype differences in variance, we should be sure that no monotonic transformation of the data can reduce or eliminate these differences. We show theoretically that for an autosomal diallelic SNP, when the three corresponding means are distinct so that the variance can be expressed as a quadratic function of the mean, there implicitly exists a transformation that will tend to equalize the three variances; we also demonstrate how to find a transformation that will do this. We investigate the validity of Bartlett's test, Box's modification of it, and a modified Levene's test to test for differences in variances when normality does not hold. We find that, although they may detect differences in variability, these tests do not necessarily detect differences in variance. The same is true for permutation tests that use these three statistics.

Potential role of fructose on human colon DNA methylation in racial disparities observed for colorectal cancer risk.

Background and aims: An increasing body of observational studies has linked fructose intake to colorectal cancer (CRC). African Americans (AAs) are significantly more likely than European Americans to consume greater quantities of fructose and to develop right-side colon cancer. Yet, a mechanistic link between these two associations remains poorly defined. We aimed to identify differentially methylated regions (DMRs) associated with dietary fructose consumption measures obtained from food frequency questionnaires in a cohort of normal colon biopsies derived from AA men and women (n=79). Methods: DNA methylation data from this study was obtained using the Illumina Infinium MethylationEPIC kit and is housed under accession GSE151732. DMR analysis was carried out using DMRcate in right and matched left colon, separately. Secondary analysis of CRC tumors was carried out using data derived from TCGA-COAD, GSE101764 and GSE193535. Differential expression analysis was carried out on CRC tumors from TCGA-COAD using DESeq2 . Results: We identified 4,263 right-side fructose-DMRs. In contrast, only 24 DMRs survived multiple testing corrections (FDR<0.05) in matched, left colon. To identify targets by which dietary fructose drives CRC risk, we overlaid these findings with data from three CRC tumor datasets. Remarkably, almost 50% of right-side fructose-DMRs overlapped regions associated with CRC in at least one of three datasets. TNXB and CDX2 ranked among the most significant fructose risk DMRs in right and left colon respectively that also displayed altered gene expression in CRC tumors. Conclusions: Our mechanistic data support the notion that fructose has a greater CRC-related effect in right than left AA colon, alluding to a potential role for fructose in contributing to racial disparities in CRC.

A Race-Specific, DNA Methylation Analysis of Aging in Normal Rectum: Implications for the Biology of Aging and Its Relationship to Rectal Cancer.

Approximately 90% of colorectal cancer (CRC) develop over the age of 50, highlighting the important role of aging in CRC risk. African Americans (AAs) shoulder a greater CRC burden than European Americans (EA) and are more likely to develop CRC at a younger age. The effects of aging in AA and EA normal rectal tissue have yet to be defined. Here, we performed epigenome-wide DNA methylation analysis in the first, large-scale biracial cohort of normal rectum (n = 140 samples). We identified increased epigenetic age acceleration in EA than AA rectum (p = 3.91 × 10-4) using linear regression. We also identified differentially methylated regions (DMRs) associated with chronological aging in AA and EA, separately using DMRcate. Next, a consensus set of regions associated with cancer was identified through DMR analysis of two rectal cancer cohorts. The vast majority of AA DMRs were present in our analysis of aging in rectum of EA subjects, though rates of epigenetic drift were significantly greater in AA (p = 1.94 × 10-45). However, 3.66-fold more DMRs were associated with aging in rectum of EA subjects, many of which were also associated with rectal cancer. Our findings reveal a novel relationship between race, age, DNA methylation and rectal cancer risk that warrants further investigation.

Linkage analysis of plasma dopamine ß-hydroxylase activity in families of patients with schizophrenia.

Dopamine ß-hydroxylase (DßH) catalyzes the conversion of dopamine to norepinephrine. DßH enters the plasma after vesicular release from sympathetic neurons and the adrenal medulla. Plasma DßH activity (pDßH) varies widely among individuals, and genetic inheritance regulates that variation. Linkage studies suggested strong linkage of pDßH to ABO on 9q34, and positive evidence for linkage to the complement fixation locus on 19p13.2-13.3. Subsequent association studies strongly supported DBH, which maps adjacent to ABO, as the locus regulating a large proportion of the heritable variation in pDßH. Prior studies have suggested that variation in pDßH, or genetic variants at DßH, associate with differences in expression of psychotic symptoms in patients with schizophrenia and other idiopathic or drug-induced brain disorders, suggesting that DBH might be a genetic modifier of psychotic symptoms. As a first step toward investigating that hypothesis, we performed linkage analysis on pDßH in patients with schizophrenia and their relatives. The results strongly confirm linkage of markers at DBH to pDßH under several models (maximum multipoint LOD score, 6.33), but find no evidence to support linkage anywhere on chromosome 19. Accounting for the contributions to the linkage signal of three SNPs at DBH, rs1611115, rs1611122, and rs6271 reduced but did not eliminate the linkage peak, whereas accounting for all SNPs near DBH eliminated the signal entirely. Analysis of markers genome-wide uncovered positive evidence for linkage between markers at chromosome 20p12 (multi-point LOD = 3.1 at 27.2 cM). The present results provide the first direct evidence for linkage between DBH and pDßH, suggest that rs1611115, rs1611122, rs6271 and additional unidentified variants at or near DBH contribute to the genetic regulation of pDßH, and suggest that a locus near 20p12 also influences pDßH.

Racial Disparities in Epigenetic Aging of the Right vs Left Colon.

There are well-documented racial differences in age-of-onset and laterality of colorectal cancer. Epigenetic age acceleration is postulated to be an underlying factor. However, comparative studies of side-specific colonic tissue epigenetic aging are lacking. Here, we performed DNA methylation analysis of matched right and left biopsies of normal colon from 128 individuals. Among African Americans (n = 88), the right colon showed accelerated epigenetic aging as compared with individual-matched left colon (1.51 years; 95% confidence interval [CI] = 0.62 to 2.40 years; 2-sided P = .001). In contrast, among European Americans (n = 40), the right colon shows remarkable age deceleration (1.93 years; 95% CI = 0.65 to 3.21 years; 2-sided P = .004). Further, epigenome-wide analysis of DNA methylation identifies a unique pattern of hypermethylation in African American right colon. Our study is the first to report such race and side-specific differences in epigenetic aging of normal colon, providing novel insight into the observed younger age-of-onset and relative preponderance of right-side colon neoplasia in African Americans.

Segregation Analysis Using the Unified Model.

Segregation analysis is a basic tool in human genetics. It is a statistical method to determine if a trait, continuous or binary, has a transmission pattern in pedigrees that is consistent with Mendelian segregation. Major locus segregation is combined together with multifactorial/polygenic inheritance in the unified model. Segregation analysis as a procedure to identify the presence of segregation at a major Mendelian locus, with/without multifactorial inheritance, is introduced in this chapter. It is illustrated with the program SEGREG in the Statistical Analysis for Genetic Epidemiology (S.A.G.E.) package, which can use either regressive models or the finite polygenic mixed model to incorporate the multifactorial/polygenic component.

Genomic regions associated with susceptibility to Barrett's esophagus and esophageal adenocarcinoma in African Americans: The cross BETRNet admixture study.

BACKGROUND: Barrett's esophagus (BE) and esophageal adenocarcinoma (EAC) are far more prevalent in European Americans than in African Americans. Hypothesizing that this racial disparity in prevalence might represent a genetic susceptibility, we used an admixture mapping approach to interrogate disease association with genomic differences between European and African ancestry. METHODS: Formalin fixed paraffin embedded samples were identified from 54 African Americans with BE or EAC through review of surgical pathology databases at participating Barrett's Esophagus Translational Research Network (BETRNet) institutions. DNA was extracted from normal tissue, and genotyped on the Illumina OmniQuad SNP chip. Case-only admixture mapping analysis was performed on the data from both all 54 cases and also on a subset of 28 cases with high genotyping quality. Haplotype phases were inferred with Beagle 3.3.2, and local African and European ancestries were inferred with SABER plus. Disease association was tested by estimating and testing excess European ancestry and contrasting it to excess African ancestry. RESULTS: Both datasets, the 54 cases and the 28 cases, identified two admixture regions. An association of excess European ancestry on chromosome 11p reached a 5% genome-wide significance threshold, corresponding to -log10(P) = 4.28. A second peak on chromosome 8q reached -log10(P) = 2.73. The converse analysis examining excess African ancestry found no genetic regions with significant excess African ancestry associated with BE and EAC. On average, the regions on chromosomes 8q and 11p showed excess European ancestry of 15% and 20%, respectively. CONCLUSIONS: Chromosomal regions on 11p15 and 8q22-24 are associated with excess European ancestry in African Americans with BE and EAC. Because GWAS have not reported any variants in these two regions, low frequency and/or rare disease associated variants that confer susceptibility to developing BE and EAC may be driving the observed European ancestry association evidence.

Linkage and related analyses of Barrett's esophagus and its associated adenocarcinomas.

BACKGROUND: Familial aggregation and segregation analysis studies have provided evidence of a genetic basis for esophageal adenocarcinoma (EAC) and its premalignant precursor, Barrett's esophagus (BE). We aim to demonstrate the utility of linkage analysis to identify the genomic regions that might contain the genetic variants that predispose individuals to this complex trait (BE and EAC). METHODS: We genotyped 144 individuals in 42 multiplex pedigrees chosen from 1000 singly ascertained BE/EAC pedigrees, and performed both model-based and model-free linkage analyses, using S.A.G.E. and other software. Segregation models were fitted, from the data on both the 42 pedigrees and the 1000 pedigrees, to determine parameters for performing model-based linkage analysis. Model-based and model-free linkage analyses were conducted in two sets of pedigrees: the 42 pedigrees and a subset of 18 pedigrees with female affected members that are expected to be more genetically homogeneous. Genome-wide associations were also tested in these families. RESULTS: Linkage analyses on the 42 pedigrees identified several regions consistently suggestive of linkage by different linkage analysis methods on chromosomes 2q31, 12q23, and 4p14. A linkage on 15q26 is the only consistent linkage region identified in the 18 female-affected pedigrees, in which the linkage signal is higher than in the 42 pedigrees. Other tentative linkage signals are also reported. CONCLUSION: Our linkage study of BE/EAC pedigrees identified linkage regions on chromosomes 2, 4, 12, and 15, with some reported associations located within our linkage peaks. Our linkage results can help prioritize association tests to delineate the genetic determinants underlying susceptibility to BE and EAC.

Predicting Barrett's Esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm.

BACKGROUND: Barrett's esophagus is often asymptomatic and only a small portion of Barrett's esophagus patients are currently diagnosed and under surveillance. Therefore, it is important to develop risk prediction models to identify high-risk individuals with Barrett's esophagus. Familial aggregation of Barrett's esophagus and esophageal adenocarcinoma, and the increased risk of esophageal adenocarcinoma for individuals with a family history, raise the necessity of including genetic factors in the prediction model. Methods to determine risk prediction models using both risk covariates and ascertained family data are not well developed. METHODS: We developed a Barrett's Esophagus Translational Research Network (BETRNet) risk prediction model from 787 singly ascertained Barrett's esophagus pedigrees and 92 multiplex Barrett's esophagus pedigrees, fitting a multivariate logistic model that incorporates family history and clinical risk factors. The eight risk factors, age, sex, education level, parental status, smoking, heartburn frequency, regurgitation frequency, and use of acid suppressant, were included in the model. The prediction accuracy was evaluated on the training dataset and an independent validation dataset of 643 multiplex Barrett's esophagus pedigrees. RESULTS: Our results indicate family information helps to predict Barrett's esophagus risk, and predicting in families improves both prediction calibration and discrimination accuracy. CONCLUSIONS: Our model can predict Barrett's esophagus risk for anyone with family members known to have, or not have, had Barrett's esophagus. It can predict risk for unrelated individuals without knowing any relatives' information. IMPACT: Our prediction model will shed light on effectively identifying high-risk individuals for Barrett's esophagus screening and surveillance, consequently allowing intervention at an early stage, and reducing mortality from esophageal adenocarcinoma. Cancer Epidemiol Biomarkers Prev; 25(5); 727-35. ©2016 AACR.

Multi-locus association study of schizophrenia susceptibility genes with a posterior probability method.

Schizophrenia is a serious neuropsychiatric illness affecting about 1% of the world's population. It is considered a complex inheritance disorder. A number of genes are involved in combination in the etiology of the disorder. Evidence implicates the altered dopaminergic transmission in schizophrenia. In the present study, in order to identify susceptibility genes for schizophrenia in dopaminergic metabolism, we analyzed 59 single nucleotide polymorphisms (SNPs) in 24 genes of the dopaminergic pathway among 82 unrelated patients with schizophrenia and 108 matched normal controls. Considering that traditional single-locus association studies ignore the multigenic nature of complex diseases and do not take into account possible interactions between susceptibility genes, we proposed a multi-locus analysis method, using the posterior probability of morbidity as a measure of absolute disease risk for a multi-locus genotype combination, and developed an algorithm based on perturbation and average to detect the susceptibility multi-locus genotype combinations, as well as to repress noise and avoid false positive results at our best. A three-locus SNP genotype combination involved in the interactions of COMT and ALDH3B1 genes was detected to be significantly susceptible to schizophrenia.

Putative linkage signals identified for breast cancer in African American families.

BACKGROUND: Genome-wide association studies have identified polymorphisms associated with breast cancer subtypes and across multiple population subgroups; however, few studies to date have applied linkage analysis to other population groups. METHODS: We performed the first genome-wide breast cancer linkage analysis in 106 African American families (comprising 179 affected and 79 unaffected members) not known to be segregating BRCA mutations to search for novel breast cancer loci. We performed regression-based model-free multipoint linkage analyses of the sibling pairs using SIBPAL, and two-level Haseman-Elston linkage analyses of affected relative pairs using RELPAL. RESULTS: We identified -log10 P values that exceed 4 on chromosomes 3q and 12q, as well as a region near BRCA1 on chromosome 17 (-log10 P values in the range of 3.0-3.2) using both sibling-based and relative-based methods; the latter observation may suggest that undetected BRCA1 mutations or other mutations nearby such as HOXB13 may be segregating in our sample. CONCLUSIONS: In summary, these results suggest novel putative regions harboring risk alleles in African Americans that deserve further study. IMPACT: We hope that our study will spur further family-based investigation into specific mechanisms for breast cancer disparities.

Analysis pipeline for the epistasis search - statistical versus biological filtering.

Gene-gene interactions may contribute to the genetic variation underlying complex traits but have not always been taken fully into account. Statistical analyses that consider gene-gene interaction may increase the power of detecting associations, especially for low-marginal-effect markers, and may explain in part the "missing heritability." Detecting pair-wise and higher-order interactions genome-wide requires enormous computational power. Filtering pipelines increase the computational speed by limiting the number of tests performed. We summarize existing filtering approaches to detect epistasis, after distinguishing the purposes that lead us to search for epistasis. Statistical filtering includes quality control on the basis of single marker statistics to avoid the analysis of bad and least informative data, and limits the search space for finding interactions. Biological filtering includes targeting specific pathways, integrating various databases based on known biological and metabolic pathways, gene function ontology and protein-protein interactions. It is increasingly possible to target single-nucleotide polymorphisms that have defined functions on gene expression, though not belonging to protein-coding genes. Filtering can improve the power of an interaction association study, but also increases the chance of missing important findings.

Segregation analysis using the unified model.

Segregation analysis is a basic tool in human genetics. It is a statistical method to determine if a trait, continuous or binary, has a transmission pattern in pedigrees that is consistent with Mendelian segregation. Major locus segregation is combined together with multifactorial/polygenic inheritance in the unified model. Segregation analysis as a procedure to identify the presence of segregation at a major Mendelian locus, with/without multifactorial inheritance, is introduced in this chapter. It is illustrated with the program SEGREG in the Statistical Analysis for Genetic Epidemiology (S.A.G.E.) package, which can use either regressive models or the finite polygenic mixed model to incorporate the multifactorial/polygenic component.

Model-based linkage analysis of a quantitative trait.

Linkage analysis is a family-based method of analysis to examine whether any typed genetic markers co-segregate with a given trait, in this case a quantitative trait. If linkage exists, this is taken as evidence in support of a genetic basis for the trait. Historically, linkage analysis was performed using a binary disease trait, but has been extended to include quantitative disease measures. Quantitative traits are desirable as they provide more information than binary traits. Linkage analysis can be performed using single marker methods (one marker at a time) or multipoint (using multiple markers simultaneously). In model-based linkage analysis, the genetic model for the trait of interest is specified. There are many software options for performing linkage analysis. Here, we use the program package Statistical Analysis for Genetic Epidemiology (S.A.G.E.). S.A.G.E. was chosen because it includes programs to perform data cleaning procedures and to generate and test genetic models for a quantitative trait, in addition to performing linkage analysis. We demonstrate in detail the process of running the program LODLINK to perform single marker analysis and MLOD to perform multipoint analysis using output from SEGREG, where SEGREG was used to determine the best fitting statistical model for the trait.

A variable age of onset segregation model for linkage analysis, with correction for ascertainment, applied to glioma.

BACKGROUND: We propose a 2-step model-based approach, with correction for ascertainment, to linkage analysis of a binary trait with variable age of onset and apply it to a set of multiplex pedigrees segregating for adult glioma. METHODS: First, we fit segregation models by formulating the likelihood for a person to have a bivariate phenotype, affection status and age of onset, along with other covariates, and from these we estimate population trait allele frequencies and penetrance parameters as a function of age (N = 281 multiplex glioma pedigrees). Second, the best fitting models are used as trait models in multipoint linkage analysis (N = 74 informative multiplex glioma pedigrees). To correct for ascertainment, a prevalence constraint is used in the likelihood of the segregation models for all 281 pedigrees. Then the trait allele frequencies are reestimated for the pedigree founders of the subset of 74 pedigrees chosen for linkage analysis. RESULTS: Using the best-fitting segregation models in model-based multipoint linkage analysis, we identified 2 separate peaks on chromosome 17; the first agreed with a region identified by Shete and colleagues who used model-free affected-only linkage analysis, but with a narrowed peak: and the second agreed with a second region they found but had a larger maximum log of the odds (LOD). CONCLUSIONS: Our approach was able to narrow the linkage peak previously published for glioma. IMPACT: We provide a practical solution to model-based linkage analysis for disease affection status with variable age of onset for the kinds of pedigree data often collected for linkage analysis.

Capability of common SNPs to tag rare variants.

Genome-wide association studies are based on the linkage disequilibrium pattern between common tagging single-nucleotide polymorphisms (SNPs) (i.e., SNPs having only common alleles) and true causal variants, and association studies with rare SNP alleles aim to detect rare causal variants. To better understand and explain the findings from both types of studies and to provide clues to improve the power of an association study with only common SNPs genotyped, we study the correlation between common SNPs and the presence of rare alleles within a region in the genome and look at the capability of common SNPs in strong linkage disequilibrium with each other to capture single rare alleles. Our results indicate that common SNPs can, to some extent, tag the presence of rare alleles and that including SNPs in strong linkage disequilibrium with each other among the tagging SNPs helps to detect rare alleles.

A segregation analysis of Barrett's esophagus and associated adenocarcinomas.

Familial aggregation of esophageal adenocarcinomas, esophagogastric junction adenocarcinomas, and their precursor Barrett's esophagus (BE) has been termed familial BE (FBE). Numerous studies documenting increased familial risk for these diseases raise the hypothesis that there may be an inherited susceptibility to the development of BE and its associated cancers. In this study, using segregation analysis for a binary trait as implemented in S.A.G.E. 6.0.1, we analyzed data on 881 singly ascertained pedigrees to determine whether FBE is caused by a common environmental or genetic agent and, if genetic, to identify the mode of inheritance of FBE. The inheritance models were compared by likelihood ratio tests and Akaike's A Information Criterion. Results indicated that random environmental and/or multifactorial components were insufficient to fully explain the familial nature of FBE, but rather, there is segregation of a major type transmitted from one generation to the next (P < 10(-10)). An incompletely dominant inheritance model together with a polygenic component fits the data best. For this dominant model, the estimated penetrance of the dominant allele is 0.1005 [95% confidence interval (95% CI), 0.0587-0.1667] and the sporadic rate is 0.0012 (95% CI, 0.0004-0.0042), corresponding to a relative risk of 82.53 (95% CI, 28.70-237.35) or odds ratio of 91.63 (95% CI, 32.01-262.29). This segregation analysis provides epidemiologic evidence in support of one or more rare autosomally inherited dominant susceptibility allele(s) in FBE families and, hence, motivates linkage analyses.