A two-stage search strategy for detecting multiple loci associated with rheumatoid arthritis

Gene × gene interactions play important roles in the etiology of complex multi-factorial diseases like rheumatoid arthritis (RA). In this paper, we describe our use of a two-stage search strategy consisting of information theoretic methods and logistic regression to detect gene × gene interactions associated with RA using the data in Problem 1 of Genetic Analysis Workshop 16. Our method detected interactions of several SNPs (single-SNP and SNP × SNP) that are located on chromosomal regions linked to RA and related diseases in previous studies

Information-theoretic gene-gene and gene-environment interaction analysis of quantitative traits

<p>Abstract</p> <p>Background</p> <p>The purpose of this research was to develop a novel information theoretic method and an efficient algorithm for analyzing the gene-gene (GGI) and gene-environmental interactions (GEI) associated with quantitative traits (QT). The method is built on two information-theoretic metrics, the <it>k</it>-way interaction information (KWII) and phenotype-associated information (PAI). The PAI is a novel information theoretic metric that is obtained from the total information correlation (TCI) information theoretic metric by removing the contributions for inter-variable dependencies (resulting from factors such as linkage disequilibrium and common sources of environmental pollutants).</p> <p>Results</p> <p>The KWII and the PAI were critically evaluated and incorporated within an algorithm called CHORUS for analyzing QT. The combinations with the highest values of KWII and PAI identified each known GEI associated with the QT in the simulated data sets. The CHORUS algorithm was tested using the simulated GAW15 data set and two real GGI data sets from QTL mapping studies of high-density lipoprotein levels/atherosclerotic lesion size and ultra-violet light-induced immunosuppression. The KWII and PAI were found to have excellent sensitivity for identifying the key GEI simulated to affect the two quantitative trait variables in the GAW15 data set. In addition, both metrics showed strong concordance with the results of the two different QTL mapping data sets.</p> <p>Conclusion</p> <p>The KWII and PAI are promising metrics for analyzing the GEI of QT.</p

Comparison of information-theoretic to statistical methods for gene-gene interactions in the presence of genetic heterogeneity

<p>Abstract</p> <p>Background</p> <p>Multifactorial diseases such as cancer and cardiovascular diseases are caused by the complex interplay between genes and environment. The detection of these interactions remains challenging due to computational limitations. Information theoretic approaches use computationally efficient directed search strategies and thus provide a feasible solution to this problem. However, the power of information theoretic methods for interaction analysis has not been systematically evaluated. In this work, we compare power and Type I error of an information-theoretic approach to existing interaction analysis methods.</p> <p>Methods</p> <p>The <it>k-</it>way interaction information (KWII) metric for identifying variable combinations involved in gene-gene interactions (GGI) was assessed using several simulated data sets under models of genetic heterogeneity driven by susceptibility increasing loci with varying allele frequency, penetrance values and heritability. The power and proportion of false positives of the KWII was compared to multifactor dimensionality reduction (MDR), restricted partitioning method (RPM) and logistic regression.</p> <p>Results</p> <p>The power of the KWII was considerably greater than MDR on all six simulation models examined. For a given disease prevalence at high values of heritability, the power of both RPM and KWII was greater than 95%. For models with low heritability and/or genetic heterogeneity, the power of the KWII was consistently greater than RPM; the improvements in power for the KWII over RPM ranged from 4.7% to 14.2% at for α = 0.001 in the three models at the lowest heritability values examined. KWII performed similar to logistic regression.</p> <p>Conclusions</p> <p>Information theoretic models are flexible and have excellent power to detect GGI under a variety of conditions that characterize complex diseases.</p

Genetic polymorphisms in chronic hyperplastic sinusitis with nasal polyposis

Objectives/Hypothesis: Although many proinflammatory cytokines have been identified in nasal polyp tissue, the initial trigger that causes this inflammation characterized by edema, lymphocytosis, and eosinophilia, is still unknown. The purpose of the present study is to identify the presence of genetic polymorphisms in proinflammatory, anti-inflammatory, and chemokine genes that might contribute to genetic susceptibility to chronic hyperplastic sinusitis with nasal polyposis (CHSwNP). Study Design: Case control study. Methods: Buccal swabs were taken from the left and right oral mucosal surfaces from 179 patients with CHSwNP and 153 nonpolyposis controls with the Purgene DNA purification protocol (Gentra). Genotyping assays for cytokine gene loci were performed on 14 cytokine genes using the iPlex Gold and the Mass Array Compact system (Sequenom, San Diego, CA). Tests of Hardy-Weinberg equilibrium proportions were performed separately in the cases and controls. Tests for evidence of association between alleles at each single-nucleotide polymorphism (SNP) and case-control status were performed using unconditional logistic regression. Results: The frequency of the A allele in a SNP located in tumor necrosis factor (TNF)-Α (rs1800629) is significantly different in patients with nasal polyposis versus controls without nasal polyposis, 18.6% and 11.5%, respectively with an individuals' odds of susceptibility to nasal polyps increasing almost two-fold (odds ratio, 1.86; confidence interval, 1.4–3.09) given at least one copy of the A allele at this SNP. All other cytokine gene polymorphisms of both inflammatory, anti-inflammatory, and chemokine genes were not statistically different between the two groups. Conclusions: TNF-Α-308, a SNP in the promoter region of this cytokine gene is associated with increased odds of developing nasal polyposis. TNF-Α is a potent immuno-mediator and proinflammatory cytokine that has been implicated in the pathogenesis of a large number of human diseases. The location of this gene on the short arm of chromosome 6, with the major histocompatibility complex genes and complement, has raised the probability that polymorphism within this locus may contribute to a genetic association of this region of the genome with a wide variety of infectious and autoimmune diseases. Laryngoscope, 2009Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63071/1/20239_ftp.pd

Complex Segregation Analysis of Pedigrees from the Gilda Radner Familial Ovarian Cancer Registry Reveals Evidence for Mendelian Dominant Inheritance

Familial component is estimated to account for about 10% of ovarian cancer. However, the mode of inheritance of ovarian cancer remains poorly understood. The goal of this study was to investigate the inheritance model that best fits the observed transmission pattern of ovarian cancer among 7669 members of 1919 pedigrees ascertained through probands from the Gilda Radner Familial Ovarian Cancer Registry at Roswell Park Cancer Institute, Buffalo, New York.Using the Statistical Analysis for Genetic Epidemiology program, we carried out complex segregation analyses of ovarian cancer affection status by fitting different genetic hypothesis-based regressive multivariate logistic models. We evaluated the likelihood of sporadic, major gene, environmental, general, and six types of Mendelian models. Under each hypothesized model, we also estimated the susceptibility allele frequency, transmission probabilities for the susceptibility allele, baseline susceptibility and estimates of familial association. Comparisons between models were carried out using either maximum likelihood ratio test in the case of hierarchical models, or Akaike information criterion for non-nested models. When assessed against sporadic model without familial association, the model with both parent-offspring and sib-sib residual association could not be rejected. Likewise, the Mendelian dominant model that included familial residual association provided the best-fitting for the inheritance of ovarian cancer. The estimated disease allele frequency in the dominant model was 0.21.This report provides support for a genetic role in susceptibility to ovarian cancer with a major autosomal dominant component. This model does not preclude the possibility of polygenic inheritance of combined effects of multiple low penetrance susceptibility alleles segregating dominantly

Common Genetic Variants Are Associated with Accelerated Bone Mineral Density Loss after Hematopoietic Cell Transplantation

BACKGROUND: Bone mineral density (BMD) loss commonly occurs after hematopoietic cell transplantation (HCT). Hypothesizing that genetic variants may influence post-HCT BMD loss, we conducted a prospective study to examine the associations of single nucleotide polymorphisms (SNP) in bone metabolism pathways and acute BMD loss after HCT. METHODS AND FINDINGS: We genotyped 122 SNPs in 45 genes in bone metabolism pathways among 121 autologous and allogeneic HCT patients. BMD changes from pre-HCT to day +100 post-HCT were analyzed in relation to these SNPs in linear regression models. After controlling for clinical risk factors, we identified 16 SNPs associated with spinal or femoral BMD loss following HCT, three of which have been previously implicated in genome-wide association studies of bone phenotypes, including rs2075555 in COL1A1, rs9594738 in RANKL, and rs4870044 in ESR1. When multiple SNPs were considered simultaneously, they explained 5-35% of the variance in post-HCT BMD loss. There was a significant trend between the number of risk alleles and the magnitude of BMD loss, with patients carrying the most risk alleles having the greatest loss. CONCLUSION: Our data provide the first evidence that common genetic variants play an important role in BMD loss among HCT patients similar to age-related BMD loss in the general population. This infers that the mechanism for post-HCT bone loss is a normal aging process that is accelerated during HCT. A limitation of our study comes from its small patient population; hence future larger studies are warranted to validate our findings

Family History of Cancer in Relation to Breast Cancer Subtypes in African American Women

Evidence on the relation of family history of cancers other than breast cancer to breast cancer risk is conflicting and most studies have not assessed specific breast cancer subtypes

An exome-wide analysis of low frequency and rare variants in relation to risk of breast cancer in African American Women: the AMBER Consortium

A large percentage of breast cancer heritability remains unaccounted for, and most of the known susceptibility loci have been established in European and Asian populations. Rare variants may contribute to the unexplained heritability of this disease, including in women of African ancestry (AA). We conducted an exome-wide analysis of rare variants in relation to risk of overall and subtype-specific breast cancer in the African American Breast Cancer Epidemiology and Risk (AMBER) Consortium, which includes data from four large studies of AA women. Genotyping on the Illumina Human Exome Beadchip yielded data for 170 812 SNPs and 8287 subjects: 3629 cases (1093 estrogen receptor negative (ER−), 1968 ER+, 568 ER unknown) and 4658 controls, the largest exome chip study to date for AA breast cancer. Pooled gene-based association analyses were performed using the unified optimal sequence kernel association test (SKAT-O) for variants with minor allele frequency (MAF) ≤ 5%. In addition, each variant with MAF >0.5% was tested for association using logistic regression. There were no significant associations with overall breast cancer. However, a novel gene, FBXL22 (P = 8.2×10–6), and a gene previously identified in GWAS of European ancestry populations, PDE4D (P = 1.2×10–6), were significantly associated with ER− breast cancer after correction for multiple testing. Cases with the associated rare variants were also negative for progesterone and human epidermal growth factor receptors—thus, triple-negative cancer. Replication is required to confirm these gene-level associations, which are based on very small counts at extremely rare SNPs