Novel breast cancer susceptibility loci under linkage peaks identified in African ancestry consortia.

BACKGROUND: Expansion of genome-wide association studies across population groups is needed to improve our understanding of shared and unique genetic contributions to breast cancer. We performed association and replication studies guided by a priori linkage findings from African ancestry (AA) relative pairs. METHODS: We performed fixed-effect inverse-variance weighted meta-analysis under three significant AA breast cancer linkage peaks (3q26-27, 12q22-23, and 16q21-22) in 9241 AA cases and 10 193 AA controls. We examined associations with overall breast cancer as well as estrogen receptor (ER)-positive and negative subtypes (193,132 SNPs). We replicated associations in the African-ancestry Breast Cancer Genetic Consortium (AABCG). RESULTS: In AA women, we identified two associations on chr12q for overall breast cancer (rs1420647, OR = 1.15, p = 2.50×10-6; rs12322371, OR = 1.14, p = 3.15×10-6), and one for ER-negative breast cancer (rs77006600, OR = 1.67, p = 3.51×10-6). On chr3, we identified two associations with ER-negative disease (rs184090918, OR = 3.70, p = 1.23×10-5; rs76959804, OR = 3.57, p = 1.77×10-5) and on chr16q we identified an association with ER-negative disease (rs34147411, OR = 1.62, p = 8.82×10-6). In the replication study, the chr3 associations were significant and effect sizes were larger (rs184090918, OR: 6.66, 95% CI: 1.43, 31.01; rs76959804, OR: 5.24, 95% CI: 1.70, 16.16). CONCLUSION: The two chr3 SNPs are upstream to open chromatin ENSR00000710716, a regulatory feature that is actively regulated in mammary tissues, providing evidence that variants in this chr3 region may have a regulatory role in our target organ. Our study provides support for breast cancer variant discovery using prioritization based on linkage evidence.

Statistical interactions and Bayes estimation of log odds in case-control studies.

This paper is concerned with the estimation of the logarithm of disease odds (log odds) when evaluating two risk factors, whether or not interactions are present. Statisticians define interaction as a departure from an additive model on a certain scale of measurement of the outcome. Certain interactions, known as removable interactions, may be eliminated by fitting an additive model under an invertible transformation of the outcome. This can potentially provide more precise estimates of log odds than fitting a model with interaction terms. In practice, we may also encounter nonremovable interactions. The model must then include interaction terms, regardless of the choice of the scale of the outcome. However, in practical settings, we do not know at the outset whether an interaction exists, and if so whether it is removable or nonremovable. Rather than trying to decide on significance levels to test for the existence of removable and nonremovable interactions, we develop a Bayes estimator based on a squared error loss function. We demonstrate the favorable bias-variance trade-offs of our approach using simulations, and provide empirical illustrations using data from three published endometrial cancer case-control studies. The methods are implemented in an R program, and available freely at http://www.mskcc.org/biostatistics/~satagopj .

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.

A Germline Variant on Chromosome 4q31.1 Associates with Susceptibility to Developing Colon Cancer Metastasis.

We tested for germline variants showing association to colon cancer metastasis using a genome-wide association study that compared Ashkenazi Jewish individuals with stage IV metastatic colon cancers versus those with stage I or II non-metastatic colon cancers. In a two-stage study design, we demonstrated significant association to developing metastatic disease for rs60745952, that in Ashkenazi discovery and validation cohorts, respectively, showed an odds ratio (OR) = 2.3 (P = 2.73E-06) and OR = 1.89 (P = 8.05E-04) (exceeding validation threshold of 0.0044). Significant association to metastatic colon cancer was further confirmed by a meta-analysis of rs60745952 in these datasets plus an additional Ashkenazi validation cohort (OR = 1.92; 95% CI: 1.28-2.87), and by a permutation test that demonstrated a significantly longer haplotype surrounding rs60745952 in the stage IV samples. rs60745952, located in an intergenic region on chromosome 4q31.1, and not previously associated with cancer, is, thus, a germline genetic marker for susceptibility to developing colon cancer metastases among Ashkenazi Jews.

Novel recurrently mutated genes in African American colon cancers.

We used whole-exome and targeted sequencing to characterize somatic mutations in 103 colorectal cancers (CRC) from African Americans, identifying 20 new genes as significantly mutated in CRC. Resequencing 129 Caucasian derived CRCs confirmed a 15-gene set as a preferential target for mutations in African American CRCs. Two predominant genes, ephrin type A receptor 6 (EPHA6) and folliculin (FLCN), with mutations exclusive to African American CRCs, are by genetic and biological criteria highly likely African American CRC driver genes. These previously unsuspected differences in the mutational landscapes of CRCs arising among individuals of different ethnicities have potential to impact on broader disparities in cancer behaviors.

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.

Genetic variation in 15-hydroxyprostaglandin dehydrogenase and colon cancer susceptibility.

BACKGROUND: 15-Hydroxyprostaglandin dehydrogenase (15-PGDH) is a metabolic antagonist of COX-2, catalyzing the degradation of inflammation mediator prostaglandin E2 (PGE2) and other prostanoids. Recent studies have established the 15-PGDH gene as a colon cancer suppressor. METHODS: We evaluated 15-PDGH as a colon cancer susceptibility locus in a three-stage design. We first genotyped 102 single-nucleotide polymorphisms (SNPs) in the 15-PGDH gene, spanning ∼50 kb up and down-stream of the coding region, in 464 colon cancer cases and 393 population controls. We then genotyped the same SNPs, and also assayed the expression levels of 15-PGDH in colon tissues from 69 independent patients for whom colon tissue and paired germline DNA samples were available. In the final stage 3, we genotyped the 9 most promising SNPs from stages 1 and 2 in an independent sample of 525 cases and 816 controls (stage 3). RESULTS: In the first two stages, three SNPs (rs1365611, rs6844282 and rs2332897) were statistically significant (p<0.05) in combined analysis of association with risk of colon cancer and of association with 15-PGDH expression, after adjustment for multiple testing. For one additional SNP, rs2555639, the T allele showed increased cancer risk and decreased 15-PGDH expression, but just missed statistical significance (p-adjusted = 0.063). In stage 3, rs2555639 alone showed evidence of association with an odds ratio (TT compared to CC) of 1.50 (95% CI = 1.05-2.15, p = 0.026). CONCLUSIONS: Our data suggest that the rs2555639 T allele is associated with increased risk of colon cancer, and that carriers of this risk allele exhibit decreased expression of 15-PGDH in the colon.

Evaluation of removable statistical interaction for binary traits.

This paper is concerned with evaluating whether an interaction between two sets of risk factors for a binary trait is removable and, when it is removable, fitting a parsimonious additive model using a suitable link function to estimate the disease odds (on the natural logarithm scale). Statisticians define the term 'interaction' as a departure from additivity in a linear model on a specific scale on which the data are measured. Certain interactions may be eliminated via a transformation of the outcome such that the relationship between the risk factors and the outcome is additive on the transformed scale. Such interactions are known as removable interactions. We develop a novel test statistic for detecting the presence of a removable interaction in case-control studies. We consider the Guerrero and Johnson family of transformations and show that this family constitutes an appropriate link function for fitting an additive model when an interaction is removable. We use simulation studies to examine the type I error and power of the proposed test and to show that, when an interaction is removable, an additive model based on the Guerrero and Johnson link function leads to more precise estimates of the disease odds parameters and a better fit. We illustrate the proposed test and use of the transformation by using case-control data from three published studies. Finally, we indicate how one can check that, after transformation, no further interaction is significant.

Detecting genetic interactions for quantitative traits with U-statistics.

The genetic etiology of complex human diseases has been commonly viewed as a process that involves multiple genetic variants, environmental factors, as well as their interactions. Statistical approaches, such as the multifactor dimensionality reduction (MDR) and generalized MDR (GMDR), have recently been proposed to test the joint association of multiple genetic variants with either dichotomous or continuous traits. In this study, we propose a novel Forward U-Test to evaluate the combined effect of multiple loci on quantitative traits with consideration of gene-gene/gene-environment interactions. In this new approach, a U-Statistic-based forward algorithm is first used to select potential disease-susceptibility loci and then a weighted U-statistic is used to test the joint association of the selected loci with the disease. Through a simulation study, we found the Forward U-Test outperformed GMDR in terms of greater power. Aside from that, our approach is less computationally intensive, making it feasible for high-dimensional gene-gene/gene-environment research. We illustrate our method with a real data application to nicotine dependence (ND), using three independent datasets from the Study of Addiction: Genetics and Environment. Our gene-gene interaction analysis of 155 SNPs in 67 candidate genes identified two SNPs, rs16969968 within gene CHRNA5 and rs1122530 within gene NTRK2, jointly associated with the level of ND (P-value = 5.31e-7). The association, which involves essential interaction, is replicated in two independent datasets with P-values of 1.08e-5 and 0.02, respectively. Our finding suggests that joint action may exist between the two gene products.

Detecting rare and common variants for complex traits: sibpair and odds ratio weighted sum statistics (SPWSS, ORWSS).

It is generally known that risk variants segregate together with a disease within families, but this information has not been used in the existing statistical methods for detecting rare variants. Here we introduce two weighted sum statistics that can apply to either genome-wide association data or resequencing data for identifying rare disease variants: weights calculated based on sibpairs and odd ratios, respectively. We evaluated the two methods via extensive simulations under different disease models. We compared the proposed methods with the weighted sum statistic (WSS) proposed by Madsen and Browning, keeping the same genotyping or resequencing cost. Our methods clearly demonstrate more statistical power than the WSS. In addition, we found that using sibpair information can increase power over using only unrelated samples by more than 40%. We applied our methods to the Framingham Heart Study (FHS) and Wellcome Trust Case Control Consortium (WTCCC) hypertension datasets. Although we did not identify any genes as reaching a genome-wide significance level, we found variants in the candidate gene angiotensinogen significantly associated with hypertension at P = 6.9 × 10(-4), whereas the most significant single SNP association evidence is P = 0.063. We further applied the odds ratio weighted method to the IFIH1 gene for type-1 diabetes in the WTCCC data. Our method yielded a P-value of 4.82 × 10(-4), much more significant than that obtained by haplotype-based methods. We demonstrated that family data are extremely informative in searching for rare variants underlying complex traits, and the odds ratio weighted sum statistic is more efficient than currently existing methods.

Infrequent detection of germline allele-specific expression of TGFBR1 in lymphoblasts and tissues of colon cancer patients.

Recently, germline allele-specific expression (ASE) of the gene encoding for transforming growth factor-beta type I receptor (TGFBR1) has been proposed to be a major risk factor for cancer predisposition in the colon. Germline ASE results in a lowered expression of one of the TGFBR1 alleles (>1.5-fold), and was shown to occur in approximately 20% of informative familial and sporadic colorectal cancer (CRC) cases. In the present study, using the highly quantitative pyrosequencing technique, we estimated the frequency of ASE in TGFBR1 in a cohort of affected individuals from familial clusters of advanced colon neoplasias (cancers and adenomas with high-grade dysplasia), and also from a cohort of individuals with sporadic CRCs. Cases were considered positive for the presence of ASE if demonstrating an allelic expression ratio <0.67 or >1.5. Using RNA derived from lymphoblastoid cell lines, we find that of 46 informative Caucasian advanced colon neoplasia cases with a family history, only 2 individuals display a modest ASE, with allelic ratios of 1.65 and 1.73, respectively. Given that ASE of TGFBR1, if present, would likely be more pronounced in the colon compared with other tissues, we additionally determined the allele ratios of TGFBR1 in the RNA derived from normal-appearing colonic mucosa of sporadic CRC cases. We, however, found no evidence of ASE in any of 44 informative sporadic cases analyzed. Taken together, we find that germline ASE of TGFBR1, as assayed in lymphoblastoid and colon epithelial cells of colon cancer patients, is a relatively rare event.

A partial least-square approach for modeling gene-gene and gene-environment interactions when multiple markers are genotyped.

Genetic association studies achieve an unprecedented level of resolution in mapping disease genes by genotyping dense single nucleotype polymorphisms (SNPs) in a gene region. Meanwhile, these studies require new powerful statistical tools that can optimally handle a large amount of information provided by genotype data. A question that arises is how to model interactions between two genes. Simply modeling all possible interactions between the SNPs in two gene regions is not desirable because a greatly increased number of degrees of freedom can be involved in the test statistic. We introduce an approach to reduce the genotype dimension in modeling interactions. The genotype compression of this approach is built upon the information on both the trait and the cross-locus gametic disequilibrium between SNPs in two interacting genes, in such a way as to parsimoniously model the interactions without loss of useful information in the process of dimension reduction. As a result, it improves power to detect association in the presence of gene-gene interactions. This approach can be similarly applied for modeling gene-environment interactions. We compare this method with other approaches, the corresponding test without modeling any interaction, that based on a saturated interaction model, that based on principal component analysis, and that based on Tukey's one-degree-of-freedom model. Our simulations suggest that this new approach has superior power to that of the other methods. In an application to endometrial cancer case-control data from the Women's Health Initiative, this approach detected AKT1 and AKT2 as being significantly associated with endometrial cancer susceptibility by taking into account their interactions with body mass index.

Modeling Genetic and Environmental Factors in Biological Systems Using Structural Equation Modeling: An Application to Energy Balance.

To improve our understanding of the role(s) that genes and environmental factors play in a complex disease, we need statistical approaches that model multiple factors simultaneously in a hierarchical manner that aims to reflect the underlying biological system(s). We present an approach that models genes as latent constructs, defined by multiple variants (single nucleotide polymorphisms, SNPs) within each gene, using the multivariate statistical framework of structural equation modeling (SEM) to model multiple, putative genetic and environmental factors involved in energy imbalance ('obesity') using subjects from a colon polyp case-control study. We found that modeling constructs for the leptin receptor (LEPR) gene (defined by SNPs rs1137100, rs1137101, rs1805096, rs6588147) and the fat mass-and-obesity-associated (FTO) gene (defined by SNPs rs9939609, rs1421085, rs8044769) together with demographic (age, race, gender), physical activity, diet and sleep variables increased the strength of the association (ß(std)=-0.13 ± 0.06; p=0.03) between the FTO and obesity constructs compared to that observed in a reduced model with only the FTO and LEPR constructs and demographic variables (ß(std)=-0.05 ± 0.03; p=0.08). Several indirect paths, including an association between the LEPR and physical activity constructs (ß(std)=-0.15 ± 0.04; p=0.01), were found. Interestingly, removing FTO revealed a marginal association between the LEPR and obesity constructs (ß(std)=0.24 ± 0.14; p=0.09), which was not present when FTO was in the model. These results illustrate the importance of modeling multiple relevant genes and other factors in the same model, which is a major strength of this approach. Moreover, our latent gene construct approach exploits the correlation structure between SNPs while capturing overall effects of variation in that gene, which will enable better utilization of candidate gene and genome-wide SNP array data.

A combinatorial approach to detecting gene-gene and gene-environment interactions in family studies.

Widespread multifactor interactions present a significant challenge in determining risk factors of complex diseases. Several combinatorial approaches, such as the multifactor dimensionality reduction (MDR) method, have emerged as a promising tool for better detecting gene-gene (G x G) and gene-environment (G x E) interactions. We recently developed a general combinatorial approach, namely the generalized multifactor dimensionality reduction (GMDR) method, which can entertain both qualitative and quantitative phenotypes and allows for both discrete and continuous covariates to detect G x G and G x E interactions in a sample of unrelated individuals. In this article, we report the development of an algorithm that can be used to study G x G and G x E interactions for family-based designs, called pedigree-based GMDR (PGMDR). Compared to the available method, our proposed method has several major improvements, including allowing for covariate adjustments and being applicable to arbitrary phenotypes, arbitrary pedigree structures, and arbitrary patterns of missing marker genotypes. Our Monte Carlo simulations provide evidence that the PGMDR method is superior in performance to identify epistatic loci compared to the MDR-pedigree disequilibrium test (PDT). Finally, we applied our proposed approach to a genetic data set on tobacco dependence and found a significant interaction between two taste receptor genes (i.e., TAS2R16 and TAS2R38) in affecting nicotine dependence.

Association of vitamin D receptor gene variants, adiposity and colon cancer.

Vitamin D receptor (VDR) gene variants have been variably associated with risk of colon cancer in epidemiologic studies. We sought to further clarify the relationship between colon cancer and three single-nucleotide polymorphisms (SNPs) in the VDR gene (Cdx-2, FokI and TaqI) in a population-based case-control study of 250 incident cases and 246 controls. Colon cancer cases were more frequently homozygous for the Cdx-2 A allele (9.2 versus 4.1%, P = 0.06). Cdx-2 AA homozygotes were at increased risk with an unadjusted odds ratio (OR) of 2.47 [95% confidence interval (CI): 1.13-5.37, P = 0.022]; adjustment for age, sex, body mass index (BMI), non-steroidal anti-inflammatory use and family history of colorectal cancer yielded an OR of 2.27 (CI: 0.95-5.41, P = 0.065). Carriers of the FokI TT genotype were also at increased risk with an adjusted OR of 1.87 (CI: 1.03-3.38, P = 0.038). Haplotype analyses showed significant increased colon cancer risk for carriers of the Cdx-2-FokI A-T haplotype and the FokI-TaqI T-G haplotype. The three-SNP Cdx-2-FokI-TaqI (A-T-G) haplotype showed a similar association with an adjusted OR of 3.63 (CI: 1.01-13.07). A strong positive association was observed for the Cdx-2 variant among individuals with low BMI or low waist circumference. Our results suggest that genetic variation at the VDR locus, in particular Cdx-2 and FokI SNPs, may influence colon cancer risk and these associations may be modified by adiposity.

Gene-gene interactions among CHRNA4, CHRNB2, BDNF, and NTRK2 in nicotine dependence.

BACKGROUND: Epidemiological data indicate that nicotine dependence (ND) are influenced by genes, environmental factors, and their interactions. Although it has been documented from molecular experiments that brain-derived neurotrophic factor (BDNF) exerts its functions via neurotrophic tyrosine kinase receptor 2 (NTRK2) and both alpha 4 (CHRNA4) and beta 2 (CHRNB2) subunits are required to form functional alpha 4 beta 2-containing nicotinic receptors (nAChRs), no study is reported demonstrating that there exist gene-gene interactions among the four genes in affecting ND. METHODS: To determine if gene-gene interactions exist among the four genes, we genotyped six single nucleotide polymorphisms (SNPs) for CHRNA4 and BDNF, nine SNPs for NTRK2, and four SNPs for CHRNB2 in a case-control sample containing 275 unrelated smokers with a Fagerström Test for Nicotine Dependence score of 4.0 or more and 348 unrelated nonsmokers. RESULTS: By using a generalized multifactor dimensionality reduction algorithm recently developed by us, we found highly significant gene-gene interactions for the gene pairs of CHRNA4 and CHRNB2, CHRNA4 and NTRK2, CHRNB2 and NTRK2, and BDNF and NTRK2 (p < .01 for all four gene pairs) and significant gene-gene interaction between CHRNA4 and BDNF (p = .031) on ND. No significant interaction was detected for CHRNB2 and BDNF (p = .068). CONCLUSIONS: Our study provides first evidence on the presence of gene-gene interaction among the four genes in affecting ND. Although CHRNB2 alone was not significantly associated with ND in several previously reported association studies on ND, we found it affects ND through interactions with CHRNA4 and NTRK2.

Immunoglobulin allotypes influence IgG antibody responses to hepatitis C virus envelope proteins E1 and E2.

Immunoglobulin (Ig) GM and KM allotypes-genetic markers of gamma and kappa chains, respectively-are associated with the outcome of hepatitis C virus (HCV) infection, but the underlying mechanisms are not well understood. We hypothesized that GM and KM allotypes could contribute to the outcome of HCV infection by influencing the levels of IgG antibodies to the HCV glycoproteins E1E2. We serologically allotyped 100 African American individuals with persistent HCV infection for GM and KM markers and measured anti-E1E2 antibodies. Subjects with the GM 1,17 5,13 phenotype had significantly higher levels of anti-E1E2 antibodies than subjects who lacked this phenotype (p = 0.008). Likewise, subjects with the KM 1-carrying phenotypes had higher levels of anti-E1E2 antibodies than subjects who lacked these phenotypes (p = 0.041). Median titers were fourfold higher in persons expressing both GM 1,17 5,13 and KM 1-carrying phenotypes compared with those who lacked these phenotypes (p = 0.011). Interactive effects of these GM-KM phenotypes were previously found to be highly significantly associated with spontaneous HCV clearance. Results presented here show that Ig allotypes contribute to the interindividual differences in humoral immunity to the HCV epitopes, a finding that may provide a mechanistic explanation for their involvement in the outcome of HCV infection.

Identification of susceptibility genes for cancer in a genome-wide scan: results from the colon neoplasia sibling study.

Colorectal cancer (CRC) is the third most commonly diagnosed cancer in Americans and is the second leading cause of cancer mortality. Only a minority ( approximately 5%) of familial CRC can be explained by known genetic variants. To identify susceptibility genes for familial colorectal neoplasia, the colon neoplasia sibling study conducted a comprehensive, genome-wide linkage scan of 194 kindreds. Clinical information (histopathology, size and number of polyps, and other primary cancers) was used in conjunction with age at onset and family history for classification of the families into five phenotypic subgroups (severe histopathology, oligopolyposis, young, colon/breast, and multiple cancer) prior to analysis. By expanding the traditional affected-sib-pair design to include unaffected and discordant sib pairs, analytical power and robustness to type I error were increased. Sib-pair linkage statistics and Haseman-Elston regression identified 19 linkage peaks, with interesting results for chromosomes 1p31.1, 15q14-q22, 17p13.3, and 21. At marker D1S1665 (1p31.1), there was strong evidence for linkage in the multiple-cancer subgroup (p = 0.00007). For chromosome 15q14-q22, a linkage peak was identified in the full-sample (p = 0.018), oligopolyposis (p = 0.003), and young (p = 0.0009) phenotypes. This region includes the HMPS/CRAC1 locus associated with hereditary mixed polyposis syndrome (HMPS) in families of Ashkenazi descent. We provide compelling evidence linking this region in families of European descent with oligopolyposis and/or young age at onset (

Genome scan of M. tuberculosis infection and disease in Ugandans.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is an enduring public health problem globally, particularly in sub-Saharan Africa. Several studies have suggested a role for host genetic susceptibility in increased risk for TB but results across studies have been equivocal. As part of a household contact study of Mtb infection and disease in Kampala, Uganda, we have taken a unique approach to the study of genetic susceptibility to TB, by studying three phenotypes. First, we analyzed culture confirmed TB disease compared to latent Mtb infection (LTBI) or lack of Mtb infection. Second, we analyzed resistance to Mtb infection in the face of continuous exposure, defined by a persistently negative tuberculin skin test (PTST-); this outcome was contrasted to LTBI. Third, we analyzed an intermediate phenotype, tumor necrosis factor-alpha (TNFalpha) expression in response to soluble Mtb ligands enriched with molecules secreted from Mtb (culture filtrate). We conducted a full microsatellite genome scan, using genotypes generated by the Center for Medical Genetics at Marshfield. Multipoint model-free linkage analysis was conducted using an extension of the Haseman-Elston regression model that includes half sibling pairs, and HIV status was included as a covariate in the model. The analysis included 803 individuals from 193 pedigrees, comprising 258 full sibling pairs and 175 half sibling pairs. Suggestive linkage (p<10(-3)) was observed on chromosomes 2q21-2q24 and 5p13-5q22 for PTST-, and on chromosome 7p22-7p21 for TB; these findings for PTST- are novel and the chromosome 7 region contains the IL6 gene. In addition, we replicated recent linkage findings on chromosome 20q13 for TB (p = 0.002). We also observed linkage at the nominal alpha = 0.05 threshold to a number of promising candidate genes, SLC11A1 (PTST- p = 0.02), IL-1 complex (TB p = 0.01), IL12BR2 (TNFalpha p = 0.006), IL12A (TB p = 0.02) and IFNGR2 (TNFalpha p = 0.002). These results confirm not only that genetic factors influence the interaction between humans and Mtb but more importantly that they differ according to the outcome of that interaction: exposure but no infection, infection without progression to disease, or progression of infection to disease. Many of the genetic factors for each of these stages are part of the innate immune system.