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We previously observed T-bet+ lymphocytes to be associated with a good prognosis in a cohort of women with familial breast cancer. To validate this finding, we evaluated lymphocyte T-bet expression in an independent unselected prospectively accrued series of women with lymph node–negative breast carcinoma. T-bet and clinicopathologic data were available for 614 women. Hormone receptors, HER2, Ki-67, CK5, EGFR, p53, and T-bet status were determined using IHC and/or biochemical methods. Tumors were assigned to luminal A, luminal B, HER2, and basal subtypes based on the expression of IHC markers. Multiple cutpoints were examined in a univariate penalized Cox model to stratify tumors into T-bet+/high and T-bet−/low. Fisher exact test was used to analyze T-bet associations with clinicopathologic variables, IHC markers, and molecular subtype. Survival analyses were by the Cox proportional hazards model. All tests were two sided. A test with a P value < 0.05 was considered statistically significant. T-bet+/high tumor status was significantly associated with large tumor size, high grade, hormone receptor negativity, CK5, EGFR and p53 positivity, high Ki-67, and basal subtype. With a median follow-up of 96.5 months, T-bet−/low tumor status was associated with a reduced disease-free survival compared with T-bet+/high tumor status in multivariate analysis (P = 0.0027; relative risk = 5.62; 95% confidence intervals, 1.48–50.19). Despite being associated with adverse clinicopathologic characteristics, T-bet+ tumor-infiltrating lymphoid cells are associated with a favorable outcome. This supports their role in Th1-mediated antitumor activity and may provide insight for the development of new therapeutic strategies. Cancer Immunol Res; 4(1); 41–48. ©2015 AACR.

Table S1 gives the relative risks by univariate Cox proportional hazards model of DFS for di- categorization (low, high) of T-bet for several (10, 15, 20, 25, 30) cut points. T-bet-/low was associated with reduced DFS at cut points 15, 25, and 30 as relative risks and p-values suggested.

This figure compares K-M curves of DFS by di- and tri- categorizations of T-bet for several (10, 15, 20, 25,) cut points. It is evident in the three-level KM plots that the absent and low count groups of T-bet are indistinguishable for the cut-points used to define the high versus the low count group of T-bet.

The identification of genetic variation that directly impacts infection susceptibility to SARS-CoV-2 and disease severity of COVID-19 is an important step towards risk stratification, personalized treatment plans, therapeutic, and vaccine development and deployment. Given the importance of study design in infectious disease genetic epidemiology, we use simulation and draw on current estimates of exposure, infectivity, and test accuracy of COVID-19 to demonstrate the feasibility of detecting host genetic factors associated with susceptibility and severity in published COVID-19 study designs. We demonstrate that limited phenotypic data and exposure/infection information in the early stages of the pandemic significantly impact the ability to detect most genetic variants with moderate effect sizes, especially when studying susceptibility to SARS-CoV-2 infection. Our insights can aid in the interpretation of genetic findings emerging in the literature and guide the design of future host genetic studies. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-020-00818-2.

Objective We aimed to demonstrate that electroarthrography (EAG) measures streaming potentials originating in the cartilage extracellular matrix during load bearing through electrodes adhered to skin surrounding an articular joint. Design Equine metacarpophalangeal joints were subjected to simulated physiological loads while (1) replacing synovial fluid with immersion buffers of different electrolyte concentrations and (2) directly degrading cartilage with trypsin. Results An inverse relationship between ionic strength and EAG coefficient was detected. Compared to native synovial fluid, EAG coefficients increased ( P < 0.05) for 5 of 6 electrodes immersed in 0.1X phosphate-buffered saline (PBS) (0.014 M NaCl), decreased ( P < 0.05) for 4 of 6 electrodes in 1X PBS (0.14 M NaCl), and decreased ( P < 0.05) for all 6 electrodes in 10X PBS (1.4 M NaCl). This relationship corresponds to similar studies where streaming potentials were directly measured on cartilage. EAG coefficients, obtained after trypsin degradation, were reduced ( P < 0.05) in 6 of 8, and 7 of 8 electrodes, during simulated standing and walking, respectively. Trypsin degradation was confirmed by direct cartilage assessments. Streaming potentials, measured by directly contacting cartilage, indicated lower cartilage stiffness ( P < 10−5). Unconfined compression data revealed reduced Em, representing proteoglycan matrix stiffness ( P = 0.005), no change in Ef, representing collagen network stiffness ( P = 0.15), and no change in permeability ( P = 0.24). Trypsin depleted proteoglycan as observed by both dimethylmethylene blue assay ( P = 0.0005) and safranin-O stained histological sections. Conclusion These data show that non-invasive EAG detects streaming potentials produced by cartilage during joint compression and has potential to become a diagnostic tool capable of detecting early cartilage degeneration.

Next generation sequencing technologies have made it possible to investigate the role of rare variants (RVs) in disease etiology. Because RVs associated with disease susceptibility tend to be enriched in families with affected individuals, study designs based on affected sib pairs (ASP) can be more powerful than case–control studies. We construct tests of RV‐set association in ASPs for single genomic regions as well as for multiple regions. Single‐region tests can efficiently detect a gene region harboring susceptibility variants, while multiple‐region extensions are meant to capture signals dispersed across a biological pathway, potentially as a result of locus heterogeneity. Within ascertained ASPs, the test statistics contrast the frequencies of duplicate rare alleles (usually appearing on a shared haplotype) against frequencies of a single rare allele copy (appearing on a nonshared haplotype); we call these allelic parity tests. Incorporation of minor allele frequency estimates from reference populations can markedly improve test efficiency. Under various genetic penetrance models, application of the tests in simulated ASP data sets demonstrates good type I error properties as well as power gains over approaches that regress ASP rare allele counts on sharing state, especially in small samples. We discuss robustness of the allelic parity methods to the presence of genetic linkage, misspecification of reference population allele frequencies, sequencing error and de novo mutations, and population stratification. As proof of principle, we apply single‐ and multiple‐region tests in a motivating study data set consisting of whole exome sequencing of sisters ascertained with early onset breast cancer.

Post-GWAS analysis, in many cases, focuses on fine-mapping targeted genetic regions discovered at GWAS-stage; that is, the aim is to pinpoint potential causal variants and susceptibility genes for complex traits and disease outcomes using next-generation sequencing (NGS) technologies. Large-scale GWAS cohorts are necessary to identify target regions given the typically modest genetic effect sizes. In this context, two-phase sampling design and analysis is a cost-reduction technique that utilizes data collected during phase 1 GWAS to select an informative subsample for phase 2 sequencing. The main goal is to make inference for genetic variants measured via NGS by efficiently combining data from phases 1 and 2. We propose two approaches for selecting a phase 2 design under a budget constraint. The first method identifies sampling fractions that select a phase 2 design yielding an asymptotic variance covariance matrix with certain optimal characteristics, for example, smallest trace, via Lagrange multipliers (LM). The second relies on a genetic algorithm (GA) with a defined fitness function to identify exactly a phase 2 subsample. We perform comprehensive simulation studies to evaluate the empirical properties of the proposed designs for a genetic association study of a quantitative trait. We compare our methods against two ranked designs: residual-dependent sampling and a recently identified optimal design. Our findings demonstrate that the proposed designs, GA in particular, can render competitive power in combined phase 1 and 2 analysis compared with alternative designs while preserving type 1 error control. These results are especially evident under the more practical scenario where design values need to be defined a priori and are subject to misspecification. We illustrate the proposed methods in a study of triglyceride levels in the North Finland Birth Cohort of 1966. R code to reproduce our results is available at github.com/egosv/TwoPhase_postGWAS.

Introduction & Objective: Rare variants with allele frequency smaller than 1% are postulated to be associated with disease susceptibility. Since allele frequencies vary globally, the use of population control data that does not match the study population can produce bias. The research question is to identify factors that explain variation in allele frequency across populations. The secondary question is to evaluate the potential bias in using population as control data when studying variants. We use data from gnomAD (Genome Aggregation Database) to answer these questions. Methods: We apply each of three model formulations: Linear, Logistic, and Poisson to explain how the frequency or count of variants depends on population subgroup/ancestry, functional annotation, sex, and disease status. We also evaluate interactions between population subgroups and functional annotation. Results: For very rare variants (allele frequency < 0.1%), likelihood ratio testing (LRT) provides evidence that allele frequencies vary with functional annotation and population in all three model formulations. By LRT, interactions of population and functional annotation are significant in the Logistic model and the Poisson model. The goodness-of-fit statistics show a better fit in the linear model compared to low frequency variants. Conclusion: We observe that population & functional annotation affect variant frequencies, and conclude that detection of differences across populations and annotations is model scale-dependent, especially for different degrees of rareness. Therefore, statisticians need to carefully consider the potential for bias when using gnomAD as control data. Moreover, gnomAD is a great resource for studies dealing with rare variants.

The proper handling of unstained paraffin slides for immunohistochemistry has been a matter of debate, with several studies demonstrating loss of antigenicity with prolonged storage at room temperature, 4°C and -20°C. The purpose of this study was to determine whether long-term storage of unstained slides at -80°C would impact the staining intensity and expression distribution of markers used to molecularly subtype breast cancer specimens [estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), cytokeratin 5 (CK5), epidermal growth factor receptor (EGFR), and Ki67]. The staining pattern of previously unstained breast tumor slides (n=39 to 64) stored at -80°C for a minimum of 9.93 years (avg., 12.8 y) was compared with the staining pattern of fresh cut slides from the same tumors. The Allred scoring method was used to score ER (0 to 2, negative; 3 to 8, positive), CK5 (≥4, positive), and EGFR (≥4, positive). ASCO/CAP guidelines were used to assess HER2 (0/1+, 2+, or 3+). Ki67 scores were determined based on the proportion of cells stained of any intensity, with 20% staining used as a cut-off. Agreement was assessed using concordance rates and chance-corrected agreement statistics. The chance-corrected agreements were as follows: 0.94 (38/39) for ER, 0.92 (53/55) for CK5, 0.87 (61/64) for EGFR, 0.86 (37/39) for HER2, and 0.67 (46/54) for Ki67. Long-term storage of cut unstained slides at -80°C does not significantly impact the scoring interpretation of ER, CK5, EGFR, and HER2.

Post-GWAS analysis, in many cases, focuses on fine-mapping targeted genetic regions discovered at GWAS-stage; that is, the aim is to pinpoint potential causal variants and susceptibility genes for complex traits and disease outcomes using next-generation sequencing (NGS) technologies. Large-scale GWAS cohorts are necessary to identify target regions given the typically modest genetic effect sizes. In this context, two-phase sampling design and analysis is a cost-reduction technique that utilizes data collected during phase 1 GWAS to select an informative subsample for phase 2 sequencing. The main goal is to make inference for genetic variants measured via NGS by efficiently combining data from phases 1 and 2. We propose two approaches for selecting a phase 2 design under a budget constraint. The first method identifies sampling fractions that select a phase 2 design yielding an asymptotic variance covariance matrix with certain optimal characteristics, e.g. smallest trace, via Lagrange multipliers (LM). The second relies on a genetic algorithm (GA) with a defined fitness function to identify exactly a phase 2 subsample. We perform comprehensive simulation studies to evaluate the empirical properties of the proposed designs for a genetic association study of a quantitative trait. We compare our methods against two ranked designs: residual-dependent sampling and a recently identified optimal design. Our findings demonstrate that the proposed designs, GA in particular, can render competitive power in combined phase 1 and 2 analysis compared to alternative designs while preserving type 1 error control. These results are especially apparent under the more practical scenario where design values need to be defined a priori and are subject to mispecification. We illustrate the proposed methods in a study of triglyceride levels in the North Finland Birth Cohort of 1966. R code to reproduce our results is available at github.com/egosv/TwoPhase_postGWAS.

When quantitative longitudinal traits are risk factors for disease progression and subject to random biological variation, joint model analysis of time-to-event and longitudinal traits can effectively identify direct and/or indirect genetic association of single nucleotide polymorphisms (SNPs) with time-to-event. We present a joint model that integrates:i)a multivariate linear mixed model describing trajectories of multiple longitudinal traits as a function of time, SNP effects, and subject-specific random effects, andii)a frailty Cox survival model that depends on SNPs, longitudinal trajectory effects, and subject-specific frailty accounting for dependence among multiple time-to-event traits. Motivated by complex genetic architecture of type 1 diabetes complications (T1DC) observed in the Diabetes Control and Complications Trial (DCCT), we implement a two-stage approach to inference with bootstrap joint covariance estimation and develop a hypothesis testing procedure to classify direct and/or indirect SNP association with each time-to-event trait. By realistic simulation study, we show that joint modelling of two time-to-T1DC (retinopathy, nephropathy) and two longitudinal risk factors (HbA1c, systolic blood pressure) reduces estimation bias in genetic effects and improves classification accuracy of direct and/or indirect SNP associations, compared to methods that ignore within-subject risk factor variability and dependence among longitudinal and time-to-event traits. Through DCCT data analysis, we demonstrate feasibility for candidate SNP modelling, and quantify effects of sample size and Winner’s curse bias on classification for two SNPs identified as having indirect associations with time-to-T1DC traits. Overall, joint analysis of multiple longitudinal and multiple time-to-event traits provides insight into complex trait architecture.

The identification of genetic variation that directly impacts infection susceptibility and disease severity of COVID-19 is an important step towards risk stratification, personalized treatment plans, therapeutic and vaccine development and deployment. Given the importance of study design in infectious disease genetic epidemiology, we use simulation and draw on current estimates of exposure, infectivity and test accuracy of COVID-19 to demonstrate the feasibility of detecting host genetic factors associated with susceptibility and severity with published COVID-19 study designs. We demonstrate why studying susceptibility to SARS-CoV-2 infection could be futile at the early stages of the pandemic. Our insights can aid in the interpretation of genetic findings emerging in the literature and guide the design of future host genetic studies.

Background Many CpGs become hyper or hypo-methylated with age. Multiple methods have been developed by Horvath et al. to estimate DNA methylation (DNAm) age including Pan-tissue, Skin & Blood, PhenoAge, and GrimAge. Pan-tissue and Skin & Blood try to estimate chronological age in the normal population whereas PhenoAge and GrimAge use surrogate markers associated with mortality to estimate biological age and its departure from chronological age. Here, we applied Horvath’s four methods to calculate and compare DNAm age in 499 subjects with type 1 diabetes (T1D) from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study using DNAm data measured by Illumina EPIC array in the whole blood. Association of the four DNAm ages with development of diabetic complications including cardiovascular diseases (CVD), nephropathy, retinopathy, and neuropathy, and their risk factors were investigated. Results Pan-tissue and GrimAge were higher whereas Skin & Blood and PhenoAge were lower than chronological age (p < 0.0001). DNAm age was not associated with the risk of CVD or retinopathy over 18–20 years after DNAm measurement. However, higher PhenoAge (β = 0.023, p = 0.007) and GrimAge (β = 0.029, p = 0.002) were associated with higher albumin excretion rate (AER), an indicator of diabetic renal disease, measured over time. GrimAge was also associated with development of both diabetic peripheral neuropathy (OR = 1.07, p = 9.24E−3) and cardiovascular autonomic neuropathy (OR = 1.06, p = 0.011). Both HbA1c (β = 0.38, p = 0.026) and T1D duration (β = 0.01, p = 0.043) were associated with higher PhenoAge. Employment (β = − 1.99, p = 0.045) and leisure time (β = − 0.81, p = 0.022) physical activity were associated with lower Pan-tissue and Skin & Blood, respectively. BMI (β = 0.09, p = 0.048) and current smoking (β = 7.13, p = 9.03E−50) were positively associated with Skin & Blood and GrimAge, respectively. Blood pressure, lipid levels, pulse rate, and alcohol consumption were not associated with DNAm age regardless of the method used. Conclusions Various methods of measuring DNAm age are sub-optimal in detecting people at higher risk of developing diabetic complications although some work better than the others.

Aims/hypothesis The aim of this study was to identify genetic variants associated with beta cell function in type 1 diabetes, as measured by serum C-peptide levels, through meta-genome-wide association studies (meta-GWAS). Methods We performed a meta-GWAS to combine the results from five studies in type 1 diabetes with cross-sectionally measured stimulated, fasting or random C-peptide levels, including 3479 European participants. The p values across studies were combined, taking into account sample size and direction of effect. We also performed separate meta-GWAS for stimulated (n = 1303), fasting (n = 2019) and random (n = 1497) C-peptide levels. Results In the meta-GWAS for stimulated/fasting/random C-peptide levels, a SNP on chromosome 1, rs559047 (Chr1:238753916, T>A, minor allele frequency [MAF] 0.24–0.26), was associated with C-peptide (p = 4.13 × 10−8), meeting the genome-wide significance threshold (p T, MAF 0.07–0.10, p = 8.43 × 10−8). In the stimulated C-peptide meta-GWAS, rs61211515 (Chr6:30100975, T/–, MAF 0.17–0.19) in the MHC region was associated with stimulated C-peptide (β [SE] = − 0.39 [0.07], p = 9.72 × 10−8). rs61211515 was also associated with the rate of stimulated C-peptide decline over time in a subset of individuals (n = 258) with annual repeated measures for up to 6 years (p = 0.02). In the meta-GWAS of random C-peptide, another MHC region, SNP rs3135002 (Chr6:32668439, C>A, MAF 0.02–0.06), was associated with C-peptide (p = 3.49 × 10−8). Conditional analyses suggested that the three identified variants in the MHC region were independent of each other. rs9260151 and rs3135002 have been associated with type 1 diabetes, whereas rs559047 and rs61211515 have not been associated with a risk of developing type 1 diabetes. Conclusions/interpretation We identified a locus on chromosome 1 and multiple variants in the MHC region, at least some of which were distinct from type 1 diabetes risk loci, that were associated with C-peptide, suggesting partly non-overlapping mechanisms for the development and progression of type 1 diabetes. These associations need to be validated in independent populations. Further investigations could provide insights into mechanisms of beta cell loss and opportunities to preserve beta cell function. Electronic supplementary material The online version of this article (10.1007/s00125-018-4555-9) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

We evaluate two‐phase designs to follow‐up findings from genome‐wide association study (GWAS) when the cost of regional sequencing in the entire cohort is prohibitive. We develop novel expectation‐maximization‐based inference under a semiparametric maximum likelihood formulation tailored for post‐GWAS inference. A GWAS‐SNP (where SNP is single nucleotide polymorphism) serves as a surrogate covariate in inferring association between a sequence variant and a normally distributed quantitative trait (QT). We assess test validity and quantify efficiency and power of joint QT‐SNP‐dependent sampling and analysis under alternative sample allocations by simulations. Joint allocation balanced on SNP genotype and extreme‐QT strata yields significant power improvements compared to marginal QT‐ or SNP‐based allocations. We illustrate the proposed method and evaluate the sensitivity of sample allocation to sampling variation using data from a sequencing study of systolic blood pressure.

Molecular and genetic association studies conducted in well-characterized longitudinal cohorts offer a powerful approach to investigate factors influencing disease course or complex trait expression. As measurement technologies continue to develop and evolve, studies based on existing cohorts raise methodological challenges. Five such challenges are illustrated in two long-term inter-disciplinary collaborations. In one, molecular genetic prognostic factors in the natural history of node-negative breast cancer are investigated using a combination of hypothesis-testing and hypothesis-generating molecular approaches. In the other, genome-wide association methods are applied to identify genes for multiple traits in extended follow-up data from participants of a therapeutic RCT in type 1 diabetes. The Canadian Journal of Statistics xx: 1–17; 2017 © 2017 Statistical Society of Canada

Motivation Linkage disequilibrium (LD) block construction is required for research in population genetics and genetic epidemiology, including specification of sets of single nucleotide polymorphisms (SNPs) for analysis of multi-SNP based association and identification of haplotype blocks in high density sequencing data. Existing methods based on a narrow sense definition do not allow intermediate regions of low LD between strongly associated SNP pairs and tend to split high density SNP data into small blocks having high between-block correlation. Results We present Big-LD, a block partition method based on interval graph modeling of LD bins which are clusters of strong pairwise LD SNPs, not necessarily physically consecutive. Big-LD uses an agglomerative approach that starts by identifying small communities of SNPs, i.e. the SNPs in each LD bin region, and proceeds by merging these communities. We determine the number of blocks using a method to find maximum-weight independent set. Big-LD produces larger LD blocks compared to existing methods such as MATILDE, Haploview, MIG ++, or S-MIG ++ and the LD blocks better agree with recombination hotspot locations determined by sperm-typing experiments. The observed average runtime of Big-LD for 13 288 240 non-monomorphic SNPs from 1000 Genomes Project autosome data (286 East Asians) is about 5.83 h, which is a significant improvement over the existing methods. Availability and implementation Source code and documentation are available for download at http://github.com/sunnyeesl/BigLD. Supplementary information Supplementary data are available at Bioinformatics online.

BACKGROUND: Although diabetic kidney disease demonstrates both familial clustering and single nucleotide polymorphism heritability, the specific genetic factors influencing risk remain largely unknown.METHODS: To identify genetic variants predisposing to diabetic kidney disease, we performed genome-wide association study (GWAS) analyses. Through collaboration with the Diabetes Nephropathy Collaborative Research Initiative, we assembled a large collection of type 1 diabetes cohorts with harmonized diabetic kidney disease phenotypes. We used a spectrum of ten diabetic kidney disease definitions based on albuminuria and renal function.RESULTS: Our GWAS meta-analysis included association results for up to 19,406 individuals of European descent with type 1 diabetes. We identified 16 genome-wide significant risk loci. The variant with the strongest association (rs55703767) is a common missense mutation in the collagen type IV alpha 3 chain (COL4A3) gene, which encodes a major structural component of the glomerular basement membrane (GBM). Mutations in COL4A3 are implicated in heritable nephropathies, including the progressive inherited nephropathy Alport syndrome. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of diabetic kidney disease, including albuminuria and ESKD, and demonstrated a significant association with GBM width; protective allele carriers had thinner GBM before any signs of kidney disease, and its effect was dependent on glycemia. Three other loci are in or near genes with known or suggestive involvement in this condition (BMP7) or renal biology (COLEC11 and DDR1).CONCLUSIONS: The 16 diabetic kidney disease-associated loci may provide novel insights into the pathogenesis of this condition and help identify potential biologic targets for prevention and treatment.

Diabetic kidney disease (DKD) is a heritable but poorly understood complication of diabetes. To identify genetic variants predisposing to DKD, we performed genome-wide association analyses in 19,406 individuals with type 1 diabetes (T1D) using a spectrum of DKD definitions basedon albuminuria and renal function. We identified 16 genome-wide significant loci. The variant with the strongest association (rs55703767) is a common missense mutation in the collagen type IV alpha 3 chain(COL4A3)gene, which encodes a major structural component of the glomerular basement membrane (GBM) implicated in heritable nephropathies. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of DKD, including albuminuria and end-stage renal disease. Three other loci are in or near genes with known or suggestive involvement in DKD(BMP7)or renal biology (COLEC11andDDR1). The 16 DKD-associated loci provide novel insights into the pathogenesis of DKD, identifying potential biological targets for prevention and treatment.

By jointly analyzing multiple variants within a gene, instead of one at a time, gene‐based multiple regression can improve power, robustness, and interpretation in genetic association analysis. We investigate multiple linear combination (MLC) test statistics for analysis of common variants under realistic trait models with linkage disequilibrium (LD) based on HapMap Asian haplotypes. MLC is a directional test that exploits LD structure in a gene to construct clusters of closely correlated variants recoded such that the majority of pairwise correlations are positive. It combines variant effects within the same cluster linearly, and aggregates cluster‐specific effects in a quadratic sum of squares and cross‐products, producing a test statistic with reduced degrees of freedom (df) equal to the number of clusters. By simulation studies of 1000 genes from across the genome, we demonstrate that MLC is a well‐powered and robust choice among existing methods across a broad range of gene structures. Compared to minimum P‐value, variance‐component, and principal‐component methods, the mean power of MLC is never much lower than that of other methods, and can be higher, particularly with multiple causal variants. Moreover, the variation in gene‐specific MLC test size and power across 1000 genes is less than that of other methods, suggesting it is a complementary approach for discovery in genome‐wide analysis. The cluster construction of the MLC test statistics helps reveal within‐gene LD structure, allowing interpretation of clustered variants as haplotypic effects, while multiple regression helps to distinguish direct and indirect associations.

We previously observed T-bet+ lymphocytes to be associated with a good prognosis in a cohort of women with familial breast cancer. To validate this finding, we evaluated lymphocyte T-bet expression in an independent unselected prospectively accrued series of women with lymph node–negative breast carcinoma. T-bet and clinicopathologic data were available for 614 women. Hormone receptors, HER2, Ki-67, CK5, EGFR, p53, and T-bet status were determined using IHC and/or biochemical methods. Tumors were assigned to luminal A, luminal B, HER2, and basal subtypes based on the expression of IHC markers. Multiple cutpoints were examined in a univariate penalized Cox model to stratify tumors into T-bet+/high and T-bet−/low. Fisher exact test was used to analyze T-bet associations with clinicopathologic variables, IHC markers, and molecular subtype. Survival analyses were by the Cox proportional hazards model. All tests were two sided. A test with a P value < 0.05 was considered statistically significant. T-bet+/high tumor status was significantly associated with large tumor size, high grade, hormone receptor negativity, CK5, EGFR and p53 positivity, high Ki-67, and basal subtype. With a median follow-up of 96.5 months, T-bet−/low tumor status was associated with a reduced disease-free survival compared with T-bet+/high tumor status in multivariate analysis (P = 0.0027; relative risk = 5.62; 95% confidence intervals, 1.48–50.19). Despite being associated with adverse clinicopathologic characteristics, T-bet+ tumor-infiltrating lymphoid cells are associated with a favorable outcome. This supports their role in Th1-mediated antitumor activity and may provide insight for the development of new therapeutic strategies. Cancer Immunol Res; 4(1); 41–48. ©2015 AACR.

Background There has been significant interest in investigating genome-wide and epigenome-wide associations with lipids. Testing at the gene or region level may improve power in such studies. Methods We analyze chromosome 11 cytosine-phosphate-guanine (CpG) methylation levels and single-nucleotide polymorphism (SNP) genotypes from the original Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, aiming to explore the association between triglyceride levels and genetic/epigenetic factors. We apply region-based tests of association to methylation and genotype data, in turn, which seek to increase power by reducing the dimension of the gene-region variables. We also investigate whether integrating 2 omics data sets (methylation and genotype) into the triglyceride association analysis helps or hinders detection of candidate gene regions. Results Gene-region testing identified 1 CpG region that had been previously reported in the GOLDN study data and another 2 gene regions that are also associated with triglyceride levels. Testing on the combined genetic and epigenetic data detected the same genes as using epigenetic or genetic data alone. Conclusions Region-based testing can uncover additional association signals beyond those detected using single-variant testing.

Background We previously observed that T-bet+ tumor-infiltrating T lymphocytes (T-bet+ TILs) in primary breast tumors were associated with adverse clinicopathological features, yet favorable clinical outcome. We identified BRD4 (Bromodomain-Containing Protein 4), a member of the Bromodomain and Extra Terminal domain (BET) family, as a gene that distinguished T-bet+/high and T-bet−/low tumors. In clinical studies, BET inhibitors have been shown to suppress inflammation in various cancers, suggesting a potential link between BRD4 and immune infiltration in cancer. Hence, we examined the BRD4 expression and clinicopathological features of breast cancer. Methods The cohort consisted of a prospectively ascertained consecutive series of women with axillary node-negative breast cancer with long follow-up. Gene expression microarray data were used to detect mRNAs differentially expressed between T-bet+/high (n = 6) and T-bet−/low (n = 41) tumors. Tissue microarrays (TMAs) constructed from tumors of 612 women were used to quantify expression of BRD4 by immunohistochemistry, which was analyzed for its association with T-bet+ TILs, Jagged1, clinicopathological features, and disease-free survival. Results Microarray analysis indicated that BRD4 mRNA expression was up to 44-fold higher in T-bet+/high tumors compared to T-bet−/low tumors (p = 5.38E-05). Immunohistochemical expression of BRD4 in cancer cells was also shown to be associated with T-bet+ TILs (p = 0.0415) as well as with Jagged1 mRNA and protein expression (p = 0.0171, 0.0010 respectively). BRD4 expression correlated with larger tumor size (p = 0.0049), pre-menopausal status (p = 0.0018), and high Ki-67 proliferative index (p = 0.0009). Women with high tumoral BRD4 expression in the absence of T-bet+ TILs exhibited a significantly poorer outcome (log rank test p = 0.0165) relative to other subgroups. Conclusions The association of BRD4 expression with T-bet+ TILs, and T-bet+ TIL-dependent disease-free survival suggests a potential link between BRD4-mediated tumor development and tumor immune surveillance, possibly through BRD4’s regulation of Jagged1 signaling pathways. Further understanding BRD4’s role in different immune contexts may help to identify an appropriate subset of breast cancer patients who may benefit from BET inhibitors without the risk of diminishing the anti-tumoral immune activity.

We previously reported that the presence of Lymphovascular invasion (LVI) is an independent predictor of recurrence in axillary node-negative breast cancer (ANNBC). We produced a unique set of gene expression microarrays specifically designed to identify genes with altered expression in LVI+ tumors (n= 37) compared to LVI- tumors (n = 68). We further examined expression differences in a second analysis designed to discover, within LVI+ tumors alone, clinically relevant genes involved in early recurrence (n = 4, smaller than 4 years) vs. no recurrence for at least 10 years (n = 41). To identify genes and pathways involved in LVI and recurrence in ANNBC, we performed a functional analysis of the expression data. We used the publicly available database tool via WebGestalt to perform a web-based enrichment analysis including Gene ontology, KEGG and the Cytogenetic band. In parallel, using Ingenuity pathway® (IPA), we identified networks, upstream regulators and canonical pathways. IPA provides the probability that the association between the genes in the dataset and the canonical pathways could be explained by chance alone through a right-tailed Fisher's exact test p-value. IPA use a z-score algorithm for predicting activation which reduces the chance that random data will generate significant predictions. In LVI+ vs LVI- analysis, the top IPA activated biofunctions are related to proliferation and migration. In LVI+REC+ vs LVI+REC- analysis, they are related to cell survival and migration as well as lipid synthesis and metabolism. The subset of genes identified in both comparisons include those involved in polarization of cells, oxidation of lipids, cellular homeostasis and growth of the lymphatic system component, with decreased cell death and apoptosis biofunctions. Our bioinformatics analyses are consistent with a migratory phenotype for cancer cells associated with LVI. In addition, we observed that inflammation related pathways are enriched in the LVI+ subgroup that undergoes early recurrence. In the upstream regulator IPA analysis, TGFb NFKb and PI3K are involved in the LVI+ in respect to LVI tumors as well as in LVI+REC+ tumors in respect to LVI+REC- tumors. The results suggest important pathways as well as upstream regulators associated with LVI and recurrence that are involved in invasion and cell fate biofunctions. The findings are currently being evaluated through mRNA and protein validation as well as functional studies. Citation Format: Mathieu Blais, Dushanthi Pinnaduwage, Shelley B. Bull, Irene L. Andrulis. Functional enrichment and pathway analysis of the transcriptome related to lymphovascular invasion and recurrence in axillary-node negative breast cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-15.

We previously reported that the presence of Lymphovascular invasion (LVI) is an independent predictor of recurrence in axillary node-negative breast cancer (ANNBC). We produced a unique set of gene expression microarrays specifically designed to identify genes with altered expression in LVI+ tumors (n= 37) compared to LVI- tumors (n = 68). We further examined expression differences in a second analysis designed to discover, within LVI+ tumors alone, clinically relevant genes involved in early recurrence (n = 4, smaller than 4 years) vs. no recurrence for at least 10 years (n = 41). To identify genes and pathways involved in LVI and recurrence in ANNBC, we performed a functional analysis of the expression data. We used the publicly available database tool via WebGestalt to perform a web-based enrichment analysis including Gene ontology, KEGG and the Cytogenetic band. In parallel, using Ingenuity pathway® (IPA), we identified networks, upstream regulators and canonical pathways. IPA provides the probability that the association between the genes in the dataset and the canonical pathways could be explained by chance alone through a right-tailed Fisher's exact test p-value. IPA use a z-score algorithm for predicting activation which reduces the chance that random data will generate significant predictions. In LVI+ vs LVI- analysis, the top IPA activated biofunctions are related to proliferation and migration. In LVI+REC+ vs LVI+REC- analysis, they are related to cell survival and migration as well as lipid synthesis and metabolism. The subset of genes identified in both comparisons include those involved in polarization of cells, oxidation of lipids, cellular homeostasis and growth of the lymphatic system component, with decreased cell death and apoptosis biofunctions. Our bioinformatics analyses are consistent with a migratory phenotype for cancer cells associated with LVI. In addition, we observed that inflammation related pathways are enriched in the LVI+ subgroup that undergoes early recurrence. In the upstream regulator IPA analysis, TGFb NFKb and PI3K are involved in the LVI+ in respect to LVI tumors as well as in LVI+REC+ tumors in respect to LVI+REC- tumors. The results suggest important pathways as well as upstream regulators associated with LVI and recurrence that are involved in invasion and cell fate biofunctions. The findings are currently being evaluated through mRNA and protein validation as well as functional studies. Citation Format: Mathieu Blais, Shelley B. Bull, Dushanthi Pinnaduwage, Irene L. Andrulis. Functional enrichment and pathway analysis of the transcriptome related to lymphovascular invasion and recurrence in axillary-node negative breast cancer. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-61.

The “winner's curse” is a subtle and difficult problem in interpretation of genetic association, in which association estimates from large‐scale gene detection studies are larger in magnitude than those from subsequent replication studies. This is practically important because use of a biased estimate from the original study will yield an underestimate of sample size requirements for replication, leaving the investigators with an underpowered study. Motivated by investigation of the genetics of type 1 diabetes complications in a longitudinal cohort of participants in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Genetics Study, we apply a bootstrap resampling method in analysis of time to nephropathy under a Cox proportional hazards model, examining 1,213 single‐nucleotide polymorphisms (SNPs) in 201 candidate genes custom genotyped in 1,361 white probands. Among 15 top‐ranked SNPs, bias reduction in log hazard ratio estimates ranges from 43.1% to 80.5%. In simulation studies based on the observed DCCT/EDIC genotype data, genome‐wide bootstrap estimates for false‐positive SNPs and for true‐positive SNPs with low‐to‐moderate power are closer to the true values than uncorrected naïve estimates, but tend to overcorrect SNPs with high power. This bias‐reduction technique is generally applicable for complex trait studies including quantitative, binary, and time‐to‐event traits.

Genetic linkage analysis aims to detect chromosomal regions containing genetic variants that influence risk of specific inherited diseases. The presence of linkage is indicated when a disease or trait cosegregates through the families with genetic markers at a particular region of the genome. Two main types of genetic linkage analysis are in common use, namely model-based linkage analysis and model-free linkage analysis. In this chapter, we focus solely on the latter type and specifically on binary traits or phenotypes, such as the presence or absence of a specific disease. Model-free linkage analysis is based on allele-sharing, where patterns of genetic similarity among affected relatives are compared to chance expectations. Because the model-free methods do not require the specification of the inheritance parameters of a genetic model, they are preferred by many researchers at early stages in the study of a complex disease. We introduce the history of model-free linkage analysis in Subheading 1. Table 1 describes a standard model-free linkage analysis workflow. We describe three popular model-free linkage analysis methods, the nonparametric linkage (NPL) statistic, the affected sib-pair (ASP) likelihood ratio test, and a likelihood approach for pedigrees. The theory behind each linkage test is described in this section together with a simple example of the relevant calculations. Table 4 provides a summary of popular genetic analysis software packages that implement model-free linkage models. In Subheading 2, we work through the methods on a rich example providing sample software code and output. Subheading 3 contains notes with additional details on various topics that may need further consideration during analysis.

Summary In this work, we propose a single nucleotide polymorphism set association test for survival phenotypes in the presence of a non-susceptible fraction. We consider a mixture model with a logistic regression for the susceptibility indicator and a proportional hazards regression to model survival in the susceptible group. We propose a joint test to assess the significance of the genetic variant in both logistic and survival regressions simultaneously. We adopt the spirit of SKAT and conduct a variance-component test treating the genetic effects of multiple variants as random. We derive score-type test statistics, and we investigate several approaches to compute their $p$-values. The finite-sample properties of the proposed tests are assessed and compared to existing approaches by simulations and their use is illustrated through an application to ovarian cancer data from the Consortium of Investigators of Modifiers of BRCA1 and BRCA2.

Genetic linkage analysis aims to detect chromosomal regions containing genetic variants that influence risk of specific inherited diseases. The presence of linkage is indicated when a disease or trait cosegregates through the families with genetic markers at a particular region of the genome. Two main types of genetic linkage analysis are in common use, namely model-based linkage analysis and model-free linkage analysis. In this chapter, we focus solely on the latter type and specifically on binary traits or phenotypes, such as the presence or absence of a specific disease. Model-free linkage analysis is based on allele-sharing, where patterns of genetic similarity among affected relatives are compared to chance expectations. Because the model-free methods do not require the specification of the inheritance parameters of a genetic model, they are preferred by many researchers at early stages in the study of a complex disease. We introduce the history of model-free linkage analysis in Subheading 1. Table 1 describes a standard model-free linkage analysis workflow. We describe three popular model-free linkage analysis methods, the nonparametric linkage (NPL) statistic, the affected sib-pair (ASP) likelihood ratio test, and a likelihood approach for pedigrees. The theory behind each linkage test is described in this section together with a simple example of the relevant calculations. Table 4 provides a summary of popular genetic analysis software packages that implement model-free linkage models. In Subheading 2, we work through the methods on a rich example providing sample software code and output. Subheading 3 contains notes with additional details on various topics that may need further consideration during analysis.

In focused studies designed to follow up associations detected in a genome-wide association study (GWAS), investigators can proceed to fine-map a genomic region by targeted sequencing or dense genotyping of all variants in the region, aiming to identify a functional sequence variant. For the analysis of a quantitative trait, we consider a Bayesian approach to fine-mapping study design that incorporates stratification according to a promising GWAS tag SNP in the same region. Improved cost-efficiency can be achieved when the fine-mapping phase incorporates a two-stage design, with identification of a smaller set of more promising variants in a subsample taken in stage 1, followed by their evaluation in an independent stage 2 subsample. To avoid the potential negative impact of genetic model misspecification on inference we incorporate genetic model selection based on posterior probabilities for each competing model. Our simulation study shows that, compared to simple random sampling that ignores genetic information from GWAS, tag-SNP-based stratified sample allocation methods reduce the number of variants continuing to stage 2 and are more likely to promote the functional sequence variant into confirmation studies.

Objectives. We conducted a casecontrol study to determine the association between KIR2D and KIR3D gene polymorphisms and their interaction with HLA alleles in PsA. Methods. A total of 678 subjects with PsA and 688 healthy controls were studied. Differences between cases and controls in the frequency of individual KIR polymorphisms were tested for significance by an asymptotic � 2 test and Fisher’s exact test. Trends for increasing susceptibility to PsA from combined genotypes (HLA-KIR and HLA) were evaluated by the CochranArmitage trend test. Multigene logistic regression analysis was conducted to identify independent associations and interactions. Results. In univariate analyses, KIR2DL2 and KIR2DS2 polymorphisms were significantly associated with PsA. Only KIR2DS2 was associated with PsA compared with healthy controls in multivariate analysis [odds ratio (OR) 1.25, 95% CI 1.01, 1.54, P = 0.044]. The presence of HLA-C group 2 alleles was associated with a higher risk of PsA (trend test P = 0.006). The risk of PsA is higher when KIR2DS2 is present with the HLA-C ligands (C group 1) for the corresponding inhibitory KIRs, and is highest when KIR2DS2 is present in the absence of HLA-C ligands for homologous inhibitor KIRs, compared with the state when KIR2DS2 is absent (trend test P = 0.027). The presence of HLA-C alleles that have high cell surface expression was also associated with a higher risk of PsA (trend test P < 0.001). HLA-B Bw4 and HLA-B Bw4 80ile allele groups were associated with a higher PsA risk (trend test P < 0.0001 for both analyses). Conclusion. This study confirms the association of the KIR2DS gene, especially KIR2DS2, with PsA.

Lymphatic invasion (LVI) is associated with disease recurrence in axillary node-negative (ANN) breast cancer. Using gene expression profiling of 105 ANN tumors, we found that podocalyxin (PODXL) was more highly expressed in tumors with LVI (LVI+) than in those without LVI (LVI-). Differences in PODXL expression were validated using real-time polymerase chain reaction as well as by immunohistochemistry in an independent set of 652 tumors on tissue microarrays. Disease-free survival (DFS) analyses were conducted for association of high PODXL protein expression with risk of distant recurrence overall and within breast cancer subtypes using both Cox and cure-rate models. High PODXL expression was associated with poor prognosis features including large tumor size, high histological grade, estrogen and progesterone receptor negativity, and with clinical alterations characteristic of the basal-like breast cancer phenotype. Surprisingly, despite having other poor prognosis characteristics, women with high PODXL expressing tumors had better long-term DFS in multivariate analysis with traditional clinicopathologic factors including LVI and HER2 status (P = 0.001). PODXL has the potential to be a useful biomarker for identifying good prognosis patients in characteristically poor prognosis breast cancer groups and may impact treatment of women with this disease.

We carried out a randomized, double-blind, placebo-controlled trial of hyperbaric oxygen therapy (HBO) in patients with chronic stable MS. Eighty-two patients were treated in a multiplace hyperbaric chamber with gas supplied by mask. Forty-one patients received 20 consecutive daily treatments of 100% O2 followed by 7 "booster" treatments in the next 6 months; 41 control patients received "air" (12.5% O2 at 1.75 atmospheres absolute). There was no significant difference in treatment and control groups in the Extended Kurtzke Disability scores, Kurtzke Functional scores, magnetic resonance imaging, or evoked potentials after the initial 20 treatments or after the boosters. HBO is not effective in treating chronic stable MS.

Long-term complications of type 1 diabetes, including nephropathy and retinopathy, share diabetes duration and hyperglycemia as major risk factors. Cross-sectional studies of sibpairs, both with type 1 diabetes, have shown familial clustering of specific complications, leading to the hypothesis that there are genetic contributors. However, because of the cross-sectional design of these studies, they were not able to account for the long-term effect of glycemia. Glycemia, measured by HbA1c, is correlated in sibs with type 1 diabetes. Recently, specific genetic loci that are associated with differences in HbA1c between people with type 1 diabetes have been convincingly identified, and they have also been shown to be associated with diabetic complications. This raises the question: how much of the familial clustering of diabetic complications is due to genes that influence risk without acting through the conventional risk factors? Implications for the design of genetic studies of diabetic complications are discussed.

In genetic association studies, analyses integrating data or estimates from unrelated case-control individuals and case trios (case offspring and their parents) can increase statistical power to identify disease susceptibility loci. Data on control trios may also be available, but how and when their use is advantageous is less familiar and is described here. In addition, the authors examine assumptions and properties of hybrid analyses combining association estimates from unrelated case-control individuals together with case and control family trios, focusing on low-prevalence disease. One such assumption is absence of population stratification bias (PSB), a potential source of confounding in case-control analyses. For detection of PSB, the authors discuss 4 possible tests that assess equality between individual-level and family-based estimates. Furthermore, a weighted framework is presented, in which estimates from analyses combining unrelated individuals and families (most powerful but subject to PSB) and family-based analyses (robust to PSB) are weighted according to the observed PSB test P value. In contrast to existing hybrid designs that combine individuals and families only if no significant PSB is detected, the weighted framework does not require specification of an arbitrary PSB testing level to establish significance. The statistical methods are evaluated using simulations and applied to a candidate gene study of childhood leukemia (Quebec Childhood Leukemia Study, 1980-2000).

By systematic examination of common tag single-nucleotide polymorphisms (SNPs) across the genome, the genome-wide association study (GWAS) has proven to be a successful approach to identify genetic variants that are asso- ciated with complex diseases and traits. Although the per base pair cost of sequencing has dropped dramatically with the advent of the next-generation technologies, it may still only be feasible to obtain DNA sequence data for a portion of available study subjects due to financial constraints. Two-phase sampling designs have been used frequently in large-scale surveys and epidemiological studies where certain variables are too costly to be measured on all subjects. We consider two-phase strati- fied sampling designs for genetic association, in which tag SNPs for candidate genes or regions are genotyped on all subjects in phase 1, and a proportion of subjects are selected into phase 2 based on genotypes at one or more tag SNPs. Deep se- quencing in the region is then applied to genotype phase 2 subjects at sequence SNPs. We investigate alternative sampling designs for selection of phase 2 subjects within strata defined by tag SNP genotypes and develop methods of inference for sequence SNP variant associations using data from both phases. In comparison to methods that use data from phase 2 alone, the combined analysis improves efficiency. Genet. Epidemiol. 00:1-13, 2012. C � 2012 Wiley Periodicals, Inc.

The phenomenon known as the winner's curse is a form of selection bias that affects estimates of genetic association. In genome-wide association studies (GWAS) the bias is exacerbated by the use of stringent selection thresholds and ranking over hundreds of thousands of single nucleotide polymorphisms (SNPs). We develop an improved multi-locus bootstrap point estimate and confidence interval, which accounts for both ranking- and threshold-selection bias in the presence of genome-wide SNP linkage disequilibrium structure. The bootstrap method easily adapts to various study designs and alternative test statistics as well as complex SNP selection criteria. The latter is demonstrated by our application to the Wellcome Trust Case Control Consortium findings, in which the selection criterion was the minimum of the p-values for the additive and genotypic genetic effect models. In contrast, existing likelihood-based bias-reduced estimators account for the selection criterion applied to an SNP as if it were the only one tested, and so are more simple computationally, but do not address ranking across SNPs. Our simulation studies show that the bootstrap bias-reduced estimates are usually closer to the true genetic effect than the likelihood estimates and are less variable with a narrower confidence interval. Replication study sample size requirements computed from the bootstrap bias-reduced estimates are adequate 75–90 per cent of the time compared to 53-60 per cent of the time for the likelihood method. The bootstrap methods are implemented in a user-friendly package able to provide point and interval estimation for both binary and quantitative phenotypes in large-scale GWAS. Copyright © 2011 John Wiley & Sons, Ltd.

Early studies of breast cancer microarray data used linear models to quantify the relationship between measures of gene expression (GE) and copy number (CN) obtained from tumour samples. Motivated by a study of women with axillary node-negative breast cancer, we propose a regression-based scan statistic to identify within-chromosome clusters of genetic probes that exhibit association between GE and CN, while accounting for tumour characteristics known to be prognostic for clinical outcome. As a measure of the association between GE and CN, for each genetic probe available from a microarray we regress GE on CN, and include subject-specific covariates. In the development of the scan statistic, the within-chromosome spatial distribution of the subset of probes with a statistically significant association is approximated by a Poisson process. By incorporating the distance between the probe positions, the scan statistic accounts for the spatial nature of CN alterations. Regions identified as clusters of significant associations are hypothesized to harbour genes involved in breast cancer progression. Using simulations, we examine the sensitivity of the method to certain factors, and to address issues of repeatability, we consider reappearance probabilities for each probe within detected regions and assess the utility of a quantity estimated by bootstrap sample frequencies. Applications of the proposed method to joint analysis of GE and CN in breast tumours, with and without an informative covariate, and comparisons with alternative methods suggest that inclusion of covariates and the use of a regional test statistic can serve to refine regions for further investigation including the analysis of their association with outcome. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract Data from the Ontario Cancer Registry (OCR) were compared with data from a multi-centred prospective cohort of 1655 node-negative breast cancer patients with intensive clinical follow-up. Agreement in cause of death was evaluated using kappa statistics. The accuracy of OCR classification was evaluated against the Mount Sinai Hospital (MSH) study oncologist’s interpretation of intensely followed, cohort-collected data as the reference standard. The two sources showed a high level of agreement (kappa statistic [k] = 0.88; 95% confidence interval [CI]: 0.86, 0.90) in vital status and cause of death. Among those cases where both sources reported a death, the OCR had a sensitivity of 95% (95% CI: 90.5, 98.8) and a specificity of 88% (95% CI: 79.6, 92.4). The OCR is a valuable tool for epidemiologic studies of breast cancer to acquire adequate and easily attainable cause-of-death information.

Résumé Une comparaison a été faite entre les données tirées du Registre d’inscription des cas de cancer de l’Ontario (RICCO) et les données d’une cohorte prospective multicentrique formée de 1 655 patientes atteintes d’un cancer du sein sans envahissement ganglionnaire faisant l’objet d’un suivi clinique rigoureux. La concordance des causes de décès a été évaluée au moyen des statistiques Kappa. L’exactitude de la classification du RICCO a été évaluée par rapport à l’interprétation, par l’oncologue de l’étude du Mount Sinai Hospital (étude MSH), des données de cohorte rigoureusement suivies et servant d’étalon de référence. Les deux sources montraient un degré de concordance élevé (valeur Kappa [κ] de 0,88; intervalle de confiance [IC] à 95 % : 0,86 à 0,90) concernant le statut vital et la cause de décès. Lorsque les deux sources faisaient état d’un décès, le RICCO avait une sensibilité de 95 % (IC à 95 % : 90,5 à 98,8) et une spécificité de 88 % (IC à 95 % : 79,6 à 92,4). Le RICCO est un outil utile dans le cadre d’études épidémiologiques sur le cancer du sein, car il permet d’obtenir de l’information adaptée et facilement accessible concernant les causes de décès.

Background— Elevated serum soluble E-selectin levels have been associated with a number of diseases. Although E-selectin levels are heritable, little is known about the specific genetic factors involved. E-selectin levels have been associated with the ABO blood group phenotype. Methods and Results— We performed a high-resolution genome-wide association study of serum soluble E-selectin levels in 685 white individuals with type 1 diabetes from the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Intervention and Complications (EDIC) study to identify major loci influencing levels. Highly significant evidence for association ( P =10 −29 ) was observed for rs579459 near the ABO blood group gene, accounting for 19% of the variance in E-selectin levels. Levels of E-selectin were higher in O/O than O/A heterozygotes, which were likewise higher than A/A genotypes. Analysis of subgroups of A alleles reveals heterogeneity in the association, and even after this was accounted for, an intron 1 SNP remained significantly associated. We replicate the ABO association in nondiabetic individuals. Conclusion— ABO is a major locus for serum soluble E-selectin levels. We excluded population stratification, fine-mapped the association to sub-A alleles, and also document association with additional variation in the ABO region.

OBJECTIVE Glycemia is a major risk factor for the development of long-term complications in type 1 diabetes; however, no specific genetic loci have been identified for glycemic control in individuals with type 1 diabetes. To identify such loci in type 1 diabetes, we analyzed longitudinal repeated measures of A1C from the Diabetes Control and Complications Trial. RESEARCH DESIGN AND METHODS We performed a genome-wide association study using the mean of quarterly A1C values measured over 6.5 years, separately in the conventional (n = 667) and intensive (n = 637) treatment groups of the DCCT. At loci of interest, linear mixed models were used to take advantage of all the repeated measures. We then assessed the association of these loci with capillary glucose and repeated measures of multiple complications of diabetes. RESULTS We identified a major locus for A1C levels in the conventional treatment group near SORCS1 (10q25.1, P = 7 × 10−10), which was also associated with mean glucose (P = 2 × 10−5). This was confirmed using A1C in the intensive treatment group (P = 0.01). Other loci achieved evidence close to genome-wide significance: 14q32.13 (GSC) and 9p22 (BNC2) in the combined treatment groups and 15q21.3 (WDR72) in the intensive group. Further, these loci gave evidence for association with diabetic complications, specifically SORCS1 with hypoglycemia and BNC2 with renal and retinal complications. We replicated the SORCS1 association in Genetics of Diabetes in Kidneys (GoKinD) study control subjects (P = 0.01) and the BNC2 association with A1C in nondiabetic individuals. CONCLUSIONS A major locus for A1C and glucose in individuals with diabetes is near SORCS1. This may influence the design and analysis of genetic studies attempting to identify risk factors for long-term diabetic complications.