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Laurent Briollais

Publications and source records attributed to Laurent Briollais.

11 recordsLinked to original sources

SNP-SNP interactions in breast cancer susceptibility.

BACKGROUND: Breast cancer predisposition genes identified to date (e.g., BRCA1 and BRCA2) are responsible for less than 5% of all breast cancer cases. Many studies have shown that the cancer risks associated with individual commonly occurring single nucleotide polymorphisms (SNPs) are incremental. However, polygenic models suggest that multiple commonly occurring low to modestly penetrant SNPs of cancer related genes might have a greater effect on a disease when considered in combination. METHODS: In an attempt to identify the breast cancer risk conferred by SNP interactions, we have studied 19 SNPs from genes involved in major cancer related pathways. All SNPs were genotyped by TaqMan 5'nuclease assay. The association between the case-control status and each individual SNP, measured by the odds ratio and its corresponding 95% confidence interval, was estimated using unconditional logistic regression models. At the second stage, two-way interactions were investigated using multivariate logistic models. The robustness of the interactions, which were observed among SNPs with stronger functional evidence, was assessed using a bootstrap approach, and correction for multiple testing based on the false discovery rate (FDR) principle. RESULTS: None of these SNPs contributed to breast cancer risk individually. However, we have demonstrated evidence for gene-gene (SNP-SNP) interaction among these SNPs, which were associated with increased breast cancer risk. Our study suggests cross talk between the SNPs of the DNA repair and immune system (XPD-[Lys751Gln] and IL10-[G(-1082)A]), cell cycle and estrogen metabolism (CCND1-[Pro241Pro] and COMT-[Met108/158Val]), cell cycle and DNA repair (BARD1-[Pro24Ser] and XPD-[Lys751Gln]), and within carcinogen metabolism (GSTP1-[Ile105Val] and COMT-[Met108/158Val]) pathways. CONCLUSION: The importance of these pathways and their communication in breast cancer predisposition has been emphasized previously, but their biological interactions through SNPs have not been described. The strategy used here has the potential to identify complex biological links among breast cancer genes and processes. This will provide novel biological information, which will ultimately improve breast cancer risk management.

Adult↗

Comparison of genotype- and haplotype-based approaches for fine-mapping of alcohol dependence using COGA data.

It is generally assumed that the detection of disease susceptibility genes via fine-mapping association study is facilitated by consideration of marker haplotypes. In this study, we compared the performance of genotype-based and haplotype-based association studies using the Collaborative Study of Genetics of Alcoholism dataset, on several chromosomal regions showing evidence for linkage with ALDX1. After correction for multiple testing, the most significant results were observed with the genotype-based analyses on two regions of chromosomes 2 and 7. Interestingly, the analyses results from this dataset showed that there was no advantage of the haplotype-based analyses over genotype-based (single-locus) analyses. However, caution should be taken when generalizing these results to other chromosomal regions or to other populations.

Alcoholism↗

Fine mapping by linkage and association in nuclear family and case-control designs.

This report summarizes the Genetic Analysis Workshop 14 contributions related to fine-mapping strategies, in which examining smaller regions by association with single-nucleotide polymorphisms (SNPs) can yield savings in genotyping and multiple-testing penalties. The aim of the analyses conducted in Group 7 contributions was to localize disease susceptibility loci from either the simulated or the Collaborative Study on the Genetics of Alcoholism (COGA) data within identified regions of linkage. Among the 10 contributions, most groups analyzed the simulated data, one group analyzed the COGA data only, and one group analyzed both data sets. The research questions included evaluation of new methods of analysis, as well as comparisons among alternative methods, analytic strategies, and study designs. Methods of interest included an algorithm for SNP marker ordering, a locally weighted transmission disequilibrium test statistic, a likelihood-ratio test statistic for family-based association in nuclear families, a robust test statistic for case-control association studies, and Bayesian spatial modeling methods for haplotype clustering and association. Evaluations included comparisons among confidence intervals for loci detected via linkage, effects of multiple testing adjustments and trade-offs between type I error and power, comparisons among haplotype-based (multilocus) and genotype-based (multilocus and single-locus) association analyses, and design of fine-mapping and replication studies. While several promising new approaches were identified, further development and evaluation of methods for multiple testing, regression modeling of association with multiple markers and haplotypes, and combined treatment of linkage and association data are necessary if we are to identify many of the genes that contribute to complex traits.

Alcoholism↗

Tests for covariate-associated heterogeneity in IBD allele sharing of affected relatives.

Linkage studies that aim to map susceptibility genes for complex diseases commonly test for excess allele sharing among affected relatives. Conventional methods based on identical-by-descent IBD allele sharing do not allow for possible differences among families, such as arise in the case of locus heterogeneity, and thus have reduced ability to detect linkage in the presence of such heterogeneity. We investigated two approaches to test for heterogeneity in allele sharing, using a family-level covariate that may be associated with different disease mechanisms leading to differences in allele sharing. Likelihood ratio tests for heterogeneity were formulated based on an extension of the linear and exponential likelihood models developed by Kong and Cox. Alternatively, we examined the asymptotic and permutation distributions of T-tests for differences between mean allele-sharing linkage scores from two covariate-defined family subgroups, assuming exchangeability. The size and power of heterogeneity tests were evaluated for S(all) and S(pairs) allele-sharing scoring functions using data sets of families with affected sibling and cousin pairs, generated under a model of locus heterogeneity. In certain simulation scenarios, the likelihood ratio test statistics did not follow the expected asymptotic distributions. The type I error estimates for the T-statistics conformed to nominal 5 and 1% levels in all scenarios considered, and corresponding power was comparable to that of the likelihood ratio tests. Application of these tests for heterogeneity detected significant differences in allele sharing between subgroups of families with inflammatory bowel disease.

Algorithms↗

Apoptotic and growth regulatory genes as mutational targets in mismatch repair deficient endometrioid adenocarcinomas of young patients.

Among multiple genetic pathways involved in endometrial cancer (EC), the mutator pathway is characterized by defective mismatch repair (MMR) causing microsatellite instability (MSI+). Inactivation of MMR genes allows elevation of mutation rates in key target genes involved in important cellular pathways, providing a selective growth advantage. Our aim was to investigate apoptotic and growth regulatory target genes in young endometrioid adenocarcinoma patients and their association with stepwise neoplastic progression through distinct stages of hyperplasia and cancer. Screening of 184 ECs revealed 38 microsatellite high (MSI-H), 10 microsatellite low (MSI-L) and 136 microsatellite stable (MSS) tumors. We observed somatic frameshift mutations in the coding region repeats of the target genes in 12/38 MSI-H tumors (T) and in 3/8 of available associated hyperplasias (HY). Mutations were detected in FAS (T=1, HY=1), BAX (T=6, HY=1), CASP5 (T=2) and IGFIIR (T=3, HY=1) genes. None of the MSI-L or MSS tumors showed alterations in these coding repeats. Increased mutation frequency in apoptotic and growth regulatory genes demonstrated a significant relationship with advancing tumor grade (p=0.02) by Fisher's exact test. Furthermore, a significant trend was found by Bartholomew's test (P<0.05) for the apoptotic pathway and close to significant (p approximately 0.06) for the overall mutation status for both pathways combined. Our results suggest that genes implicated in apoptosis may serve as targets in the progression of MSI+ EC in young patients.

Adenocarcinoma↗

Polymorphisms XRCC1-R399Q and XRCC3-T241M and the risk of breast cancer at the Ontario site of the Breast Cancer Family Registry.

This study investigates the role of two nonsynonymous single nucleotide polymorphisms in DNA repair genes, X-ray repair cross-complementing group 1 (XRCC1)-R399Q and X-ray repair cross-complementing group 3 (XRCC3)-T241M, in breast cancer. Incident cases of invasive breast cancer in Caucasian women [n = 402, mean age = 45.7 (SD = 6.2) years] and female Caucasian controls [n = 402, mean age = 45.2 (6.5) years] frequency matched on 5-year age intervals were identified from the Ontario Familial Breast Cancer Registry. No evidence for a main effect of the XRCC1-R399Q genotype on breast cancer risk was observed. Estimates of risk for a family history (FH) of breast cancer compared with no FH differed by XRCC1-R399Q genotype (P value for interaction = 0.001). Homozygote XRCC1-399 R/R individuals and FH+ were at a 2.92-fold [95% confidence interval (95% CI) = 1.47-5.79] increased risk of disease compared with FH- individuals; the estimate of risk increased for R/Q heterozygotes with FH+ [odds ratio (OR) = 3.85, 95% CI = 1.94-7.65] but not for Q/Q homozygotes with FH+ (OR = 0.54, 95% CI = 0.20-1.47) compared with homozygous R/R and FH- individuals. A marginal positive association for XRCC3-241 M/M compared with T/T genotype was found (OR = 1.44, 95% CI = 0.94-2.19), but the heterozygous T/M was not associated with an increase in risk (OR = 0.96, 95% CI = 0.71-1.32). There was also some evidence for a combined effect of body mass index and XRCC3-T241M on estimates of risk. Our results suggest that these polymorphisms may influence breast cancer risk by modifying the effect of risk factors such as FH. There is a need for further study into the role of these polymorphisms as effect modifiers.

Adult↗

Multilevel modeling for the analysis of longitudinal blood pressure data in the Framingham Heart Study pedigrees.

BACKGROUND: The data arising from a longitudinal familial study have a complex correlation structure that cannot be modeled using classical methods for the analysis of familial data at a single time point. METHODS: To fit the longitudinal systolic blood pressure (SBP) pedigree data arising from the Framingham Heart Study, we proposed to use multilevel modeling. That approach was used to distinguish multiple levels of information with individual repeated measurements (Level 1) being made within individuals (Level 2), and individuals clustered within pedigrees (Level 3). Residuals from the subject-specific and pedigree-specific regression models were summed both for the mean SBP and slope of SBP change over time, in order to define two new outcomes that were then used in a genome-wide linkage analysis. RESULTS: Evidence for linkage for the two outcomes (mean SBP and slope) was found in several chromosomal regions with a maximum LOD score of 3.6 on chromosome 8 and 3.5 on chromosome 17 for the mean SBP, and 2.5 on chromosome 1 for SBP slope. However, the linkage on chromosome 8 was only detected when the sample was restricted to subjects between age 25 and 75 and with at least four exams (Cohort 1) or 3 exams (Cohort 2). DISCUSSION: Multilevel modeling is a powerful approach to detect genes involved in complex traits when longitudinal data are available. It allows for complex hierarchical data structure to be taken into account and therefore, a better partitioning of random within-individual variation from other sources of variability (genetic or nongenetic).

Adult↗

Using an age-at-onset phenotype with interval censoring to compare methods of segregation and linkage analysis in a candidate region for elevated systolic blood pressure.

BACKGROUND: Genetic studies of complex disorders such as hypertension often utilize families selected for this outcome, usually with information obtained at a single time point. Since age-at-onset for diagnosed hypertension can vary substantially between individuals, a phenotype based on long-term follow up in unselected families can yield valuable insights into this disorder for the general population. METHODS: Genetic analyses were conducted using 2884 individuals from the largest 330 families of the Framingham Heart Study. A longitudinal phenotype was constructed using the age at an examination when systolic blood pressure (SBP) first exceeds 139 mm Hg. An interval for age-at-onset was created, since the exact time of onset was unknown. Time-fixed (sex, study cohort) and time-varying (body mass index, daily cigarette and alcohol consumption) explanatory variables were included. RESULTS: Segregation analysis for a major gene effect demonstrated that the major gene effect parameter was sensitive to the choice for age-at-onset. Linkage analyses for age-at-onset were conducted using 1537 individuals in 52 families. Evidence for putative genes identified on chromosome 17 in a previous linkage study using a quantitative SBP phenotype for these data was not confirmed. CONCLUSIONS: Interval censoring for age-at-onset should not be ignored. Further research is needed to explain the inconsistent segregation results between the different age-at-onset models (regressive threshold and proportional hazards) as well as the inconsistent linkage results between the longitudinal phenotypes (age-at-onset and quantitative).

Adolescent↗

Longitudinal data analysis in pedigree studies.

Longitudinal family studies provide a valuable resource for investigating genetic and environmental factors that influence long-term averages and changes over time in a complex trait. This paper summarizes 13 contributions to Genetic Analysis Workshop 13, which include a wide range of methods for genetic analysis of longitudinal data in families. The methods can be grouped into two basic approaches: 1) two-step modeling, in which repeated observations are first reduced to one summary statistic per subject (e.g., a mean or slope), after which this statistic is used in a standard genetic analysis, or 2) joint modeling, in which genetic and longitudinal model parameters are estimated simultaneously in a single analysis. In applications to Framingham Heart Study data, contributors collectively reported evidence for genes that affected trait mean on chromosomes 1, 2, 3, 5, 8, 9, 10, 13, and 17, but most did not find genes affecting slope. Applications to simulated data suggested that even for a gene that only affected slope, use of a mean-type statistic could provide greater power than a slope-type statistic for detecting that gene. We report on the results of a small experiment that sheds some light on this apparently paradoxical finding, and indicate how one might form a more powerful test for finding a slope-affecting gene. Several areas for future research are discussed.

Cardiovascular Diseases↗

Heterogeneity in IBD allele sharing among covariate-defined subgroups: issues and findings for affected relatives.

OBJECTIVES: Modelling of variation in identical-by-descent (IBD) allele sharing using covariates can increase power to detect linkage, identify covariate-defined subgroups linked to particular marker regions, and improve the design of subsequent studies to localize genes and characterize their effects. In this report, we highlight issues that arise in studies of families with affected relatives. METHODS: Mirea et al. [Genet Epidemiol 2003, in press] extended linear and exponential linkage likelihood models [Kong and Cox, Am J Hum Genet 1997;61: 1179-1188] to model variation in NPL scores among covariate-defined groups of families, and proposed likelihood ratio (LR) and t statistics to detect differences in allele sharing between groups defined by a binary covariate. Here we evaluate factors affecting the power of these tests analytically and by example, as well as effects of constraints, nuisance parameters, and incomplete data on test validity by simulation of locus heterogeneity in families with affected siblings or affected cousins. RESULTS: Provided constraints on the parameters are avoided, these tests are particularly useful when one subgroup has less than expected IBD sharing. The distribution of the LR statistic depends on the extent of linkage, particularly in the presence of constraints. The t statistic may be biased by group differences in information content. CONCLUSIONS: We recommend that constraints be applied cautiously, and covariate effects in IBD allele sharing models interpreted with care.

Alleles↗

Genes involved in DNA repair are mutational targets in endometrial cancers with microsatellite instability.

Microsatellite instability (MSI) is observed in a subset of endometrial cancers (ECs) and is attributed to defects in mismatch repair. Mismatch repair deficiency allows for accumulation of mutations in the coding repeats of key target genes, which may be involved in the initiation and progression of MSI+ EC. We examined genes implicated in DNA repair pathways in 38 MSI-high (MSI-H), 10 MSI-low, 25 microsatellite stable ECs, and a selected panel of associated premalignant hyperplasias. Genetic alterations were correlated to histopathological data, including tumor grade and stage. Somatic frameshift mutations were observed in hMLH3, hMSH3, hMSH6, CHK1, and BAX genes in MSI-H endometrial hyperplasias and cancers, whereas mutations in ATR and CDC25C were observed only in MSI-H ECs. Increased mutation frequency in DNA damage response pathway genes including ATR, CHK1, and BAX demonstrated a significant trend with advancing tumor grade (P < 0.05). Our observations of the same mutations at short coding mononucleotide repeats in both premalignant lesions and tumors and association of increased frequency of mutation accumulation with advancing tumor grade suggest that these alterations may play a role in the development and progression of MSI+ EC.

Adenocarcinoma↗