Oligogenic combinations associated with breast cancer risk in women under 53 years of age

Genetic evaluation of patients with multiple primary cancers

Regarding inherited cancer predisposition, single gene carriers of pathogenic variants (PVs) have been extensively reported on in the literature, whereas the oligogenic coinheritance of heterozygous PVs in cancer-related genes is a poorly studied event. Currently, due to the increased number of cancer survivors, the probability of patients presenting with multiple primary cancers (MPCs) is higher. The present study included patients with MPCs aged ≤45 years without known PVs in common cancer predisposition genes. This study used whole exome sequencing (WES) of germline and tumoral DNA, chromosomal microarray analysis (CMA) of germline DNA (patients 1-7, 9 and 10), and a karyotype test of patient 8 to detect variants associated with the disease. The 10 patients included in the study presented a mean of 3 cancers per patient. CMA showed two microduplications and one microdeletion, while WES of the germline DNA identified 1-3 single nucleotide variants of potential interest to the disease in each patient and two additional copy number variants. Most of the identified variants were classified as variants of uncertain significance. The mapping of the germline variants into their pathways showed a possible additive effect of these as the cause of the cancer. A total of 12 somatic samples from 5 patients were available for sequencing. All of the germline variants were also present in the somatic samples, while no second hits were identified in the same genes. The sequencing of patients with early cancers, family history and multiple tumors is already a standard of care. However, growing evidence has suggested that the assessment of patients should not stop at the identification of one PV in a cancer predisposition gene.

Pathogenesis of FUS-associated ALS and FTD: insights from rodent models

Disruptions to genes linked to RNA processing and homeostasis are implicated in the pathogenesis of two pathologically related but clinically heterogeneous neurodegenerative diseases, amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Mutations in the Fused-in-Sarcoma (FUS) gene encoding a 526 amino-acid RNA-binding protein are found in a small subset of ALS cases, but FUS mutations do not appear to be a direct cause of FTD. Structural and functional similarities between FUS and another ALS-related RNA-binding protein, TDP-43, highlight the potential importance of aberrant RNA processing in ALS/FTD, and this pathway is now a major focus of interest. Recently, several research groups have reported transgenic vertebrate models of FUSopathy, with varying results. Here, we discuss the evidence for FUS pathogenicity in ALS/FTD, review the experimental approaches used and phenotypic features of FUS rodent models reported to date, and outline their contribution to our understanding of pathogenic mechanisms. Further refinement of vertebrate models will likely aid our understanding of the role of FUS in both diseases.

A steroid metabolizing gene variant in a polyfactorial model improves risk prediction in a high incidence breast cancer population

Background

We have combined functional gene polymorphisms with clinical factors to improve prediction and understanding of sporadic breast cancer risk, particularly within a high incidence Caucasian population.

Methods

A polyfactorial risk model (PFRM) was built from both clinical data and functional single nucleotide polymorphism (SNP) gene candidates using multivariate logistic regression analysis on data from 5022 US Caucasian females (1671 breast cancer cases, 3351 controls), validated in an independent set of 1193 women (400 cases, 793 controls), and reassessed in a unique high incidence breast cancer population (165 cases, 173 controls) from Marin County, CA.

Results

The optimized PFRM consisted of 22 SNPs (19 genes, 6 regulating steroid metabolism) and 5 clinical risk factors, and its 5-year and lifetime risk prediction performance proved significantly superior (~ 2-fold) over the Gail model (Breast Cancer Risk Assessment Tool, BCRAT), whether assessed by odds (OR) or positive likelihood (PLR) ratios over increasing model risk levels. Improved performance of the PFRM in high risk Marin women was due in part to genotype enrichment by a CYP11B2 (-344T/C) variant.

Conclusions and general significance

Since the optimized PFRM consistently outperformed BCRAT in all Caucasian study populations, it represents an improved personalized risk assessment tool. The finding of higher Marin County risk linked to a CYP11B2 aldosterone synthase SNP associated with essential hypertension offers a new genetic clue to sporadic breast cancer predisposition.

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A polyfactorial breast cancer risk assessment model (PFRM) was built and validated.

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The optimized PFRM incorporates both genetic (22 SNPs/19 genes) and clinical risk factors.

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The PFRM was further validated in a high risk USA/Marin breast cancer population.

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This PFRM consistently performed significantly better than the BCRAT (Gail model).

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A functional aldosterone synthase SNP in PFRM improved predictive performance in Marin.

The NQO1 Pro187Ser polymorphism and breast cancer susceptibility: evidence from an updated meta-analysis

Background

NAD(P)H: quinone oxidoreductase 1 (NQO1) plays a central role in catalyzing the two-electron reduction of quinoid compounds into hydroquinones. The NQO1 Pro187Ser polymorphism was found to correlate with a lower enzymatic activity, which may result in increased incidence of carcinomas including breast cancer. Previous studies investigating the association between NQO1 Pro187Ser polymorphism and breast cancer risk showed inconsistent results. We performed a meta-analysis to summarize the possible association.

Methods

All studies published from January 1966 to February 2014 on the association between NQO1 Pro187Ser polymorphism and breast cancer risk were identified by searching electronic databases PubMed, EMBASE, Cochrane library, and Chinese Biomedical Literature database (CBM). The association between NQO1 Pro187Ser polymorphism and breast cancer risk was assessed by odds ratios (ORs) together with their 95% confidence intervals (CIs).

Results

Ten studies with 2,773 cases and 4,076 controls were finally included in the meta-analysis. We did not observe a significant association between NQO1 Pro187Ser polymorphism and breast cancer risk when all studies were pooled into the meta-analysis. In subgroup analysis by ethnicity, significant increased breast cancer risk was found in Caucasians (Ser/Pro vs. Pro/Pro: OR = 1.145, 95% CI = 1.008–1.301, P = 0.038; Ser/Ser + Ser/Pro vs. Pro/Pro: OR = 1.177, 95% CI = 1.041–1.331, P = 0.009). When stratified by source of control, significant increased breast cancer risk was found in population-based studies (Ser/Pro vs. Pro/Pro: OR = 1.180, 95% CI = 1.035–1.344, P = 0.013; Ser/Ser + Ser/Pro vs. Pro/Pro: OR = 1.191, 95% CI = 1.050–1.350, P = 0.007). However, in subgroup analyses according to menopausal status, quality score, and HWE in controls, no any significant association was detected.

Conclusions

Our meta-analysis provides the evidence that the NQO1 Pro187Ser polymorphism contributed to the breast cancer susceptibility among Caucasians. Further large and well-designed studies are needed to confirm this association.

Virtual slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1248639991252504

The NQO1 polymorphism C609T (Pro187Ser) and cancer susceptibility: a comprehensive meta-analysis

Background:

Evidence is increasingly emerging about multiple roles for the NAD(P)H quinone oxidoreductase 1 enzyme in cancer. The C609T (rs1800566, Pro187Ser) null polymorphism of the NQO1 gene contributes significantly to the variation in enzymatic activity across different populations. NQO1 C609T polymorphism was thoroughly investigated with respect to cancer susceptibility. The results were inconsistent partly due to low sample sizes. The aim of the present work was to perform a meta-analysis to assess association for all common cancer sites separately and in combination.

Methods:

Our meta-analysis involved 92 studies including 21 178 cases and 25 157 controls. Statistical analysis involved individual cancer sites and the combined cancer risk. Association was tested under different genetic models.

Results:

We found a statistically significant association between the variant T allele and overall cancer risk in the worldwide population (for the TT vs CC model, OR=1.18 (1.07–1.31), P=0.002, I²=36%). Stratified analysis revealed that this association was largely attributed to the Caucasian ethnicity (for the TT vs CC model, OR=1.28 (1.12–1.46), P=0.0002, I²=1%). Stratification by tumour site showed significant association for bladder cancer in the worldwide population (for the TT vs CC model, OR=1.70 (1.17–2.46), P=0.005, I²=0%), and in the Asian population (for the TT vs CC model, 1.48 (1.14–1.93), P=0.003, I²=16%). Positive association was also found for gastric cancer in the worldwide population under the dominant model (OR=1.34 (1.09–1.65), P=0.006, I²=15%).

Conclusion:

Our results indicate that the C609T polymorphism of the NQO1 gene is an important genetic risk factor in cancer.

Association between polymorphisms in apoptotic genes and susceptibility for developing breast cancer in Syrian women

Apoptosis is a major protective mechanism against cancer. The tumor suppressor protein p53 is the central protein in the apoptotic pathway and was shown to harbor mutations in a considerable fraction of breast cancer tumors. The NQO1 was shown to act as a p53 stabilizer and was suggested to play an important role in the protection against carcinogenic catechol estrogens. Functional polymorphisms in TP53 and NQO1 were investigated in relation to breast cancer susceptibility in several studies, primarily involving Asian and Caucasian populations. The aim of the present study was to investigate TP53 and NQO1 polymorphisms and their combined effects with respect to breast cancer susceptibility in a Syrian study cohort. The study cohort consisted of 122 cases and 139 controls. The tetra-primer ARMS-PCR method was used to genotype three TP53 polymorphisms; namely, exon 4 G>C Arg72Pro, IVS3 16 bp Del/Ins, and MspI IVS6+62A>G, and NQO1 C609T (Pro187Ser) polymorphism. Association was tested under six genetic models. We found a significant association for the heterozygous Arg/Pro genotype when combined with heterozygosity for IVS3 16 bp Del/Ins and MspI IVS6+62A>G (OR = 2.05 (1.22-3.47), P = 0.006). No significant association was found for NQO1 C609T or its combinations with TP53 polymorphisms. Our results support an association for TP53 polymorphisms with breast cancer susceptibility in the Syrian population.

Prediction of Colorectal Cancer Risk Using a Genetic Risk Score: The Korean Cancer Prevention Study-II (KCPS-II)

Colorectal cancer (CRC) is among the leading causes of cancer deaths and can be caused by environmental factors as well as genetic factors. Therefore, we developed a prediction model of CRC using genetic risk scores (GRS) and evaluated the effects of conventional risk factors, including family history of CRC, in combination with GRS on the risk of CRC in Koreans. This study included 187 cases (men, 133; women, 54) and 976 controls (men, 554; women, 422). GRS were calculated with most significantly associated single-nucleotide polymorphism with CRC through a genomewide association study. The area under the curve (AUC) increased by 0.5% to 5.2% when either counted or weighted GRS was added to a prediction model consisting of age alone (AUC 0.687 for men, 0.598 for women) or age and family history of CRC (AUC 0.692 for men, 0.603 for women) for both men and women. Furthermore, the risk of CRC significantly increased for individuals with a family history of CRC in the highest quartile of GRS when compared to subjects without a family history of CRC in the lowest quartile of GRS (counted GRS odds ratio [OR], 47.9; 95% confidence interval [CI], 4.9 to 471.8 for men; OR, 22.3; 95% CI, 1.4 to 344.2 for women) (weighted GRS OR, 35.9; 95% CI, 5.9 to 218.2 for men; OR, 18.1, 95% CI, 3.7 to 88.1 for women). Our findings suggest that in Koreans, especially in Korean men, GRS improve the prediction of CRC when considered in conjunction with age and family history of CRC.

Vitamin D receptor polymorphisms and breast cancer risk in a high-incidence population: a pilot study

BACKGROUND

Marin County, CA has very high incidence of breast cancer. Traditional risk factors, such as those included in the Gail model, do not effectively stratify breast cancer in this population. This retrospective case-control pilot study evaluates DNA from volunteers from a previous Marin County breast cancer epidemiology study. A polyfactorial risk model (OncoVue; InterGenetics Incorporated) that incorporates 22 polymorphisms in 19 genes and 5 clinical risk factors was used to stratify risk in Marin County women.

STUDY DESIGN

DNA genotyping was performed on 164 Caucasian women diagnosed with primary breast cancer in Marin County from 1997 to 1999 and 174 age- and ethnicity-matched control subjects. Individual lifetime risks were determined using the polyfactorial risk model and genotype frequencies in women at elevated risk were compared with the overall genotypes.

RESULTS

The vitamin D receptor VDR ApaI A2/A2 (rs7975232) homozygous polymorphism was present in high frequency in elevated-risk women. Sixty-four percent of elevated-risk women had the VDR Apa1 A2/A2 genotype compared with only 34% in the overall study, a statistically significant 1.9-fold difference (p = 0.0003). VDR Apa1 A2/a1 and a1/a1 genotypes were also present, but in lower frequencies.

CONCLUSIONS

The high frequency of the VDR Apa1 A2/A2 homozygous polymorphism in women designated as elevated risk for breast cancer by the polyfactorial risk model might be related to the high incidence rates of breast cancer in Marin County, CA. Vitamin D supplementation could modify risk of breast cancer in this population.

SNP-set analysis replicates acute lung injury genetic risk factors

BACKGROUND

We used a gene - based replication strategy to test the reproducibility of prior acute lung injury (ALI) candidate gene associations.

METHODS

We phenotyped 474 patients from a prospective severe trauma cohort study for ALI. Genomic DNA from subjects' blood was genotyped using the IBC chip, a multiplex single nucleotide polymorphism (SNP) array. Results were filtered for 25 candidate genes selected using prespecified literature search criteria and present on the IBC platform. For each gene, we grouped SNPs according to haplotype blocks and tested the joint effect of all SNPs on susceptibility to ALI using the SNP-set kernel association test. Results were compared to single SNP analysis of the candidate SNPs. Analyses were separate for genetically determined ancestry (African or European).

RESULTS

We identified 4 genes in African ancestry and 2 in European ancestry trauma subjects which replicated their associations with ALI. Ours is the first replication of IL6, IL10, IRAK3, and VEGFA associations in non-European populations with ALI. Only one gene - VEGFA - demonstrated association with ALI in both ancestries, with distinct haplotype blocks in each ancestry driving the association. We also report the association between trauma-associated ALI and NFKBIA in European ancestry subjects.

CONCLUSIONS

Prior ALI genetic associations are reproducible and replicate in a trauma cohort. Kernel - based SNP-set analysis is a more powerful method to detect ALI association than single SNP analysis, and thus may be more useful for replication testing. Further, gene-based replication can extend candidate gene associations to diverse ethnicities.

Assessing the joint effect of population stratification and sample selection in studies of gene-gene (environment) interactions

BACKGROUND

It is well known that the presence of population stratification (PS) may cause the usual test in case-control studies to produce spurious gene-disease associations. However, the impact of the PS and sample selection (SS) is less known. In this paper, we provide a systematic study of the joint effect of PS and SS under a more general risk model containing genetic and environmental factors. We provide simulation results to show the magnitude of the bias and its impact on type I error rate of the usual chi-square test under a wide range of PS level and selection bias.

RESULTS

The biases to the estimation of main and interaction effect are quantified and then their bounds derived. The estimated bounds can be used to compute conservative p-values for the association test. If the conservative p-value is smaller than the significance level, we can safely claim that the association test is significant regardless of the presence of PS or not, or if there is any selection bias. We also identify conditions for the null bias. The bias depends on the allele frequencies, exposure rates, gene-environment odds ratios and disease risks across subpopulations and the sampling of the cases and controls.

CONCLUSION

Our results show that the bias cannot be ignored even the case and control data were matched in ethnicity. A real example is given to illustrate application of the conservative p-value. These results are useful to the genetic association studies of main and interaction effects.

Powerful tests for detecting a gene effect in the presence of possible gene-gene interactions using garrote kernel machines

We propose in this article a powerful testing procedure for detecting a gene effect on a continuous outcome in the presence of possible gene-gene interactions (epistasis) in a gene set, e.g., a genetic pathway or network. Traditional tests for this purpose require a large number of degrees of freedom by testing the main effect and all the corresponding interactions under a parametric assumption, and hence suffer from low power. In this article, we propose a powerful kernel machine based test. Specifically, our test is based on a garrote kernel method and is constructed as a score test. Here, the term garrote refers to an extra nonnegative parameter that is multiplied to the covariate of interest so that our score test can be formulated in terms of this nonnegative parameter. A key feature of the proposed test is that it is flexible and developed for both parametric and nonparametric models within a unified framework, and is more powerful than the standard test by accounting for the correlation among genes and hence often uses a much smaller degrees of freedom. We investigate the theoretical properties of the proposed test. We evaluate its finite sample performance using simulation studies, and apply the method to the Michigan prostate cancer gene expression data.

Modeling interactions with known risk loci-a Bayesian model averaging approach

Genome-wide association studies (GWAS) are now clearly established as a powerful method for detecting loci involved in the etiology of common complex diseases. Most diseases and traits studied using the GWAS approach now have several loci that have been shown to be convincingly replicated. It is generally the case that these loci have been identified using single locus association scans of genotyped or imputed SNPs and very few loci have been identified by taking interactions into account. We propose a method that assesses the evidence of association at each SNP by modeling the effect of the locus in combination with other known loci. We use a Bayesian model averaging approach that combines the evidence across several different plausible models for the way in which the loci interact. We show that the method has good power both when the association is the result of marginal effects only, and when interaction with a known locus occurs. The method is implemented as an option in the program SNPTEST.

Identification of interacting genes in genome-wide association studies using a model-based two-stage approach

In this paper, we propose a two-stage approach based on 17 biologically plausible models to search for two-locus combinations that have significant joint effects on the disease status in genome-wide association (GWA) studies. In the two-stage analyses, we only test two-locus joint effects of SNPs that show modest marginal effects. We use simulation studies to compare the power of our two-stage analysis with a single-marker analysis and a two-stage analysis by using a full model. We find that for most plausible interaction effects, our two-stage analysis can dramatically increase the power to identify two-locus joint effects compared to a single-marker analysis and a two-stage analysis based on the full model. We also compare two-stage methods with one-stage methods. Our simulation results indicate that two-stage methods are more powerful than one-stage methods. We applied our two-stage approach to a GWA study for identifying genetic factors that might be relevant in the pathogenesis of sporadic Amyotrophic Lateral Sclerosis (ALS). Our proposed two-stage approach found that two SNPs have significant joint effect on sporadic ALS while the single-marker analysis and the two-stage analysis based on the full model did not find any significant results.

Evidence on the association between NQO1 Pro187Ser polymorphism and breast cancer risk in the current studies: a meta-analysis

Several molecular epidemiological studies were conducted in recent years to evaluate the association between NQO1 Pro187Ser polymorphism and breast cancer risk in diverse populations. However, the results remain conflicting rather than conclusive. This meta-analysis on 3177 cases with breast cancer and 4038 controls from seven published case-control studies showed that the 187Ser allele was not associated with a significantly increased risk of breast cancer (Ser versus Pro: P = 0.33, OR = 1.08, 95% CI = 0.92-1.28; Ser/Ser versus Pro/Pro: P = 0.58, OR = 1.16, 95% CI = 0.68-2.00; Ser/Ser versus Pro/Ser + Pro/Pro: P = 0.62, OR = 1.14, 95% CI = 0.68-1.90; Ser/Ser + Pro/Ser versus Pro/Pro: P = 0.30, OR = 1.07, 95% CI = 0.94-1.22). In the stratified analysis by ethnicity, we found that the Pro187Ser polymorphism was associated with increased breast cancer risk in Caucasians in the additive genetic model and dominant genetic model (P = 0.03, OR = 1.13, 95% CI = 1.01-1.26; P = 0.03, OR = 1.15, 95% CI = 1.01-1.30, respectively), whereas no significant in Asians (P = 0.44, OR = 0.94, 95% CI = 0.80-1.10) and postmenopausal women (P = 0.99, OR = 1.00, 95% CI = 0.84-1.19). The results suggest that NQO1 Pro187Ser polymorphism may contribute to breast cancer development in Caucasians.

No association between CYP17 T-34C polymorphism and breast cancer risk: a meta-analysis involving 58,814 subjects

Breast cancer is one of the most common malignant tumors worldwide. To date, many articles have evaluated the association between Cytochrome P450c17 (CYP17) T-34C polymorphism and breast cancer risk. However, the results remain inconclusive. In order to derive a more precise estimation of the association, a meta-analysis was performed in this study. By searching Medline, ISI Web of Knowledge, Cochrane, ScienceDirect, EBSCO, CNKI, and SinoMed databases, 43 studies including 26,008 cases and 32,806 controls were collected for CYP17 T-34C polymorphism. Crude ORs with 95% CIs were used to assess the strength of association between CYP17 T-34C polymorphism and breast cancer risk. The pooled ORs were performed for codominant model, dominant model, and recessive model, respectively. Overall, no significant associations between CYP17 T-34C polymorphism and breast cancer susceptibility were found for TT versus CC (OR = 0.96; 95% CI: 0.89-1.05), TC versus CC (OR = 0.97; 95% CI: 0.89-1.06), TT + TC versus CC (OR = 0.97; 95% CI: 0.89-1.05) and TT versus TC + CC (OR = 0.98; 95% CI: 0.93-1.03). In the stratified analysis by ethnicity, menopausal status, and sources of controls, significant associations were still not detected in all genetic models. In conclusion, this meta-analysis strongly suggests that CYP17 T-34C polymorphism is not associated with breast cancer risk.

Identifying gene interaction enrichment for gene expression data

Gene set analysis allows the inclusion of knowledge from established gene sets, such as gene pathways, and potentially improves the power of detecting differentially expressed genes. However, conventional methods of gene set analysis focus on gene marginal effects in a gene set, and ignore gene interactions which may contribute to complex human diseases. In this study, we propose a method of gene interaction enrichment analysis, which incorporates knowledge of predefined gene sets (e.g. gene pathways) to identify enriched gene interaction effects on a phenotype of interest. In our proposed method, we also discuss the reduction of irrelevant genes and the extraction of a core set of gene interactions for an identified gene set, which contribute to the statistical variation of a phenotype of interest. The utility of our method is demonstrated through analyses on two publicly available microarray datasets. The results show that our method can identify gene sets that show strong gene interaction enrichments. The enriched gene interactions identified by our method may provide clues to new gene regulation mechanisms related to the studied phenotypes. In summary, our method offers a powerful tool for researchers to exhaustively examine the large numbers of gene interactions associated with complex human diseases, and can be a useful complement to classical gene set analyses which only considers single genes in a gene set.

Genome-wide association reveals three SNPs associated with sporadic amyotrophic lateral sclerosis through a two-locus analysis

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a fatal, degenerative neuromuscular disease characterized by a progressive loss of voluntary motor activity. About 95% of ALS patients are in "sporadic form"-meaning their disease is not associated with a family history of the disease. To date, the genetic factors of the sporadic form of ALS are poorly understood.

METHODS

We proposed a two-stage approach based on seventeen biological plausible models to search for two-locus combinations that have significant joint effects to the disease in a genome-wide association study (GWAS). We used a two-stage strategy to reduce the computational burden associated with performing an exhaustive two-locus search across the genome. In the first stage, all SNPs were screened using a single-marker test. In the second stage, all pairs made from the 1000 SNPs with the lowest p-values from the first stage were evaluated under each of the 17 two-locus models.

RESULTS

we performed the two-stage approach on a GWAS data set of sporadic ALS from the SNP Database at the NINDS Human Genetics Resource Center DNA and Cell Line Repository http://ccr.coriell.org/ninds/. Our two-locus analysis showed that two two-locus combinations--rs4363506 (SNP1) and rs3733242 (SNP2), and rs4363506 and rs16984239 (SNP3) -- were significantly associated with sporadic ALS. After adjusting for multiple tests and multiple models, the combination of SNP1 and SNP2 had a p-value of 0.032 under the Dom intersection Dom epistatic model; SNP1 and SNP3 had a p-value of 0.042 under the Dom x Dom multiplicative model.

CONCLUSION

The proposed two-stage analytical method can be used to search for joint effects of genes in GWAS. The two-stage strategy decreased the computational time and the multiple testing burdens associated with GWAS. We have also observed that the loci identified by our two-stage strategy can not be detected by single-locus tests.

Polymorphisms in genes involved in sex hormone metabolism, estrogen plus progestin hormone therapy use, and risk of postmenopausal breast cancer

Hormone therapy, estrogen plus progestin (E+P) particularly, is associated with increased risk of breast cancer. Functionally relevant polymorphisms in genes involved in sex hormone metabolism may alter exposure to exogenous sex hormones and affect risk of postmenopausal breast cancer. We evaluated associations of common polymorphisms in genes involved in estrogen and/or progesterone metabolism, E+P use, and their interactions with breast cancer risk in a case-control study of postmenopausal women (324 cases; 651 controls) nested within the VITAL cohort. None of the polymorphisms studied was, by itself, statistically significantly associated with breast cancer risk. E+P use was significantly associated with increased breast cancer risk (> or =10 years versus never; odds ratio, 1.9; 95% confidence interval, 1.3-2.8; P(trend) = 0.0002). Statistically significant interactions between CYP1A1 Ile(462)Val (P(interaction) = 0.04), CYP1A1 MspI (P(interaction) = 0.003), CYP1B1 Val(432)Leu (P(interaction) = 0.007), CYP1B1 Asn(453)Ser (P(interaction) = 0.04) and PGR Val(660)Leu (P(interaction) = 0.01), and E+P use were observed. The increased risk of breast cancer associated with E+P use was greater among women with at least one rare allele of the CYP1A1 Ile(462)Val, CYP1A1 MspI, CYP1B1 Asn(453)Ser, and PGR Val(660)Leu polymorphisms than among women homozygous for the common allele of these polymorphisms. Risk of breast cancer increased little with increasing years of E+P use among women with at least one CYP1B1 Val(432) allele; a large increase in risk was seen among women homozygous for CYP1B1 Leu(432). Our results support the hypothesis that specific polymorphisms in genes involved in sex hormone metabolism may modify the effect of E+P use on breast cancer risk.

An ensemble learning approach jointly modeling main and interaction effects in genetic association studies

Complex diseases are presumed to be the results of interactions of several genes and environmental factors, with each gene only having a small effect on the disease. Thus, the methods that can account for gene-gene interactions to search for a set of marker loci in different genes or across genome and to analyze these loci jointly are critical. In this article, we propose an ensemble learning approach (ELA) to detect a set of loci whose main and interaction effects jointly have a significant association with the trait. In the ELA, we first search for "base learners" and then combine the effects of the base learners by a linear model. Each base learner represents a main effect or an interaction effect. The result of the ELA is easy to interpret. When the ELA is applied to analyze a data set, we can get a final model, an overall P-value of the association test between the set of loci involved in the final model and the trait, and an importance measure for each base learner and each marker involved in the final model. The final model is a linear combination of some base learners. We know which base learner represents a main effect and which one represents an interaction effect. The importance measure of each base learner or marker can tell us the relative importance of the base learner or marker in the final model. We used intensive simulation studies as well as a real data set to evaluate the performance of the ELA. Our simulation studies demonstrated that the ELA is more powerful than the single-marker test in all the simulation scenarios. The ELA also outperformed the other three existing multi-locus methods in almost all cases. In an application to a large-scale case-control study for Type 2 diabetes, the ELA identified 11 single nucleotide polymorphisms that have a significant multi-locus effect (P-value=0.01), while none of the single nucleotide polymorphisms showed significant marginal effects and none of the two-locus combinations showed significant two-locus interaction effects.

SNP and Mutation Analysis

Genetic variation and SNP analysis starts with generation of sequence-specific signal, followed by the collection of that signal. The final step is extensive data analysis, which starts with conversion of quantifiable raw data and ends up with identified SNPs, frequencies, and sometimes tissue-specific expression patterns (levels). In this chapter we describe and compare the mechanisms of signal generation of several representative SNP analysis platforms. DNA microarray no doubt has its advantage in applications involving the classification and identification of tumor classes, gene discovery, drug dependent transcription mechanisms, as well as prediction of drug response. PCR, xMAP, invader assay, mass spectrometry, and pyrosequencing, on the other hand, are alternative methods of genotyping employed following the large scale screening and discovery of genetic variations. In addition, they offer higher specificity and sensitivity in analysis of both genomic DNA, as well as RNA. By exploiting these technologies, correlative study of the effects of putative genetic variations on cells, tissue-specific and developmentally specific expression is possible. Of extreme value are the many forms of Mass Spectrometry in the areas of sensitive, early cancer diagnosis. Finally, microarray and xMAP are suitable for protein analysis. While protein array offers higher throughput, xMAP is more amendable to the native 3D structure of protein molecules.

Prediction of coronary heart disease risk using a genetic risk score: the Atherosclerosis Risk in Communities Study

Recent studies have evaluated whether incorporating nontraditional risk factors improves coronary heart disease (CHD) prediction models. This 1986-2001 US study aggregated the contribution of multiple single nucleotide polymorphisms into a genetic risk score (GRS) and assessed whether the GRS plus traditional risk factors predict CHD better than traditional risk factors alone. The Atherosclerosis Risk in Communities (ARIC) cohort was followed for a median of 13 years for CHD events (n = 1,452). Individuals were genotyped for 116 single nucleotide polymorphisms associated with CHD in multiple case-control studies. Single nucleotide polymorphisms nominally predicting incident CHD in the ARIC study were included in the GRS. The GRS was significantly associated with incident CHD in Blacks (hazard rate ratio = 1.20, 95% confidence interval: 1.11, 1.29) and Whites (hazard rate ratio = 1.10, 95% confidence interval: 1.06, 1.14) as well as in each tertile defined by the traditional cardiovascular risk score (p < or = 0.02). When receiver operating characteristic curves based on traditional risk factors were recalculated after the GRS was added, the increase in the area under the receiver operating characteristic curve was statistically significant for Blacks and suggestive of improved CHD prediction for Whites. This study demonstrates the concept of aggregating information from multiple single nucleotide polymorphisms into a risk score and indicates that it can improve prediction of incident CHD in the ARIC study.

Detection of gene x gene interactions in genome-wide association studies of human population data

Empirical evidence supporting the commonality of gene x gene interactions, coupled with frequent failure to replicate results from previous association studies, has prompted statisticians to develop methods to handle this important subject. Nonparametric methods have generated intense interest because of their capacity to handle high-dimensional data. Genome-wide association analysis of large-scale SNP data is challenging mathematically and computationally. In this paper, we describe major issues and questions arising from this challenge, along with methodological implications. Data reduction and pattern recognition methods seem to be the new frontiers in efforts to detect gene x gene interactions comprehensively. Currently, there is no single method that is recognized as the 'best' for detecting, characterizing, and interpreting gene x gene interactions. Instead, a combination of approaches with the aim of balancing their specific strengths may be the optimal approach to investigate gene x gene interactions in human data.

A statistical model for assessing genetic susceptibility as a risk factor in multifactorial diseases: lessons from occupational asthma

BACKGROUND

Incorporating the influence of genetic variation in the risk assessment process is often considered, but no generalized approach exists. Many common human diseases such as asthma, cancer, and cardiovascular disease are complex in nature, as they are influenced variably by environmental, physiologic, and genetic factors. The genetic components most responsible for differences in individual disease risk are thought to be DNA variants (polymorphisms) that influence the expression or function of mediators involved in the pathological processes.

OBJECTIVE

The purpose of this study was to estimate the combinatorial contribution of multiple genetic variants to disease risk.

METHODS

We used a logistic regression model to help estimate the joint contribution that multiple genetic variants would have on disease risk. This model was developed using data collected from molecular epidemiology studies of allergic asthma that examined variants in 16 susceptibility genes.

RESULTS

Based on the product of single gene variant odds ratios, the risk of developing asthma was assigned to genotype profiles, and the frequency of each profile was estimated for the general population. Our model predicts that multiple disease variants broaden the risk distribution, facilitating the identification of susceptible populations. This model also allows for incorporation of exposure information as an independent variable, which will be important for risk variants associated with specific exposures.

CONCLUSION

The present model provided an opportunity to estimate the relative change in risk associated with multiple genetic variants. This will facilitate identification of susceptible populations and help provide a framework to model the genetic contribution in probabilistic risk assessment.

Age-specific association of steroid hormone pathway gene polymorphisms with breast cancer risk

BACKGROUND

Breast cancer (BC) is a complex disease, and the incidence rates for BC increase with age. Both environmental factors and genetics have an impact on the risk of BC. Although the effects of environmental factors may vary with age, it has been assumed generally that the penetrance of single nucleotide polymorphisms (SNPs) is constant throughout life. In the current study, the results demonstrated that certain SNPs exhibit BC risk associations that vary considerably with age.

METHODS

SNPs in 12 steroid hormone pathway genes were investigated for associations with BC risk in white women who were enrolled in an age-matched, case-control (1:2 for cases and controls, respectively) study that consisted of a discovery set (n = 5000 women) and an independent validation set (n = 1583 women).

RESULTS

Significant age-related trends were identified and confirmed for SNPs in 4 genes associated with BC risk. The cytosine/cytosine (C/C) genotype of cytochrome P450 XIB2 (CYP11B2) was associated with decreased risk at younger ages (ages 30-44 years) but an increased risk at older ages (ages 55-69 years). The homozygous cytosine-guanine (CG/CG) genotype of uridine phosphorylase glycosyltransferase 1A7 (UGT1A7) was associated with increased risk at younger ages but decreased risk at older ages. Associations in cytochrome P450 19 (CYP19) and progesterone receptor (PGR) were confined to middle age (ages 45-54 years).

CONCLUSIONS

The identification of age-specific genetic associations may have profound implications for future etiologic studies of BC and for the use of SNP genotyping to accurately predict the risk of BC in women.

Risk Communication During Screening for Genomic Breast Cancer Susceptibility

Screening for genomic predisposition to diseases of adulthood is emerging in standard medical practice. Breast cancer predisposition testing provides a useful example for understanding the potential implications of risk-related screening in practice. Risk is an important and complex psychological construct that is understood differently both between scientific disciplines and between medical and lay persons. The process of establishing thresholds for classifying whether one's risk reaches levels warranting departure from standard preventive strategies is an important predictor of both physician feedback and patient interpretation of their risk. Messages about genomic breast cancer risk arising during the pedigree screening context are conveyed via explicit and implicit modes of communication. Best practices for communicating about genomic risk and the implications for health disparities related to screening for genomic susceptibility remain unknown. More work is urgently needed given the societal forces that are catalyzing population-based screening for genomic predisposition into the routine practice of preventive medicine.

Two-stage two-locus models in genome-wide association

Studies in model organisms suggest that epistasis may play an important role in the etiology of complex diseases and traits in humans. With the era of large-scale genome-wide association studies fast approaching, it is important to quantify whether it will be possible to detect interacting loci using realistic sample sizes in humans and to what extent undetected epistasis will adversely affect power to detect association when single-locus approaches are employed. We therefore investigated the power to detect association for an extensive range of two-locus quantitative trait models that incorporated varying degrees of epistasis. We compared the power to detect association using a single-locus model that ignored interaction effects, a full two-locus model that allowed for interactions, and, most important, two two-stage strategies whereby a subset of loci initially identified using single-locus tests were analyzed using the full two-locus model. Despite the penalty introduced by multiple testing, fitting the full two-locus model performed better than single-locus tests for many of the situations considered, particularly when compared with attempts to detect both individual loci. Using a two-stage strategy reduced the computational burden associated with performing an exhaustive two-locus search across the genome but was not as powerful as the exhaustive search when loci interacted. Two-stage approaches also increased the risk of missing interacting loci that contributed little effect at the margins. Based on our extensive simulations, our results suggest that an exhaustive search involving all pairwise combinations of markers across the genome might provide a useful complement to single-locus scans in identifying interacting loci that contribute to moderate proportions of the phenotypic variance.

Although there is growing appreciation that attempting to map genetic interactions in humans may be a fruitful endeavor, there is no consensus as to the best strategy for their detection, particularly in the case of genome-wide association where the number of potential comparisons is enormous. In this article, the authors compare the performance of four different search strategies to detect loci which interact in genome-wide association—a single-locus search, an exhaustive two-locus search, and two, two-stage procedures in which a subset of loci initially identified with single-locus tests are analyzed using a full two-locus model. Their results show that when loci interact, an exhaustive two-locus search across the genome is superior to a two-stage strategy, and in many situations can identify loci which would not have been identified solely using a single-locus search. Their findings suggest that an exhaustive search involving all pairwise combinations of markers across the genome may provide a useful complement to single-locus scans in identifying interacting loci that contribute to moderate proportions of the phenotypic variance.

A systematic analysis of disease-associated variants in the 3' regulatory regions of human protein-coding genes II: the importance of mRNA secondary structure in assessing the functionality of 3' UTR variants

In an attempt both to catalogue 3' regulatory region (3' RR)-mediated disease and to improve our understanding of the structure and function of the 3' RR, we have performed a systematic analysis of disease-associated variants in the 3' RRs of human protein-coding genes. We have previously analysed the variants that have occurred in two specific domains/motifs of the 3' untranslated region (3' UTR) as well as in the 3' flanking region. Here we have focused upon 83 known variants within the upstream sequence (USS; between the translational termination codon and the upstream core polyadenylation signal sequence) of the 3' UTR. To place these variants in their proper context, we first performed a comprehensive survey of known cis-regulatory elements within the USS and the mechanisms by which they effect post-transcriptional gene regulation. Although this survey supports the view that RNA regulatory elements function within the context of specific secondary structures, there are no general rules governing how secondary structure might exert its influence. We have therefore addressed this question by systematically evaluating both functional and non-functional (based upon in vitro reporter gene and/or electrophoretic mobility shift assay data) USS variant-containing sequences against known cis-regulatory motifs within the context of predicted RNA secondary structures. This has allowed us not only to establish a reliable and objective means to perform secondary structure prediction but also to identify consistent patterns of secondary structural change that could potentiate the discrimination of functional USS variants from their non-functional counterparts. The resulting rules were then used to infer potential functionality in the case of some of the remaining functionally uncharacterized USS variants, from their predicted secondary structures. This not only led us to identify further patterns of secondary structural change but also several potential novel cis-regulatory motifs within the 3' UTRs studied.

A testing framework for identifying susceptibility genes in the presence of epistasis

An efficient testing strategy called the "focused interaction testing framework" (FITF) was developed to identify susceptibility genes involved in epistatic interactions for case-control studies of candidate genes. In the FITF approach, likelihood-ratio tests are performed in stages that increase in the order of interaction considered. Joint tests of main effects and interactions are performed conditional on significant lower-order effects. A reduction in the number of tests performed is achieved by prescreening gene combinations with a goodness-of-fit chi2 statistic that depends on association among candidate genes in the pooled case-control group. Multiple testing is accounted for by controlling false-discovery rates. Simulation analysis demonstrated that the FITF approach is more powerful than marginal tests of candidate genes. FITF also outperformed multifactor dimensionality reduction when interactions involved additive, dominant, or recessive genes. In an application to asthma case-control data from the Children's Health Study, FITF identified a significant multilocus effect between the nicotinamide adenine dinucleotide (phosphate) reduced:quinone oxidoreductase gene (NQO1), myeloperoxidase gene (MPO), and catalase gene (CAT) (unadjusted P = .00026), three genes that are involved in the oxidative stress pathway. In an independent data set consisting primarily of African American and Asian American children, these three genes also showed a significant association with asthma status (P = .0008).