Choosing haplotype-tagging SNPS based on unphased genotype data using a preliminary sample of unrelated subjects with an example from the Multiethnic Cohort Study

Growth factor genes and change in mammographic density after stopping combined hormone therapy in the California Teachers Study

Background

The contribution of genetic polymorphisms to the large inter-individual variation in mammographic density (MD) changes following starting and stopping use of estrogen and progestin combined therapy (EPT) has not been well-studied. Previous studies have shown that circulating levels of insulin-like growth factors are associated with MD and cross-talk between estrogen signaling and growth factors is necessary for cell proliferation in the breast. We evaluated single nucleotide polymorphisms (SNPs) in growth factor genes in association with MD changes after women stop EPT use.

Methods

We genotyped 191 SNPs in 13 growth factor pathway genes in 284 non-Hispanic white California Teachers Study participants who previously used EPT and collected their mammograms before and after quitting EPT. Percent MD was assessed using a computer-assisted method. Change in percent MD was calculated by subtracting percent MD of an ‘off-EPT’ mammogram from percent MD of an ‘on-EPT’ (i.e. baseline) mammogram. We used multivariable linear regression analysis to investigate the association between SNPs and change in percent MD. We calculated P-values corrected for multiple testing within a gene (P_adj).

Results

Rs1983210 in INHA and rs35539615 in IGFBP1/3 showed the strongest associations. Per minor allele of rs1983210, the absolute change in percent MD after stopping EPT use decreased by 1.80% (a difference in absolute change in percent MD) (P_adj= 0.021). For rs35539615, change in percent MD increased by 1.79% per minor allele (P_adj= 0.042). However, after applying a Bonferroni correction for the number of genes tested, these associations were no longer statistically significant.

Conclusions

Genetic variation in growth factor pathway genes INHA and IGFBP1/3 may predict longitudinal MD change after women quit EPT. The observed differences in EPT-associated changes in percent MD in association with these genetic polymorphisms are modest but may be clinically significant considering that the magnitude of absolute increase in percent MD reported from large clinical trials of EPT ranged from 3% to 7%.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4981-6) contains supplementary material, which is available to authorized users.

Tagging SNP-set selection with maximum information based on linkage disequilibrium structure in genome-wide association studies

Motivation

Effective tagging single-nucleotide polymorphism (SNP)-set selection is crucial to SNP-set analysis in genome-wide association studies (GWAS). Most of the existing tagging SNP-set selection methods cannot make full use of the information hidden in common or rare variants associated diseases. It is noticed that some SNPs have overlapping genetic information owing to linkage disequilibrium (LD) structure between SNPs. Therefore, when testing the association between SNPs and disease susceptibility, it is sufficient to elect the representative SNPs (called tag SNP-set or tagSNP-set) with maximum information.

Results

It is proposed a new tagSNP-set selection method based on LD information between SNPs, namely TagSNP-Set with Maximum Information. Compared with classical SNP-set analytical method, our method not only has higher power, but also can minimize the number of selected tagSNPs and maximize the information provided by selected tagSNPs with less genotyping cost and lower time complexity.

Contact

hesicheng12@163.com.

Supplementary information

Supplementary data are available at Bioinformatics online.

Better ILP models for haplotype assembly

Background

The haplotype assembly problem for diploid is to find a pair of haplotypes from a given set of aligned Single Nucleotide Polymorphism (SNP) fragments (reads). It has many applications in association studies, drug design, and genetic research. Since this problem is computationally hard, both heuristic and exact algorithms have been designed for it. Although exact algorithms are much slower, they are still of great interest because they usually output significantly better solutions than heuristic algorithms in terms of popular measures such as the Minimum Error Correction (MEC) score, the number of switch errors, and the QAN50 score. Exact algorithms are also valuable because they can be used to witness how good a heuristic algorithm is. The best known exact algorithm is based on integer linear programming (ILP) and it is known that ILP can also be used to improve the output quality of every heuristic algorithm with a little decline in speed. Therefore, faster ILP models for the problem are highly demanded.

Results

As in previous studies, we consider not only the general case of the problem but also its all-heterozygous case where we assume that if a column of the input read matrix contains at least one 0 and one 1, then it corresponds to a heterozygous SNP site. For both cases, we design new ILP models for the haplotype assembly problem which aim at minimizing the MEC score. The new models are theoretically better because they contain significantly fewer constraints. More importantly, our experimental results show that for both simulated and real datasets, the new model for the all-heterozygous (respectively, general) case can usually be solved via CPLEX (an ILP solver) at least 5 times (respectively, twice) faster than the previous bests. Indeed, the running time can sometimes be 41 times better.

Conclusions

This paper proposes a new ILP model for the haplotype assembly problem and its all-heterozygous case, respectively. Experiments with both real and simulated datasets show that the new models can be solved within much shorter time by CPLEX than the previous bests. We believe that the models can be used to improve heuristic algorithms as well.

Association of Air Pollution Exposure and Interleukin-13 Haplotype with the Risk of Aggregate Bronchitic Symptoms in Children

Interleukin-13(IL-13) might play an important role in driving aggregate bronchitic symptoms pathogenesis. However, none of the studies assessed the interaction between air pollutants exposure and IL-13 gene on the risk of aggregate bronchitic symptoms in non-asthma children. To assess the independent and joint effects of the exposure to air pollution and IL-13 haplotypes on the risk of aggregate bronchitic symptoms, we conducted a cross-sectional study and focused on non-asthma children. The study population consisted of 2944 children. The effect of each air pollutant on the risk of aggregate bronchitic symptoms was estimated as odds ratios per interquartile range (IQR) change. In the multiple logistic regressions, adjusted for confounding factors, the risk of chronic phlegm was associated with PM_2.5 exposure (aOR, 1.59; 95% CI, 1.07–2.37 per 12.51 μg/m3 change), O₃ exposure (aOR, 1.54 95% CI, 1.05–2.27 per 8.28 ppb change) and SO₂ exposure (aOR, 1.19; 95% CI, 1.02–1.39 per 0.98 ppb change). Our study further provides the evidence that gene-environment interactions between IL-13 haplotype and O₃ exposure on chronic phlegm (95% CI for interaction, 1.01–1.38). Identifying children who are more sensitive to air pollution helps us to provide them an efficient prevention to avoid aggregate bronchitic symptoms.

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Limited studies explored the interactions between IL-13 gene and air pollutants exposure on the risk of bronchitic symptoms.

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Genetic susceptibility of IL-13 may interact with O₃ exposure causing the pathogenesis of bronchitic symptoms.

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Identifying children susceptible to air pollutants helps us to provide them an efficient prevention of bronchitic symptoms.

Genetic susceptibility may interact with specific environmental factors causing the pathogenesis of aggregate bronchitic symptoms. Limited studies have explored the interactions between Interleukin-13 (IL-13) gene and air pollutants exposure on the risk of aggregate bronchitic symptoms. Our study further provides the evidence that gene-environment interactions between IL-13 gene and O₃ exposure may play an important role in aggregate bronchitic symptoms. Identifying children who are more sensitive to air pollutants helps us to provide them an efficient prevention to avoid aggregate bronchitic symptoms.

New multilocus linkage disequilibrium measure for tag SNP selection

Numerous approaches have been proposed for selecting an optimal tag single-nucleotide polymorphism (SNP) set. Most of these approaches are based on linkage disequilibrium (LD). Classical LD measures, such as D′ and r2, are frequently used to quantify the relationship between two marker (pairwise) linkage disequilibria. Despite of their successful use in many applications, these measures cannot be used to measure the LD between multiple-marker. These LD measures need information about the frequencies of alleles collected from haplotype dataset. In this study, a cluster algorithm is proposed to cluster SNPs according to multilocus LD measure which is based on information theory. After that, tag SNPs are selected in each cluster optimized by the number of tag SNPs, prediction accuracy and so on. The experimental results show that this new LD measure can be directly applied to genotype dataset collected from the HapMap project, so that it saves the cost of haplotyping. More importantly, the proposed method significantly improves the efficiency and prediction accuracy of tag SNP selection.

Multi-SNP Haplotype Analysis Methods for Association Analysis

Haplotype analysis forms the basis of much of genetic association analysis using both related and unrelated individuals (we concentrate on unrelated). For example, haplotype analysis indirectly underlies the SNP imputation methods that are used for testing trait associations with known but unmeasured variants and for performing collaborative post-GWAS meta-analysis. This chapter is focused on the direct use of haplotypes in association testing. It reviews the rationale for haplotype-based association testing, discusses statistical issues related to haplotype uncertainty that affect the analysis, then gives practical guidance for testing haplotype-based associations with phenotype or outcome trait, first of candidate gene regions and then for the genome as a whole. Haplotypes are interesting for two reasons, first they may be in closer LD with a causal variant than any single measured SNP, and therefore may enhance the coverage value of the genotypes over single SNP analysis. Second, haplotypes may themselves be the causal variants of interest and some solid examples of this have appeared in the literature.This chapter discusses three possible approaches to incorporation of SNP haplotype analysis into generalized linear regression models: (1) a simple substitution method involving imputed haplotypes, (2) simultaneous maximum likelihood (ML) estimation of all parameters, including haplotype frequencies and regression parameters, and (3) a simplified approximation to full ML for case-control data.Examples of the various approaches for a haplotype analysis of a candidate gene are provided. We compare the behavior of the approximation-based methods and argue that in most instances the simpler methods hold up well in practice. We also describe the practical implementation of haplotype risk estimation genome-wide and discuss several shortcuts that can be used to speed up otherwise potentially very intensive computational requirements.

Polymorphisms in the 5' upstream regulatory region of p21(WAF1/CIP1) and susceptibility to oesophageal squamous cell carcinoma

This study aims to scan the 5'-upstream regulatory region of the p21 gene to identify all putative functional single nucleotide polymorphisms (SNPs) and to evaluate the contribution of p21 variants to oesophageal squamous cell carcinoma (ESCC) in the Chinese Han population. Common SNPs were identified, and both locus-based and haplotype-based association tests were used to evaluate the potential risk of these p21 gene polymorphisms for ESCC. Immunohistochemistry assay was further performed to detect the P21 protein expression in ESCC specimens. Twenty three SNPs were identified and seven Tagging SNPs were chosen to represent all 23 SNPs. Univariate analysis indicated that the rs3829963 C and the rs2395655 G alleles increased susceptibility to ESCC (OR = 1.606 and OR = 1.572, respectively). The rs3829963 C and rs2395655 G alleles, combined with cigarette smoking, could further increase the risk for ESCC (OR = 2.657 and OR = 2.828, respectively). Additionally, the rs2395655 G allele appeared to elevate the positive rate of P21 expression in ESCC tissues, as compared to the A allele. This report demonstrates for the first time that rs3829963 and rs2395655, in the promoter of the p21 gene are potentially functional, modulating susceptibility to ESCC among the high-risk cigarette-smoking Chinese population.

An efficient weighted tag SNP-set analytical method in genome-wide association studies

BACKGROUND

Single-nucleotide polymorphism (SNP)-set analysis in Genome-wide association studies (GWAS) has emerged as a research hotspot for identifying genetic variants associated with disease susceptibility. But most existing methods of SNP-set analysis are affected by the quality of SNP-set, and poor quality of SNP-set can lead to low power in GWAS.

RESULTS

In this research, we propose an efficient weighted tag-SNP-set analytical method to detect the disease associations. In our method, we first design a fast algorithm to select a subset of SNPs (called tag SNP-set) from a given original SNP-set based on the linkage disequilibrium (LD) between SNPs, then assign a proper weight to each of the selected tag SNP respectively and test the joint effect of these weighted tag SNPs. The intensive simulation results show that the power of weighted tag SNP-set-based test is much higher than that of weighted original SNP-set-based test and that of un-weighted tag SNP-set-based test. We also compare the powers of the weighted tag SNP-set-based test based on four types of tag SNP-sets. The simulation results indicate the method of selecting tag SNP-set impacts the power greatly and the power of our proposed method is the highest.

CONCLUSIONS

From the analysis of simulated replicated data sets, we came to a conclusion that weighted tag SNP-set-based test is a powerful SNP-set test in GWAS. We also designed a faster algorithm of selecting tag SNPs which include most of information of original SNP-set, and a better weighted function which can describe the status of each tag SNP in GWAS.

Hormone metabolism pathway genes and mammographic density change after quitting estrogen and progestin combined hormone therapy in the California Teachers Study

INTRODUCTION

Mammographic density (MD) is a strong biomarker of breast cancer risk. MD increases after women start estrogen plus progestin therapy (EPT) and decreases after women quit EPT. A large interindividual variation in EPT-associated MD change has been observed, but few studies have investigated genetic predictors of the EPT-associated MD change. Here, we evaluate the association between polymorphisms in hormone metabolism pathway genes and MD changes when women quit EPT.

METHODS

We collected mammograms before and after women quit EPT and genotyped 405 tagging single nucleotide polymorphisms (SNPs) in 30 hormone metabolism pathway genes in 284 non-Hispanic white participants of the California Teachers Study (CTS). Participants were ages 49 to 71 years at time of mammography taken after quitting EPT. We assessed percent MD using a computer-assisted method. MD change was calculated by subtracting MD of an 'off-EPT' mammogram from MD of an 'on-EPT' (that is baseline) mammogram. Linear regression analysis was used to investigate the SNP-MD change association, adjusting for the baseline 'on-EPT' MD, age and BMI at time of baseline mammogram, and time interval and BMI change between the two mammograms. An overall pathway and gene-level summary was obtained using the adaptive rank truncated product (ARTP) test. We calculated 'P values adjusted for correlated tests (P(ACT))' to account for multiple testing within a gene.

RESULTS

The strongest associations were observed for rs7489119 in SLCO1B1, and rs5933863 in ARSC. SLCO1B1 and ARSC are involved in excretion and activation of estrogen metabolites of EPT, respectively. MD change after quitting was 4.2% smaller per minor allele of rs7489119 (P = 0.0008; P(ACT) = 0.018) and 1.9% larger per minor allele of rs5933863 (P = 0.013; P(ACT) = 0.025). These individual SNP associations did not reach statistical significance when we further used Bonferroni correction to consider the number of tested genes. The pathway level summary ARTP P value was not statistically significant.

CONCLUSIONS

Data from this longitudinal study of EPT quitters suggest that genetic variation in two hormone metabolism pathway genes, SLCO1B1 and ARSC, may be associated with change in MD after women stop using EPT. Larger longitudinal studies are needed to confirm our findings.

Multi-marker-LD based genetic algorithm for tag SNP selection

Despite the advances in genotyping technologies which have led to large reduction in genotyping cost, the Tag SNP Selection problem remains an important problem for computational biologists and geneticists. Selecting the smallest subset of tag SNPs that can predict the other SNPs would considerably minimize the complexity of genome-wide or block-based SNP-disease association studies. These studies would lead to better diagnosis and treatment of diseases. In this work, we propose three variations of a genetic algorithm based on two-marker linkage disequilibrium, multi-marker linkage disequilibrium, and a third measure that we denote by prediction power. The performance of the three algorithms are compared with those of a recognized tag SNP selection algorithm using three different real data sets from the HapMap project. The results indicate that the multi-marker linkage disequilibrium based genetic algorithm yields better prediction accuracy.

Associations between CYP19A1 polymorphisms, Native American ancestry, and breast cancer risk and mortality: the Breast Cancer Health Disparities Study

The cytochrome p450 family 19 gene (CYP19A1) encodes for aromatase, which catalyzes the final step in estrogen biosynthesis and conversion of androgens to estrogens. Genetic variation in CYP19A1 is linked to higher circulating estrogen levels and increased aromatase expression. Using data from the Breast Cancer Health Disparities Study, a consortium of three population-based case-control studies in the United States (n = 3,030 non-Hispanic Whites; n = 2,893 Hispanic/Native Americans (H/NA) and Mexico (n = 1,810), we examined influence of 25 CYP19A1 tagging single-nucleotide polymorphisms (SNPs) on breast cancer risk and mortality, considering NA ancestry. Odds ratios (ORs) and 95 % confidence intervals (CIs) and hazard ratios estimated breast cancer risk and mortality. After multiple comparison adjustment, none of the SNPs were significantly associated with breast cancer risk or mortality. Two SNPs remained significantly associated with increased breast cancer risk in women of moderate to high NA ancestry (≥29 %): rs700518, ORGG 1.36, 95 % CI 1.11-1.67 and rs11856927, ORGG 1.35, 95 % CI 1.05-1.72. A significant interaction was observed for rs2470144 and menopausal status (p adj = 0.03); risk was increased in postmenopausal (ORAA 1.22, 95 % CI 1.05-1.14), but not premenopausal (ORAA 0.78, 95 % CI 0.64-0.95) women. The absence of an overall association with CYP19A1 and breast cancer risk is similar to previous literature. However, this analysis provides support that variation in CYP19A1 may influence breast cancer risk differently in women with moderate to high NA ancestry. Additional research is warranted to investigate the how variation in an estrogen-regulating gene contributes to racial/ethnic disparities in breast cancer.

A novel haplotype within C-reactive protein gene influences CRP levels and coronary heart disease risk in Northwest Indians

According to several epidemiological and clinical studies, the concentration of C-reactive protein (CRP) in blood is associated with the risk of coronary heart disease (CHD). However, these studies are limited in high incidence and prevalence area of North-West India. The present case control study investigated the contribution of three relevant CRP single nucleotide polymorphisms: -717A>G located in the promoter region (rs2794521), +1059G>C on exon2 (rs1800947) and +1444C>T in the 3' UTR (rs1130864) in 180 angiographically verified CHD cases and 175 control subjects. Minor allele frequencies (G, C and T) of rs2794521, rs1800947 and rs1130864 are observed to be 21.1, 11.7, 29.4 and 11.4, 10.0, 19.7 % in CHD cases and controls respectively. AA genotype of -717A>G and TT genotype of +1444C>T were significantly associated (P = 0.02 & 0.03 respectively) with the risk of CHD whereas, +1059G and +1444T were found to be strongly related (P = 0.023 & P = 0.008 respectively) with multivariable adjusted CRP levels. AGT Haplotype was significantly associated with the adjusted CRP levels (P < 0.05). Disease association analysis revealed that haplotype AGT influences CHD risk (OR 2.4, 95 % CI 1.23-4.84, P = 0.006) which exacerbates after correcting the confounding effects of risk variables (OR 2.5, 95 % CI 1.27-4.99, P = 0.004). With the global index of Akaike information criterion, it has been observed that the carrying each single unit of this susceptibility haplotype increases CHD risk by a value of 2.41 ± 0.439 (β ± SE) in the recessive mode.

Association of the common genetic polymorphisms and haplotypes of the chymase gene with left ventricular mass in male patients with symptomatic aortic stenosis

We investigated the association between polymorphisms and haplotypes of the chymase 1 gene (CMA1) and the left ventricular mass index (LVM/BSA) in a large cohort of patients with aortic stenosis (AS). Additionally, the gender differences in cardiac remodeling and hypertrophy were analyzed. The genetic background may affect the myocardial response to pressure overload. In human cardiac tissue, CMA1 is involved in angiotensin II production and TGF-β activation, which are two major players in the pathogenesis of hypertrophy and fibrosis. Preoperative echocardiographic data from 648 patients with significant symptomatic AS were used. The LVM/BSA was significantly lower (p<0.0001), but relative wall thickness (RWT) was significantly higher (p = 0.0009) in the women compared with the men. The haplotypes were reconstructed using six genotyped polymorphisms: rs5248, rs4519248, rs1956932, rs17184822, rs1956923, and rs1800875. The haplotype h1.ACAGGA was associated with higher LVM/BSA (p = 9.84×10⁻⁵), and the haplotype h2.ATAGAG was associated with lower LVM/BSA (p = 0.0061) in men, and no significant differences were found in women. Two polymorphisms within the promoter region of the CMA1 gene, namely rs1800875 (p = 0.0067) and rs1956923 (p = 0.0015), influenced the value of the LVM/BSA in males. The polymorphisms and haplotypes of the CMA1 locus are associated with cardiac hypertrophy in male patients with symptomatic AS. Appropriate methods for the indexation of heart dimensions revealed substantial sex-related differences in the myocardial response to pressure overload.

Imputation without doing imputation: a new method for the detection of non-genotyped causal variants

Genome-wide association studies allow detection of non-genotyped disease-causing variants through testing of nearby genotyped SNPs. This approach may fail when there are no genotyped SNPs in strong LD with the causal variant. Several genotyped SNPs in weak LD with the causal variant may, however, considered together, provide equivalent information. This observation motivates popular but computationally intensive approaches based on imputation or haplotyping. Here we present a new method and accompanying software designed for this scenario. Our approach proceeds by selecting, for each genotyped "anchor" SNP, a nearby genotyped "partner" SNP, chosen via a specific algorithm we have developed. These two SNPs are used as predictors in linear or logistic regression analysis to generate a final significance test. In simulations, our method captures much of the signal captured by imputation, while taking a fraction of the time and disc space, and generating a smaller number of false-positives. We apply our method to a case/control study of severe malaria genotyped using the Affymetrix 500K array. Previous analysis showed that fine-scale sequencing of a Gambian reference panel in the region of the known causal locus, followed by imputation, increased the signal of association to genome-wide significance levels. Our method also increases the signal of association from P ≈ 2 × 10⁻⁶ to P ≈ 6 × 10⁻¹¹. Our method thus, in some cases, eliminates the need for more complex methods such as sequencing and imputation, and provides a useful additional test that may be used to identify genetic regions of interest.

Genetic variants in MUC4 gene are associated with lung cancer risk in a Chinese population

Mucin MUC4, which is encoded by the MUC4 gene, plays an important role in epithelial cell proliferation and differentiation. Aberrant MUC4 overexpression is associated with invasive tumor proliferation and poor outcome in epithelial cancers. Collectively, the existing evidence suggests that MUC4 has tumor-promoter functions. In this study, we performed a case-control study of 1,048 incident lung cancer cases and 1,048 age- and sex frequency-matched cancer-free controls in a Chinese population to investigate the role of MUC4 gene polymorphism in lung cancer etiology. We identified nine SNPs that were significantly associated with increased lung cancer risk (P = 0.0425 for rs863582, 0.0333 for rs842226, 0.0294 for rs842225, 0.0010 for rs2550236, 0.0149 for rs2688515, 0.0191 for rs 2641773, 0.0058 for rs3096337, 0.0077 for rs859769, and 0.0059 for rs842461 in an additive model). Consistent with these single-locus analysis results, the haplotype analyses revealed an adverse effect of the haplotype “GGC” of rs3096337, rs859769, and rs842461 on lung cancer. Both the haplotype and diplotype “CTGAGC” of rs863582, rs842226, rs2550236, rs842225, and rs2688515 had an adverse effect on lung cancer, which is also consistent with the single-locus analysis. Moreover, we observed statistically significant interactions for rs863582 and rs842461 in heavy smokers. Our results suggest that MUC4 gene polymorphisms and their interaction with smoking may contribute to lung cancer etiology.

Variations in bitter-taste receptor genes, dietary intake, and colorectal adenoma risk

Genetic variants in bitter-taste receptor genes have been hypothesized to negatively impact health outcomes and/or influence dietary intake and, consequently, could increase the risk of colorectal neoplasia. Using a case-control study of 914 colorectal adenoma cases/1188 controls, we explored associations among colorectal adenoma risk, dietary intake, and genetic variation in 3 bitter-taste receptor genes: TAS2R38 (rs713598, rs1726866, rs10246939), TAS2R16 (rs846672), and TAS2R50 (rs1376251). Analysis of covariance was conducted to detect trends in dietary intake across TAS2R genotypes/haplotypes. Odds ratios and 95% confidence intervals were estimated by logistic regression to test gene-adenoma risk associations. No significant associations were observed between the TAS2R38 PAV/PAV diplotype or the TAS2R16 (rs846672) polymorphism with the selected diet variables. We observed weak inverse associations between the TAS2R50 (rs1376251) C allele and dietary fiber and vegetable intake (Ps < 0.015). Odds ratios for adenoma risk were not significantly different from the null. Our findings do not support a link between these TAS2R genotypes/haplotypes and dietary intake that could impact colorectal adenoma risk. However, given the paucity of data, we cannot dismiss the possibility that these genes may influence colorectal adenoma risk in other ways, such as through impaired gastrointestinal function, particularly in subgroups of the population.

Functional variants at the 11q13 risk locus for breast cancer regulate cyclin D1 expression through long-range enhancers

Analysis of 4,405 variants in 89,050 European subjects from 41 case-control studies identified three independent association signals for estrogen-receptor-positive tumors at 11q13. The strongest signal maps to a transcriptional enhancer element in which the G allele of the best candidate causative variant rs554219 increases risk of breast cancer, reduces both binding of ELK4 transcription factor and luciferase activity in reporter assays, and may be associated with low cyclin D1 protein levels in tumors. Another candidate variant, rs78540526, lies in the same enhancer element. Risk association signal 2, rs75915166, creates a GATA3 binding site within a silencer element. Chromatin conformation studies demonstrate that these enhancer and silencer elements interact with each other and with their likely target gene, CCND1.

Androgen receptor genetic variants in male patients with ankylosing spondylitis in Taiwan

AIM

Ankylosing spondylitis (AS) is a chronic rheumatic disorder with gender differences. The aim of study was to investigate the association between polymorphisms of the androgen receptor (AR) gene and the susceptibility to AS in Taiwanese men of Han Chinese descent.

METHODS

We conducted a case-control study with 92 male AS patients and 108 healthy controls. Trinucleotide (CAG and GGC) repeats and seven single nucleotide polymorphisms (SNPs) rs962458, rs6152, rs1204038, rs5918757, rs2361634, rs6624304 and rs1337080 in the AR gene were genotyped.

RESULTS

We found that only one patient had polymorphic SNPs of the AR gene. None of the genotyped SNPs in the AR gene, originally found in Caucasians, was polymorphic in the Taiwanese men. Neither CAG nor GGC repeat lengths in the AR gene had a significant relationship with human leukocyte antigen (HLA)-B27 positivity or disease severity in AS.

CONCLUSION

There were no differences in CAG and GGC lengths in the AR gene between AS and the controls. None of the genotyped SNPs in AR gene are detected to be polymorphic in male Taiwanese, which indicates that the effect of AR gene on AS may be ethnic-specific and may be conserved in East Asians compared to Caucasian populations. Still, additional studies using large sets of subjects deserve further attention, since our sample size was small with limited statistical power and supporting evidence for association between the AR gene and AS risk in the Japanese population exists.

Variations in sex hormone metabolism genes, postmenopausal hormone therapy and risk of endometrial cancer

We investigated whether variants in sex steroid hormone metabolism genes modify the effect of hormone therapy (HT) on endometrial cancer risk in postmenopausal non-Hispanic white women. A nested case-control study was conducted within the California Teachers Study (CTS). We genotyped htSNPs in six genes involved in the hormone metabolism in 286 endometrial cancer cases and 488 controls. Odds ratio (OR) and 95% confidence interval (CI) were estimated for each haplotype using unconditional logistic regression, adjusting for age. The strongest interaction was observed between duration of estrogen therapy (ET) use and haplotype 1A in CYP11A1 (p(interaction) = 0.0027; p(interaction) = 0.010 after correcting for multiple testing within each gene). The OR for endometrial cancer per copy of haplotype 1A was 2.00 (95% CI: 1.05-3.96) for long-term ET users and 0.90 (95% CI: 0.69-1.18) for never users. The most significant interaction with estrogen-progestin therapy (EPT) was found for two haplotypes on CYP19A1 and EPT use (haplotype 4A, p(interaction) = 0.024 and haplotype 3B, p(interaction) = 0.043). However, neither this interaction, nor the ET or EPT interactions for any other genes, was statistically significant after correction for multiple testing. Variations in CYP11A1 may modify the effect of ET use on risk of postmenopausal endometrial cancer; however, larger studies are needed to explore these findings further.

Catechol O-methyltransferase pharmacogenomics and selective serotonin reuptake inhibitor response

We applied a systematic pharmacogenetic approach to investigate the role of genetic variation in the gene encoding catechol O-methyltransferase (COMT) in individual variation in selective serotonin reuptake inhibitor (SSRI) response among depressed patients. In all, 23 single-nucleotide polymorphisms (SNPs) in COMT were genotyped using DNA from the Sequenced Treatment Alternatives to Relieve Depression (STAR(*)D) study (N=1914). One SNP, rs13306278, located in the distal promoter region of COMT, showed significant association with remission in White non-Hispanic (WNH) subjects (P=0.038). Electromobility shift assay for rs13306278 showed alternation in the ability of the variant sequence to bind nuclear proteins. A replication study was performed using samples from the Mayo Clinic Pharmacogenetics Research Network Citalopram/Escitalopram Pharmacogenomic study (N=422) that demonstrated a similar trend for association. Our findings suggest that novel genetic markers in the COMT distal promoter may influence SSRI response phenotypes.

Multi-SNP haplotype analysis methods for association analysis

This chapter reviews the rationale for the use of haplotypes in association-based testing, discusses statistical issues related to haplotype uncertainty that complicate the analysis, then gives practical guidance for testing haplotype-based associations with phenotype or outcome trait, first of candidate gene regions and then for the genome as a whole. Haplotypes are interesting for two reasons: First, they may be in closer LD with a causal variant than any single measured SNP, and therefore may enhance the coverage value of the genotypes over single SNP analysis. Second, haplotypes may themselves be the causal variants of interest and some solid examples of this have appeared in the literature. This chapter discusses three possible approaches to incorporation of SNP haplotype analysis into generalized linear regression models: (1) a simple substitution method involving imputed haplotypes; (2) simultaneous maximum likelihood (ML) estimation of all parameters, including haplotype frequencies and regression parameters; and (3) a simplified approximation to full ML for case-control data. Examples of the various approaches for a haplotype analysis of a candidate gene are provided. We compare the behavior of the approximation-based methods and show that in most instances the simpler methods hold up well in practice. We also describe the practical implementation of genome-wide haplotype risk estimation and discuss several shortcuts that can be used to speed up otherwise potentially very intensive computational requirements.

Genetic and epigenetic variations in inducible nitric oxide synthase promoter, particulate pollution, and exhaled nitric oxide levels in children

BACKGROUND

Inducible nitric oxide synthase (iNOS; encoded by nitric oxide synthase isoform 2 [NOS2]) is the major enzyme for nitric oxide synthesis in airways. As such, measurement of fractional concentration of exhaled nitric oxide (Feno) provides an in vivo assessment of iNOS activity. Short-term exposure to air pollution, haplotypes, and DNA methylation in the NOS2 promoter has been associated independently with iNOS expression, Feno levels, or both.

OBJECTIVE

We aimed to examine the effects of ambient air pollutants, NOS2 promoter haplotypes, and NOS2 promoter methylation on Feno levels in children.

METHODS

We selected 940 participants in the Children's Health Study who provided buccal samples and had undergone Feno measurement on the same day. DNA methylation was measured with a bisulfite-PCR Pyrosequencing assay. Seven single nucleotide polymorphisms captured the haplotype diversity in the NOS2 promoter. Average particulate matter with an aerodynamic diameter of 2.5 μm or less (PM(2.5)) and 10 μm (PM(10)) or less and ozone and nitrogen dioxide levels 7 days before Feno measurement were estimated based on air pollution data obtained at central monitoring sites.

RESULTS

We found interrelated effects of PM(2.5), NOS2 promoter haplotypes, and iNOS methylation on Feno levels. Increased 7-day average PM(2.5) exposure was associated with lower iNOS methylation (P = .01). NOS2 promoter haplotypes were globally associated with NOS2 promoter methylation (P = 6.2 × 10(-8)). There was interaction among 1 common promoter haplotype, iNOS methylation level, and PM(2.5) exposure on Feno levels (P(interaction) = .00007).

CONCLUSION

Promoter variants in NOS2 and short-term PM(2.5) exposure affect iNOS methylation. This is one of the first studies showing contributions of genetic and epigenetic variations in air pollution-mediated phenotype expression.

Association of TNFSF8 polymorphisms with peripheral neutrophil count

OBJECTIVE

To investigate the association between 347 single-nucleotide polymorphisms within candidate genes of the tumor necrosis factor, interleukin 1 and interleukin 6 families with neutrophil count.

PATIENTS AND METHODS

Four hundred cases with heart failure after myocardial infarction (MI) were matched by age, sex, and date of incident MI to 694 controls (MI without post-MI heart failure). Both genotypes and neutrophil count at admission for incident MI were available in 314 cases and 515 controls.

RESULTS

We found significant associations between the TNFSF8 poly morphisms rs927374 (P=5.1 x 10(-5)) and rs2295800 (P=1.3 x 10(-4)) and neutrophil count; these single-nucleotide polymorphisms are in high linkage disequilibrium (r(2)=0.97). Associations persisted after controlling for clinical characteristics and were unchanged after adjusting for case-control status. For rs927374, the neutrophil count of GG homozygotes (7.6±5.1) was 16% lower than that of CC homozygotes (9.0±5.2).

CONCLUSION

The TNFSF8 polymorphisms rs927374 and rs2295800 were associated with neutrophil count. This finding suggests that post-MI inflammatory response is genetically modulated.

Genetic polymorphisms in AURKA and BRCA1 are associated with breast cancer susceptibility in a Chinese Han population

Centrosome defects can result in aneuploidy and genomic instability, and have important implications for breast cancer development. The Aurora-A and BRCA1 proteins interact and both are strongly involved in centrosome regulation. Genetic variants in these two genes may have an effect on breast cancer development. Here, we report a comprehensive single nucleotide polymorphism (SNP) and haplotype-tagging association study on these two genes in 1334 breast cancer cases and 1568 unaffected controls among the Chinese Han population. Apart from a missense SNP, rs2273535 (Phe31Ile), and a probable risk SNP, rs2064863, six htSNPs were analysed in three high-LD blocks of AURKA spanning from 10 kb upstream to 2 kb downstream of AURKA. For BRCA1, six htSNPs were analysed in a large high-LD region covering 98 kb (10 kb was extended to each end of BRCA1). The results showed that four SNPs in AURKA (data in recessive model, rs2273535: OR = 2.19, 95% CI = 1.03-4.66, p = 0.0422; rs2298016: OR = 0.38, 95% CI = 0.18-0.82, p = 0.0141; rs6024836: OR = 1.54, 95% CI = 1.18-2.00, p = 0.0014; rs10485805: OR = 0.68, 95% CI = 0.47-0.98, p = 0.0380) and one SNP in BRCA1 (rs3737559, dominant model OR = 1.35, 95% CI = 1.11-1.64, p = 0.0030) were associated with breast cancer susceptibility. After correction for multiple comparisons (FDR = 0.05), only rs6024836 and rs3737559 remained significant. Two haplotypes (CC of block 2, OR = 20.74, 95% CI = 4.35-98.88, p = 0.0001; GG of block 3, OR = 1.32, 95% CI = 1.12-1.56, p = 0.0010) and one diplotype (AG-GG of block 3, OR = 1.63, 95% CI = 1.18-2.26, p = 0.0031) within AURKA showed strong associations with breast cancer risk. One haplotype of BRCA1 (CTGTTG, OR = 1.30, 95% CI = 1.06-1.59, p = 0.0118) was also associated with breast cancer risk. However, women harbouring both at-risk genotypes of Aurora-A and BRCA1 were at a slightly increased risk compared with those harbouring either at-risk variant alone. Common genetic variants in the AURKA and BRCA1 genes may contribute to breast cancer development.

Association of specific genotypes in metastatic suppressor HTPAP with tumor metastasis and clinical prognosis in hepatocellular carcinoma

The phosphatidic acid phosphatase HTPAP has been defined as a metastatic suppressor of hepatocellular carcinoma (HCC), but little is known about its function or potential applications as a prognostic marker. In this study, we analyzed patterns of HTPAP genetic variation and gene expression in 864 patients who underwent HCC resection, assessing these patterns for correlations to tumor metastasis potential. Focusing on two tagSNPs that were selected (+357G/C and +1838A/G), we found that only the +357G/C genotype was significantly associated with HTPAP mRNA and protein expression levels and the probability of metastasis. In an independent cohort of 665 HCC patients, we determined that the +357G/C genotype was associated with shorter time to recurrence and overall survival. Together, these results indicated that the HTPAP tagSNP +357 GG+GC genotypes may influence HCC metastatic potential and clinical prognosis by down-regulating HTPAP expression. Extending these results, a global expression profiling analysis identified 41 genes including the pro-inflammatory genes IL-8 and TLR2 that were significantly overexpressed in the +357 GG+GC group, as possible coregulated markers with HTPAP. Together, our findings identify an HTPAP genotype and associated gene expression pattern that favors metastasis progression and that could be used to predict tumor metastasis and prognosis in HCC patients.

Common alleles in candidate susceptibility genes associated with risk and development of epithelial ovarian cancer

Common germline genetic variation in the population is associated with susceptibility to epithelial ovarian cancer. Microcell-mediated chromosome transfer and expression microarray analysis identified nine genes associated with functional suppression of tumorogenicity in ovarian cancer cell lines; AIFM2, AKTIP, AXIN2, CASP5, FILIP1L, RBBP8, RGC32, RUVBL1 and STAG3. Sixty-three tagging single nucleotide polymorphisms (tSNPs) in these genes were genotyped in 1,799 invasive ovarian cancer cases and 3,045 controls to look for associations with disease risk. Two SNPs in RUVBL1, rs13063604 and rs7650365, were associated with increased risk of serous ovarian cancer [HetOR = 1.42 (1.15-1.74) and the HomOR = 1.63 (1.10-1.42), p-trend = 0.0002] and [HetOR = 0.97 (0.80-1.17), HomOR = 0.74 (0.58-0.93), p-trend = 0.009], respectively. We genotyped rs13063604 and rs7650365 in an additional 4,590 cases and 6,031 controls from ten sites from the United States, Europe and Australia; however, neither SNP was significant in Stage 2. We also evaluated the potential role of tSNPs in these nine genes in ovarian cancer development by testing for allele-specific loss of heterozygosity (LOH) in 286 primary ovarian tumours. We found frequent LOH for tSNPs in AXIN2, AKTIP and RGC32 (64, 46 and 34%, respectively) and one SNP, rs1637001, in STAG3 showed significant allele-specific LOH with loss of the common allele in 94% of informative tumours (p = 0.015). Array comparative genomic hybridisation indicated that this nonrandom allelic imbalance was due to amplification of the rare allele. In conclusion, we show evidence for the involvement of a common allele of STAG3 in the development of epithelial ovarian cancer.

The association of polymorphisms in hormone metabolism pathway genes, menopausal hormone therapy, and breast cancer risk: a nested case-control study in the California Teachers Study cohort

Introduction

The female sex steroids estrogen and progesterone are important in breast cancer etiology. It therefore seems plausible that variation in genes involved in metabolism of these hormones may affect breast cancer risk, and that these associations may vary depending on menopausal status and use of hormone therapy.

Methods

We conducted a nested case-control study of breast cancer in the California Teachers Study cohort. We analyzed 317 tagging single nucleotide polymorphisms (SNPs) in 24 hormone pathway genes in 2746 non-Hispanic white women: 1351 cases and 1395 controls. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated by fitting conditional logistic regression models using all women or subgroups of women defined by menopausal status and hormone therapy use. P values were adjusted for multiple correlated tests (P_ACT).

Results

The strongest associations were observed for SNPs in SLCO1B1, a solute carrier organic anion transporter gene, which transports estradiol-17β-glucuronide and estrone-3-sulfate from the blood into hepatocytes. Ten of 38 tagging SNPs of SLCO1B1 showed significant associations with postmenopausal breast cancer risk; 5 SNPs (rs11045777, rs11045773, rs16923519, rs4149057, rs11045884) remained statistically significant after adjusting for multiple testing within this gene (P_ACT = 0.019-0.046). In postmenopausal women who were using combined estrogen-progestin therapy (EPT) at cohort enrollment, the OR of breast cancer was 2.31 (95% CI = 1.47-3.62) per minor allele of rs4149013 in SLCO1B1 (P = 0.0003; within-gene P_ACT = 0.002; overall P_ACT = 0.023). SNPs in other hormone pathway genes evaluated in this study were not associated with breast cancer risk in premenopausal or postmenopausal women.

Conclusions

We found evidence that genetic variation in SLCO1B1 is associated with breast cancer risk in postmenopausal women, particularly among those using EPT.

POMC and TP53 genetic variability and risk of basal cell carcinoma of skin: Interaction between host and genetic factors

BACKGROUND

Basal cell carcinoma (BCC) of the skin is the most common neoplasm among the Caucasian population of the western world. Ultraviolet (UV) radiation-induced p53 activation promotes cutaneous pigmentation by increasing transcriptional activity of pro-opiomelanocortin (POMC) in the skin. Induction of POMC/α-melanocyte-stimulating hormone (α-MSH) activates the melanocortin 1 receptor (MC1R), resulting in skin pigmentation. The tumor suppressor p53 is a key player in stress responses that preserve genomic stability, responding to a variety of insults including DNA damage, hypoxia, metabolic stress and oncogene activation. Malfunction of the p53 pathway is an almost universal hallmark of human tumors. Polymorphisms in the gene encoding p53 (TP53) alter its transcriptional activity, which in turn may influence the UV radiation-induced tanning response.

OBJECTIVE

The aim of the present work is to test association between POMC and TP53 genetic variability, the possible interplay with host factors and the risk of basal cell carcinoma of skin.

METHODS

We covered the variability of the two genes we used 17 tagging polymorphisms in 529 BCC cases and 532 healthy controls. We have also tested the possible interactions between the genetic variants and three known risk factors for BCC: skin complexion, sun effect and skin response to sun exposure.

RESULTS

We did not observe any statistically significant association between SNPs in these two genes and BCC risk overall, nor interactions of SNPs with known BCC risk factors. However we found that, in the group of subjects with lower sun exposure, carriers of one copy of the C allele of the TP53 SNP rs12951053 had a decreased risk of BCC (OR=0.28, 95% CI 0.12-0.62, P=0.002).

CONCLUSIONS

We have observed that the interplay of an environmental risk factor and one polymorphism in TP53 gene could modulate the risk of BCC.

The A/G allele of rs16906252 predicts for MGMT methylation and is selectively silenced in premalignant lesions from smokers and in lung adenocarcinomas

PURPOSE

To address the association between sequence variants within the MGMT (O(6)-methylguanine-DNA methyltransferase) promoter-enhancer region and methylation of MGMT in premalignant lesions from smokers and lung adenocarcinomas, their biological effects on gene regulation, and targeting MGMT for therapy.

EXPERIMENTAL DESIGN

Single nucleotide polymorphisms (SNP) identified through sequencing a 1.9 kb fragment 5' of MGMT were examined in relation to MGMT methylation in 169 lung adenocarcinomas and 1,731 sputum samples from smokers. The effect of promoter haplotypes on MGMT expression was tested using a luciferase reporter assay and cDNA expression analysis along with allele-specific sequencing for methylation. The response of MGMT methylated lung cancer cell lines to the alkylating agent temozolomide (TMZ) was assessed.

RESULTS

The A allele of rs16906252 and the haplotype containing this SNP were strongly associated with increased risk for MGMT methylation in adenocarcinomas (ORs ≥ 94). This association was observed to a lesser extent in sputum samples in both smoker cohorts. The A allele was selectively methylated in primary lung tumors and cell lines heterozygous for rs16906252. With the most common haplotype as the reference, a 20 to 41% reduction in promoter activity was seen for the haplotype carrying the A allele that correlated with lower MGMT expression. The sensitivity of lung cancer cell lines to TMZ was strongly correlated with levels of MGMT methylation and expression.

CONCLUSIONS

These studies provide strong evidence that the A allele of a MGMT promoter-enhancer SNP is a key determinant for MGMT methylation in lung carcinogenesis. Moreover, TMZ treatment may benefit a subset of lung cancer patients methylated for MGMT.

Genetic variations in nitric oxide synthase and arginase influence exhaled nitric oxide levels in children

BACKGROUND

Exhaled nitric oxide (FeNO) is a biomarker of airway inflammation. In the nitric oxide (NO) synthesis pathway, nitric oxide synthases (encoded by NOS1, NOS2A, and NOS3) and arginases (encoded by ARG1 and ARG2) compete for L-arginine. Although FeNO levels are higher in children with asthma/allergy, influence of these conditions on the relationships between variations in these genes and FeNO remains unknown. The aims of the study were to evaluate the role of genetic variations in nitric oxide synthases and arginases on FeNO in children and to assess the influence of asthma and respiratory allergy on these genetic associations.

METHODS

Among children (6-11 years) who participated in the southern California Children's Health Study, variations in these five genetic loci were characterized by tagSNPs. FeNO was measured in two consecutive years (N = 2298 and 2515 in Years 1 and 2, respectively). Repeated measures analysis of variance was used to evaluate the associations between these genetic variants and FeNO.

RESULTS

Sequence variations in the NOS2A and ARG2 loci were globally associated with FeNO (P = 0.0002 and 0.01, respectively). The ARG2 association was tagged by intronic variant rs3742879 with stronger association with FeNO in asthmatic children (P-interaction = 0.01). The association of a NOS2A promoter haplotype with FeNO varied significantly by rs3742879 genotypes and by asthma.

CONCLUSION

Variants in the NO synthesis pathway genes jointly contribute to differences in FeNO concentrations. Some of these genetic influences were stronger in children with asthma. Further studies are required to confirm our findings.

Betaine-homocysteine methyltransferase: human liver genotype-phenotype correlation

Betaine-homocysteine methyltransferase (BHMT) catalyzes the remethylation of homocysteine. BHMT is highly expressed in the human liver. In the liver, BHMT catalyzes up to 50% of homocysteine metabolism. Understanding the relationship between BHMT genetic polymorphisms and function might increase our understanding of the role of this reaction in homocysteine remethylation and in S-adenosylmethionine-dependent methylation. To help achieve those goals, we measured levels of BHMT enzyme activity and immunoreactive protein in 268 human hepatic surgical biopsy samples from adult subjects as well as 73 fetal hepatic tissue samples obtained at different gestational ages. BHMT protein levels were correlated significantly (p<0.001) with levels of enzyme activity in both fetal and adult tissues, but both were decreased in fetal tissue when compared with levels in the adult hepatic biopsies. To determine possible genotype-phenotype correlations, 12 tag SNPs for BHMT and the closely related BHMT2 gene were selected from SNPs observed during our own gene resequencing studies as well as from HapMap. These SNPs data were used to genotype DNA from the adult hepatic surgical biopsy samples, and genotype-phenotype association analysis was performed. Three SNPs (rs41272270, rs16876512, and rs6875201), located 28kb upstream, in the 5'-UTR and in intron 1 of BHMT, respectively, were significantly correlated with both BHMT activity (p=3.41E-8, 2.55E-9 and 2.46E-10, respectively) and protein levels (p=5.78E-5, 1.08E-5 and 6.92E-6, respectively). We also imputed 230 additional SNPs across the BHMT and BHMT2 genes, identifying an additional imputed SNP, rs7700790, that was also highly associated with hepatic BHMT enzyme activity and protein. However, none of the 3 genotyped or one imputed SNPs displayed a "shift" during electrophoretic mobility shift assays. These observations may help us to understand individual variation in the regulation of BHMT in the human liver and its possible relationship to variation in methylation.

Common genetic variation within IGFI, IGFII, IGFBP-1, and IGFBP-3 and endometrial cancer risk

OBJECTIVE

The insulin-like growth factor (IGF) pathway plays a critical role in the growth and development of the uterus and is believed to function as a mediator of steroid hormone actions in the endometrium. The local expression of genes encoding IGFs and IGF-binding proteins (IGFBPs) is important in determining IGF bioactivity in the uterus. Genetic variation in key genes within the IGF pathway may influence the rate of cellular proliferation and differentiation in the uterus and ultimately affect the risk of endometrial cancer. Our hypothesis is that variant alleles in key genes involved in the IGF pathway will influence the development of endometrial cancer.

METHODS

We conducted a case-control study nested within the Nurses' Health Study (NHS) and the Women's Health Study (WHS) to investigate the association between forty-four polymorphisms within IGFI, IGFII, IGFBP-1, and IGFBP-3 with endometrial cancer risk using 692 invasive endometrial cancer cases and 1723 matched controls. We used conditional logistic regression to calculate odds ratios (ORs) and 95% confidence intervals (CIs) to assess the risk of endometrial cancer.

RESULTS

We observed an inverse association with IGFII rs3741211 and endometrial cancer risk (OR=0.79 (95% CI: 0.63, 0.99)) and IGFII rs1004446 and endometrial cancer risk (OR=0.80 (95% CI: 0.68, 0.94)). We also observed an inverse association with IGFBP-3 rs2453839 and endometrial cancer risk (OR=0.81 (95%CI: 0.67, 0.98). However, we did not observe any statistically significant associations with the polymorphisms in IGFI and IGFBP1 and endometrial cancer risk.

CONCLUSIONS

Genetic variation with IGFII and IGFBP-3 may influence endometrial cancer risk in Caucasians. Polymorphisms in IGFI and IGFBP-1 were not associated with endometrial cancer risk, but further research is needed.

Statistical issues in gene association studies

This chapter reviews statistical issues related to gene association studies. The goal is to review various aspects of study design and analysis for individuals who do not have an extensive statistical background. We will review statistical issues as they relate to both genome-wide and candidate gene studies. Topics reviewed include study design, power and sample size, data checking, statistical methods, population stratification, and multiple testing. We draw examples from the type 2 diabetes genetics literature to illustrate some of the issues discussed.

Eighteen insulin-like growth factor pathway genes, circulating levels of IGF-I and its binding protein, and risk of prostate and breast cancer

BACKGROUND

Circulating levels of insulin-like growth factor I (IGF-I) and its main binding protein, IGF binding protein 3 (IGFBP-3), have been associated with risk of several types of cancer. Heritable factors explain up to 60% of the variation in IGF-I and IGFBP-3 in studies of adult twins.

METHODS

We systematically examined common genetic variation in 18 genes in the IGF signaling pathway for associations with circulating levels of IGF-I and IGFBP-3. A total of 302 single nucleotide polymorphisms (SNP) were genotyped in >5,500 Caucasian men and 5,500 Caucasian women from the Breast and Prostate Cancer Cohort Consortium.

RESULTS

After adjusting for multiple testing, SNPs in the IGF1 and SSTR5 genes were significantly associated with circulating IGF-I (P < 2.1 × 10(-4)); SNPs in the IGFBP3 and IGFALS genes were significantly associated with circulating IGFBP-3. Multi-SNP models explained R(2) = 0.62% of the variation in circulating IGF-I and 3.9% of the variation in circulating IGFBP-3. We saw no significant association between these multi-SNP predictors of circulating IGF-I or IGFBP-3 and risk of prostate or breast cancers.

CONCLUSION

Common genetic variation in the IGF1 and SSTR5 genes seems to influence circulating IGF-I levels, and variation in IGFBP3 and IGFALS seems to influence circulating IGFBP-3. However, these variants explain only a small percentage of the variation in circulating IGF-I and IGFBP-3 in Caucasian men and women.

IMPACT

Further studies are needed to explore contributions from other genetic factors such as rare variants in these genes and variation outside of these genes.

Genetic variation in the androgen estrogen conversion pathway in relation to breast cancer prognosticators

Genetic variation in the androgen-to-estrogen conversion pathway has been shown to be associated with risk of breast cancer and, in particular, with estrogen receptor (ER) positive tumours. We aimed at studying how the genetic alterations, which have been identified for risk, are associated with breast cancer prognosticators, with a prior hypothesis that, in general, hormone-related breast cancers have a better prognosis than non-hormone-related breast cancers. Association between tagging SNPs in genes involved in estrogen metabolism and patient's lymph node status, tumour size and histological grade were estimated in a sample of 1569 Swedish breast cancer patients. Polymorphisms in CYP19A1, which have previously been linked to breast cancer risk, are shown to be associated with breast cancer prognosticators. The strongest association was observed for rs4646, with histological grade. The common allele of rs4646, which has been associated with increased breast cancer risk, was associated with low-histological grade and small tumour size (P = 0.001 and 0.015; 1-sided, respectively). We also found evidence that SNP rs7167936 is associated with histological grade and tumour size (P = 0.010 and 0.005; 1-sided, respectively). We show that rs4646 and rs7167936 are associated with histological grade even amongst only ER-positive tumours (P = 0.008 and 0.011; 1-sided, respectively). Our results provide new evidence that CYP19A1 is involved in both breast cancer risk and prognosis.

Comprehensive analysis of common genetic variation in 61 genes related to steroid hormone and insulin-like growth factor-I metabolism and breast cancer risk in the NCI breast and prostate cancer cohort consortium

There is extensive evidence that increases in blood and tissue concentrations of steroid hormones and of insulin-like growth factor I (IGF-I) are associated with breast cancer risk. However, studies of common variation in genes involved in steroid hormone and IGF-I metabolism have yet to provide convincing evidence that such variants predict breast cancer risk. The Breast and Prostate Cancer Cohort Consortium (BPC3) is a collaboration of large US and European cohorts. We genotyped 1416 tagging single nucleotide polymorphisms (SNPs) in 37 steroid hormone metabolism genes and 24 IGF-I pathway genes in 6292 cases of breast cancer and 8135 controls, mostly Caucasian, postmenopausal women from the BPC3. We also imputed 3921 additional SNPs in the regions of interest. None of the SNPs tested was significantly associated with breast cancer risk, after correction for multiple comparisons. The results remained null when cases and controls were stratified by age at diagnosis/recruitment, advanced or nonadvanced disease, body mass index, with or without in situ cases; or restricted to Caucasians. Among 770 estrogen receptor-negative cases, an SNP located 3' of growth hormone receptor (GHR) was marginally associated with increased risk after correction for multiple testing (P(trend) = 1.5 × 10(-4)). We found no significant overall associations between breast cancer and common germline variation in 61 genes involved in steroid hormone and IGF-I metabolism in this large, comprehensive study. Although previous studies have shown that variations in these genes can influence endogenous hormone levels, the magnitude of the effect of single SNPs does not appear to be sufficient to alter breast cancer risk.

Methods: genetic epidemiology

Given the potential benefits of gene identification in psychiatry, genetic epidemiology has become a mainstream discipline within the field. This article discusses the main tools for gene discovery. The focus is on the designs and analytic approaches for each of these methods. Because most gene discovery has now moved to genetic association studies, and most recently to genome-wide association studies, the focus is on methods for this design. Also highlighted are the current challenges of genetic epidemiology as a prelude to future approaches that may be applied to psychiatric disorders in the coming years.

Genetic variation in the progesterone receptor gene and risk of endometrial cancer: a haplotype-based approach

BACKGROUND

It is well established that estrogen increases endometrial cancer risk, whereas progesterone opposes the estrogen effects. The PROGINS allele of the progesterone receptor (PGR) gene reduces the function of PGR and has been associated with increased risk of the endometrioid type ovarian cancer. We investigated whether genetic variation in PGR is also associated with endometrial cancer risk using a haplotype-based approach.

METHODS

We pooled data from two endometrial cancer case-control studies that were nested within two prospective cohorts, the Multiethnic Cohort Study and the California Teachers Study. Seventeen haplotype-tagging single nucleotide polymorphisms (SNPs) across four linkage disequilibrium (LD) blocks spanning the PGR locus were genotyped in 583 incident cases and 1936 control women. Odds ratios (ORs) and 95% confidence intervals (CIs) associated with each haplotype were estimated using conditional logistic regression, stratified by age and ethnicity.

RESULTS

Genetic variation in LD block 3 of the PGR locus was associated with endometrial cancer risk (P(global test) = 0.002), with haplotypes 3C, 3D and 3F associated with 31-34% increased risk. Among whites (383 cases/840 controls), genetic variation in all four blocks was associated with increased endometrial cancer risk (P(global test) = 0.010, 0.013, 0.005 and 0.020). Haplotypes containing the PROGINS allele and several haplotypes in blocks 1, 3 and 4 were associated with 34-77% increased risk among whites. SNP analyses for whites suggested that rs608995, partially linked to the PROGINS allele (r(2) = 0.6), was associated with increased risk (OR = 1.30, 95% CI = 1.06-1.59).

CONCLUSIONS

Our results suggest that genetic variation in the PGR region is associated with endometrial cancer risk.

A comprehensive analysis of common IGF1, IGFBP1 and IGFBP3 genetic variation with prospective IGF-I and IGFBP-3 blood levels and prostate cancer risk among Caucasians

The insulin-like growth factor (IGF) pathway has been implicated in prostate development and carcinogenesis. We conducted a comprehensive analysis, utilizing a resequencing and tagging single-nucleotide polymorphism (SNP) approach, between common genetic variation in the IGF1, IGF binding protein (BP) 1, and IGFBP3 genes with IGF-I and IGFBP-3 blood levels, and prostate cancer (PCa) risk, among Caucasians in the NCI Breast and Prostate Cancer Cohort Consortium. We genotyped 14 IGF1 SNPs and 16 IGFBP1/IGFBP3 SNPs to capture common [minor allele frequency (MAF) >or= 5%] variation among Caucasians. For each SNP, we assessed the geometric mean difference in IGF blood levels (N = 5684) across genotypes and the association with PCa risk (6012 PCa cases/6641 controls). We present two-sided statistical tests and correct for multiple comparisons. A non-synonymous IGFBP3 SNP in exon 1, rs2854746 (Gly32Ala), was associated with IGFBP-3 blood levels (P(adj) = 8.8 x 10(-43)) after adjusting for the previously established IGFBP3 promoter polymorphism A-202C (rs2854744); IGFBP-3 blood levels were 6.3% higher for each minor allele. For IGF1 SNP rs4764695, the risk estimates among heterozygotes was 1.01 (99% CI: 0.90-1.14) and 1.20 (99% CI: 1.06-1.37) for variant homozygotes with overall PCa risk. The corrected allelic P-value was 8.7 x 10(-3). IGF-I levels were significantly associated with PCa risk (P(trend) = 0.02) with a 21% increase of PCa risk when compared with the highest quartile to the lowest quartile. We have identified SNPs significantly associated with IGFBP-3 blood levels, but none of these alter PCa risk; however, a novel IGF1 SNP, not associated with IGF-I blood levels, shows preliminary evidence for association with PCa risk among Caucasians.

Haplotype misclassification resulting from statistical reconstruction and genotype error, and its impact on association estimates

Haplotypes are an important concept for genetic association studies, but involve uncertainty due to statistical reconstruction from single nucleotide polymorphism (SNP) genotypes and genotype error. We developed a re-sampling approach to quantify haplotype misclassification probabilities and implemented the MC-SIMEX approach to tackle this as a 3 x 3 misclassification problem. Using a previously published approach as a benchmark for comparison, we evaluated the performance of our approach by simulations and exemplified it on real data from 15 SNPs of the APM1 gene. Misclassification due to reconstruction error was small for most, but notable for some, especially rarer haplotypes. Genotype error added misclassification to all haplotypes resulting in a non-negligible drop in sensitivity. In our real data example, the bias of association estimates due to reconstruction error alone reached -48.2% for a 1% genotype error, indicating that haplotype misclassification should not be ignored if high genotype error can be expected. Our 3 x 3 misclassification view of haplotype error adds a novel perspective to currently used methods based on genotype intensities and expected number of haplotype copies. Our findings give a sense of the impact of haplotype error under realistic scenarios and underscore the importance of high-quality genotyping, in which case the bias in haplotype association estimates is negligible.

Multi-variant pathway association analysis reveals the importance of genetic determinants of estrogen metabolism in breast and endometrial cancer susceptibility

Despite the central role of estrogen exposure in breast and endometrial cancer development and numerous studies of genes in the estrogen metabolic pathway, polymorphisms within the pathway have not been consistently associated with these cancers. We posit that this is due to the complexity of multiple weak genetic effects within the metabolic pathway that can only be effectively detected through multi-variant analysis. We conducted a comprehensive association analysis of the estrogen metabolic pathway by interrogating 239 tagSNPs within 35 genes of the pathway in three tumor samples. The discovery sample consisted of 1,596 breast cancer cases, 719 endometrial cancer cases, and 1,730 controls from Sweden; and the validation sample included 2,245 breast cancer cases and 1,287 controls from Finland. We performed admixture maximum likelihood (AML)-based global tests to evaluate the cumulative effect from multiple SNPs within the whole metabolic pathway and three sub-pathways for androgen synthesis, androgen-to-estrogen conversion, and estrogen removal. In the discovery sample, although no single polymorphism was significant after correction for multiple testing, the pathway-based AML global test suggested association with both breast (p(global) = 0.034) and endometrial (p(global) = 0.052) cancers. Further testing revealed the association to be focused on polymorphisms within the androgen-to-estrogen conversion sub-pathway, for both breast (p(global) = 0.008) and endometrial cancer (p(global) = 0.014). The sub-pathway association was validated in the Finnish sample of breast cancer (p(global) = 0.015). Further tumor subtype analysis demonstrated that the association of the androgen-to-estrogen conversion sub-pathway was confined to postmenopausal women with sporadic estrogen receptor positive tumors (p(global) = 0.0003). Gene-based AML analysis suggested CYP19A1 and UGT2B4 to be the major players within the sub-pathway. Our study indicates that the composite genetic determinants related to the androgen-estrogen conversion are important for the induction of two hormone-associated cancers, particularly for the hormone-driven breast tumour subtypes.

Fine scale mapping of the breast cancer 16q12 locus

Recent genome-wide association studies have identified a breast cancer susceptibility locus on 16q12 with an unknown biological basis. We used a set of single nucleotide polymorphism (SNP) markers to generate a fine-scale map and narrowed the region of association to a 133 kb DNA segment containing the largely uncharacterized hypothetical gene LOC643714, a short intergenic region and the 5' end of TOX3. Re-sequencing this segment in European subjects identified 293 common polymorphisms, including a set of 26 highly correlated candidate causal variants. By evaluation of these SNPs in five breast cancer case-control studies involving more than 23 000 subjects from populations of European and Southeast Asian ancestry, all but 14 variants could be excluded at odds of <1:100. Most of the remaining variants lie in the intergenic region, which exhibits evolutionary conservation and open chromatin conformation, consistent with a regulatory function. African-American case-control studies exhibit a different pattern of association suggestive of an additional causative variant.

Efficient genome-wide TagSNP selection across populations via the linkage disequilibrium criterion

In this article, we studied the tag single-nucleotide polymorphism (tagSNP) selection problem on multiple populations using the pairwise r(2) linkage disequilibrium criterion. We proposed a novel combinatorial optimization model for the tagSNP selection problem, called the minimum common tagSNP selection (MCTS) problem, and presented efficient solutions for MCTS. Our approach consists of the following three main steps: (i) partitioning the SNP markers into small disjoint components, (ii) applying some data reduction rules to simplify the problem, and (iii) applying either a fast greedy algorithm or a Lagrangian relaxation algorithm to solve the remaining (general) MCTS. These algorithms also provide lower bounds on tagging (i.e., the minimum number of tagSNPs needed). The lower bounds allow us to evaluate how far our solution is from the optimum. To the best of our knowledge, it is the first time the tagging lower bounds are discussed in the literature. We assessed the performance of our algorithms on real HapMap data for genome-wide tagging. The experiments demonstrated that our algorithms run 3-4 orders of magnitude faster than the existing single-population tagging programs such as FESTA, LD-Select, and the multiple-population tagging method MultiPop-TagSelect. Our method also greatly reduced the required tagSNPs compared with LD-Select on a single population and MultiPop-TagSelect on multiple populations. Moreover, the numbers of tagSNPs selected by our algorithms are almost optimal because they are very close to the corresponding lower bounds obtained by our method.

Genetic variation in the estrogen metabolic pathway and mammographic density as an intermediate phenotype of breast cancer

Introduction

Several studies have examined the effect of genetic variants in genes involved in the estrogen metabolic pathway on mammographic density, but the number of loci studied and the sample sizes evaluated have been small and pathways have not been evaluated comprehensively. In this study, we evaluate the association between mammographic density and genetic variants of the estrogen metabolic pathway.

Methods

A total of 239 SNPs in 34 estrogen metabolic genes were studied in 1,731 Swedish women who participated in a breast cancer case-control study, of which 891 were cases and 840 were controls. Film mammograms of the medio-lateral oblique view were digitalized and the software Cumulus was used for computer-assisted semi-automated thresholding of mammographic density. Generalized linear models controlling for possible confounders were used to evaluate the effects of SNPs on mammographic density. Results found to be nominally significant were examined in two independent populations. The admixture maximum likelihood-based global test was performed to evaluate the cumulative effect from multiple SNPs within the whole metabolic pathway and three subpathways for androgen synthesis, androgen-to-estrogen conversion and estrogen removal.

Results

Genetic variants of genes involved in estrogen metabolism exhibited no appreciable effect on mammographic density. None of the nominally significant findings were validated. In addition, global analyses on the overall estrogen metabolic pathway and its subpathways did not yield statistically significant results.

Conclusions

Overall, there is no conclusive evidence that genetic variants in genes involved in the estrogen metabolic pathway are associated with mammographic density in postmenopausal women.

Genetic variation in insulin-like growth factor signaling genes and breast cancer risk among BRCA1 and BRCA2 carriers

Introduction

Women who carry mutations in BRCA1 and BRCA2 have a substantially increased risk of developing breast cancer as compared with the general population. However, risk estimates range from 20 to 80%, suggesting the presence of genetic and/or environmental risk modifiers. Based on extensive in vivo and in vitro studies, one important pathway for breast cancer pathogenesis may be the insulin-like growth factor (IGF) signaling pathway, which regulates both cellular proliferation and apoptosis. BRCA1 has been shown to directly interact with IGF signaling such that variants in this pathway may modify risk of cancer in women carrying BRCA mutations. In this study, we investigate the association of variants in genes involved in IGF signaling and risk of breast cancer in women who carry deleterious BRCA1 and BRCA2 mutations.

Methods

A cohort of 1,665 adult, female mutation carriers, including 1,122 BRCA1 carriers (433 cases) and 543 BRCA2 carriers (238 cases) were genotyped for SNPs in IGF1, IGF1 receptor (IGF1R), IGF1 binding protein (IGFBP1, IGFBP2, IGFBP5), and IGF receptor substrate 1 (IRS1). Cox proportional hazards regression was used to model time from birth to diagnosis of breast cancer for BRCA1 and BRCA2 carriers separately. For linkage disequilibrium (LD) blocks with multiple SNPs, an additive genetic model was assumed; and for single SNP analyses, no additivity assumptions were made.

Results

Among BRCA1 carriers, significant associations were found between risk of breast cancer and LD blocks in IGF1R (global P = 0.011 for LD block 2 and global P = 0.012 for LD block 11). Among BRCA2 carriers, an LD block in IGFBP2 (global P = 0.0145) was found to be associated with the time to breast cancer diagnosis. No significant LD block associations were found for the other investigated genes among BRCA1 and BRCA2 carriers.

Conclusions

This is the first study to investigate the role of genetic variation in IGF signaling and breast cancer risk in women carrying deleterious mutations in BRCA1 and BRCA2. We identified significant associations in variants in IGF1R and IRS1 in BRCA1 carriers and in IGFBP2 in BRCA2 carriers. Although there is known to be interaction of BRCA1 and IGF signaling, further replication and identification of causal mechanisms are needed to better understand these associations.

Methods: genetic epidemiology

Given the potential benefits of gene identification in psychiatry, genetic epidemiology has become a mainstream discipline within the field. This article discusses the main tools for gene discovery. The focus is on the designs and analytic approaches for each of these methods. Because most gene discovery has now moved to genetic association studies, and most recently to genome-wide association studies, the focus is on methods for this design. Also highlighted are the current challenges of genetic epidemiology as a prelude to future approaches that may be applied to psychiatric disorders in the coming years.