Sajjad Rafiq

Exome sequence read depth methods for identifying copy number changes

Copy number variants (CNVs) play important roles in a number of human diseases and in pharmacogenetics. Powerful methods exist for CNV detection in whole genome sequencing (WGS) data, but such data are costly to obtain. Many disease causal CNVs span or are found in genome coding regions (exons), which makes CNV detection using whole exome sequencing (WES) data attractive. If reliably validated against WGS-based CNVs, exome-derived CNVs have potential applications in a clinical setting. Several algorithms have been developed to exploit exome data for CNV detection and comparisons made to find the most suitable methods for particular data samples. The results are not consistent across studies. Here, we review some of the exome CNV detection methods based on depth of coverage profiles and examine their performance to identify problems contributing to discrepancies in published results. We also present a streamlined strategy that uses a single metric, the likelihood ratio, to compare exome methods, and we demonstrated its utility using the VarScan 2 and eXome Hidden Markov Model (XHMM) programs using paired normal and tumour exome data from chronic lymphocytic leukaemia patients. We use array-based somatic CNV (SCNV) calls as a reference standard to compute prevalence-independent statistics, such as sensitivity, specificity and likelihood ratio, for validation of the exome-derived SCNVs. We also account for factors known to influence the performance of exome read depth methods, such as CNV size and frequency, while comparing our findings with published results.

Identification of inherited genetic variations influencing prognosis in early onset breast cancer

Genome-Wide Association Studies (GWAS) have begun to investigate associations between inherited genetic variations and breast cancer prognosis. Here, we report our findings from a GWAS conducted in 536 patients with early-onset breast cancer aged 40 or less at diagnosis and with a mean follow-up period of 4.1 years (SD = 1.96). Patients were selected from the Prospective Study of Outcomes in Sporadic versus Hereditary breast cancer. A Bonferroni correction for multiple testing determined that a P value of 1.0 × 10(-7) was a statistically significant association signal. Following quality control, we identified 487,496 single nucleotide polymorphisms (SNP) for association tests in stage 1. In stage 2, 35 SNPs with the most significant associations were genotyped in 1,516 independent cases from the same early-onset cohort. In stage 2, 11 SNPs remained associated in the same direction (P ≤ 0.05). Fixed effects meta-analysis models identified one SNP associated at close to genome wide level of significance 556 kb upstream of the ARRDC3 locus [HR = 1.61; 95% confidence interval (CI), 1.33-1.96; P = 9.5 × 10(-7)]. Four further associations at or close to the PBX1, RORα, NTN1, and SYT6 loci also came close to genome-wide significance levels (P = 10(-6)). In the first ever GWAS for the identification of SNPs associated with prognosis in patients with early-onset breast cancer, we report a SNP upstream of the ARRDC3 locus as potentially associated with prognosis (median follow-up time for genotypes: CC = 4 years, CT = 3 years, and TT = 2.7 years; Wilcoxon rank-sum test CC vs. CT, P = 4 × 10(-4) and CT vs. TT, P = 0.76). Four further loci may also be associated with prognosis.

Association analysis of 31 common polymorphisms with type 2 diabetes and its related traits in Indian sib pairs

Aims/hypothesisEvaluation of the association of 31 common single nucleotide polymorphisms (SNPs) with fasting glucose, fasting insulin, HOMA-beta cell function (HOMA-β), HOMA-insulin resistance (HOMA-IR) and type 2 diabetes in the Indian population.MethodsWe genotyped 3,089 sib pairs recruited in the Indian Migration Study from four cities in India (Lucknow, Nagpur, Hyderabad and Bangalore) for 31 SNPs in 24 genes previously associated with type 2 diabetes in European populations. We conducted within-sib-pair analysis for type 2 diabetes and its related quantitative traits.ResultsThe risk-allele frequencies of all the SNPs were comparable with those reported in western populations. We demonstrated significant associations of CXCR4 (rs932206), CDKAL1 (rs7756992) and TCF7L2 (rs7903146, rs12255372) with fasting glucose, with β values of 0.007 (p = 0.05), 0.01 (p = 0.01), 0.007 (p = 0.05), 0.01 (p = 0.003) and 0.08 (p = 0.01), respectively. Variants in NOTCH2 (rs10923931), TCF-2 (also known as HNF1B) (rs757210), ADAM30 (rs2641348) and CDKN2A/B (rs10811661) significantly predicted fasting insulin, with β values of −0.06 (p = 0.04), 0.05 (p = 0.05), −0.08 (p = 0.01) and −0.08 (p = 0.02), respectively. For HOMA-IR, we detected associations with TCF-2, ADAM30 and CDKN2A/B, with β values of 0.05 (p = 0.04), −0.07 (p = 0.03) and −0.08 (p = 0.02), respectively. We also found significant associations of ADAM30 (β = −0.05; p = 0.01) and CDKN2A/B (β = −0.05; p = 0.03) with HOMA-β. THADA variant (rs7578597) was associated with type 2 diabetes (OR 1.5; 95% CI 1.04, 2.22; p = 0.03).Conclusions/interpretationWe validated the association of seven established loci with intermediate traits related to type 2 diabetes in an Indian population using a design resistant to population stratification.

Association Study of 25 Type 2 Diabetes Related Loci with Measures of Obesity in Indian Sib Pairs

Obesity is an established risk factor for type 2 diabetes (T2D) and they are metabolically related through the mechanism of insulin resistance. In order to explore how common genetic variants associated with T2D correlate with body mass index (BMI), we examined the influence of 25 T2D associated loci on obesity risk. We used 5056 individuals (2528 sib-pairs) recruited in Indian Migration Study and conducted within sib-pair analysis for six obesity phenotypes. We found associations of variants in CXCR4 (rs932206) and HHEX (rs5015480) with higher body mass index (BMI) (β = 0.13, p = 0.001) and (β = 0.09, p = 0.002), respectively and weight (β = 0.13, p = 0.001) and (β = 0.09, p = 0.001), respectively. CXCR4 variant was also strongly associated with body fat (β = 0.10, p = 0.0004). In addition, we demonstrated associations of CXCR4 and HHEX with overweight/obesity (OR = 1.6, p = 0.003) and (OR = 1.4, p = 0.002), respectively, in 1333 sib-pairs (2666 individuals). We observed marginal evidence of associations between variants at six loci (TCF7L2, NGN3, FOXA2, LOC646279, FLJ3970 and THADA) and waist hip ratio (WHR), BMI and/or overweight which needs to be validated in larger set of samples. All the above findings were independent of daily energy consumption and physical activity level. The risk score estimates based on eight significant loci (including nominal associations) showed associations with WHR and body fat which were independent of BMI. In summary, we establish the role of T2D associated loci in influencing the measures of obesity in Indian population, suggesting common underlying pathophysiology across populations.

A Genome Wide Meta-Analysis Study for Identification of Common Variation Associated with Breast Cancer Prognosis

Objective Genome wide association studies (GWAs) of breast cancer mortality have identified few potential associations. The concordance between these studies is unclear. In this study, we used a meta-analysis of two prognostic GWAs and a replication cohort to identify the strongest associations and to evaluate the loci suggested in previous studies. We attempt to identify those SNPs which could impact overall survival irrespective of the age of onset. Methods To facilitate the meta-analysis and to refine the association signals, SNPs were imputed using data from the 1000 genomes project. Cox-proportional hazard models were used to estimate hazard ratios (HR) in 536 patients from the POSH cohort (Prospective study of Outcomes in Sporadic versus Hereditary breast cancer) and 805 patients from the HEBCS cohort (Helsinki Breast Cancer Study). These hazard ratios were combined using a Mantel-Haenszel fixed effects meta-analysis and a p-value threshold of 5×10−8 was used to determine significance. Replication was performed in 1523 additional patients from the POSH study. Results Although no SNPs achieved genome wide significance, three SNPs have significant association in the replication cohort and combined p-values less than 5.6×10−6. These SNPs are; rs421379 which is 556 kb upstream of ARRDC3 (HR = 1.49, 95% confidence interval (CI) = 1.27–1.75, P = 1.1×10−6), rs12358475 which is between ECHDC3 and PROSER2 (HR = 0.75, CI = 0.67–0.85, P = 1.8×10−6), and rs1728400 which is between LINC00917 and FOXF1. Conclusions In a genome wide meta-analysis of two independent cohorts from UK and Finland, we identified potential associations at three distinct loci. Phenotypic heterogeneity and relatively small sample sizes may explain the lack of genome wide significant findings. However, the replication at three SNPs in the validation cohort shows promise for future studies in larger cohorts. We did not find strong evidence for concordance between the few associations highlighted by previous GWAs of breast cancer survival and this study.

Support Vector Machine Classifier for Estrogen Receptor Positive and Negative Early-Onset Breast Cancer

Two major breast cancer sub-types are defined by the expression of estrogen receptors on tumour cells. Cancers with large numbers of receptors are termed estrogen receptor positive and those with few are estrogen receptor negative. Using genome-wide single nucleotide polymorphism genotype data for a sample of early-onset breast cancer patients we developed a Support Vector Machine (SVM) classifier from 200 germline variants associated with estrogen receptor status (p<0.0005). Using a linear kernel Support Vector Machine, we achieved classification accuracy exceeding 93%. The model indicates that polygenic variation in more than 100 genes is likely to underlie the estrogen receptor phenotype in early-onset breast cancer. Functional classification of the genes involved identifies enrichment of functions linked to the immune system, which is consistent with the current understanding of the biological role of estrogen receptors in breast cancer.

Evaluation of seven common lipid associated loci in a large Indian sib pair study.

BackgroundGenome wide association studies (GWAS), mostly in Europeans have identified several common variants as associated with key lipid traits. Replication of these genetic effects in South Asian populations is important since it would suggest wider relevance for these findings. Given the rising prevalence of metabolic disorders and heart disease in the Indian sub-continent, these studies could be of future clinical relevance.MethodsWe studied seven common variants associated with a variety of lipid traits in previous GWASs. The study sample comprised of 3178 sib-pairs recruited as participants for the Indian Migration Study (IMS). Associations with various lipid parameters and quantitative traits were analyzed using the Fulker genetic association model.ResultsWe replicated five of the 7 main effect associations with p-values ranging from 0.03 to 1.97x10-7. We identified particularly strong association signals at rs662799 in APOA5 (beta=0.18 s.d, p=1.97 x 10-7), rs10503669 in LPL (beta =−0.18 s.d, p=1.0 x 10-4) and rs780094 in GCKR (beta=0.11 s.d, p=0.001) loci in relation to triglycerides. In addition, the GCKR variant was also associated with total cholesterol (beta=0.11 s.d, p=3.9x10-4). We also replicated the association of rs562338 in APOB (p=0.03) and rs4775041 in LIPC (p=0.007) with LDL-cholesterol and HDL-cholesterol respectively.ConclusionsWe report associations of five loci with various lipid traits with the effect size consistent with the same reported in Europeans. These results indicate an overlap of genetic effects pertaining to lipid traits across the European and Indian populations.

Assessment of variation in immunosuppressive pathway genes reveals TGFBR2 to be associated with prognosis of estrogen receptor-negative breast cancer after chemotherapy

IntroductionTumor lymphocyte infiltration is associated with clinical response to chemotherapy in estrogen receptor (ER) negative breast cancer. To identify variants in immunosuppressive pathway genes associated with prognosis after adjuvant chemotherapy for ER-negative patients, we studied stage I-III invasive breast cancer patients of European ancestry, including 9,334 ER-positive (3,151 treated with chemotherapy) and 2,334 ER-negative patients (1,499 treated with chemotherapy).MethodsWe pooled data from sixteen studies from the Breast Cancer Association Consortium (BCAC), and employed two independent studies for replications. Overall 3,610 single nucleotide polymorphisms (SNPs) in 133 genes were genotyped as part of the Collaborative Oncological Gene-environment Study, in which phenotype and clinical data were collected and harmonized. Multivariable Cox proportional hazard regression was used to assess genetic associations with overall survival (OS) and breast cancer-specific survival (BCSS). Heterogeneity according to chemotherapy or ER status was evaluated with the log-likelihood ratio test.ResultsThree independent SNPs in TGFBR2 and IL12B were associated with OS (P <10−3) solely in ER-negative patients after chemotherapy (267 events). Poorer OS associated with TGFBR2 rs1367610 (G > C) (per allele hazard ratio (HR) 1.54 (95% confidence interval (CI) 1.22 to 1.95), P = 3.08 × 10−4) was not found in ER-negative patients without chemotherapy or ER-positive patients with chemotherapy (P for interaction <10−3). Two SNPs in IL12B (r2 = 0.20) showed different associations with ER-negative disease after chemotherapy: rs2546892 (G > A) with poorer OS (HR 1.50 (95% CI 1.21 to 1.86), P = 1.81 × 10−4), and rs2853694 (A > C) with improved OS (HR 0.73 (95% CI 0.61 to 0.87), P = 3.67 × 10−4). Similar associations were observed with BCSS. Association with TGFBR2 rs1367610 but not IL12B variants replicated using BCAC Asian samples and the independent Prospective Study of Outcomes in Sporadic versus Hereditary Breast Cancer Study and yielded a combined HR of 1.57 ((95% CI 1.28 to 1.94), P = 2.05 × 10−5) without study heterogeneity.ConclusionsTGFBR2 variants may have prognostic and predictive value in ER-negative breast cancer patients treated with adjuvant chemotherapy. Our findings provide further insights into the development of immunotherapeutic targets for ER-negative breast cancer.

Polymorphism at 19q13.41 Predicts Breast Cancer Survival Specifically after Endocrine Therapy

Purpose: Although most patients with estrogen receptor (ER)–positive breast cancer benefit from endocrine therapies, a significant proportion do not. Our aim was to identify inherited genetic variations that might predict survival among patients receiving adjuvant endocrine therapies. Experimental Design: We performed a meta-analysis of two genome-wide studies; Helsinki Breast Cancer Study, 805 patients, with 240 receiving endocrine therapy and Prospective study of Outcomes in Sporadic versus Hereditary breast cancer, 536 patients, with 155 endocrine therapy patients, evaluating 486,478 single-nucleotide polymorphisms (SNP). The top four associations from the endocrine treatment subgroup were further investigated in two independent datasets totaling 5,011 patients, with 3,485 receiving endocrine therapy. Results: A meta-analysis identified a common SNP rs8113308, mapped to 19q13.41, associating with reduced survival among endocrine-treated patients [hazard ratio (HR), 1.69; 95% confidence interval (CI), 1.37–2.07; P = 6.34 × 10−7] and improved survival among ER-negative patients, with a similar trend in ER-positive cases not receiving endocrine therapy. In a multivariate analysis adjusted for conventional prognostic factors, we found a significant interaction between the rs8113308 and endocrine treatment, indicating a predictive, treatment-specific effect of the SNP rs8113308 on breast cancer survival, with the per-allele HR for interaction 2.16 (95% CI, 1.30–3.60; Pinteraction = 0.003) and HR = 7.77 (95% CI, 0.93–64.71) for the homozygous genotype carriers. A biologic rationale is suggested by in silico functional analyses. Conclusions: Our findings suggest carrying the rs8113308 rare allele may identify patients who will not benefit from adjuvant endocrine treatment. Clin Cancer Res; 21(18); 4086–96. ©2015 AACR.

Quantifying the cumulative effect of low-penetrance genetic variants on breast cancer risk.

Many common diseases have a complex genetic basis in which large numbers of genetic variations combine with environmental factors to determine risk. However, quantifying such polygenic effects has been challenging. In order to address these difficulties we developed a global measure of the information content of an individuals genome relative to a reference population, which may be used to assess differences in global genome structure between cases and appropriate controls. Informally this measure, which we call relative genome information (RGI), quantifies the relative “disorder” of an individuals genome. In order to test its ability to predict disease risk we used RGI to compare single‐nucleotide polymorphism genotypes from two independent samples of women with early‐onset breast cancer with three independent sets of controls. We found that RGI was significantly elevated in both sets of breast cancer cases in comparison with all three sets of controls, with disease risk rising sharply with RGI. Furthermore, these differences are not due to associations with common variants at a small number of disease‐associated loci, but rather are due to the combined associations of thousands of markers distributed throughout the genome. Our results indicate that the information content of an individuals genome may be used to measure the risk of a complex disease, and suggest that early‐onset breast cancer has a strongly polygenic component.