A case-control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density.

Panels of single nucleotide polymorphisms (SNPs) stratify risk for breast cancer in women from the general population, but studies are needed assess their use in a fully comprehensive model including classical risk factors, mammographic density and more than 100 SNPs associated with breast cancer. A case-control study was designed (1,668 controls, 405 cases) in women aged 47-73 years attending routine screening in Manchester UK, and enrolled in a wider study to assess methods for risk assessment. Risk from classical questionnaire risk factors was assessed using the Tyrer-Cuzick model; mean percentage visual mammographic density was scored by two independent readers. DNA extracted from saliva was genotyped at selected SNPs using the OncoArray. A predefined polygenic risk score based on 143 SNPs was calculated (SNP143). The odds ratio (OR, and 95% confidence interval, CI) per interquartile range (IQ-OR) of SNP143 was estimated unadjusted and adjusted for Tyrer-Cuzick and breast density. Secondary analysis assessed risk by oestrogen receptor (ER) status. The primary polygenic risk score was well calibrated (O/E OR 1.10, 95% CI 0.86-1.34) and accuracy was retained after adjustment for Tyrer-Cuzick risk and mammographic density (IQ-OR unadjusted 2.12, 95% CI% 1.75-2.42; adjusted 2.06, 95% CI 1.75-2.42). SNP143 was a risk factor for ER+ and ER- breast cancer (adjusted IQ-OR, ER+ 2.11, 95% CI 1.78-2.51; ER- 1.81, 95% CI 1.16-2.84). In conclusion, polygenic risk scores based on a large number of SNPs improve risk stratification in combination with classical risk factors and mammographic density, and SNP143 was similarly predictive for ER-positive and ER-negative disease.

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.

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

INTRODUCTION: Tumor 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). METHODS: We 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. RESULTS: Three 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. CONCLUSIONS: TGFBR2 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.

Genome-wide association study identifies 25 known breast cancer susceptibility loci as risk factors for triple-negative breast cancer.

Triple-negative (TN) breast cancer is an aggressive subtype of breast cancer associated with a unique set of epidemiologic and genetic risk factors. We conducted a two-stage genome-wide association study of TN breast cancer (stage 1: 1529 TN cases, 3399 controls; stage 2: 2148 cases, 1309 controls) to identify loci that influence TN breast cancer risk. Variants in the 19p13.1 and PTHLH loci showed genome-wide significant associations (P < 5 × 10(-) (8)) in stage 1 and 2 combined. Results also suggested a substantial enrichment of significantly associated variants among the single nucleotide polymorphisms (SNPs) analyzed in stage 2. Variants from 25 of 74 known breast cancer susceptibility loci were also associated with risk of TN breast cancer (P < 0.05). Associations with TN breast cancer were confirmed for 10 loci (LGR6, MDM4, CASP8, 2q35, 2p24.1, TERT-rs10069690, ESR1, TOX3, 19p13.1, RALY), and we identified associations with TN breast cancer for 15 additional breast cancer loci (P < 0.05: PEX14, 2q24.1, 2q31.1, ADAM29, EBF1, TCF7L2, 11q13.1, 11q24.3, 12p13.1, PTHLH, NTN4, 12q24, BRCA2, RAD51L1-rs2588809, MKL1). Further, two SNPs independent of previously reported signals in ESR1 [rs12525163 odds ratio (OR) = 1.15, P = 4.9 × 10(-) (4)] and 19p13.1 (rs1864112 OR = 0.84, P = 1.8 × 10(-) (9)) were associated with TN breast cancer. A polygenic risk score (PRS) for TN breast cancer based on known breast cancer risk variants showed a 4-fold difference in risk between the highest and lowest PRS quintiles (OR = 4.03, 95% confidence interval 3.46-4.70, P = 4.8 × 10(-) (69)). This translates to an absolute risk for TN breast cancer ranging from 0.8% to 3.4%, suggesting that genetic variation may be used for TN breast cancer risk prediction.

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

BACKGROUND: Genome 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. METHODS: We 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. RESULTS: We 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. CONCLUSIONS: We 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.

A genome-wide association study identifies protein quantitative trait loci (pQTLs).

There is considerable evidence that human genetic variation influences gene expression. Genome-wide studies have revealed that mRNA levels are associated with genetic variation in or close to the gene coding for those mRNA transcripts - cis effects, and elsewhere in the genome - trans effects. The role of genetic variation in determining protein levels has not been systematically assessed. Using a genome-wide association approach we show that common genetic variation influences levels of clinically relevant proteins in human serum and plasma. We evaluated the role of 496,032 polymorphisms on levels of 42 proteins measured in 1200 fasting individuals from the population based InCHIANTI study. Proteins included insulin, several interleukins, adipokines, chemokines, and liver function markers that are implicated in many common diseases including metabolic, inflammatory, and infectious conditions. We identified eight Cis effects, including variants in or near the IL6R (p = 1.8x10(-57)), CCL4L1 (p = 3.9x10(-21)), IL18 (p = 6.8x10(-13)), LPA (p = 4.4x10(-10)), GGT1 (p = 1.5x10(-7)), SHBG (p = 3.1x10(-7)), CRP (p = 6.4x10(-6)) and IL1RN (p = 7.3x10(-6)) genes, all associated with their respective protein products with effect sizes ranging from 0.19 to 0.69 standard deviations per allele. Mechanisms implicated include altered rates of cleavage of bound to unbound soluble receptor (IL6R), altered secretion rates of different sized proteins (LPA), variation in gene copy number (CCL4L1) and altered transcription (GGT1). We identified one novel trans effect that was an association between ABO blood group and tumour necrosis factor alpha (TNF-alpha) levels (p = 6.8x10(-40)), but this finding was not present when TNF-alpha was measured using a different assay , or in a second study, suggesting an assay-specific association. Our results show that protein levels share some of the features of the genetics of gene expression. These include the presence of strong genetic effects in cis locations. The identification of protein quantitative trait loci (pQTLs) may be a powerful complementary method of improving our understanding of disease pathways.

An interleukin-18 polymorphism is associated with reduced serum concentrations and better physical functioning in older people.

BACKGROUND: The proinflammatory cytokine interleukin-18 (IL-18) is associated with major disabling conditions, although whether as byproduct or driver is unclear. The role of common variation in the IL-18 gene on serum concentrations and functioning in old age is unknown. METHODS: We used 1671 participants aged 65-80 years from two studies: the InCHIANTI study and wave 6 of the Iowa-Established Populations for Epidemiological Study of the Elderly (EPESE). We tested three common polymorphisms against IL-18 concentration and measures of functioning. RESULTS: In the InCHIANTI study, a 1 standard deviation increase in serum IL-18 concentrations was associated with an increased chance of being in the 20% of slowest walkers (odds ratio 1.45; 95% confidence interval, 1.17-1.80; p =.0007) and 20% of those with poorest function based on the Short Physical Performance Battery Score (odds ratio 1.52; 95% confidence interval, 1.22-1.89; p =.00016) in age sex adjusted logistic regression models. There was no association with Activities of Daily Living (p =.26) or Mini-Mental State Examination score (p =.66). The C allele of the IL-18 polymorphism rs5744256 reduced serum concentrations of IL-18 by 39 pmol/mL per allele (p =.00001). The rs5744256 single nucleotide polymorphism was also associated with shorter walk times in InCHIANTI (n = 662, p =.016) and Iowa-EPESE (n = 995, p =.026). In pooled ranked models rs5744256 was also associated with higher SPPB scores (n = 1671, p =.019). Instead of adjusting for confounders in the IL-18 walk time association, we used rs5744256 in a Mendelian randomization analysis: The association remained in instrumental variable models (p =.021). CONCLUSION: IL-18 concentrations are associated with physical function in 65- to 80-year-olds. A polymorphism in the IL-18 gene alters IL-18 concentrations and is associated with an improvement in walk speed. IL-18 may play an active role in age-related functional impairment, but these findings need independent replication.

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.

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 individual's 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 individual's 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 individual's genome may be used to measure the risk of a complex disease, and suggest that early-onset breast cancer has a strongly polygenic component.

The SNP rs6500843 in 16p13.3 is associated with survival specifically among chemotherapy-treated breast cancer patients.

We have utilized a two-stage study design to search for SNPs associated with the survival of breast cancer patients treated with adjuvant chemotherapy. Our initial GWS data set consisted of 805 Finnish breast cancer cases (360 treated with adjuvant chemotherapy). The top 39 SNPs from this stage were analyzed in three independent data sets: iCOGS (n=6720 chemotherapy-treated cases), SUCCESS-A (n=3596), and POSH (n=518). Two SNPs were successfully validated: rs6500843 (any chemotherapy; per-allele HR 1.16, 95% C.I. 1.08-1.26, p=0.0001, p(adjusted)=0.0091), and rs11155012 (anthracycline therapy; per-allele HR 1.21, 95% C.I. 1.08-1.35, p=0.0010, p(adjusted)=0.0270). The SNP rs6500843 was found to specifically interact with adjuvant chemotherapy, independently of standard prognostic markers (p(interaction)=0.0009), with the rs6500843-GG genotype corresponding to the highest hazard among chemotherapy-treated cases (HR 1.47, 95% C.I. 1.20-1.80). Upon trans-eQTL analysis of public microarray data, the rs6500843 locus was found to associate with the expression of a group of genes involved in cell cycle control, notably AURKA, the expression of which also exhibited differential prognostic value between chemotherapy-treated and untreated cases in our analysis of microarray data. Based on previously published information, we propose that the eQTL genes may be connected to the rs6500843 locus via a RBFOX1-FOXM1 -mediated regulatory pathway.

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.

2q36.3 is associated with prognosis for oestrogen receptor-negative breast cancer patients treated with chemotherapy.

Large population-based registry studies have shown that breast cancer prognosis is inherited. Here we analyse single-nucleotide polymorphisms (SNPs) of genes implicated in human immunology and inflammation as candidates for prognostic markers of breast cancer survival involving 1,804 oestrogen receptor (ER)-negative patients treated with chemotherapy (279 events) from 14 European studies in a prior large-scale genotyping experiment, which is part of the Collaborative Oncological Gene-environment Study (COGS) initiative. We carry out replication using Asian COGS samples (n=522, 53 events) and the Prospective Study of Outcomes in Sporadic versus Hereditary breast cancer (POSH) study (n=315, 108 events). Rs4458204_A near CCL20 (2q36.3) is found to be associated with breast cancer-specific death at a genome-wide significant level (n=2,641, 440 events, combined allelic hazard ratio (HR)=1.81 (1.49-2.19); P for trend=1.90 × 10(-9)). Such survival-associated variants can represent ideal targets for tailored therapeutics, and may also enhance our current prognostic prediction capabilities.

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.

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 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) (ß=0.13, p=0.001) and (ß=0.09, p=0.002), respectively and weight (ß=0.13, p=0.001) and (ß=0.09, p=0.001), respectively. CXCR4 variant was also strongly associated with body fat (ß=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, FLJ39370 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.

Serological response to influenza vaccination among children vaccinated for multiple influenza seasons.

BACKGROUND: To evaluate if, among children aged 3 to 15 years, influenza vaccination for multiple seasons affects the proportion sero-protected. METHODOLOGY/PRINCIPAL FINDINGS: Participants were 131 healthy children aged 3-15 years. Participants were vaccinated with trivalent inactivated seasonal influenza vaccine (TIV) over the 2005-06, 2006-07 and 2007-8 seasons. Number of seasons vaccinated were categorized as one (2007-08); two (2007-08 and 2006-07 or 2007-08 and 2005-06) or three (2005-06, 2006-07, and 2007-08). Pre- and post-vaccination sera were collected four weeks apart. Antibody titres were determined by hemagglutination inhibition (HAI) assay using antigens to A/Solomon Islands/03/06 (H1N1), A/Wisconsin/67/05 (H3N2) and B/Malaysia/2506/04. The proportions sero-protected were compared by number of seasons vaccinated using cut-points for seroprotection of 1:40 vs. 1:320. The proportions of children sero-protected against H1N1 and H3N2 was high (>85%) regardless of number of seasons vaccinated and regardless of cut-point for seroprotection. For B Malaysia there was no change in proportions sero-protected by number of seasons vaccinated; however the proportions protected were lower than for H1N1 and H3N2, and there was a lower proportion sero-protected when the higher, compared to lower, cut-point was used for sero-protection. CONCLUSION/SIGNIFICANCE: The proportion of children sero-protected is not affected by number of seasons vaccinated.

Genome-wide association studies of hypertension: have they been fruitful?

Over the last two decades candidate gene association studies and genome-wide linkage scans have met with little success in characterizing risk variants for hypertension. Several factors could be responsible for the relative lack of success, although our understanding of the genetics has evolved to support the belief that there are multiple common risk variants, which are associated with hypertension with modest effect sizes. Genome-wide association studies (GWAS) have successfully identified risk loci for several complex polygenic disease states. Until recently, the productivity of GWAS with respect to identifying risk loci for hypertension was limited. In this paper we describe the recent success of GWAS of hypertension in identifying over a dozen loci associated with essential hypertension. We will review these findings, and place these results in the context of the future potential of pharmocogenetics of hypertension.

Interleukin-18 polymorphism and physical functioning in older people: a replication study and meta-analysis.

BACKGROUND: Levels of the proinflammatory cytokine interleukin-18 (IL-18) are raised in old age and are associated with reduced physical functioning. Previous studies have indicated that the C allele of the rs5744256 polymorphism in the IL-18 gene is strongly associated with reduced circulating IL-18 levels. This variant has previously been associated with improved locomotor performance in old age, but the finding requires independent replication. METHODS: We examined the association between the IL-18 polymorphism rs5744256 and physical functioning in three cohorts with a total of 4,107 participants aged 60-85 years: the English Longitudinal Study of Ageing, Caerphilly, and Boyd Orr. We meta-analyzed (N = 6,141) the results with data from the original paper reporting this association: Iowa-Established Populations for Epidemiological Study of the Elderly and InCHIANTI cohorts. Physical functioning was assessed by timed walks or the get up and go test. As locomotor performance tests differed between the cohorts and the distributions of times to complete the test (in seconds) were positively skewed, we used the reciprocal transformation and computed study-specific z scores. RESULTS: Based on the three new studies, the estimated linear regression coefficient per C allele was 0.011 (95% confidence interval [95% CI]: -0.04 to 0.06). A meta-analysis that pooled the data from all studies showed weak evidence of an effect, with a regression coefficient of 0.047 (95% CI: 0.010 to 0.083). CONCLUSIONS: We did not replicate an association between the IL-18 rs5744256 polymorphism and the physical function in people aged 60-85 years. However, pooling data from all studies suggested a weak association of the C allele of the rs5744256 single nucleotide polymorphism on improving walking times in old age.