Clonal composition of human ovarian cancer based on copy number analysis reveals a reciprocal relation with oncogenic mutation status.

Intratumoral heterogeneity of cancer cells remains largely unexplored. Here we investigated the composition of ovarian cancer and its biological relevance. A whole-genome single nucleotide polymorphism array was applied to detect the clonal composition of 24 formalin-fixed, paraffin-embedded samples of human ovarian cancer. Genome-wide segmentation data consisting of the log2 ratio (log2R) and B allele frequency (BAF) were used to calculate an estimate of the clonal composition number (CC number) for each tumor. Somatic mutation profiles of cancer-related genes were also determined for the same 24 samples by next-generation sequencing. The CC number was estimated successfully for 23 of the 24 cancer samples. The mean ± SD value for the CC number was 1.7 ± 1.1 (range of 0-4). A somatic mutation in at least one gene was identified in 22 of the 24 ovarian cancer samples, with the mutations including those in the oncogenes KRAS (29.2%), PIK3CA (12.5%), BRAF (8.3%), FGFR2 (4.2%), and JAK2 (4.2%) as well as those in the tumor suppressor genes TP53 (54.2%), FBXW7 (8.3%), PTEN (4.2%), and RB1 (4.2%). Tumors with one or more oncogenic mutations had a significantly lower CC number than did those without such a mutation (1.0 ± 0.8 versus 2.3 ± 0.9, P = 0.0027), suggesting that cancers with driver oncogene mutations are less heterogeneous than those with other mutations. Our results thus reveal a reciprocal relation between oncogenic mutation status and clonal composition in ovarian cancer using the established method for the estimation of the CC number.

Variation in the glucose transporter gene SLC2A2 is associated with glycemic response to metformin.

Metformin is the first-line antidiabetic drug with over 100 million users worldwide, yet its mechanism of action remains unclear. Here the Metformin Genetics (MetGen) Consortium reports a three-stage genome-wide association study (GWAS), consisting of 13,123 participants of different ancestries. The C allele of rs8192675 in the intron of SLC2A2, which encodes the facilitated glucose transporter GLUT2, was associated with a 0.17% (P = 6.6 × 10(-14)) greater metformin-induced reduction in hemoglobin A1c (HbA1c) in 10,577 participants of European ancestry. rs8192675 was the top cis expression quantitative trait locus (cis-eQTL) for SLC2A2 in 1,226 human liver samples, suggesting a key role for hepatic GLUT2 in regulation of metformin action. Among obese individuals, C-allele homozygotes at rs8192675 had a 0.33% (3.6 mmol/mol) greater absolute HbA1c reduction than T-allele homozygotes. This was about half the effect seen with the addition of a DPP-4 inhibitor, and equated to a dose difference of 550 mg of metformin, suggesting rs8192675 as a potential biomarker for stratified medicine.

Quantitative trait locus mapping methods for diversity outbred mice.

Genetic mapping studies in the mouse and other model organisms are used to search for genes underlying complex phenotypes. Traditional genetic mapping studies that employ single-generation crosses have poor mapping resolution and limit discovery to loci that are polymorphic between the two parental strains. Multiparent outbreeding populations address these shortcomings by increasing the density of recombination events and introducing allelic variants from multiple founder strains. However, multiparent crosses present new analytical challenges and require specialized software to take full advantage of these benefits. Each animal in an outbreeding population is genetically unique and must be genotyped using a high-density marker set; regression models for mapping must accommodate multiple founder alleles, and complex breeding designs give rise to polygenic covariance among related animals that must be accounted for in mapping analysis. The Diversity Outbred (DO) mice combine the genetic diversity of eight founder strains in a multigenerational breeding design that has been maintained for >16 generations. The large population size and randomized mating ensure the long-term genetic stability of this population. We present a complete analytical pipeline for genetic mapping in DO mice, including algorithms for probabilistic reconstruction of founder haplotypes from genotyping array intensity data, and mapping methods that accommodate multiple founder haplotypes and account for relatedness among animals. Power analysis suggests that studies with as few as 200 DO mice can detect loci with large effects, but loci that account for <5% of trait variance may require a sample size of up to 1000 animals. The methods described here are implemented in the freely available R package DOQTL.

Genome-wide association studies of maximum number of drinks.

Maximum number of drinks (MaxDrinks) defined as "Maximum number of alcoholic drinks consumed in a 24-h period" is an intermediate phenotype that is closely related to alcohol dependence (AD). Family, twin and adoption studies have shown that the heritability of MaxDrinks is approximately 0.5. We conducted the first genome-wide association (GWA) study and meta-analysis of MaxDrinks as a continuous phenotype. 1059 individuals were from the Collaborative Study on the Genetics of Alcoholism (COGA) sample and 1628 individuals were from the Study of Addiction - Genetics and Environment (SAGE) sample. Family sample with 3137 individuals was from the Australian twin-family study of alcohol use disorder (OZALC). Two population-based Caucasian samples (COGA and SAGE) with 1 million single-nucleotide polymorphisms (SNPs) were used for gene discovery and one family-based Caucasian sample was used for replication. Through meta-analysis we identified 162 SNPs associated with MaxDirnks (p < 10(-4)). The most significant association with MaxDrinks was observed with SNP rs11128951 (p = 4.27 × 10(-8)) near SGOL1 gene at 3p24.3. Furthermore, several SNPs (rs17144687 near DTWD2, rs12108602 near NDST4, and rs2128158 in KCNB2) showed significant associations with MaxDrinks (p < 5 × 10(-7)) in the meta-analysis. Especially, 8 SNPs in DDC gene showed significant associations with MaxDrinks (p < 5 × 10(-7)) in the SAGE sample. Several flanking SNPs in above genes/regions were confirmed in the OZALC family sample. In conclusions, we identified several genes/regions associated with MaxDrinks. These findings can improve the understanding about the pathogenesis of alcohol consumption phenotypes and alcohol-related disorders.

Common variants in HLA-DRA gene are associated with alcohol dependence in two Caucasian samples.

HLA-DRA gene polymorphisms might play an important role in alcohol dependence (AD). We examined genetic associations of 29 single-nucleotide polymorphisms (SNPs) within the HLA-DRA gene with AD using two Caucasian samples-the Collaborative Study on the Genetics of Alcoholism (COGA) sample (660 AD cases and 400 controls) and the Study of Addiction: Genetics and Environment (SAGE) sample (623 cases and 1,016 controls). Logistic regression analysis using PLINK showed that 16 SNPs were associated with AD in the COGA sample and 13 SNPs were associated with AD in the SAGE sample (p < 0.05). The best novel signal was SNP rs2239803 associated with AD in both samples (p = 0.000817 for the COGA sample and p = 0.0026 for the SAGE sample, respectively) while one flanking SNP rs4935356 also showed strong association in both samples (p = 0.00219 and 0.0026 for the COGA and SAGE samples, respectively). Furthermore, these two SNPs revealed stronger associations in meta-analysis of these two samples (p = 8.97 × 10(-6) and 2.02 × 10(-5) for rs2239803 and rs4935356, respectively). In addition, the G-A haplotype from these two SNPs revealed a significant association with AD in both the COGA and SAGE samples (p = 0.0007 and 0.0019, respectively). These findings highlight the novel associations with HLA-DRA that may play an important role in the etiology of AD.

Associations of smoking status and serious psychological distress with chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) has been a major public health problem due to its high prevalence, morbidity, and mortality. Smoking is a major risk factor for COPD, while serious psychological distress (SPD) is prevalent among COPD patients. However, no study focusing on the effect of SPD on COPD has been so far conducted, while few studies have focused on the associations of SPD and behavioral factors with COPD by smoking status. OBJECTIVES: This study aimed to examine the associations of SPD and behavioral factors (such as smoking and physical activity) with COPD. MATERIALS AND METHODS: Weighted logistic regression models were used for the analysis of 1,248 cases and 39,995 controls from the 2005 California Health Interview Survey (CHIS). RESULTS: The prevalence of SPD was 10% in cases and 4% in controls, respectively. The percentages of past and current smoking were higher in cases than controls (50% vs. 24% and 27% vs. 15%, respectively). After adjusting for other factors, smoking (OR = 4.56, 95% CI = 3.41-6.11 and OR = 3.24, 95% CI = 2.57-4.08 for current and past smoking, respectively), physical activity (OR = 0.69, 95% CI = 0.55-0.87), obesity (OR = 1.25, 95% CI = 1.03-1.52), older age (OR = 2.86, 95% CI = 2.15-3.82, and OR = 5.97, 95% CI = 4.42-8.08 for middle-aged and elder groups, respectively), SPD (OR = 2.11, 95% CI = 1.47-3.04), employment (OR = 0.62, 95% CI = 0.51-0.76), race (OR = 0.35, 95% CI = 0.23-0.54, OR = 0.59, 95% CI = 0.36-0.97, and OR = 0.47, 95% CI=0.29-0.75 for Latino, Asian, and African American, respectively) and lower federal poverty level (OR=1.89, 95% CI = 1.35-2.63, OR = 1.65, 95% CI = 1.27-2.14, and OR = 1.39, 95% CI = 1.12-1.72 for 0-99% FPL, 100-199% FPL and 200-299% FPL, respectively) were all associated with COPD (P < 0.05). Age group, SPD, race, and employment showed significant interactions with smoking status. Stratified by smoking status, aging was the only risk factor for COPD in the never smoking group; whereas, lack of physical activity, older age, SPD, race, unemployment, and lower federal poverty level were associated with COPD in the smoking groups. CONCLUSIONS: Smoking and aging were major risk factors for COPD, while lack of physical activity and SPD were strongly associated with COPD in the smoking groups.

Misspecification of Cox regression models with composite endpoints.

Researchers routinely adopt composite endpoints in multicenter randomized trials designed to evaluate the effect of experimental interventions in cardiovascular disease, diabetes, and cancer. Despite their widespread use, relatively little attention has been paid to the statistical properties of estimators of treatment effect based on composite endpoints. We consider this here in the context of multivariate models for time to event data in which copula functions link marginal distributions with a proportional hazards structure. We then examine the asymptotic and empirical properties of the estimator of treatment effect arising from a Cox regression model for the time to the first event. We point out that even when the treatment effect is the same for the component events, the limiting value of the estimator based on the composite endpoint is usually inconsistent for this common value. We find that in this context the limiting value is determined by the degree of association between the events, the stochastic ordering of events, and the censoring distribution. Within the framework adopted, marginal methods for the analysis of multivariate failure time data yield consistent estimators of treatment effect and are therefore preferred. We illustrate the methods by application to a recent asthma study.

PKNOX2 is associated with formal thought disorder in schizophrenia: a meta-analysis of two genome-wide association studies.

Formal thought disorder (FTD), or disorganized speech, is one of the central signs of schizophrenia; however, little is known about the etiology of FTD. To identify new genetic loci associated with FTD, we conducted the first genome-wide association meta-analysis of two datasets of 835 cases of FTD and 2,694 controls with 729,454 single-nucleotide polymorphisms (SNPs). Logistic regression analysis of FTD as a binary trait, adjusted for age and sex, was performed using PLINK. For meta-analysis of two datasets, the fixed-effect model in PLINK was applied. Through meta-analysis we identified 61 SNPs associated with FTD with p < 10(-4). The most significant association with FTD was observed with rs1783925 (p = 4.4 × 10(-7)) within PKNOX2 gene at 11q24.2 while the second interesting locus was rs2277644 (p = 1.18 × 10(-5)) within MYH13 at 17p13. Haplotype analyses of PKNOX2 and MYH13 loci further supported the associations with FTD. The third locus was PHF2 at 9q22.31 (the top SNP was rs12238738 with p = 2.08 × 10(-5)) while the fourth locus was GPC6 at 13q32 (the top SNP was rs17196161 with p = 3.12 × 10(-5)). In conclusion, we identified four new loci (PKNOX2, MYH13, PHF2, and GPC6) associated with FTD. These findings offer the potential for new insights into the pathogenesis of FTD and schizophrenia.

Genome-wide association study identifies 5q21 and 9p24.1 (KDM4C) loci associated with alcohol withdrawal symptoms.

Several genome-wide association (GWA) studies of alcohol dependence (AD) and alcohol-related phenotypes have been conducted; however, little is known about genetic variants influencing alcohol withdrawal symptoms (AWS). We conducted the first GWA study of AWS using 461 cases of AD with AWS and 408 controls in Caucasian population in the Collaborative Study on the Genetics of Alcoholism (COGA) sample. Logistic regression analysis of AWS as a binary trait, adjusted for age and sex, was performed using PLINK. We identified 51 SNPs associated with AWS with p < 10(-4). The first best signal was rs770182 (p = 3.65 × 10(-6)) at 5q21 near EFNA5 gene which was replicated in the Australian twin-family study of 273 families (p = 0.0172). Furthermore, three SNPs (rs10975990, rs10758821 and rs1407862) within KDM4C gene at 9p24.1 showed p < 10(-4) (p = 7.15 × 10(-6), 2.79 × 10(-5) and 4.93 × 10(-5), respectively) in the COGA sample while one SNP rs12001158 within KDM4C with p = 1.97 × 10(-4) in the COGA sample was replicated in the family sample (p = 0.01). Haplotype analysis further supported the associations of single-marker analyses of KDM4C in the COGA sample. Moreover, two SNPs (rs2046593 and rs10497668) near FSIP2 at 2q32.1 with moderate associations with AWS in the COGA sample (p = 2.66 × 10(-4) and 9.48 × 10(-5), respectively) were replicated in the family sample (p = 0.0013 and 0.0162, respectively). In addition, several SNPs in GABRA1, GABRG1, and GABRG3 were associated with AWS (p < 10(-2)) in the COGA sample. In conclusion, we identified several loci associated with AWS. These findings offer the potential for new insights into the pathogenesis of AD and AWS.