The extent to which RNA stability differs between individuals and its contribution to the interindividual expression variation remain unknown. We conducted a genome-wide analysis of RNA stability in seven human HapMap lymphoblastoid cell lines (LCLs) and analyzed the effect of DNA sequence variation on RNA half-life differences. Twenty-six percent of the expressed genes exhibited RNA half-life differences between LCLs at a false discovery rate (FDR) < 0.05, which accounted for ~ 37% of the gene expression differences between individuals. Nonsense polymorphisms were associated with reduced RNA half-lives. In genes presenting interindividual RNA half-life differences, higher coding GC3 contents (G and C percentages at the third-codon positions) were correlated with increased RNA half-life. Consistently, G and C alleles of single nucleotide polymorphisms (SNPs) in protein coding sequences were associated with enhanced RNA stability. These results suggest widespread interindividual differences in RNA stability related to DNA sequence and composition variation.

Background

High systemic estrogen levels contribute to breast cancer risk for postmenopausal women, whereas low levels contribute to osteoporosis risk. Except for obesity, determinants of non-ovarian systemic estrogen levels are undefined. We sought to identify members and functions of the intestinal microbial community associated with estrogen levels via enterohepatic recirculation.

Methods

Fifty-one epidemiologists at the National Institutes of Health, including 25 men, 7 postmenopausal women, and 19 premenopausal women, provided urine and aliquots of feces, using methods proven to yield accurate and reproducible results. Estradiol, estrone, 13 estrogen metabolites (EM), and their sum (total estrogens) were quantified in urine and feces by liquid chromatography/tandem mass spectrometry. In feces, β-glucuronidase and β-glucosidase activities were determined by realtime kinetics, and microbiome diversity and taxonomy were estimated by pyrosequencing 16S rRNA amplicons. Pearson correlations were computed for each loge estrogen level, loge enzymatic activity level, and microbiome alpha diversity estimate. For the 55 taxa with mean relative abundance of at least 0.1%, ordinal levels were created [zero, low (below median of detected sequences), high] and compared to loge estrogens, β-glucuronidase and β-glucosidase enzymatic activity levels by linear regression. Significance was based on two-sided tests with α=0.05.

Results

In men and postmenopausal women, levels of total urinary estrogens (as well as most individual EM) were very strongly and directly associated with all measures of fecal microbiome richness and alpha diversity (R≥0.50, P≤0.003). These non-ovarian systemic estrogens also were strongly and significantly associated with fecal Clostridia taxa, including non-Clostridiales and three genera in the Ruminococcaceae family (R=0.57−0.70, P=0.03−0.002). Estrone, but not other EM, in urine correlated significantly with functional activity of fecal β-glucuronidase (R=0.36, P=0.04). In contrast, fecal β-glucuronidase correlated inversely with fecal total estrogens, both conjugated and deconjugated (R≤-0.47, P≤0.01). Premenopausal female estrogen levels, which were collected across menstrual cycles and thus highly variable, were completely unrelated to fecal microbiome and enzyme parameters (P≥0.6).

Conclusions

Intestinal microbial richness and functions, including but not limited to β-glucuronidase, influence levels of non-ovarian estrogens via enterohepatic circulation. Thus, the gut microbial community likely affects the risk for estrogen-related conditions in older adults. Understanding how Clostridia taxa relate to systemic estrogens may identify targets for interventions.

Trial registration

Not applicable.

BACKGROUND

Tobacco-induced lung cancer is characterized by a deregulated inflammatory microenvironment. Variants in multiple genes in inflammation pathways may contribute to risk of lung cancer.

METHODS

We therefore conducted a three-stage comprehensive pathway analysis (discovery, replication and meta-analysis) of inflammation gene variants in ever smoking lung cancer cases and controls. A discovery set (1096 cases; 727 controls) and an independent and non-overlapping internal replication set (1154 cases; 1137 controls) were derived from an ongoing case-control study. For discovery, we used an iSelect BeadChip to interrogate a comprehensive panel of 11737 inflammation pathway SNPs and selected nominally significant (p<0.05) SNPs for internal replication.

RESULTS

There were 6 SNPs that achieved statistical significance (p<0.05) in the internal replication dataset with concordant risk estimates for former smokers and 5 concordant and replicated SNPs in current smokers. Replicated hits were further tested in a subsequent meta-analysis using external data derived from two published GWAS and a case-control study. Two of these variants (a BCL2L14 SNP in former smokers and a SNP in IL2RB in current smokers) were further validated. In risk score analyses, there was a 26% increase in risk with each additional adverse allele when we combined the genotyped SNP and the most significant imputed SNP in IL2RB in current smokers and a 36% similar increase in risk for former smokers associated with genotyped and imputed BCL2L14 SNPs.

CONCLUSIONS/IMPACT

Before they can be applied for risk prediction efforts, these SNPs should be subject to further external replication and more extensive fine mapping studies.

Meta-analysis of genome-wide association studies involves testing single nucleotide polymorphisms (SNPs) using summary statistics that are weighted sums of site-specific score or Wald statistics. This approach avoids having to pool individual-level data. We describe the weights that maximize the power of the summary statistics. For small effect-sizes, any choice of weights yields summary Wald and score statistics with the same power, and the optimal weights are proportional to the square roots of the sites' Fisher information for the SNP's regression coefficient. When SNP effect size is constant across sites, the optimal summary Wald statistic is the well-known inverse-variance-weighted combination of estimated regression coefficients, divided by its standard deviation. We give simple approximations to the optimal weights for various phenotypes, and show that weights proportional to the square roots of study sizes are suboptimal for data from case-control studies with varying case-control ratios, for quantitative trait data when the trait variance differs across sites, for count data when the site-specific mean counts differ, and for survival data with different proportions of failing subjects. Simulations suggest that weights that accommodate inter-site variation in imputation error give little power gain compared to those obtained ignoring imputation uncertainties. We note advantages to combining site-specific score statistics, and we show how they can be used to assess effect-size heterogeneity across sites. The utility of the summary score statistic is illustrated by application to a meta-analysis of schizophrenia data in which only site-specific p-values and directions of association are available.

While lung cancer is largely caused by tobacco smoking, inherited genetic factors play a role in its etiology. Genome-wide association studies (GWAS) in Europeans have robustly demonstrated only three polymorphic variations influencing lung cancer risk. Tumor heterogeneity may have hampered the detection of association signal when all lung cancer subtypes were analyzed together. In a GWAS of 5,355 European smoking lung cancer cases and 4,344 smoking controls, we conducted a pathway-based analysis in lung cancer histologic subtypes with 19,082 SNPs mapping to 917 genes in the HuGE-defined “inflammation” pathway. We identified a susceptibility locus for squamous cell lung carcinoma (SQ) at 12p13.33 (RAD52, rs6489769), and replicated the association in three independent samples totaling 3,359 SQ cases and 9,100 controls (odds ratio=1.20, Pcombined=2.3×10−8).

Significance

The combination of pathway-based approaches and information on disease specific subtypes can improve the identification of cancer susceptibility loci in heterogeneous diseases.

Genome-wide association studies (GWAS) of body mass index (BMI) using large samples have yielded approximately a dozen robustly associated variants and implicated additional loci. Individually these variants have small effects and in aggregate explain a small proportion of the variance. As a result, replication attempts have limited power to achieve genome-wide significance, even with several thousand subjects. Since there is strong prior evidence for genetic influence on BMI for specific variants, alternative approaches to replication can be applied. Instead of testing individual loci sequentially, a genetic risk sum score (GRSS) summarizing the total number of risk alleles can be tested. In the current study, GRSS comprising 56 top variants catalogued from two large meta-analyses was tested for association with BMI in the Molecular Genetics of Schizophrenia controls (2,653 European-Americans, 973 African-Americans). After accounting for covariates known to influence BMI (ancestry, sex, age), GRSS was highly associated with BMI (p value = 3.19E−06) although explained a limited amount of the variance (0.66%). However, area under receiver operator criteria curve (AUC) estimates indicated that the GRSS and covariates significantly predicted overweight and obesity classification with maximum discriminative ability for predicting class III obesity (AUC = 0.697). The relative contributions of the individual loci to GRSS were examined post hoc and the results were not due to a few highly significant variants, but rather the result of numerous variants of small effect. This study provides evidence of the utility of a GRSS as an alternative approach to replication of common polygenic variation in complex traits.

Few microbial functions have been compared to a comprehensive survey of the human fecal microbiome. We evaluated determinants of fecal microbial β-glucuronidase and β-glucosidase activities, focusing especially on associations with microbial alpha and beta diversity and taxonomy. We enrolled 51 healthy volunteers (26 female, mean age 39) who provided questionnaire data and multiple aliquots of a stool, from which proteins were extracted to quantify β-glucuronidase and β-glucosidase activities, and DNA was extracted to amplify and pyrosequence 16S rRNA gene sequences to classify and quantify microbiome diversity and taxonomy. Fecal β-glucuronidase was elevated with weight loss of at least 5 lb. (P = 0.03), whereas β-glucosidase was marginally reduced in the four vegetarians (P = 0.06). Both enzymes were correlated directly with microbiome richness and alpha diversity measures, directly with the abundance of four Firmicutes Clostridia genera, and inversely with the abundance of two other genera (Firmicutes Lactobacillales Streptococcus and Bacteroidetes Rikenellaceae Alistipes) (all P = 0.05–0.0001). Beta diversity reflected the taxonomic associations. These observations suggest that these enzymatic functions are performed by particular taxa and that diversity indices may serve as surrogates of bacterial functions. Independent validation and deeper understanding of these associations are needed, particularly to characterize functions and pathways that may be amenable to manipulation.

Recent studies suggest that variation in complex disorders (e.g., schizophrenia) is explained by a large number of genetic variants with small effect size (Odds Ratio∼1.05–1.1). The statistical power to detect these genetic variants in Genome Wide Association (GWA) studies with large numbers of cases and controls (∼15,000) is still low. As it will be difficult to further increase sample size, we decided to explore an alternative method for analyzing GWA data in a study of schizophrenia, dramatically reducing the number of statistical tests. The underlying hypothesis was that at least some of the genetic variants related to a common outcome are collocated in segments of chromosomes at a wider scale than single genes. Our approach was therefore to study the association between relatively large segments of DNA and disease status. An association test was performed for each SNP and the number of nominally significant tests in a segment was counted. We then performed a permutation-based binomial test to determine whether this region contained significantly more nominally significant SNPs than expected under the null hypothesis of no association, taking linkage into account. Genome Wide Association data of three independent schizophrenia case/control cohorts with European ancestry (Dutch, German, and US) using segments of DNA with variable length (2 to 32 Mbp) was analyzed. Using this approach we identified a region at chromosome 5q23.3-q31.3 (128–160 Mbp) that was significantly enriched with nominally associated SNPs in three independent case-control samples. We conclude that considering relatively wide segments of chromosomes may reveal reliable relationships between the genome and schizophrenia, suggesting novel methodological possibilities as well as raising theoretical questions.

Background

While genetic influences on Alcohol Dependence (AD) are substantial, progress in the identification of individual genetic variants that impact on risk has been difficult.

Methods

We performed a genome-wide association study on 3,169 alcohol consuming subjects from the population-based Molecular Genetics of Schizophrenia (MGS2) control sample. Subjects were asked 7 questions about symptoms of AD which were analyzed by confirmatory factor analysis. Genotyping was performed using the Affymetrix 6.0 array. Three sets of analyses were conducted separately for European American (EA, n=2,357) and African-American (AA, n=812) subjects: individual SNPs, candidate genes and enriched pathways using Gene Ontology (GO) categories.

Results

The symptoms of AD formed a highly coherent single factor. No SNP approached genome-wide significance. In the EA sample, the most significant intragenic SNP was in KCNMA1, the human homolog of the slo-1 gene in C. Elegans. Genes with clusters of significant SNPs included AKAP9, PIGG and KCNMA1. In the AA sample, the most significant intragenic SNP was CEACAM6 and genes showing empirically significant SNPs included KCNQ5, SLC35B4 and MGLL. In the candidate gene based analyses, the most significant findings were with ADH1C, NFKB1 and ANKK1 in the EA sample, and ADH5, POMC, and CHRM2 in the AA sample. The ALIGATOR program identified a significant excess of associated SNPs within and near genes in a substantial number of GO categories over a range of statistical stringencies in both the EA and AA sample.

Conclusions

While we cannot be highly confident about any single result from these analyses, a number of findings were suggestive and worthy of follow-up. Although quite large samples will be needed to obtain requisite power, the study of AD symptoms in general population samples is a viable complement to case-control studies in identifying genetic risk variants for AD.

Three recent genome-wide association studies of testicular germ cell tumors have uncovered predisposition alleles in or near several genes, including KITLG, BAK1, SPRY4, TERT, ATF7IP, and DMRT1. The calculated per-allele odds ratio for variants in the region of KITLG is the highest reported for any malignancy so far. These findings are in agreement with epidemiological data indicating that testicular cancer has a higher heritability than most other cancers. Here, we discuss the question of whether the newly identified risk polymorphisms can be used to guide patient care.

Background

Recent genome-wide association studies have associated polymorphisms in the gene CACNA1C, which codes for Cav1.2, with a bipolar disorder and depression diagnosis.

Methods

The behaviors of wild type and Cacna1c heterozygous mice of both sexes were evaluated in a number of tests. Based upon sex differences in our mouse data, we assessed a gene x sex interaction for diagnosis of mood disorders in human subjects. Data from the NIMH-BP Consortium and the GenRED Consortium were examined utilizing a combined dataset that included 2,021 mood disorder cases (1,223 females) and 1,840 controls (837 females).

Results

In both male and female mice, Cacna1c haploinsufficiency is associated with lower exploratory behavior, decreased response to amphetamine, and antidepressant-like behavior in the forced swim and tail suspension tests. Female, but not male, heterozygous mice displayed decreased risk-taking behavior or increased anxiety in multiple tests, greater attenuation of amphetamine-induced hyperlocomotion, decreased development of learned helplessness, and a decreased acoustic startle response indicating a sex-specific role of Cacna1c. In humans, sex-specific genetic association was seen for two intronic single nucleotide polymorphisms (SNPs), rs2370419 and rs2470411, in CACNA1C, with effects in females (OR=1.64, 1.32), but not in males (OR=0.82, 0.86). The interactions by sex were significant after correction for testing 190 SNPs (P=1.4 x 10−4, 2.1 x 10−4; Pcorrected=0.03, 0.04), and were consistent across two large data sets.

Conclusions

Our preclinical results support a role for CACNA1C in mood disorder pathophysiology, and the combination of human genetic and preclinical data support an interaction between sex and genotype.

Linkage analysis has been widely used to identify from family data genetic variants influencing quantitative traits. Common approaches have both strengths and limitations. Likelihood ratio tests typically computed in variance component analysis can accommodate large families but are highly sensitive to departure from normality assumptions. Regression-based approaches are more robust but their use has primarily been restricted to nuclear families. In this paper, we develop methods for mapping quantitative traits in moderately large pedigrees. Our methods are based on the score statistic which in contrast to the likelihood ratio statistic, can use nonparametric estimators of variability to achieve robustness of the false positive rate against departures from the hypothesized phenotypic model. Because the score statistic is easier to calculate than the likelihood ratio statistic, our basic mapping methods utilize relatively simple computer code that performs statistical analysis on output from any program that computes estimates of identity-by-descent. This simplicity also permits development and evaluation of methods to deal with multivariate and ordinal phenotypes, and with gene-gene and gene-environment interaction. We demonstrate our methods on simulated data and on fasting insulin, a quantitative trait measured in the Framingham Heart Study.

Schizophrenia, a devastating psychiatric disorder, has a prevalence of 0.5–1%, with high heritability (80–85%) and complex transmission.1 Recent studies implicate rare, large, high-penetrance copy number variants (CNVs) in some cases2, but it is not known what genes or biological mechanisms underlie susceptibility. Here we show that schizophrenia is significantly associated with single nucleotide polymorphisms (SNPs) in the extended Major Histocompatibility Complex (MHC) region on chromosome 6. We carried out a genome-wide association study (GWAS) of common SNPs in the Molecular Genetics of Schizophrenia (MGS) case-control sample, and then a meta-analysis of data from the MGS, International Schizophrenia Consortium (ISC) and SGENE datasets. No MGS finding achieved genome-wide statistical significance. In the meta-analysis of European-ancestry subjects (8,008 cases, 19,077 controls), significant association with schizophrenia was observed in a region of linkage disequilibrium on chromosome 6p22.1 (P = 9.54 × 10−9). This region includes a histone gene cluster and several immunity-related genes, possibly implicating etiologic mechanisms involving chromatin modification, transcriptional regulation, auto-immunity and/or infection. These results demonstrate that common schizophrenia susceptibility alleles can be detected. The characterization of these signals will suggest important directions for research on susceptibility mechanisms.

When testing large numbers of null hypotheses, one needs to assess the evidence against the global null hypothesis that none of the hypotheses is false. Such evidence typically is based on the test statistic of the largest magnitude, whose statistical significance is evaluated by permuting the sample units to simulate its null distribution. Efron (2007) has noted that correlation among the test statistics can induce substantial interstudy variation in the shapes of their histograms, which may cause misleading tail counts. Here, we show that permutation-based estimates of the overall significance level also can be misleading when the test statistics are correlated. We propose that such estimates be conditioned on a simple measure of the spread of the observed histogram, and we provide a method for obtaining conditional significance levels. We justify this conditioning using the conditionality principle described by Cox and Hinkley (1974). Application of the method to gene expression data illustrates the circumstances when conditional significance levels are needed.

Morley et al. (Nature 2004, 430:743–747) detected significant linkages to the expression levels of 142 genes (of 3554) at a reported threshold of genome-wide p = 0.001 (LOD ≈ 5.3), using 14 three-generation Centre d'Etude du Polymorphisme Humain pedigrees. Most of the linkages (77%) were trans, i.e., more than 5 Mb from the expressed gene. However, the analysis did not account for the expected anti-conservative effect of the skewed distribution of score- or regression-based statistics in large sibships, or for the possible variance distortion due to correlations among tests. Therefore, we re-analyzed their data, using a robust score statistic for the entire pedigrees and correcting the p-values for skewness. We found that a LOD of 5.3 had a skewness-corrected genome-wide p-value of 0.016 instead of 0.001 (a result that we confirmed using simulation), with around 50 expected false positives. We then further corrected for correlation among the (skew-corrected) p-values by using Efron's method for obtaining the empirical null distribution. Setting a threshold of FDR = 10% (Z = 6.4, LOD = 8.9), we detected linkage for the expression levels of 22 genes, 19 of which are cis. Limiting the analysis to cis regions, linkage was detected to the expression levels of 46 genes with 4.6 expected false positives (FDR = 10%).