BackgroundIt has been shown that gene expression in human tissues is heritable, thus predicting gene expression using only SNPs becomes possible. The prediction of gene expression can offer important implications on the genetic architecture of individual functional associated SNPs and further interpretations of the molecular basis underlying human diseases.MethodsWe compared three types of methods for predicting gene expression using only cis-SNPs, including the polygenic model, i.e. linear mixed model (LMM), two sparse models, i.e. Lasso and elastic net (ENET), and the hybrid of LMM and sparse model, i.e. Bayesian sparse linear mixed model (BSLMM). The three kinds of prediction methods have very different assumptions of underlying genetic architectures. These methods were evaluated using simulations under various scenarios, and were applied to the Geuvadis gene expression data.ResultsThe simulations showed that these four prediction methods (i.e. Lasso, ENET, LMM and BSLMM) behaved best when their respective modeling assumptions were satisfied, but BSLMM had a robust performance across a range of scenarios. According to R 2 of these models in the Geuvadis data, the four methods performed quite similarly. We did not observe any clustering or enrichment of predictive genes (defined as genes with R 2 ≥ 0.05) across the chromosomes, and also did not see there was any clear relationship between the proportion of the predictive genes and the proportion of genes in each chromosome. However, an interesting finding in the Geuvadis data was that highly predictive genes (e.g. R 2 ≥ 0.30) may have sparse genetic architectures since Lasso, ENET and BSLMM outperformed LMM for these genes; and this observation was validated in another gene expression data. We further showed that the predictive genes were enriched in approximately independent LD blocks.ConclusionsGene expression can be predicted with only cis-SNPs using well-developed prediction models and these predictive genes were enriched in some approximately independent LD blocks. The prediction of gene expression can shed some light on the functional interpretation for identified SNPs in GWASs.

BackgroundThe impact of the various elements of metabolic syndrome (MetS) on chronic kidney disease (CKD) has been conflicting. Therefore, in the present study we aimed to examine the association of MetS and its components with decreased glomerular filtration rate (GFR).MethodsA total of 75,468 urban workers who underwent annual health examinations under the auspices of the local governments between March 2010 and September 2012 at the health examination center of Xuzhou center hospital were enrolled in the cross-sectional survey. Decreased GFR was defined as an estimated GFR <60 mL/min per 1.73 m2. The definition of MetS was based on the most-recent interim joint consensus definition, requiring any three of the five components, consisting of elevated blood pressure (BP), fasting plasma glucose (FPG), or triglycerides (TG), reduced high density lipoprotein-cholesterol (HDL-c), and obesity.ResultsMetS was related to the reduced GFR with an odds ratio [95% confidence interval (CI)] of 1.43 (1.13–1.83). In multivariable analyses, individual components of MetS that were independently associated with decreased GFR were elevated BP (OR 1.34, 95% CI 1.00–1.78), low HDL-c (OR 1.88, 95% CI 1.44–2.43), and elevated FPG (OR 1.42, 95% CI 1.09–1.85). The age-adjusted population-attributable risk percent (PARP) for reduced GFR was 27.55%, 19.67% and14.31% for elevated BP, low HDL-c and elevated FPG respectively. The multivariate-adjusted odds ratios (95% CI) of decreased GFR were 1.70(1.11–2.60), 2.38(1.53–3.71), or 4.11(2.42–6.98) for those with 1, 2, or 3 critical elements (versus zero), respectively. The corresponding multivariate-adjusted odds ratios (95% CI) of decreased GFR were 1.11(0.84–1.48) and 0.89(0.63–1.27) for those with 1 or 2 noncritical components (versus zero), respectively.ConclusionsWe concluded that various elements of MetS and the cumulative number of MetS should not be considered indiscriminately as risk factors for reduced GFR.

Background Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear. Methods We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders. Results We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders. Conclusions Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.

SummaryIt is believed that rare variants play an important role in human phenotypes; however, the detection of rare variants is extremely challenging due to their very low minor allele frequency. In this paper, the likelihood ratio test (LRT) and restricted likelihood ratio test (ReLRT) are proposed to test the association of rare variants based on the linear mixed effects model, where a group of rare variants are treated as random effects. Like the sequence kernel association test (SKAT), a state-of-the-art method for rare variant detection, LRT and ReLRT can effectively overcome the problem of directionality of effect inherent in the burden test in practice. By taking full advantage of the spectral decomposition, exact finite sample null distributions for LRT and ReLRT are obtained by simulation. We perform extensive numerical studies to evaluate the performance of LRT and ReLRT, and compare to the burden test, SKAT and SKAT-O. The simulations have shown that LRT and ReLRT can correctly control the type I error, and the controls are robust to the weights chosen and the number of rare variants under study. LRT and ReLRT behave similarly to the burden test when all the causal rare variants share the same direction of effect, and outperform SKAT across various situations. When both positive and negative effects exist, LRT and ReLRT suffer from few power reductions compared to the other two competing methods; under this case, an additional finding from our simulations is that SKAT-O is no longer the optimal test, and its power is even lower than that of SKAT. The exome sequencing SNP data from Genetic Analysis Workshop 17 were employed to illustrate the proposed methods, and interesting results are described.

Observational studies showed an inverse association between birth weight and chronic kidney disease (CKD) in adulthood existed. However, whether such an association is causal remains fully elusive. Moreover, none of prior studies distinguished the direct fetal effect from the indirect maternal effect. Herein, we aimed to investigate the causal relationship between birth weight and CKD and to understand the relative fetal and maternal contributions. Meta-analysis (n = ~22 million) showed that low birth weight led to ~83% (95% confidence interval [CI] 37–146%) higher risk of CKD in late life. With summary statistics from large scale GWASs (n = ~300 000 for birth weight and ~481 000 for CKD), linkage disequilibrium score regression demonstrated birth weight had a negative maternal, but not fetal, genetic correlation with CKD and several other kidney-function related phenotypes. Furthermore, with multiple instruments of birth weight, Mendelian randomization showed there existed a negative fetal casual association (OR = 1.10, 95% CI 1.01–1.16) between birth weight and CKD; a negative but non-significant maternal casual association (OR = 1.09, 95% CI 0.98–1.21) was also identified. Those associations were robust against various sensitivity analyses. However, no maternal/fetal casual effects of birth weight were significant for other kidney-function related phenotypes. Overall, our study confirmed the inverse association between birth weight and CKD observed in prior studies, and further revealed the shared maternal genetic foundation between low birth weight and CKD, and the direct fetal and indirect maternal causal effects of birth weight may commonly drive this negative relationship.