Improving power of genome-wide association studies with weighted false discovery rate control and prioritized subset analysis

Genome-wide association study for four measures of epigenetic age acceleration and two epigenetic surrogate markers using DNA methylation data from Taiwan biobank

To highlight the genetic architecture for epigenetic aging, McCartney et al. recently identified 137 significant SNPs based on genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and two epigenetic surrogate markers. However, none Asian ancestry studies have been included in this or previous meta-analyses. I performed a GWAS on blood DNA methylation (DNAm) levels of 2309 Taiwan Biobank (TWB) participants. Owing to the fact that the sample size of an individual GWAS of DNAm data is still not large, I adopted the "prioritized subset analysis" (PSA) method to boost the power of a GWAS. The four epigenetic clocks and the two epigenetic surrogate markers were investigated, respectively. I replicated 21 out of the 137 aging-associated genetic loci by applying the PSA method to the TWB DNAm data. Moreover, I identified five novel loci, including rs117530284 that was associated with the "epigenetic age acceleration" (EAA) according to Lu et al.'s GrimAge (called "GrimEAA"). Considering 16 covariates (sex, BMI, smoking status, drinking status, regular exercise, educational attainment, and the first 10 ancestry principal components), each "A" allele of rs117530284 in the IBA57 gene was found to be associated with a 1.5943-year GrimEAA (95% C.I. = [1.0748, 2.1138]). IBA57 is a protein coding gene and is associated with multiple mitochondrial dysfunctions syndromes. A decline in mitochondrial activity and quality is associated with aging and many age-related diseases. This is one of the first DNAm GWAS for individuals of Asian ancestry.

Statistical Methods in Genome-Wide Association Studies

Since the initial success of genome-wide association studies (GWAS) in 2005, tens of thousands of genetic variants have been identified for hundreds of human diseases and traits. In a GWAS, genotype information at up to millions of genetic markers is collected from up to hundreds of thousands of individuals, together with their phenotype information. Several scientific goals can be accomplished through the analysis of GWAS data, including the identification of variants, genes, and pathways associated with diseases and traits of interest; the inference of the genetic architecture of these traits; and the development of genetic risk prediction models. In this review, we provide an overview of the statistical challenges in achieving these goals and recent progress in statistical methodology to address these challenges.

Weighted False Discovery Rate Control in Large-Scale Multiple Testing

The use of weights provides an effective strategy to incorporate prior domain knowledge in large-scale inference. This paper studies weighted multiple testing in a decision-theoretic framework. We develop oracle and data-driven procedures that aim to maximize the expected number of true positives subject to a constraint on the weighted false discovery rate. The asymptotic validity and optimality of the proposed methods are established. The results demonstrate that incorporating informative domain knowledge enhances the interpretability of results and precision of inference. Simulation studies show that the proposed method controls the error rate at the nominal level, and the gain in power over existing methods is substantial in many settings. An application to a genome-wide association study is discussed.

Hierarchical investigation of genetic influences on response inhibition in healthy young adults

Poor inhibitory control is a known risk factor for substance use disorders, making it a priority to identify the determinants of these deficits. The aim of the current study was to identify genetic associations with inhibitory control using the stop signal task in a large sample (n = 934) of healthy young adults of European ancestry. We genotyped the subjects genome-wide and then used a hierarchical approach in which we tested seven a priori single nucleotide polymorphisms (SNPs) previously associated with stop signal task performance, approximately 9,000 SNPs designated as high-value addiction (HVA) markers by the SmokeScreen array, and approximately five million genotyped and imputed SNPs, followed by a gene-based association analysis using the resultant p values. A priori SNP analyses revealed nominally significant associations between response inhibition and one locus in HTR2A (rs6313; p = .04, dominance model, uncorrected) in the same direction as prior findings. A nominally significant association was also found in one locus in ANKK1 (rs1800497; p = .03, uncorrected), although in the opposite direction of previous reports. After accounting for multiple comparisons, the HVA, genome-wide, and gene-based analyses yielded no significant findings. This study implicates variation in serotonergic and dopaminergic genes while underscoring the difficulty of detecting the influence of individual SNPs, even when biological information is used to prioritize testing. Although such small effect sizes suggest limited utility of individual SNPs in predicting risk for addiction or other impulse control disorders, they may nonetheless shed light on complex biological processes underlying poor inhibitory control. (PsycINFO Database Record

CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data

Capture Hi-C (CHi-C) is a method for profiling chromosomal interactions involving targeted regions of interest, such as gene promoters, globally and at high resolution. Signal detection in CHi-C data involves a number of statistical challenges that are not observed when using other Hi-C-like techniques. We present a background model and algorithms for normalisation and multiple testing that are specifically adapted to CHi-C experiments. We implement these procedures in CHiCAGO ( http://regulatorygenomicsgroup.org/chicago ), an open-source package for robust interaction detection in CHi-C. We validate CHiCAGO by showing that promoter-interacting regions detected with this method are enriched for regulatory features and disease-associated SNPs.

Exome sequencing of oral squamous cell carcinoma in users of Arabian snuff reveals novel candidates for driver genes

The study sought to identify genetic aberrations driving oral squamous cell carcinoma (OSCC) development among users of shammah, an Arabian preparation of smokeless tobacco. Twenty archival OSCC samples, 15 of which with a history of shammah exposure, were whole-exome sequenced at an average depth of 127×. Somatic mutations were identified using a novel, matched controls-independent filtration algorithm. CODEX and Exomedepth coupled with a novel, Database of Genomic Variant-based filter were employed to call somatic gene-copy number variations. Significantly mutated genes were identified with Oncodrive FM and the Youn and Simon's method. Candidate driver genes were nominated based on Gene Set Enrichment Analysis. The observed mutational spectrum was similar to that reported by the TCGA project. In addition to confirming known genes of OSCC (TP53, CDKNA2, CASP8, PIK3CA, HRAS, FAT1, TP63, CCND1 and FADD) the analysis identified several candidate novel driver events including mutations of NOTCH3, CSMD3, CRB1, CLTCL1, OSMR and TRPM2, amplification of the proto-oncogenes FOSL1, RELA, TRAF6, MDM2, FRS2 and BAG1, and deletion of the recently described tumor suppressor SMARCC1. Analysis also revealed significantly altered pathways not previously implicated in OSCC including Oncostatin-M signalling pathway, AP-1 and C-MYB transcription networks and endocytosis. There was a trend for higher number of mutations, amplifications and driver events in samples with history of shammah exposure particularly those that tested EBV positive, suggesting an interaction between tobacco exposure and EBV. The work provides further evidence for the genetic heterogeneity of oral cancer and suggests shammah-associated OSCC is characterized by extensive amplification of oncogenes.

Pharmacogenetics of antidepressant drugs: an update after almost 20 years of research

Major depressive disorder (MDD) is an emergent cause of personal and socio-economic burden, both for the high prevalence of the disorder and the unsatisfying response rate of the available antidepressant treatments. No reliable predictor of treatment efficacy and tolerance in the single patient is available, thus drug choice is based on a trial and error principle with poor clinical efficiency. Among modulators of treatment outcome, genetic polymorphisms are thought to explain a significant share of the inter-individual variability. The present review collected the main pharmacogenetic findings primarily about antidepressant response and secondly about antidepressant induced side effects, and discussed the main strengths and limits of both candidate and genome-wide association studies and the most promising methodological opportunities and challenges of the field. Despite clinical applications of antidepressant pharmacogenetics are not available yet, previous findings suggest that genotyping may be applied in the clinical practice. In order to reach this objective, further rigorous pharmacogenetic studies (adequate sample size, study of better defined clinical subtypes of MDD, adequate covering of the genetic variability), their combination with the results obtained through complementary methodologies (e.g., pathway analysis, epigenetics, transcriptomics, and proteomics), and finally cost-effectiveness trials are required.

Multiscale modeling of the causal functional roles of nsSNPs in a genome-wide association study: application to hypoxia

BACKGROUND

It is a great challenge of modern biology to determine the functional roles of non-synonymous Single Nucleotide Polymorphisms (nsSNPs) on complex phenotypes. Statistical and machine learning techniques establish correlations between genotype and phenotype, but may fail to infer the biologically relevant mechanisms. The emerging paradigm of Network-based Association Studies aims to address this problem of statistical analysis. However, a mechanistic understanding of how individual molecular components work together in a system requires knowledge of molecular structures, and their interactions.

RESULTS

To address the challenge of understanding the genetic, molecular, and cellular basis of complex phenotypes, we have, for the first time, developed a structural systems biology approach for genome-wide multiscale modeling of nsSNPs--from the atomic details of molecular interactions to the emergent properties of biological networks. We apply our approach to determine the functional roles of nsSNPs associated with hypoxia tolerance in Drosophila melanogaster. The integrated view of the functional roles of nsSNP at both molecular and network levels allows us to identify driver mutations and their interactions (epistasis) in H, Rad51D, Ulp1, Wnt5, HDAC4, Sol, Dys, GalNAc-T2, and CG33714 genes, all of which are involved in the up-regulation of Notch and Gurken/EGFR signaling pathways. Moreover, we find that a large fraction of the driver mutations are neither located in conserved functional sites, nor responsible for structural stability, but rather regulate protein activity through allosteric transitions, protein-protein interactions, or protein-nucleic acid interactions. This finding should impact future Genome-Wide Association Studies.

CONCLUSIONS

Our studies demonstrate that the consolidation of statistical, structural, and network views of biomolecules and their interactions can provide new insight into the functional role of nsSNPs in Genome-Wide Association Studies, in a way that neither the knowledge of molecular structures nor biological networks alone could achieve. Thus, multiscale modeling of nsSNPs may prove to be a powerful tool for establishing the functional roles of sequence variants in a wide array of applications.

Transcriptome and proteome exploration to provide a resource for the study of Agrocybe aegerita

BACKGROUND

Agrocybe aegerita, the black poplar mushroom, has been highly valued as a functional food for its medicinal and nutritional benefits. Several bioactive extracts from A. aegerita have been found to exhibit antitumor and antioxidant activities. However, limited genetic resources for A. aegerita have hindered exploration of this species.

METHODOLOGY/PRINCIPAL FINDINGS

To facilitate the research on A. aegerita, we established a deep survey of the transcriptome and proteome of this mushroom. We applied high-throughput sequencing technology (Illumina) to sequence A. aegerita transcriptomes from mycelium and fruiting body. The raw clean reads were de novo assembled into a total of 36,134 expressed sequences tags (ESTs) with an average length of 663 bp. These ESTs were annotated and classified according to Gene Ontology (GO), Clusters of Orthologous Groups (COG), and Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways. Gene expression profile analysis showed that 18,474 ESTs were differentially expressed, with 10,131 up-regulated in mycelium and 8,343 up-regulated in fruiting body. Putative genes involved in polysaccharide and steroid biosynthesis were identified from A. aegerita transcriptome, and these genes were differentially expressed at the two stages of A. aegerita. Based on one-dimensional gel electrophoresis (1-DGE) coupled with electrospray ionization liquid chromatography tandem MS (LC-ESI-MS/MS), we identified a total of 309 non-redundant proteins. And many metabolic enzymes involved in glycolysis were identified in the protein database.

CONCLUSIONS/SIGNIFICANCE

This is the first study on transcriptome and proteome analyses of A. aegerita. The data in this study serve as a resource of A. aegerita transcripts and proteins, and offer clues to the applications of this mushroom in nutrition, pharmacy and industry.