Gene-based tests of association

Genome-wide association studies using multi-models and multi-SNP datasets provide new insights into pasmo resistance in flax

Introduction

Flax (Linum usitatissimum L.) is an economically important crop due to its oil and fiber. However, it is prone to various diseases, including pasmo caused by the fungus Septoria linicola.

Methods

In this study, we conducted field evaluations of 445 flax accessions over a five-year period (2012-2016) to assess their resistance to pasmo A total of 246,035 single nucleotide polymorphisms (SNPs) were used for genetic analysis. Four statistical models, including the single-locus model GEMMA and the multi-locus models FarmCPU, mrMLM, and 3VmrMLM, were assessed to identify quantitative trait nucleotides (QTNs) associated with pasmo resistance.

Results

We identified 372 significant QTNs or 132 tag QTNs associated with pasmo resistance from five pasmo resistance datasets (PAS2012-PAS2016 and the 5-year average, namely PASmean) and three genotypic datasets (the all SNPs/ALL, the gene-based SNPs/GB and the RGA-based SNPs/RGAB). The tag QTNs had R2 values of 0.66-16.98% from the ALL SNP dataset, 0.68-20.54%from the GB SNP dataset, and 0.52-22.42% from the RGAB SNP dataset. Of these tag QTNs, 93 were novel. Additionally, 37 resistance gene analogs (RGAs)co-localizing with 39 tag QTNs were considered as potential candidates for controlling pasmo resistance in flax and 50 QTN-by-environment interactions(QEIs) were identified to account for genes by environmental interactions. Nine RGAs were predicted as candidate genes for ten QEIs.

Discussion

Our results suggest that pasmo resistance in flax is polygenic and potentially influenced by environmental factors. The identified QTNs provide potential targets for improving pasmo resistance in flax breeding programs. This study sheds light on the genetic basis of pasmo resistance and highlights the importance of considering both genetic and environmental factors in breeding programs for flax.

Cross-phenotype association analysis of gastric cancer: in-silico functional annotation based on the disease-gene network

BACKGROUND

A gene or variant has pleiotropic effects, and genetic variant identification across multiple phenotypes can provide a comprehensive understanding of biological pathways shared among different diseases or phenotypes. Discovery of genetic loci associated with multiple diseases can simultaneously support general interventions. Several meta-analyses have shown genetic associations with gastric cancer (GC); however, no study has identified associations with other phenotypes using this approach.

METHODS

Here, we applied disease network analysis and gene-based analysis (GBA) to examine genetic variants linked to GC and simultaneously associated with other phenotypes. We conducted a single-nucleotide polymorphism (SNP) level meta-analysis and GBA through a systematic genome-wide association study (GWAS) linked to GC, to integrate published results for the SNP variants and group them into major GC-associated genes. We then performed disease network and expression quantitative trait loci (eQTL) analyses to evaluate cross-phenotype associations and expression levels of GC-related genes.

RESULTS

Seven genes (MTX1, GBAP1, MUC1, TRIM46, THBS3, PSCA, and ABO) were associated with GC as well as blood urea nitrogen (BUN), glomerular filtration rate (GFR), and uric acid (UA). In addition, 17 SNPs regulated the expression of genes located on 1q22, 24 SNPs regulated the expression of PSCA on 8q24.3, and rs7849820 regulated the expression of ABO on 9q34.2. Furthermore, rs1057941 and rs2294008 had the highest posterior causal probabilities of being a causal candidate SNP in 1q22, and 8q24.3, respectively.

CONCLUSIONS

These findings identified seven GC-associated genes exhibiting a cross-association with GFR, BUN, and UA.

Hierarchical association of COPD to principal genetic components of biological systems

Many disease-causing genetic variants converge on common biological functions and pathways. Precisely how to incorporate pathway knowledge in genetic association studies is not yet clear, however. Previous approaches employ a two-step approach, in which a regular association test is first performed to identify variants associated with the disease phenotype, followed by a test for functional enrichment within the genes implicated by those variants. Here we introduce a concise one-step approach, Hierarchical Genetic Analysis (Higana), which directly computes phenotype associations against each function in the large hierarchy of biological functions documented by the Gene Ontology. Using this approach, we identify risk genes and functions for Chronic Obstructive Pulmonary Disease (COPD), highlighting microtubule transport, muscle adaptation, and nicotine receptor signaling pathways. Microtubule transport has not been previously linked to COPD, as it integrates genetic variants spread over numerous genes. All associations validate strongly in a second COPD cohort.

Priors, population sizes, and power in genome-wide hypothesis tests

BACKGROUND

Genome-wide tests, including genome-wide association studies (GWAS) of germ-line genetic variants, driver tests of cancer somatic mutations, and transcriptome-wide association tests of RNAseq data, carry a high multiple testing burden. This burden can be overcome by enrolling larger cohorts or alleviated by using prior biological knowledge to favor some hypotheses over others. Here we compare these two methods in terms of their abilities to boost the power of hypothesis testing.

RESULTS

We provide a quantitative estimate for progress in cohort sizes and present a theoretical analysis of the power of oracular hard priors: priors that select a subset of hypotheses for testing, with an oracular guarantee that all true positives are within the tested subset. This theory demonstrates that for GWAS, strong priors that limit testing to 100-1000 genes provide less power than typical annual 20-40% increases in cohort sizes. Furthermore, non-oracular priors that exclude even a small fraction of true positives from the tested set can perform worse than not using a prior at all.

CONCLUSION

Our results provide a theoretical explanation for the continued dominance of simple, unbiased univariate hypothesis tests for GWAS: if a statistical question can be answered by larger cohort sizes, it should be answered by larger cohort sizes rather than by more complicated biased methods involving priors. We suggest that priors are better suited for non-statistical aspects of biology, such as pathway structure and causality, that are not yet easily captured by standard hypothesis tests.

Systematic review of gastric cancer-associated genetic variants, gene-based meta-analysis, and gene-level functional analysis to identify candidate genes for drug development

Objective: Despite being a powerful tool to identify novel variants, genome-wide association studies (GWAS) are not sufficient to explain the biological function of variants. In this study, we aimed to elucidate at the gene level the biological mechanisms involved in gastric cancer (GC) development and to identify candidate drug target genes.

Materials and methods: We conducted a systematic review for GWAS on GC following the PRISMA guidelines. Single nucleotide polymorphism (SNP)-level meta-analysis and gene-based analysis (GBA) were performed to identify SNPs and genes significantly associated with GC. Expression quantitative trait loci (eQTL), disease network, pathway enrichment, gene ontology, gene-drug, and chemical interaction analyses were conducted to elucidate the function of the genes identified by GBA.

Results: A review of GWAS on GC identified 226 SNPs located in 91 genes. In the comprehensive GBA, 44 genes associated with GC were identified, among which 12 genes (THBS3, GBAP1, KRTCAP2, TRIM46, HCN3, MUC1, DAP3, EFNA1, MTX1, PRKAA1, PSCA, and ABO) were eQTL. Using disease network and pathway analyses, we identified that PRKAA, THBS3, and EFNA1 were significantly associated with the PI3K-Alt-mTOR-signaling pathway, which is involved in various oncogenic processes, and that MUC1 acts as a regulator in both the PI3K-Alt-mTOR and P53 signaling pathways. Furthermore, RPKAA1 had the highest number of interactions with drugs and chemicals.

Conclusion: Our study suggests that PRKAA1, a gene in the PI3K-Alt-mTOR-signaling pathway, could be a potential target gene for drug development associated with GC in the future.

Systematic Review Registration: website, identifier registration number.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

Omnibus testing approach for gene-based gene-gene interaction

Genetic interaction is considered as one of the main heritable component of complex traits. With the emergence of genome-wide association studies (GWAS), a collection of statistical methods dedicated to the identification of interaction at the SNP level have been proposed. More recently, gene-based gene-gene interaction testing has emerged as an attractive alternative as they confer advantage in both statistical power and biological interpretation. Most of the gene-based interaction methods rely on a multidimensional modeling of the interaction, thus facing a lack of robustness against the huge space of interaction patterns. In this paper, we study a global testing approaches to address the issue of gene-based gene-gene interaction. Based on a logistic regression modeling framework, all SNP-SNP interaction tests are combined to produce a gene-level test for interaction. We propose an omnibus test that takes advantage of (1) the heterogeneity between existing global tests and (2) the complementarity between allele-based and genotype-based coding of SNPs. Through an extensive simulation study, it is demonstrated that the proposed omnibus test has the ability to detect with high power the most common interaction genetic models with one causal pair as well as more complex genetic models where more than one causal pair is involved. On the other hand, the flexibility of the proposed approach is shown to be robust and improves power compared to single global tests in replication studies. Furthermore, the application of our procedure to real datasets confirms the adaptability of our approach to replicate various gene-gene interactions.

The Panorama of Cancer Genetics

Cancer, a disease of the genome, is caused by a combination of germline predisposing variants and acquired somatic mutations. A unified view of heritable and acquired genetic factors will improve our understanding of cancer occurrence and progression. Fanfani and colleagues provide new insight into heritable cancer risk through a computational method that identifies genes and loci that contribute strongly to cancer heritability; many of these loci also harbor somatic drivers. Beyond improving cancer clinical outcomes, these methods will also be valuable across complex disorders by identifying regions responsible for missing heritability.See related article by Fanfani et al., p. 2588.

Integrating Genome-Wide Association Analysis With Transcriptome Sequencing to Identify Candidate Genes Related to Blooming Time in Prunus mume

Prunus mume is one of the most important woody perennials for edible and ornamental use. Despite a substantial variation in the flowering phenology among the P. mume germplasm resources, the genetic control for flowering time remains to be elucidated. In this study, we examined five blooming time-related traits of 235 P. mume landraces for 2 years. Based on the phenotypic data, we performed genome-wide association studies, which included a combination of marker- and gene-based association tests, and identified 1,445 candidate genes that are consistently linked with flowering time across multiple years. Furthermore, we assessed the global transcriptome change of floral buds from the two P. mume cultivars exhibiting contrasting bloom dates and detected 617 associated genes that were differentially expressed during the flowering process. By integrating a co-expression network analysis, we screened out 191 gene candidates of conserved transcriptional pattern during blooming across cultivars. Finally, we validated the temporal expression profiles of these candidates and highlighted their putative roles in regulating floral bud break and blooming time in P. mume. Our findings are important to expand the understanding of flowering time control in woody perennials and will boost the molecular breeding of novel varieties in P. mume.

Mitochondrial DNA copy number can influence mortality and cardiovascular disease via methylation of nuclear DNA CpGs

BACKGROUND

Mitochondrial DNA copy number (mtDNA-CN) has been associated with a variety of aging-related diseases, including all-cause mortality. However, the mechanism by which mtDNA-CN influences disease is not currently understood. One such mechanism may be through regulation of nuclear gene expression via the modification of nuclear DNA (nDNA) methylation.

METHODS

To investigate this hypothesis, we assessed the relationship between mtDNA-CN and nDNA methylation in 2507 African American (AA) and European American (EA) participants from the Atherosclerosis Risk in Communities (ARIC) study. To validate our findings, we assayed an additional 2528 participants from the Cardiovascular Health Study (CHS) (N = 533) and Framingham Heart Study (FHS) (N = 1995). We further assessed the effect of experimental modification of mtDNA-CN through knockout of TFAM, a regulator of mtDNA replication, via CRISPR-Cas9.

RESULTS

Thirty-four independent CpGs were associated with mtDNA-CN at genome-wide significance (P < 5 × 10- 8). Meta-analysis across all cohorts identified six mtDNA-CN-associated CpGs at genome-wide significance (P < 5 × 10- 8). Additionally, over half of these CpGs were associated with phenotypes known to be associated with mtDNA-CN, including coronary heart disease, cardiovascular disease, and mortality. Experimental modification of mtDNA-CN demonstrated that modulation of mtDNA-CN results in changes in nDNA methylation and gene expression of specific CpGs and nearby transcripts. Strikingly, the "neuroactive ligand receptor interaction" KEGG pathway was found to be highly overrepresented in the ARIC cohort (P = 5.24 × 10- 12), as well as the TFAM knockout methylation (P = 4.41 × 10- 4) and expression (P = 4.30 × 10- 4) studies.

CONCLUSIONS

These results demonstrate that changes in mtDNA-CN influence nDNA methylation at specific loci and result in differential expression of specific genes that may impact human health and disease via altered cell signaling.

Functional Annotations of Single-Nucleotide Polymorphism (SNP)-Based and Gene-Based Genome-Wide Association Studies Show Genes Affecting Keratitis Susceptibility

Background

Keratitis is a complex condition in humans and is the second most common cause of legal blindness worldwide.

Material/Methods

To reveal the genomic loci underlying keratitis, we performed functional annotations of SNP-based and gene-based genome-wide association studies of keratitis in the UK Biobank (UKB) cohort with 337 199 subjects of European ancestry.

Results

The publicly available SNP-based association results showed a total of 34 SNPs, from 14 distinct loci, associated with keratitis in the UKB. Gene-based association analysis identified 2 significant genes: IQCF3 (p=2.0×10⁻⁶) and SOD3 (p=2.0×10⁻⁶). Thirty-two candidate genes were then prioritized using information from multiple sources. The overlap of IQCF3 in these 2 analyses resulted in a total of 33 hub genes. Functional annotation of hub genes was performed and transcriptional factors of IQCF3 and SOD3 were predicted.

Conclusions

A total of 34 SNPs from 14 distinct loci were identified as being associated with keratitis, and 32 candidate genes were then prioritized. In addition, IQCF3 and SOD3 were identified by their p values through gene-based tests on the basis of individual SNP-based tests. The functional relationship between these suspect genes and keratitis suggest that IQCF3 and SOD3 are candidate genes underlying keratitis.

TS: a powerful truncated test to detect novel disease associated genes using publicly available gWAS summary data

BACKGROUND

In the last decade, a large number of common variants underlying complex diseases have been identified through genome-wide association studies (GWASs). Summary data of the GWASs are freely and publicly available. The summary data is usually obtained through single marker analysis. Gene-based analysis offers a useful alternative and complement to single marker analysis. Results from gene level association tests can be more readily integrated with downstream functional and pathogenic investigations. Most existing gene-based methods fall into two categories: burden tests and quadratic tests. Burden tests are usually powerful when the directions of effects of causal variants are the same. However, they may suffer loss of statistical power when different directions of effects exist at the causal variants. The power of quadratic tests is not affected by the directions of effects but could be less powerful due to issues such as the large number of degree of freedoms. These drawbacks of existing gene based methods motivated us to develop a new powerful method to identify disease associated genes using existing GWAS summary data.

METHODS AND RESULTS

In this paper, we propose a new truncated statistic method (TS) by utilizing a truncated method to find the genes that have a true contribution to the genetic association. Extensive simulation studies demonstrate that our proposed test outperforms other comparable tests. We applied TS and other comparable methods to the schizophrenia GWAS data and type 2 diabetes (T2D) GWAS meta-analysis summary data. TS identified more disease associated genes than comparable methods. Many of the significant genes identified by TS may have important mechanisms relevant to the associated traits. TS is implemented in C program TS, which is freely and publicly available online.

CONCLUSIONS

The proposed truncated statistic outperforms existing methods. It can be employed to detect novel traits associated genes using GWAS summary data.

An Exome-Wide Association Study Identifies New Susceptibility Loci for Age of Smoking Initiation in African- and European-American Populations

INTRODUCTION

Cigarette smoking is one of the largest causes of preventable death worldwide. This study aimed to identify susceptibility loci for age at smoking initiation (ASI) by performing an exome-wide association analysis.

METHODS

A total of 2510 smokers of either African-American (AA) or European-American (EA) origin were genotyped and analyzed at both the single nucleotide polymorphism (SNP) and gene levels. After removal of those SNPs with a minor allele frequency (<0.01), 48091 and 34933 SNPs for AAs and EAs, respectively, were used to conduct a SNP-based association analysis. Gene-based analyses were then performed for all SNPs examined within each gene. Further, we estimated the proportion of variance explained by all common SNPs included in the analysis.

RESULTS

The strongest signals were detected for SNPs rs17849904 in the pitrilysin metallopeptidase 1 gene (PITRM1) in the AA sample (p = 9.02 × 10-7) and rs34722354 in the discoidin domain of the receptor tyrosine kinase 2 gene (DDR2) in the EA sample (p = 9.74 × 10-7). Both SNPs remained significant after Bonferroni correction for the number of SNPs tested. Subsequently, the gene-based association analysis revealed a significantly associated gene, DHRS7, in the AA sample (p = 5.00 × 10-6), a gene previously implicated in nicotine metabolism.

CONCLUSIONS

Our study revealed two susceptibility loci for age of smoking initiation in the two ethnic samples, with the first being PITRM1 for AA smokers and the second DDR2 for EA smokers. In addition, we found DHRS7 to be a plausible candidate for ASI in the AA sample from our gene-based association analysis.

IMPLICATIONS

PITRM1 and DHRS7 for African-American smokers and DDR2 for European-American smokers are new candidate genes for smoking initiation. These genes represent new additions to smoking initiation, an important but less studied phenotype in nicotine dependence research.

A road map for understanding molecular and genetic determinants of osteoporosis

Osteoporosis is a highly prevalent disorder characterized by low bone mineral density and an increased risk of fracture, termed osteoporotic fracture. Notably, bone mineral density, osteoporosis and osteoporotic fracture are highly heritable; however, determining the genetic architecture, and especially the underlying genomic and molecular mechanisms, of osteoporosis in vivo in humans is still challenging. In addition to susceptibility loci identified in genome-wide association studies, advances in various omics technologies, including genomics, transcriptomics, epigenomics, proteomics and metabolomics, have all been applied to dissect the pathogenesis of osteoporosis. However, each technology individually cannot capture the entire view of the disease pathology and thus fails to comprehensively identify the underlying pathological molecular mechanisms, especially the regulatory and signalling mechanisms. A change to the status quo calls for integrative multi-omics and inter-omics analyses with approaches in 'systems genetics and genomics'. In this Review, we highlight findings from genome-wide association studies and studies using various omics technologies individually to identify mechanisms of osteoporosis. Furthermore, we summarize current studies of data integration to understand, diagnose and inform the treatment of osteoporosis. The integration of multiple technologies will provide a road map to illuminate the complex pathogenesis of osteoporosis, especially from molecular functional aspects, in vivo in humans.

Gene-based analysis of ADHD using PASCAL: a biological insight into the novel associated genes

Background

Attention-Deficit Hyperactivity Disorder (ADHD) is a complex neurodevelopmental disorder (NDD) which may significantly impact on the affected individual’s life. ADHD is acknowledged to have a high heritability component (70–80%). Recently, a meta-analysis of GWAS (Genome Wide Association Studies) has demonstrated the association of several independent loci. Our main aim here, is to apply PASCAL (pathway scoring algorithm), a new gene-based analysis (GBA) method, to the summary statistics obtained in this meta-analysis. PASCAL will take into account the linkage disequilibrium (LD) across genomic regions in a different way than the most commonly employed GBA methods (MAGMA or VEGAS (Versatile Gene-based Association Study)). In addition to PASCAL analysis a gene network and an enrichment analysis for KEGG and GO terms were carried out. Moreover, GENE2FUNC tool was employed to create gene expression heatmaps and to carry out a (DEG) (Differentially Expressed Gene) analysis using GTEX v7 and BrainSpan data.

Results

PASCAL results have revealed the association of new loci with ADHD and it has also highlighted other genes previously reported by MAGMA analysis. PASCAL was able to discover new associations at a gene level for ADHD: FEZF1 (p-value: 2.2 × 10^− 7) and FEZF1-AS1 (p-value: 4.58 × 10^− 7). In addition, PASCAL has been able to highlight association of other genes that share the same LD block with some previously reported ADHD susceptibility genes. Gene network analysis has revealed several interactors with the associated ADHD genes and different GO and KEGG terms have been associated. In addition, GENE2FUNC has demonstrated the existence of several up and down regulated expression clusters when the associated genes and their interactors were considered.

Conclusions

PASCAL has been revealed as an efficient tool to extract additional information from previous GWAS using their summary statistics. This study has identified novel ADHD associated genes that were not previously reported when other GBA methods were employed. Moreover, a biological insight into the biological function of the ADHD associated genes across brain regions and neurodevelopmental stages is provided.

Gene-set association and epistatic analyses reveal complex gene interaction networks affecting flowering time in a worldwide barley collection

Single-marker genome-wide association studies (GWAS) have successfully detected associations between single nucleotide polymorphisms (SNPs) and agronomic traits such as flowering time and grain yield in barley. However, the analysis of individual SNPs can only account for a small proportion of genetic variation, and can only provide limited knowledge on gene network interactions. Gene-based GWAS approaches provide enormous opportunity both to combine genetic information and to examine interactions among genetic variants. Here, we revisited a previously published phenotypic and genotypic data set of 895 barley varieties grown in two years at four different field locations in Australia. We employed statistical models to examine gene-phenotype associations, as well as two-way epistasis analyses to increase the capability to find novel genes that have significant roles in controlling flowering time in barley. Genetic associations were tested between flowering time and corresponding genotypes of 174 putative flowering time-related genes. Gene-phenotype association analysis detected 113 genes associated with flowering time in barley, demonstrating the unprecedented power of gene-based analysis. Subsequent two-way epistasis analysis revealed 19 pairs of gene×gene interactions involved in controlling flowering time. Our study demonstrates that gene-based association approaches can provide higher capacity for future crop improvement to increase crop performance and adaptation to different environments.

Novel Gene-Based Analysis of ASD GWAS: Insight Into the Biological Role of Associated Genes

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by its significant social impact and high heritability. The latest meta-analysis of ASD GWAS (genome-wide association studies) has revealed the association of several SNPs that were replicated in additional sets of independent samples. However, summary statistics from GWAS can be used to perform a gene-based analysis (GBA). GBA allows to combine all genetic information across the gene to create a single statistic (p-value for each gene). Thus, PASCAL (Pathway scoring algorithm), a novel GBA tool, has been applied to the summary statistics from the latest meta-analysis of ASD. GBA approach (testing the gene as a unit) provides an advantage to perform an accurate insight into the biological ASD mechanisms. Therefore, a gene-network analysis and an enrichment analysis for KEGG and GO terms were carried out. GENE2FUNC was used to create gene expression heatmaps and to carry out differential expression analysis (DEA) across GTEx v7 tissues and Brainspan data. dbMDEGA was employed to perform a DEG analysis between ASD and brain control samples for the associated genes and interactors.

Results: PASCAL has identified the following loci associated with ASD: XRN2, NKX2-4, PLK1S1, KCNN2, NKX2-2, CRHR1-IT1, C8orf74 and LOC644172. While some of these genes were previously reported by MAGMA (XRN2, PLK1S1, and KCNN2), PASCAL has been useful to highlight additional genes. The biological characterization of the ASD-associated genes and their interactors have demonstrated the association of several GO and KEGG terms. Moreover, DEA analysis has revealed several up- and down-regulated clusters. In addition, many of the ASD-associated genes and their interactors have shown association with ASD expression datasets.

Conclusions: This study identifies several associations at a gene level in ASD. Most of them were previously reported by MAGMA. This fact proves that PASCAL is an efficient GBA tool to extract additional information from previous GWAS. In addition, this study has characterized for the first time the biological role of the ASD-associated genes across brain regions, neurodevelopmental stages, and ASD gene-expression datasets.

An expanded proteome of cardiac t-tubules

BACKGROUND

Transverse tubules (t-tubules) are important structural elements, derived from sarcolemma, found on all striated myocytes. These specialized organelles create a scaffold for many proteins crucial to the effective propagation of signal in cardiac excitation-contraction coupling. The full protein composition of this region is unknown.

METHODS

We characterized the t-tubule subproteome using 52,033 immunohistochemical images covering 13,203 proteins from the Human Protein Atlas (HPA) cardiac tissue microarrays. We used HPASubC, a suite of Python tools, to rapidly review and classify each image for a specific t-tubule staining pattern. The tools Gene Cards, String 11, and Gene Ontology Consortium as well as literature searches were used to understand pathways and relationships between the proteins.

RESULTS

There were 96 likely t-tubule proteins identified by HPASubC. Of these, 12 were matrisome proteins and 3 were mitochondrial proteins. A separate literature search identified 50 known t-tubule proteins. A comparison of the 2 lists revealed only 17 proteins in common, including 8 of the matrisome proteins. String11 revealed that 94 of 127 combined t-tubule proteins generated a single interconnected network.

CONCLUSION

Using HPASubC and the HPA, we identified 78 novel, putative t-tubule proteins and validated 17 within the literature. This expands and improves our knowledge of this important subcellular structure of the cardiac myocyte. This information can be used to identify new structural targets involved in excitation-contraction coupling that may be altered in disease.

A systems biology approach uncovers cell-specific gene regulatory effects of genetic associations in multiple sclerosis

Genome-wide association studies (GWAS) have identified more than 50,000 unique associations with common human traits. While this represents a substantial step forward, establishing the biology underlying these associations has proven extremely difficult. Even determining which cell types and which particular gene(s) are relevant continues to be a challenge. Here, we conduct a cell-specific pathway analysis of the latest GWAS in multiple sclerosis (MS), which had analyzed a total of 47,351 cases and 68,284 healthy controls and found more than 200 non-MHC genome-wide associations. Our analysis identifies pan immune cell as well as cell-specific susceptibility genes in T cells, B cells and monocytes. Finally, genotype-level data from 2,370 patients and 412 controls is used to compute intra-individual and cell-specific susceptibility pathways that offer a biological interpretation of the individual genetic risk to MS. This approach could be adopted in any other complex trait for which genome-wide data is available.

Novel methods for integration and visualization of genomics and genetics data in Alzheimer's disease

INTRODUCTION

Numerous omics studies have been conducted to understand the molecular networks involved in Alzheimer's disease (AD), but the pathophysiology is still not completely understood; new approaches that enable neuroscientists to better interpret the results of omics analysis are required.

METHODS

We have developed advanced methods to analyze and visualize publicly-available genomics and genetics data. The tools include a composite clinical-neuropathological score for defining AD, gene expression maps in the brain, and networks integrating omics data to understand the impact of polymorphisms on AD pathways.

RESULTS

We have analyzed over 50 public human gene expression data sets, spanning 19 different brain regions and encompassing three separate cohorts. We integrated genome-wide association studies with expression data to identify important genes in the pathophysiology of AD, which provides further insight into the calcium signaling and calcineurin pathways.

DISCUSSION

Biologists can use these freely-available tools to obtain a comprehensive, information-rich view of the pathways in AD.

Shared genes between Alzheimer's disease and ischemic stroke

Aims

Although converging evidence from experimental and epidemiological studies indicates Alzheimer's disease (AD) and ischemic stroke (IS) are related, the genetic basis underlying their links is less well characterized. Traditional SNP‐based genome‐wide association studies (GWAS) have failed to uncover shared susceptibility variants of AD and IS. Therefore, this study was designed to investigate whether pleiotropic genes existed between AD and IS to account for their phenotypic association, although this was not reported in previous studies.

Methods

Taking advantage of large‐scale GWAS summary statistics of AD (17,008 AD cases and 37,154 controls) and IS (10,307 IS cases and 19,326 controls), we performed gene‐based analysis implemented in VEGAS2 and Fisher's meta‐analysis of the set of overlapped genes of nominal significance in both diseases. Subsequently, gene expression analysis in AD‐ or IS‐associated expression datasets was conducted to explore the transcriptional alterations of pleiotropic genes identified.

Results

16 AD‐IS pleiotropic genes surpassed the cutoff for Bonferroni‐corrected significance. Notably, MS4A4A and TREM2, two established AD‐susceptibility genes showed remarkable alterations in the spleens and brains afflicted by IS, respectively. Among the prioritized genes identified by virtue of literature‐based knowledge, most are immune‐relevant genes (EPHA1, MS4A4A, UBE2L3 and TREM2), implicating crucial roles of the immune system in the pathogenesis of AD and IS.

Conclusions

The observation that AD and IS had shared disease‐associated genes offered mechanistic insights into their common pathogenesis, predominantly involving the immune system. More importantly, our findings have important implications for future research directions, which are encouraged to verify the involvement of these candidates in AD and IS and interpret the exact molecular mechanisms of action.

Associations of Mitochondrial and Nuclear Mitochondrial Variants and Genes with Seven Metabolic Traits

Mitochondria (MT), the major site of cellular energy production, are under dual genetic control by 37 mitochondrial DNA (mtDNA) genes and numerous nuclear genes (MT-nDNA). In the CHARGEmtDNA+ Consortium, we studied genetic associations of mtDNA and MT-nDNA associations with body mass index (BMI), waist-hip-ratio (WHR), glucose, insulin, HOMA-B, HOMA-IR, and HbA1c. This 45-cohort collaboration comprised 70,775 (insulin) to 170,202 (BMI) pan-ancestry individuals. Validation and imputation of mtDNA variants was followed by single-variant and gene-based association testing. We report two significant common variants, one in MT-ATP6 associated (p ≤ 5E-04) with WHR and one in the D-loop with glucose. Five rare variants in MT-ATP6, MT-ND5, and MT-ND6 associated with BMI, WHR, or insulin. Gene-based meta-analysis identified MT-ND3 associated with BMI (p ≤ 1E-03). We considered 2,282 MT-nDNA candidate gene associations compiled from online summary results for our traits (20 unique studies with 31 dataset consortia's genome-wide associations [GWASs]). Of these, 109 genes associated (p ≤ 1E-06) with at least 1 of our 7 traits. We assessed regulatory features of variants in the 109 genes, cis- and trans-gene expression regulation, and performed enrichment and protein-protein interactions analyses. Of the identified mtDNA and MT-nDNA genes, 79 associated with adipose measures, 49 with glucose/insulin, 13 with risk for type 2 diabetes, and 18 with cardiovascular disease, indicating for pleiotropic effects with health implications. Additionally, 21 genes related to cholesterol, suggesting additional important roles for the genes identified. Our results suggest that mtDNA and MT-nDNA genes and variants reported make important contributions to glucose and insulin metabolism, adipocyte regulation, diabetes, and cardiovascular disease.

PR interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity

Electrocardiographic PR interval measures atrio-ventricular depolarization and conduction, and abnormal PR interval is a risk factor for atrial fibrillation and heart block. Our genome-wide association study of over 92,000 European-descent individuals identifies 44 PR interval loci (34 novel). Examination of these loci reveals known and previously not-yet-reported biological processes involved in cardiac atrial electrical activity. Genes in these loci are over-represented in cardiac disease processes including heart block and atrial fibrillation. Variants in over half of the 44 loci were associated with atrial or blood transcript expression levels, or were in high linkage disequilibrium with missense variants. Six additional loci were identified either by meta-analysis of ~105,000 African and European-descent individuals and/or by pleiotropic analyses combining PR interval with heart rate, QRS interval, and atrial fibrillation. These findings implicate developmental pathways, and identify transcription factors, ion-channel genes, and cell-junction/cell-signaling proteins in atrio-ventricular conduction, identifying potential targets for drug development.

Genetic contributions to lupus nephritis in a multi-ethnic cohort of systemic lupus erythematous patients

OBJECTIVE

African Americans, East Asians, and Hispanics with systemic lupus erythematous (SLE) are more likely to develop lupus nephritis (LN) than are SLE patients of European descent. The etiology of this difference is not clear, and this study was undertaken to investigate how genetic variants might explain this effect.

METHODS

In this cross-sectional study, 1244 SLE patients from multiethnic case collections were genotyped for 817,810 single-nucleotide polymorphisms (SNPs) across the genome. Continental genetic ancestry was estimated utilizing the program ADMIXTURE. Gene-based testing and pathway analysis was performed within each ethnic group and meta-analyzed across ethnicities. We also performed candidate SNP association tests with SNPs previously established as risk alleles for SLE, LN, and chronic kidney disease (CKD). Association testing and logistic regression models were performed with LN as the outcome, adjusted for continental ancestries, sex, disease duration, and age.

RESULTS

We studied 255 North European, 263 South European, 238 Hispanic, 224 African American and 264 East Asian SLE patients, of whom 606 had LN (48.7%). In genome-wide gene-based and candidate SNP analyses, we found distinct genes, pathways and established risk SNPs associated with LN for each ethnic group. Gene-based analyses showed significant associations between variation in ZNF546 (p = 1.0E-06), TRIM15 (p = 1.0E-06), and TRIMI0 (p = 1.0E-06) and LN among South Europeans, and TTC34 (p = 8.0E-06) was significantly associated with LN among Hispanics. The SNP rs8091180 in NFATC1 was associated with LN (OR 1.43, p = 3.3E-04) in the candidate SNP meta-analysis with the highest OR among African-Americans (OR 2.17, p = 0.0035).

CONCLUSION

Distinct genetic factors are associated with the risk of LN in SLE patients of different ethnicities. CKD risk alleles may play a role in the development of LN in addition to SLE-associated risk variants. These findings may further explain the clinical heterogeneity of LN risk and response to therapy observed between different ethnic groups.

Neurotransmitter Pathway Genes in Cognitive Decline During Aging: Evidence for GNG4 and KCNQ2 Genes

BACKGROUND/RATIONALE

Experimental studies support the role of neurotransmitter genes in dementia risk, but human studies utilizing single variants in candidate genes have had limited success.

METHODS

We used the gene-based testing program Versatile Gene-based Association Study to assess whether aggregate variation across 6 neurotransmitter pathways influences risk of cognitive decline in 8159 cognitively normal elderly (≥65 years old) adults from 3 community-based cohorts.

RESULTS

Common genetic variation in GNG4 and KCNQ2 was associated with cognitive decline. In human brain tissue data sets, both GNG4 and KCNQ2 show higher expression in hippocampus relative to other brain regions; GNG4 expression decreases with advancing age. Both GNG4 and KCNQ2 show highest expression in fetal astrocytes.

CONCLUSION

Genetic variation analyses and gene expression data suggest that GNG4 and KCNQ2 may be associated with cognitive decline in normal aging. Gene-based testing of neurotransmitter pathways may confirm and reveal novel risk genes in future studies of healthy cognitive aging.

Genetic variations in the p11/tPA/BDNF pathway are associated with post stroke depression

BACKGROUND

The effects of BDNF on post stroke depression (PSD) may be influenced by genetic variations in intracellular signal transduction pathways, such as the p11/tPA/BDNF pathway. In this study, we aimed to determine the association of polymorphisms in candidate genes of the gene transduction pathway with PSD, as well as the effects of the interactions between genes in our Chinese sample.

METHODS

Two-hundred-fifty-four Chinese samples with acute ischaemic stroke included 122 PSD patients and 132 nonPSD patients. Sixty-five single nucleotide polymorphisms (SNPs) in six genes (p11, tPA, PAI-1, BDNF, TrkB and p75NTR) of the p11/tPA/BDNF pathway with minor allele frequencies > 5% were successfully genotyped from an initial series of 76 SNPs. The severity of depressive symptoms was assessed by the 17-item Hamilton Depression Rating scale score. Environmental factors were measured with the life events scale and social support rating scale for all patients. SNP and haplotype associations were analysed using gPLINK software. Gene-gene interactions were evaluated with generalized multifactor dimensionality reduction software.

RESULTS

The results showed that TrkB polymorphisms (rs11140793AC genotype, rs7047042CG genotype, rs1221CT genotype, rs2277193TC genotype and rs2277192AG genotype) were significantly associated with PSD. Three haplotypes (AT, GG, and AAT) of TrkB were significantly associated with PSD. Seven haplotypes (GC, AG, ACG, CGC, GCT, ACGC and ACAT) of BDNF were significantly correlated with PSD. We identified significant gene-gene interactions between the p11 (rs11204922 SNP), tPA (rs8178895, rs2020918 SNPs) and BDNF (rs6265, rs2049046, rs16917271, rs727155 SNPs) genes in the PSD group. We also identified significant gene-gene interactions between the BDNF (rs2049046, rs7931247 SNPs) and TrkB (rs7816 SNP) genes with increased occurrence of PSD and sig gene-gene interactions between the BDNF gene (rs6265, rs56164415, rs2049046, rs4923468, rs2883187, rs16917271, rs1491850, rs727155, rs2049048 SNPs) and p75NTR gene (rs2072446, rs11466155) in the PSD group.

CONCLUSION

These findings provides evidence that the TrkB gene, BDNF and TrkB haplotypes, and gene-gene interactions between p11, tPA and BDNF are all associated with PSD, which suggests that genetic variations in the p11/tPA/BDNF pathway may play a central role in regulating the underlying mechanism of PSD.

ExomeChip-Wide Analysis of 95 626 Individuals Identifies 10 Novel Loci Associated With QT and JT Intervals

BACKGROUND

QT interval, measured through a standard ECG, captures the time it takes for the cardiac ventricles to depolarize and repolarize. JT interval is the component of the QT interval that reflects ventricular repolarization alone. Prolonged QT interval has been linked to higher risk of sudden cardiac arrest.

METHODS AND RESULTS

We performed an ExomeChip-wide analysis for both QT and JT intervals, including 209 449 variants, both common and rare, in 17 341 genes from the Illumina Infinium HumanExome BeadChip. We identified 10 loci that modulate QT and JT interval duration that have not been previously reported in the literature using single-variant statistical models in a meta-analysis of 95 626 individuals from 23 cohorts (comprised 83 884 European ancestry individuals, 9610 blacks, 1382 Hispanics, and 750 Asians). This brings the total number of ventricular repolarization associated loci to 45. In addition, our approach of using coding variants has highlighted the role of 17 specific genes for involvement in ventricular repolarization, 7 of which are in novel loci.

CONCLUSIONS

Our analyses show a role for myocyte internal structure and interconnections in modulating QT interval duration, adding to previous known roles of potassium, sodium, and calcium ion regulation, as well as autonomic control. We anticipate that these discoveries will open new paths to the goal of making novel remedies for the prevention of lethal ventricular arrhythmias and sudden cardiac arrest.

Adaptive combination of Bayes factors as a powerful method for the joint analysis of rare and common variants

Multi-marker association tests can be more powerful than single-locus analyses because they aggregate the variant information within a gene/region. However, combining the association signals of multiple markers within a gene/region may cause noise due to the inclusion of neutral variants, which usually compromises the power of a test. To reduce noise, the “adaptive combination of P-values” (ADA) method removes variants with larger P-values. However, when both rare and common variants are considered, it is not optimal to truncate variants according to their P-values. An alternative summary measure, the Bayes factor (BF), is defined as the ratio of the probability of the data under the alternative hypothesis to that under the null hypothesis. The BF quantifies the “relative” evidence supporting the alternative hypothesis. Here, we propose an “adaptive combination of Bayes factors” (ADABF) method that can be directly applied to variants with a wide spectrum of minor allele frequencies. The simulations show that ADABF is more powerful than single-nucleotide polymorphism (SNP)-set kernel association tests and burden tests. We also analyzed 1,109 case-parent trios from the Schizophrenia Trio Genomic Research in Taiwan. Three genes on chromosome 19p13.2 were found to be associated with schizophrenia at the suggestive significance level of 5 × 10⁻⁵.

A Gene-Based Analysis of Acoustic Startle Latency

Latency of the acoustic startle response is the time required from the presentation of startling auditory stimulus until the startle response is elicited and provides an index of neural processing speed. Latency is prolonged in subjects with schizophrenia compared to controls in some but not all studies and is 68-90% heritable in baseline startle trials. In order to determine the genetic association with latency as a potential inroad into genetically based vulnerability to psychosis, we conducted a gene-based study of latency followed by an independent replication study of significant gene findings with a single-nucleotide polymorphism (SNP)-based analysis of schizophrenia and control subjects. 313 subjects from an urban population of low socioeconomic status with mixed psychiatric diagnoses were included in the gene-based study. Startle testing was conducted using a Biopac M150 system according to our published methods. Genotyping was performed with the Omni-Quad 1M or the Omni Express BeadChip. The replication study was conducted on 154 schizophrenia subjects and 123 psychiatric controls. Genetic analyses were conducted with Illumina Human Omni1-Quad and OmniExpress BeadChips. Twenty-nine SNPs were selected from four genes that were significant in the gene-based analysis and also associated with startle and/or schizophrenia in the literature. Linear regressions on latency were conducted, controlling for age, race, and diagnosis as a dichotomous variable. In the gene-based study, 2,870 genes demonstrated the evidence of association after correction for multiple comparisons (false discovery rate < 0.05). Pathway analysis of these genes revealed enrichment for relevant biological processes including neural transmission (p = 0.0029), synaptic transmission (p = 0.0032), and neuronal development (p = 0.024). The subsequent SNP-based replication analysis revealed a strong association of onset latency with the SNP rs901561 on the neuregulin gene (NRG1) in an additive model (beta = 0.21, p = 0.001), indicating that subjects with the AA and AG genotypes had slower mean latency than subjects with GG genotype. In conclusion, startle latency, a highly heritable measure that is slowed in schizophrenia, may be a useful biological probe for genetic contributions to psychotic disorders. Our analyses in two independent populations point to a significant prediction of startle latency by genetic variation in NRG1.

Fine-mapping inflammatory bowel disease loci to single-variant resolution

Inflammatory bowel diseases are chronic gastrointestinal inflammatory disorders that affect millions of people worldwide. Genome-wide association studies have identified 200 inflammatory bowel disease-associated loci, but few have been conclusively resolved to specific functional variants. Here we report fine-mapping of 94 inflammatory bowel disease loci using high-density genotyping in 67,852 individuals. We pinpoint 18 associations to a single causal variant with greater than 95% certainty, and an additional 27 associations to a single variant with greater than 50% certainty. These 45 variants are significantly enriched for protein-coding changes (n = 13), direct disruption of transcription-factor binding sites (n = 3), and tissue-specific epigenetic marks (n = 10), with the last category showing enrichment in specific immune cells among associations stronger in Crohn's disease and in gut mucosa among associations stronger in ulcerative colitis. The results of this study suggest that high-resolution fine-mapping in large samples can convert many discoveries from genome-wide association studies into statistically convincing causal variants, providing a powerful substrate for experimental elucidation of disease mechanisms.

Discovery and fine-mapping of loci associated with MUFAs through trans-ethnic meta-analysis in Chinese and European populations

MUFAs are unsaturated FAs with one double bond and are derived from endogenous synthesis and dietary intake. Accumulating evidence has suggested that plasma and erythrocyte MUFA levels are associated with cardiometabolic disorders, including CVD, T2D, and metabolic syndrome (MS). Previous genome-wide association studies (GWASs) have identified seven loci for plasma and erythrocyte palmitoleic and oleic acid levels in populations of European origin. To identify additional MUFA-associated loci and the potential functional variant at each locus, we performed ethnic-specific GWAS meta-analyses and trans-ethnic meta-analyses in more than 15,000 participants of Chinese and European ancestry. We identified novel genome-wide significant associations for vaccenic acid at FADS1/2 and PKD2L1 [log10(Bayes factor) ≥ 8.07] and for gondoic acid at FADS1/2 and GCKR [log10(Bayes factor) ≥ 6.22], and also observed improved fine-mapping resolutions at FADS1/2 and GCKR loci. The greatest improvement was observed at GCKR, where the number of variants in the 99% credible set was reduced from 16 (covering 94.8 kb) to 5 (covering 19.6 kb, including a missense variant rs1260326) after trans-ethnic meta-analysis. We also confirmed the previously reported associations of PKD2L1, FADS1/2, GCKR, and HIF1AN with palmitoleic acid and of FADS1/2 and LPCAT3 with oleic acid in the Chinese-specific GWAS and the trans-ethnic meta-analyses. Pathway-based analyses suggested that the identified loci were in unsaturated FA metabolism and signaling pathways. Our findings provide novel insight into the genetic basis relevant to MUFA metabolism and biology.

Association of atrial fibrillation susceptibility genes, atrial fibrillation phenotypes and response to catheter ablation: a gene-based analysis of GWAS data

Background

Previous studies have suggested PITX2, KCNN3 and ZFHX3 as atrial fibrillation (AF) susceptibility genes. Single common genetic polymorphisms of those genes have been linked with AF phenotypes and rhythm outcome of AF catheter ablation although their mechanisms remain elusive. New gene-based association tests may help clarifying genotype–phenotype correlations. Therefore, we hypothesized that PITX2, KCNN3 and ZFHX3 associate with left atrial enlargement and persistent AF and subsequently with ablation outcome.

Methods and results

Samples from 660 patients with paroxysmal (n = 370) or persistent AF (n = 290) undergoing AF catheter ablation were genotyped for ~1,000,000 SNPs. Gene-based association was investigated using two different gene-based association tests (VEGAS, minSNP). Among the three candidate genes, only ZFHX3 associated with left atrial dilatation and AF recurrence after catheter ablation.

Conclusion

This study suggests a contribution of ZFHX3 to AF remodeling and response to therapy. Future and larger studies are necessary to replicate and apply these findings with an emphasis on designing AF pathophysiology-based multi-locus risk scores.

Blood transcriptomic comparison of individuals with and without autism spectrum disorder: A combined-samples mega-analysis

Blood-based microarray studies comparing individuals affected with autism spectrum disorder (ASD) and typically developing individuals help characterize differences in circulating immune cell functions and offer potential biomarker signal. We sought to combine the subject-level data from previously published studies by mega-analysis to increase the statistical power. We identified studies that compared ex vivo blood or lymphocytes from ASD-affected individuals and unrelated comparison subjects using Affymetrix or Illumina array platforms. Raw microarray data and clinical meta-data were obtained from seven studies, totaling 626 affected and 447 comparison subjects. Microarray data were processed using uniform methods. Covariate-controlled mixed-effect linear models were used to identify gene transcripts and co-expression network modules that were significantly associated with diagnostic status. Permutation-based gene-set analysis was used to identify functionally related sets of genes that were over- and under-expressed among ASD samples. Our results were consistent with diminished interferon-, EGF-, PDGF-, PI3K-AKT-mTOR-, and RAS-MAPK-signaling cascades, and increased ribosomal translation and NK-cell related activity in ASD. We explored evidence for sex-differences in the ASD-related transcriptomic signature. We also demonstrated that machine-learning classifiers using blood transcriptome data perform with moderate accuracy when data are combined across studies. Comparing our results with those from blood-based studies of protein biomarkers (e.g., cytokines and trophic factors), we propose that ASD may feature decoupling between certain circulating signaling proteins (higher in ASD samples) and the transcriptional cascades which they typically elicit within circulating immune cells (lower in ASD samples). These findings provide insight into ASD-related transcriptional differences in circulating immune cells. © 2016 Wiley Periodicals, Inc.

Gene-based genome-wide association study identified 19p13.3 for lean body mass

Lean body mass (LBM) is a complex trait for human health. To identify genomic loci underlying LBM, we performed a gene-based genome-wide association study of lean mass index (LMI) in 1000 unrelated Caucasian subjects, and replicated in 2283 unrelated Caucasians subjects. Gene-based association analyses highlighted the significant associations of three genes UQCR, TCF3 and MBD3 in one single locus 19p13.3 (discovery p = 6.10 × 10-5, 1.65 × 10-4 and 1.10 × 10-4; replication p = 2.21 × 10-3, 1.84 × 10-3 and 6.95 × 10-3; combined p = 2.26 × 10-6, 4.86 × 10-6 and 1.15 × 10-5, respectively). These results, together with the known functional relevance of the three genes to LMI, suggested that the 19p13.3 region containing UQCR, TCF3 and MBD3 genes was a novel locus underlying lean mass variation.

Systems biology approach to late-onset Alzheimer's disease genome-wide association study identifies novel candidate genes validated using brain expression data and Caenorhabditis elegans experiments

INTRODUCTION

We sought to determine whether a systems biology approach may identify novel late-onset Alzheimer's disease (LOAD) loci.

METHODS

We performed gene-wide association analyses and integrated results with human protein-protein interaction data using network analyses. We performed functional validation on novel genes using a transgenic Caenorhabditis elegans Aβ proteotoxicity model and evaluated novel genes using brain expression data from people with LOAD and other neurodegenerative conditions.

RESULTS

We identified 13 novel candidate LOAD genes outside chromosome 19. Of those, RNA interference knockdowns of the C. elegans orthologs of UBC, NDUFS3, EGR1, and ATP5H were associated with Aβ toxicity, and NDUFS3, SLC25A11, ATP5H, and APP were differentially expressed in the temporal cortex.

DISCUSSION

Network analyses identified novel LOAD candidate genes. We demonstrated a functional role for four of these in a C. elegans model and found enrichment of differentially expressed genes in the temporal cortex.

Bayesian variable selection for post-analytic interrogation of susceptibility loci

Understanding the complex interplay among protein coding genes and regulatory elements requires rigorous interrogation with analytic tools designed for discerning the relative contributions of overlapping genomic regions. To this aim, we offer a novel application of Bayesian variable selection (BVS) for classifying genomic class level associations using existing large meta-analysis summary level resources. This approach is applied using the expectation maximization variable selection (EMVS) algorithm to typed and imputed SNPs across 502 protein coding genes (PCGs) and 220 long intergenic non-coding RNAs (lncRNAs) that overlap 45 known loci for coronary artery disease (CAD) using publicly available Global Lipids Gentics Consortium (GLGC) (Teslovich et al., 2010; Willer et al., 2013) meta-analysis summary statistics for low-density lipoprotein cholesterol (LDL-C). The analysis reveals 33 PCGs and three lncRNAs across 11 loci with >50% posterior probabilities for inclusion in an additive model of association. The findings are consistent with previous reports, while providing some new insight into the architecture of LDL-cholesterol to be investigated further. As genomic taxonomies continue to evolve, additional classes such as enhancer elements and splicing regions, can easily be layered into the proposed analysis framework. Moreover, application of this approach to alternative publicly available meta-analysis resources, or more generally as a post-analytic strategy to further interrogate regions that are identified through single point analysis, is straightforward. All coding examples are implemented in R version 3.2.1 and provided as supplemental material.

A multi-locus genetic association test for a dichotomous trait and its secondary phenotype

Genetic association studies often collect information on secondary phenotypes related to the primary disease status. In many situations, the secondary phenotypes are only measured in subjects with the disease condition. It would be advantageous to model the primary trait and the secondary phenotype together if they share certain level of genetic heritability. We propose a family of multi-locus testing procedures to detect the composite association between a set of genetic markers and two traits (the primary trait and a secondary phenotype), in order to identify genes influencing both traits. The proposed test is derived from a random effect model with two variance components, with each presenting the genetic effect on one trait, and incorporates a model selection procedure for seeking the optimal model to represent the two sources of genetic effects. We conduct simulation studies to evaluate performance of the proposed procedure and apply the method to a genome-wide association study of prostate cancer with the Gleason score as the secondary phenotype.

Genome-wide gene-based analysis suggests an association between Neuroligin 1 (NLGN1) and post-traumatic stress disorder

Post-traumatic stress disorder (PTSD) develops in only some people following trauma exposure, but the mechanisms differentially explaining risk versus resilience remain largely unknown. PTSD is heritable but candidate gene studies and genome-wide association studies (GWAS) have identified only a modest number of genes that reliably contribute to PTSD. New gene-based methods may help identify additional genes that increase risk for PTSD development or severity. We applied gene-based testing to GWAS data from the Grady Trauma Project (GTP), a primarily African American cohort, and identified two genes (NLGN1 and ZNRD1-AS1) that associate with PTSD after multiple test correction. Although the top SNP from NLGN1 did not replicate, we observed gene-based replication of NLGN1 with PTSD in the Drakenstein Child Health Study (DCHS) cohort from Cape Town. NLGN1 has previously been associated with autism, and it encodes neuroligin 1, a protein involved in synaptogenesis, learning, and memory. Within the GTP dataset, a single nucleotide polymorphism (SNP), rs6779753, underlying the gene-based association, associated with the intermediate phenotypes of higher startle response and greater functional magnetic resonance imaging activation of the amygdala, orbitofrontal cortex, right thalamus and right fusiform gyrus in response to fearful faces. These findings support a contribution of the NLGN1 gene pathway to the neurobiological underpinnings of PTSD.

Comprehensive gene- and pathway-based analysis of depressive symptoms in older adults

Depressive symptoms are common in older adults and are particularly prevalent in those with or at elevated risk for dementia. Although the heritability of depression is estimated to be substantial, single nucleotide polymorphism-based genome-wide association studies of depressive symptoms have had limited success. In this study, we performed genome-wide gene- and pathway-based analyses of depressive symptom burden. Study participants included non-Hispanic Caucasian subjects (n = 6,884) from three independent cohorts, the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Health and Retirement Study (HRS), and the Indiana Memory and Aging Study (IMAS). Gene-based meta-analysis identified genome-wide significant associations (ANGPT4 and FAM110A, q-value = 0.026; GRM7-AS3 and LRFN5, q-value = 0.042). Pathway analysis revealed enrichment of association in 105 pathways, including multiple pathways related to ERK/MAPK signaling, GSK3 signaling in bipolar disorder, cell development, and immune activation and inflammation. GRM7, ANGPT4, and LRFN5 have been previously implicated in psychiatric disorders, including the GRM7 region displaying association with major depressive disorder. The ERK/MAPK signaling pathway is a known target of antidepressant drugs and has important roles in neuronal plasticity, and GSK3 signaling has been previously implicated in Alzheimer's disease and as a promising therapeutic target for depression. Our results warrant further investigation in independent and larger cohorts and add to the growing understanding of the genetics and pathobiology of depressive symptoms in aging and neurodegenerative disorders. In particular, the genes and pathways demonstrating association with depressive symptoms may be potential therapeutic targets for these symptoms in older adults.

A Simple Test of Class-Level Genetic Association Can Reveal Novel Cardiometabolic Trait Loci

BACKGROUND

Characterizing the genetic determinants of complex diseases can be further augmented by incorporating knowledge of underlying structure or classifications of the genome, such as newly developed mappings of protein-coding genes, epigenetic marks, enhancer elements and non-coding RNAs.

METHODS

We apply a simple class-level testing framework, termed Genetic Class Association Testing (GenCAT), to identify protein-coding gene association with 14 cardiometabolic (CMD) related traits across 6 publicly available genome wide association (GWA) meta-analysis data resources. GenCAT uses SNP-level meta-analysis test statistics across all SNPs within a class of elements, as well as the size of the class and its unique correlation structure, to determine if the class is statistically meaningful. The novelty of findings is evaluated through investigation of regional signals. A subset of findings are validated using recently updated, larger meta-analysis resources. A simulation study is presented to characterize overall performance with respect to power, control of family-wise error and computational efficiency. All analysis is performed using the GenCAT package, R version 3.2.1.

RESULTS

We demonstrate that class-level testing complements the common first stage minP approach that involves individual SNP-level testing followed by post-hoc ascribing of statistically significant SNPs to genes and loci. GenCAT suggests 54 protein-coding genes at 41 distinct loci for the 13 CMD traits investigated in the discovery analysis, that are beyond the discoveries of minP alone. An additional application to biological pathways demonstrates flexibility in defining genetic classes.

CONCLUSIONS

We conclude that it would be prudent to include class-level testing as standard practice in GWA analysis. GenCAT, for example, can be used as a simple, complementary and efficient strategy for class-level testing that leverages existing data resources, requires only summary level data in the form of test statistics, and adds significant value with respect to its potential for identifying multiple novel and clinically relevant trait associations.

Gene-based meta-analysis of genome-wide association studies implicates new loci involved in obesity

To date, genome-wide association studies (GWASs) have identified >100 loci with single variants associated with body mass index (BMI). This approach may miss loci with high allelic heterogeneity; therefore, the aim of the present study was to use gene-based meta-analysis to identify regions with high allelic heterogeneity to discover additional obesity susceptibility loci. We included GWAS data from 123 865 individuals of European descent from 46 cohorts in Stage 1 and Metabochip data from additional 103 046 individuals from 43 cohorts in Stage 2, all within the Genetic Investigation of ANthropometric Traits (GIANT) consortium. Each cohort was tested for association between ∼2.4 million (Stage 1) or ∼200 000 (Stage 2) imputed or genotyped single variants and BMI, and summary statistics were subsequently meta-analyzed in 17 941 genes. We used the 'VErsatile Gene-based Association Study' (VEGAS) approach to assign variants to genes and to calculate gene-based P-values based on simulations. The VEGAS method was applied to each cohort separately before a gene-based meta-analysis was performed. In Stage 1, two known (FTO and TMEM18) and six novel (PEX2, MTFR2, SSFA2, IARS2, CEP295 and TXNDC12) loci were associated with BMI (P < 2.8 × 10(-6) for 17 941 gene tests). We confirmed all loci, and six of them were gene-wide significant in Stage 2 alone. We provide biological support for the loci by pathway, expression and methylation analyses. Our results indicate that gene-based meta-analysis of GWAS provides a useful strategy to find loci of interest that were not identified in standard single-marker analyses due to high allelic heterogeneity.

Powerful Tests for Multi-Marker Association Analysis Using Ensemble Learning

Multi-marker approaches have received a lot of attention recently in genome wide association studies and can enhance power to detect new associations under certain conditions. Gene-, gene-set- and pathway-based association tests are increasingly being viewed as useful supplements to the more widely used single marker association analysis which have successfully uncovered numerous disease variants. A major drawback of single-marker based methods is that they do not look at the joint effects of multiple genetic variants which individually may have weak or moderate signals. Here, we describe novel tests for multi-marker association analyses that are based on phenotype predictions obtained from machine learning algorithms. Instead of assuming a linear or logistic regression model, we propose the use of ensembles of diverse machine learning algorithms for prediction. We show that phenotype predictions obtained from ensemble learning algorithms provide a new framework for multi-marker association analysis. They can be used for constructing tests for the joint association of multiple variants, adjusting for covariates and testing for the presence of interactions. To demonstrate the power and utility of this new approach, we first apply our method to simulated SNP datasets. We show that the proposed method has the correct Type-1 error rates and can be considerably more powerful than alternative approaches in some situations. Then, we apply our method to previously studied asthma-related genes in 2 independent asthma cohorts to conduct association tests.

A guide to genome-wide association analysis and post-analytic interrogation

This tutorial is a learning resource that outlines the basic process and provides specific software tools for implementing a complete genome‐wide association analysis. Approaches to post‐analytic visualization and interrogation of potentially novel findings are also presented. Applications are illustrated using the free and open‐source R statistical computing and graphics software environment, Bioconductor software for bioinformatics and the UCSC Genome Browser. Complete genome‐wide association data on 1401 individuals across 861,473 typed single nucleotide polymorphisms from the PennCATH study of coronary artery disease are used for illustration. All data and code, as well as additional instructional resources, are publicly available through the Open Resources in Statistical Genomics project: http://www.stat-gen.org. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Meta-analysis of shared genetic architecture across ten pediatric autoimmune diseases

Genome-wide association studies (GWASs) have identified hundreds of susceptibility genes, including shared associations across clinically distinct autoimmune diseases. We performed an inverse χ(2) meta-analysis across ten pediatric-age-of-onset autoimmune diseases (pAIDs) in a case-control study including more than 6,035 cases and 10,718 shared population-based controls. We identified 27 genome-wide significant loci associated with one or more pAIDs, mapping to in silico-replicated autoimmune-associated genes (including IL2RA) and new candidate loci with established immunoregulatory functions such as ADGRL2, TENM3, ANKRD30A, ADCY7 and CD40LG. The pAID-associated single-nucleotide polymorphisms (SNPs) were functionally enriched for deoxyribonuclease (DNase)-hypersensitivity sites, expression quantitative trait loci (eQTLs), microRNA (miRNA)-binding sites and coding variants. We also identified biologically correlated, pAID-associated candidate gene sets on the basis of immune cell expression profiling and found evidence of genetic sharing. Network and protein-interaction analyses demonstrated converging roles for the signaling pathways of type 1, 2 and 17 helper T cells (TH1, TH2 and TH17), JAK-STAT, interferon and interleukin in multiple autoimmune diseases.

Using Hamming Distance as Information for SNP-Sets Clustering and Testing in Disease Association Studies

The availability of high-throughput genomic data has led to several challenges in recent genetic association studies, including the large number of genetic variants that must be considered and the computational complexity in statistical analyses. Tackling these problems with a marker-set study such as SNP-set analysis can be an efficient solution. To construct SNP-sets, we first propose a clustering algorithm, which employs Hamming distance to measure the similarity between strings of SNP genotypes and evaluates whether the given SNPs or SNP-sets should be clustered. A dendrogram can then be constructed based on such distance measure, and the number of clusters can be determined. With the resulting SNP-sets, we next develop an association test HDAT to examine susceptibility to the disease of interest. This proposed test assesses, based on Hamming distance, whether the similarity between a diseased and a normal individual differs from the similarity between two individuals of the same disease status. In our proposed methodology, only genotype information is needed. No inference of haplotypes is required, and SNPs under consideration do not need to locate in nearby regions. The proposed clustering algorithm and association test are illustrated with applications and simulation studies. As compared with other existing methods, the clustering algorithm is faster and better at identifying sets containing SNPs exerting a similar effect. In addition, the simulation studies demonstrated that the proposed test works well for SNP-sets containing a large proportion of neutral SNPs. Furthermore, employing the clustering algorithm before testing a large set of data improves the knowledge in confining the genetic regions for susceptible genetic markers.

Statistical and Computational Methods for Genetic Diseases: An Overview

The identification of causes of genetic diseases has been carried out by several approaches with increasing complexity. Innovation of genetic methodologies leads to the production of large amounts of data that needs the support of statistical and computational methods to be correctly processed. The aim of the paper is to provide an overview of statistical and computational methods paying attention to methods for the sequence analysis and complex diseases.