Gene and Network Analysis of Common Variants Reveals Novel Associations in Multiple Complex Diseases

DiGAS: Differential gene allele spectrum as a descriptor in genetic studies

Diagnosing individuals with complex genetic diseases is a challenging task. Computational methodologies exploit information at the genotype level by taking into account single nucleotide polymorphisms (SNPs) leveraging the results of genome-wide association studies analysis to assign a statistical significance to each SNP. Recent methodologies extend such an approach by aggregating SNP significance at the genetic level to identify genes that are related to the condition under study. However, such methodologies still suffer from the initial SNP analysis limitations. Here, we present DiGAS, a tool for diagnosing genetic conditions by computing significance, by means of SNP information, directly at the complex level of genetic regions. Such an approach is based on a generalized notion of allele spectrum, which evaluates the complete genetic alterations of the SNP set belonging to a genetic region at the population level. The statistical significance of a region is then evaluated through a differential allele spectrum analysis between the conditions of individuals belonging to the population. Tests, performed on well-established datasets regarding Alzheimer's disease, show that DiGAS outperforms the state of the art in distinguishing between sick and healthy subjects.

Discovering non-additive heritability using additive GWAS summary statistics

LD score regression (LDSC) is a method to estimate narrow-sense heritability from genome-wide association study (GWAS) summary statistics alone, making it a fast and popular approach. In this work, we present interaction-LD score (i-LDSC) regression: an extension of the original LDSC framework that accounts for interactions between genetic variants. By studying a wide range of generative models in simulations, and by re-analyzing 25 well-studied quantitative phenotypes from 349,468 individuals in the UK Biobank and up to 159,095 individuals in BioBank Japan, we show that the inclusion of a cis-interaction score (i.e. interactions between a focal variant and proximal variants) recovers genetic variance that is not captured by LDSC. For each of the 25 traits analyzed in the UK Biobank and BioBank Japan, i-LDSC detects additional variation contributed by genetic interactions. The i-LDSC software and its application to these biobanks represent a step towards resolving further genetic contributions of sources of non-additive genetic effects to complex trait variation.

Network propagation for GWAS analysis: a practical guide to leveraging molecular networks for disease gene discovery

MOTIVATION

Genome-wide association studies (GWAS) have enabled large-scale analysis of the role of genetic variants in human disease. Despite impressive methodological advances, subsequent clinical interpretation and application remains challenging when GWAS suffer from a lack of statistical power. In recent years, however, the use of information diffusion algorithms with molecular networks has led to fruitful insights on disease genes.

RESULTS

We present an overview of the design choices and pitfalls that prove crucial in the application of network propagation methods to GWAS summary statistics. We highlight general trends from the literature, and present benchmark experiments to expand on these insights selecting as case study three diseases and five molecular networks. We verify that the use of gene-level scores based on GWAS P-values offers advantages over the selection of a set of 'seed' disease genes not weighted by the associated P-values if the GWAS summary statistics are of sufficient quality. Beyond that, the size and the density of the networks prove to be important factors for consideration. Finally, we explore several ensemble methods and show that combining multiple networks may improve the network propagation approach.

Identifying the joint signature of brain atrophy and gene variant scores in Alzheimer's Disease

The joint modeling of genetic data and brain imaging information allows for determining the pathophysiological pathways of neurodegenerative diseases such as Alzheimer's disease (AD). This task has typically been approached using mass-univariate methods that rely on a complete set of Single Nucleotide Polymorphisms (SNPs) to assess their association with selected image-derived phenotypes (IDPs). However, such methods are prone to multiple comparisons bias and, most importantly, fail to account for potential cross-feature interactions, resulting in insufficient detection of significant associations. Ways to overcome these limitations while reducing the number of traits aim at conveying genetic information at the gene level and capturing the integrated genetic effects of a set of genetic variants, rather than looking at each SNP individually. Their associations with brain IDPs are still largely unexplored in the current literature, though they can uncover new potential genetic determinants for brain modulations in the AD continuum. In this work, we explored an explainable multivariate model to analyze the genetic basis of the grey matter modulations, relying on the AD Neuroimaging Initiative (ADNI) phase 3 dataset. Cortical thicknesses and subcortical volumes derived from T1-weighted Magnetic Resonance were considered to describe the imaging phenotypes. At the same time the genetic counterpart was represented by gene variant scores extracted by the Sequence Kernel Association Test (SKAT) filtering model. Moreover, transcriptomic analysis was carried on to assess the expression of the resulting genes in the main brain structures as a form of validation. Results highlighted meaningful genotype-phenotype interactionsas defined by three latent components showing a significant difference in the projection scores between patients and controls. Among the significant associations, the model highlighted EPHX1 and BCAS1 gene variant scores involved in neurodegenerative and myelination processes, hence relevant for AD. In particular, the first was associated with decreased subcortical volumes and the second with decreasedtemporal lobe thickness. Noteworthy, BCAS1 is particularly expressed in the dentate gyrus. Overall, the proposed approach allowed capturing genotype-phenotype interactions in a restricted study cohort that was confirmed by transcriptomic analysis, offering insights into the underlying mechanisms of neurodegeneration in AD in line with previous findings and suggesting new potential disease biomarkers.

Thrsp Gene and the ADHD Predominantly Inattentive Presentation

There are three presentations of attention-deficit/hyperactivity disorder (ADHD): the predominantly inattention (ADHD-PI), predominantly hyperactive-impulsive (ADHD-HI), and combined (ADHD-C) presentations of ADHD. These may represent distinct childhood-onset neurobehavioral disorders with separate etiologies. ADHD diagnoses are behaviorally based, so investigations into potential etiologies should be founded on behavior. Animal models of ADHD demonstrate face, predictive, and construct validity when they accurately reproduce elements of the symptoms, etiology, biochemistry, and disorder treatment. Spontaneously hypertensive rats (SHR/NCrl) fulfill many validation criteria and compare well with clinical cases of ADHD-C. Compounding the difficulty of selecting an ideal model to study specific presentations of ADHD is a simple fact that our knowledge regarding ADHD neurobiology is insufficient. Accordingly, the current review has explored a potential animal model for a specific presentation, ADHD-PI, with acceptable face, predictive, and construct validity. The Thrsp gene could be a biomarker for ADHD-PI presentation, and THRSP OE mice could represent an animal model for studying this distinct ADHD presentation.

Early detection of Alzheimer's disease using single nucleotide polymorphisms analysis based on gradient boosting tree

Alzheimer's disease (AD) is a degenerative disorder that attacks nerve cells in the brain. AD leads to memory loss and cognitive & intellectual impairments that can influence social activities and decision-making. The most common type of human genetic variation is single nucleotide polymorphisms (SNPs). SNPs are beneficial markers of complex gene-disease. Many common and serious diseases, such as AD, have associated SNPs. Detection of SNP biomarkers linked with AD could help in the early prediction and diagnosis of this disease. The main objective of this paper is to predict and diagnose AD based on SNPs biomarkers with high classification accuracy in the early stages. One of the most concerning problems is the high number of features. Thus, the paper proposes a comprehensive framework for early AD detection and detecting the most significant genes based on SNPs analysis. Usage of machine learning (ML) techniques to identify new biomarkers of AD is also suggested. In the proposed system, two feature selection techniques are separately checked: the information gain filter and Boruta wrapper. The two feature selection techniques were used to select the most significant genes related to AD in this system. Filter methods measure the relevance of features by their correlation with dependent variables, while wrapper methods measure the usefulness of a subset of features by training a model on it. Gradient boosting tree (GBT) has been applied on all AD genetic data of neuroimaging initiative phase 1 (ADNI-1) and Whole-Genome Sequencing (WGS) datasets by using two feature selection techniques. In the whole-genome approach ADNI-1, results revealed that the GBT learning algorithm scored an overall accuracy of 99.06% in the case of using Boruta feature selection. Using information gain feature selection, the proposed system achieved an average accuracy of 94.87%. The results show that the proposed system is preferable for the early detection of AD. Also, the results revealed that the Boruta wrapper feature selection is superior to the information gain filter technique.

Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries

Since 2005, genome-wide association (GWA) datasets have been largely biased toward sampling European ancestry individuals, and recent studies have shown that GWA results estimated from self-identified European individuals are not transferable to non-European individuals because of various confounding challenges. Here, we demonstrate that enrichment analyses that aggregate SNP-level association statistics at multiple genomic scales-from genes to genomic regions and pathways-have been underutilized in the GWA era and can generate biologically interpretable hypotheses regarding the genetic basis of complex trait architecture. We illustrate examples of the robust associations generated by enrichment analyses while studying 25 continuous traits assayed in 566,786 individuals from seven diverse self-identified human ancestries in the UK Biobank and the Biobank Japan as well as 44,348 admixed individuals from the PAGE consortium including cohorts of African American, Hispanic and Latin American, Native Hawaiian, and American Indian/Alaska Native individuals. We identify 1,000 gene-level associations that are genome-wide significant in at least two ancestry cohorts across these 25 traits as well as highly conserved pathway associations with triglyceride levels in European, East Asian, and Native Hawaiian cohorts.

The DOMINO web-server for active module identification analysis

MOTIVATION

Active module identification (AMI) is an essential step in many omics analyses. Such algorithms receive a gene network and a gene activity profile as input and report subnetworks that show significant over-representation of accrued activity signal ('active modules'). Such modules can point out key molecular processes in the analyzed biological conditions.

RESULTS

We recently introduced a novel AMI algorithm called DOMINO and demonstrated that it detects active modules that capture biological signals with markedly improved rate of empirical validation. Here, we provide an online server that executes DOMINO, making it more accessible and user-friendly. To help the interpretation of solutions, the server provides GO enrichment analysis, module visualizations and accessible output formats for customized downstream analysis. It also enables running DOMINO with various gene identifiers of different organisms.

AVAILABILITY AND IMPLEMENTATION

The server is available at http://domino.cs.tau.ac.il. Its codebase is available at https://github.com/Shamir-Lab.

Cohort-based association study of germline genetic variants with acute and chronic health complications of childhood cancer and its treatment: Genetic Risks for Childhood Cancer Complications Switzerland (GECCOS) study protocol

INTRODUCTION

Childhood cancer and its treatment may lead to various health complications. Related impairment in quality of life, excess in deaths and accumulated healthcare costs are relevant. Genetic variations are suggested to contribute to the wide inter-individual variability of complications but have been used only rarely to risk-stratify treatment and follow-up care. This study aims to identify germline genetic variants associated with acute and late complications of childhood cancer.

METHODS AND ANALYSIS

The Genetic Risks for Childhood Cancer Complications Switzerland (GECCOS) study is a nationwide cohort study. Eligible are patients and survivors who were diagnosed with childhood cancers or Langerhans cell histiocytosis before age 21 years, were registered in the Swiss Childhood Cancer Registry (SCCR) since 1976 and have consented to the Paediatric Biobank for Research in Haematology and Oncology, Geneva, host of the national Germline DNA Biobank Switzerland for Childhood Cancer and Blood Disorders (BISKIDS).GECCOS uses demographic and clinical data from the SCCR and the associated Swiss Childhood Cancer Survivor Study. Clinical outcome data consists of organ function testing, health conditions diagnosed by physicians, second primary neoplasms and self-reported information from participants. Germline genetic samples and sequencing data are collected in BISKIDS. We will perform association analyses using primarily whole-exome or whole-genome sequencing to identify genetic variants associated with specified health conditions. We will use clustering and machine-learning techniques and assess multiple health conditions in different models.

DISCUSSION

GECCOS will improve knowledge of germline genetic variants associated with childhood cancer-associated health conditions and help to further individualise cancer treatment and follow-up care, potentially resulting in improved efficacy and reduced side effects.

ETHICS AND DISSEMINATION

The Geneva Cantonal Commission for Research Ethics has approved the GECCOS study.Research findings will be disseminated through national and international conferences, publications in peer-reviewed journals and in lay language online.

TRIAL REGISTRATION NUMBER

NCT04702321.

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

BACKGROUND AND OBJECTIVES

This paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain's genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer's disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges.

METHODS

We used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail.

RESULTS

It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes.

CONCLUSIONS

Despite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.

Multi-scale inference of genetic trait architecture using biologically annotated neural networks

In this article, we present Biologically Annotated Neural Networks (BANNs), a nonlinear probabilistic framework for association mapping in genome-wide association (GWA) studies. BANNs are feedforward models with partially connected architectures that are based on biological annotations. This setup yields a fully interpretable neural network where the input layer encodes SNP-level effects, and the hidden layer models the aggregated effects among SNP-sets. We treat the weights and connections of the network as random variables with prior distributions that reflect how genetic effects manifest at different genomic scales. The BANNs software uses variational inference to provide posterior summaries which allow researchers to simultaneously perform (i) mapping with SNPs and (ii) enrichment analyses with SNP-sets on complex traits. Through simulations, we show that our method improves upon state-of-the-art association mapping and enrichment approaches across a wide range of genetic architectures. We then further illustrate the benefits of BANNs by analyzing real GWA data assayed in approximately 2,000 heterogenous stock of mice from the Wellcome Trust Centre for Human Genetics and approximately 7,000 individuals from the Framingham Heart Study. Lastly, using a random subset of individuals of European ancestry from the UK Biobank, we show that BANNs is able to replicate known associations in high and low-density lipoprotein cholesterol content.

Genetic underpinnings of affective temperaments: a pilot GWAS investigation identifies a new genome-wide significant SNP for anxious temperament in ADGRB3 gene

Although recently a large-sample GWASs identified significant loci in the background of depression, the heterogeneity of the depressive phenotype and the lack of accurate phenotyping hinders applicability of findings. We carried out a pilot GWAS with in-depth phenotyping of affective temperaments, considered as subclinical manifestations and high-risk states for affective disorders, in a general population sample of European origin. Affective temperaments were measured by TEMPS-A. SNP-level association was assessed by linear regression models, assuming an additive genetic effect, using PLINK1.9. Gender, age, the first ten principal components (PCs) and the other four temperaments were included in the regression models as covariates. SNP-level relevances (p-values) were aggregated to gene level using the PEGASUS method¹. In SNP-based tests, a Bonferroni-corrected significance threshold of p ≤ 5.0 × 10^-8 and a suggestive significance threshold of p ≤ 1.0 × 10^-5, whereas in gene-based tests a Bonferroni-corrected significance of 2.0 × 10^-6 and a suggestive significance of p ≤ 4.0 × 10^-4 was established. To explore known functional effects of the most significant SNPs, FUMA v1.3.5 was used. We identified 1 significant and 21 suggestively significant SNPs in ADGRB3, expressed in the brain, for anxious temperament. Several other brain-relevant SNPs and genes emerged at suggestive significance for the other temperaments. Functional analyses reflecting effect on gene expression and participation in chromatin interactions also pointed to several genes expressed in the brain with potentially relevant phenotypes regulated by our top SNPs. Our findings need to be tested in larger GWA studies and candidate gene analyses in well-phenotyped samples in relation to affective disorders and related phenotypes.

Natural variation in the regulation of neurodevelopmental genes modifies flight performance in Drosophila

The winged insects of the order Diptera are colloquially named for their most recognizable phenotype: flight. These insects rely on flight for a number of important life history traits, such as dispersal, foraging, and courtship. Despite the importance of flight, relatively little is known about the genetic architecture of flight performance. Accordingly, we sought to uncover the genetic modifiers of flight using a measure of flies’ reaction and response to an abrupt drop in a vertical flight column. We conducted a genome wide association study (GWAS) using 197 of the Drosophila Genetic Reference Panel (DGRP) lines, and identified a combination of additive and marginal variants, epistatic interactions, whole genes, and enrichment across interaction networks. Egfr, a highly pleiotropic developmental gene, was among the most significant additive variants identified. We functionally validated 13 of the additive candidate genes’ (Adgf-A/Adgf-A2/CG32181, bru1, CadN, flapper (CG11073), CG15236, flippy (CG9766), CREG, Dscam4, form3, fry, Lasp/CG9692, Pde6, Snoo), and introduce a novel approach to whole gene significance screens: PEGASUS_flies. Additionally, we identified ppk23, an Acid Sensing Ion Channel (ASIC) homolog, as an important hub for epistatic interactions. We propose a model that suggests genetic modifiers of wing and muscle morphology, nervous system development and function, BMP signaling, sexually dimorphic neural wiring, and gene regulation are all important for the observed differences flight performance in a natural population. Additionally, these results represent a snapshot of the genetic modifiers affecting drop-response flight performance in Drosophila, with implications for other insects.

Insect flight is a widely recognizable phenotype of many winged insects, hence the name: flies. While fruit flies, or Drosophila melanogaster, are a genetically tractable model, flight performance is a highly integrative phenotype, and therefore challenging to identify comprehensively which genetic modifiers contribute to its genetic architecture. Accordingly, we screened 197 Drosophila Genetic Reference Panel lines for their ability to react and respond to an abrupt drop. Using several computational approaches, we identified additive, marginal, and epistatic variants, as well as whole genes and altered sub-networks of gene-gene and protein-protein interaction networks that contribute to variation in flight performance. More generally, we demonstrate the benefits of employing multiple methodologies to elucidate the genetic architecture of complex traits. Many variants and genes mapped to regions of the genome that affect neurodevelopment, wing and muscle development, and regulation of gene expression. We also introduce PEGASUS_flies, a Drosophila-adapted version of the PEGASUS platform first used in human studies, to infer gene-level significance of association based on the gene’s distribution of individual variant P-values. Our results contribute to the debate over the relative importance of individual, additive factors and epistatic, or higher order, interactions, in the mapping of genotype to phenotype.

Boosting GWAS using biological networks: A study on susceptibility to familial breast cancer

Genome-wide association studies (GWAS) explore the genetic causes of complex diseases. However, classical approaches ignore the biological context of the genetic variants and genes under study. To address this shortcoming, one can use biological networks, which model functional relationships, to search for functionally related susceptibility loci. Many such network methods exist, each arising from different mathematical frameworks, pre-processing steps, and assumptions about the network properties of the susceptibility mechanism. Unsurprisingly, this results in disparate solutions. To explore how to exploit these heterogeneous approaches, we selected six network methods and applied them to GENESIS, a nationwide French study on familial breast cancer. First, we verified that network methods recovered more interpretable results than a standard GWAS. We addressed the heterogeneity of their solutions by studying their overlap, computing what we called the consensus. The key gene in this consensus solution was COPS5, a gene related to multiple cancer hallmarks. Another issue we observed was that network methods were unstable, selecting very different genes on different subsamples of GENESIS. Therefore, we proposed a stable consensus solution formed by the 68 genes most consistently selected across multiple subsamples. This solution was also enriched in genes known to be associated with breast cancer susceptibility (BLM, CASP8, CASP10, DNAJC1, FGFR2, MRPS30, and SLC4A7, P-value = 3 × 10-4). The most connected gene was CUL3, a regulator of several genes linked to cancer progression. Lastly, we evaluated the biases of each method and the impact of their parameters on the outcome. In general, network methods preferred highly connected genes, even after random rewirings that stripped the connections of any biological meaning. In conclusion, we present the advantages of network-guided GWAS, characterize their shortcomings, and provide strategies to address them. To compute the consensus networks, implementations of all six methods are available at https://github.com/hclimente/gwas-tools.

Network propagation of rare variants in Alzheimer's disease reveals tissue-specific hub genes and communities

State-of-the-art rare variant association testing methods aggregate the contribution of rare variants in biologically relevant genomic regions to boost statistical power. However, testing single genes separately does not consider the complex interaction landscape of genes, nor the downstream effects of non-synonymous variants on protein structure and function. Here we present the NETwork Propagation-based Assessment of Genetic Events (NETPAGE), an integrative approach aimed at investigating the biological pathways through which rare variation results in complex disease phenotypes. We applied NETPAGE to sporadic, late-onset Alzheimer's disease (AD), using whole-genome sequencing from the AD Neuroimaging Initiative (ADNI) cohort, as well as whole-exome sequencing from the AD Sequencing Project (ADSP). NETPAGE is based on network propagation, a framework that models information flow on a graph and simulates the percolation of genetic variation through tissue-specific gene interaction networks. The result of network propagation is a set of smoothed gene scores that can be tested for association with disease status through sparse regression. The application of NETPAGE to AD enabled the identification of a set of connected genes whose smoothed variation profile was robustly associated to case-control status, based on gene interactions in the hippocampus. Additionally, smoothed scores significantly correlated with risk of conversion to AD in Mild Cognitive Impairment (MCI) subjects. Lastly, we investigated tissue-specific transcriptional dysregulation of the core genes in two independent RNA-seq datasets, as well as significant enrichments in terms of gene sets with known connections to AD. We present a framework that enables enhanced genetic association testing for a wide range of traits, diseases, and sample sizes.

DOMINO: a network-based active module identification algorithm with reduced rate of false calls

Algorithms for active module identification (AMI) are central to analysis of omics data. Such algorithms receive a gene network and nodes' activity scores as input and report subnetworks that show significant over-representation of accrued activity signal ("active modules"), thus representing biological processes that presumably play key roles in the analyzed conditions. Here, we systematically evaluated six popular AMI methods on gene expression and GWAS data. We observed that GO terms enriched in modules detected on the real data were often also enriched on modules found on randomly permuted data. This indicated that AMI methods frequently report modules that are not specific to the biological context measured by the analyzed omics dataset. To tackle this bias, we designed a permutation-based method that empirically evaluates GO terms reported by AMI methods. We used the method to fashion five novel AMI performance criteria. Last, we developed DOMINO, a novel AMI algorithm, that outperformed the other six algorithms in extensive testing on GE and GWAS data. Software is available at https://github.com/Shamir-Lab.

Network and pathway expansion of genetic disease associations identifies successful drug targets

Genetic evidence of disease association has often been used as a basis for selecting of drug targets for complex common diseases. Likewise, the propagation of genetic evidence through gene or protein interaction networks has been shown to accurately infer novel disease associations at genes for which no direct genetic evidence can be observed. However, an empirical test of the utility of combining these approaches for drug discovery has been lacking. In this study, we examine genetic associations arising from an analysis of 648 UK Biobank GWAS and evaluate whether targets identified as proxies of direct genetic hits are enriched for successful drug targets, as measured by historical clinical trial data. We find that protein networks formed from specific functional linkages such as protein complexes and ligand–receptor pairs are suitable for even naïve guilt-by-association network propagation approaches. In addition, more sophisticated approaches applied to global protein–protein interaction networks and pathway databases, also successfully retrieve targets enriched for clinically successful drug targets. We conclude that network propagation of genetic evidence can be used for drug target identification.

Learning gene networks underlying clinical phenotypes using SNP perturbation

Availability of genome sequence, molecular, and clinical phenotype data for large patient cohorts generated by recent technological advances provides an opportunity to dissect the genetic architecture of complex diseases at system level. However, previous analyses of such data have largely focused on the co-localization of SNPs associated with clinical and expression traits, each identified from genome-wide association studies and expression quantitative trait locus mapping. Thus, their description of the molecular mechanisms behind the SNPs influencing clinical phenotypes was limited to the single gene linked to the co-localized SNP. Here we introduce PerturbNet, a statistical framework for learning gene networks that modulate the influence of genetic variants on phenotypes, using genetic variants as naturally occurring perturbation of a biological system. PerturbNet uses a probabilistic graphical model to directly model the cascade of perturbation from genetic variants to the gene network to the phenotype network along with the networks at each layer of the biological system. PerturbNet learns the entire model by solving a single optimization problem with an efficient algorithm that can analyze human genome-wide data within a few hours. PerturbNet inference procedures extract a detailed description of how the gene network modulates the genetic effects on phenotypes. Using simulated and asthma data, we demonstrate that PerturbNet improves statistical power for detecting disease-linked SNPs and identifies gene networks and network modules mediating the SNP effects on traits, providing deeper insights into the underlying molecular mechanisms.

Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits

Traditional univariate genome-wide association studies generate false positives and negatives due to difficulties distinguishing associated variants from variants with spurious nonzero effects that do not directly influence the trait. Recent efforts have been directed at identifying genes or signaling pathways enriched for mutations in quantitative traits or case-control studies, but these can be computationally costly and hampered by strict model assumptions. Here, we present gene-ε, a new approach for identifying statistical associations between sets of variants and quantitative traits. Our key insight is that enrichment studies on the gene-level are improved when we reformulate the genome-wide SNP-level null hypothesis to identify spurious small-to-intermediate SNP effects and classify them as non-causal. gene-ε efficiently identifies enriched genes under a variety of simulated genetic architectures, achieving greater than a 90% true positive rate at 1% false positive rate for polygenic traits. Lastly, we apply gene-ε to summary statistics derived from six quantitative traits using European-ancestry individuals in the UK Biobank, and identify enriched genes that are in biologically relevant pathways.

Detecting Shared Genetic Architecture Among Multiple Phenotypes by Hierarchical Clustering of Gene-Level Association Statistics

Emerging large-scale biobanks pairing genotype data with phenotype data present new opportunities to prioritize shared genetic associations across multiple phenotypes for molecular validation. Past research, by our group and others, has shown gene-level tests of association produce biologically interpretable characterization of the genetic architecture of a given phenotype. Here, we present a new method, Ward clustering to identify Internal Node branch length outliers using Gene Scores (WINGS), for identifying shared genetic architecture among multiple phenotypes. The objective of WINGS is to identify groups of phenotypes, or "clusters," sharing a core set of genes enriched for mutations in cases. We validate WINGS using extensive simulation studies and then combine gene-level association tests with WINGS to identify shared genetic architecture among 81 case-control and seven quantitative phenotypes in 349,468 European-ancestry individuals from the UK Biobank. We identify eight prioritized phenotype clusters and recover multiple published gene-level associations within prioritized clusters.

Germline Features Associated with Immune Infiltration in Solid Tumors

The immune composition of the tumor microenvironment influences response and resistance to immunotherapies. While numerous studies have identified somatic correlates of immune infiltration, germline features that associate with immune infiltrates in cancers remain incompletely characterized. We analyze seven million autosomal germline variants in the TCGA cohort and test for association with established immune-related phenotypes that describe the tumor immune microenvironment. We identify one SNP associated with the amount of infiltrating follicular helper T cells; 23 candidate genes, some of which are involved in cytokine-mediated signaling and others containing cancer-risk SNPs; and networks with genes that are part of the DNA repair and transcription elongation pathways. In addition, we find a positive association between polygenic risk for rheumatoid arthritis and amount of infiltrating CD8+ T cells. Overall, we identify multiple germline genetic features associated with tumor-immune phenotypes and develop a framework for probing inherited features that contribute to differences in immune infiltration.

Footprints of natural selection at the mannose-6-phosphate isomerase locus in barnacles

The rocky intertidal is a natural laboratory to study how natural selection acts on the genes and proteins responsible for organismal survival and reproduction. Alternative forms of enzymes that differ across the intertidal have been known for decades and have provided examples of selection, but the genetic basis of such enzyme variation is known in only a few cases. In this paper, we present molecular evidence of natural selection at the Mpi gene, a key enzyme in energy metabolism that alters survival of barnacles living across the stress gradient imposed by the intertidal. Our study demonstrates how natural selection can facilitate survival in highly heterogeneous environments through the maintenance of multiple molecular solutions to ecological stresses.

The mannose-6-phosphate isomerase (Mpi) locus in Semibalanus balanoides has been studied as a candidate gene for balancing selection for more than two decades. Previous work has shown that Mpi allozyme genotypes (fast and slow) have different frequencies across Atlantic intertidal zones due to selection on postsettlement survival (i.e., allele zonation). We present the complete gene sequence of the Mpi locus and quantify nucleotide polymorphism in S. balanoides, as well as divergence to its sister taxon Semibalanus cariosus. We show that the slow allozyme contains a derived charge-altering amino acid polymorphism, and both allozyme classes correspond to two haplogroups with multiple internal haplotypes. The locus shows several footprints of balancing selection around the fast/slow site: an enrichment of positive Tajima’s D for nonsynonymous mutations, an excess of polymorphism, and a spike in the levels of silent polymorphism relative to silent divergence, as well as a site frequency spectrum enriched for midfrequency mutations. We observe other departures from neutrality across the locus in both coding and noncoding regions. These include a nonsynonymous trans-species polymorphism and a recent mutation under selection within the fast haplogroup. The latter suggests ongoing allelic replacement of functionally relevant amino acid variants. Moreover, predicted models of Mpi protein structure provide insight into the functional significance of the putatively selected amino acid polymorphisms. While footprints of selection are widespread across the range of S. balanoides, our data show that intertidal zonation patterns are variable across both spatial and temporal scales. These data provide further evidence for heterogeneous selection on Mpi.

A network analysis to identify mediators of germline-driven differences in breast cancer prognosis

Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies ~7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as G-alpha signaling, circadian clock, angiogenesis, and Rho-GTPases in apoptosis.

AMPK variant, a candidate of novel predictor for chemotherapy in metastatic colorectal cancer: A meta-analysis using TRIBE, MAVERICC and FIRE3

AMP-activated protein kinase (AMPK) is a key sensor of energy homeostasis and regulates cell metabolism, proliferation and chemotherapy/radiotherapy sensitivities. This study aimed to explore the relationship between the AMPK pathway-related single nucleotide polymorphisms (SNPs) and clinical outcomes in patients with metastatic colorectal cancer (mCRC). We analyzed a total of 884 patients with mCRC enrolled in three randomized clinical trials (TRIBE, MAVERICC and FIRE-3: where patients were treated with FOLFIRI, mFOLFOX6 or FOLFOXIRI combined with bevacizumab or cetuximab as the first-line chemotherapy). The association between AMPK pathway-related SNPs and clinical outcomes was analyzed across the six treatment cohorts, using a meta-analysis approach. Our meta-analysis showed that AMPK pathway had significant associations with progression-free survival (PFS; p < 0.001) and overall survival (OS; p < 0.001), but not with tumor response (TR; p = 0.220): PRKAA1 rs13361707 was significantly associated with favorable PFS (log HR = -0.219, SE = 0.073, p = 0.003), as well as PRKAA1 rs10074991 (log HR = -0.215, SE = 0.073, p = 0.003), and there were suggestive associations of PRKAG1 rs1138908 with unfavorable OS (log HR = 0.170, SE = 0.083, p = 0.041), and of UBE2O rs3803739 with unfavorable PFS (log HR = 0.137, SE = 0.068, p = 0.042) and OS (log HR = 0.210, SE = 0.077, p = 0.006), although these results were not significant after false discovery rate adjustment. AMPK pathway-related SNPs may be predictors for chemotherapy in mCRC. Upon validation, our findings would provide novel insight for selecting treatment strategies.

Early complement genes are associated with visual system degeneration in multiple sclerosis

Multiple sclerosis is a heterogeneous disease with an unpredictable course and a wide range of severity; some individuals rapidly progress to a disabled state whereas others experience only mild symptoms. Though genetic studies have identified variants that are associated with an increased risk of developing multiple sclerosis, no variants have been consistently associated with multiple sclerosis severity. In part, the lack of findings is related to inherent limitations of clinical rating scales; these scales are insensitive to early degenerative changes that underlie disease progression. Optical coherence tomography imaging of the retina and low-contrast letter acuity correlate with and predict clinical and imaging-based outcomes in multiple sclerosis. Therefore, they may serve as sensitive phenotypes to discover genetic predictors of disease course. We conducted a set of genome-wide association studies of longitudinal structural and functional visual pathway phenotypes in multiple sclerosis. First, we assessed genetic predictors of ganglion cell/inner plexiform layer atrophy in a discovery cohort of 374 patients with multiple sclerosis using mixed-effects models adjusting for age, sex, disease duration, optic neuritis and genetic ancestry and using a combination of single-variant and network-based analyses. For candidate variants identified in discovery, we conducted a similar set of analyses of ganglion cell/inner plexiform layer thinning in a replication cohort (n = 376). Second, we assessed genetic predictors of sustained loss of 5-letters in low-contrast letter acuity in discovery (n = 582) using multivariable-adjusted Cox proportional hazards models. We then evaluated candidate variants/pathways in a replication cohort. (n = 253). Results of both studies revealed novel subnetworks highly enriched for connected genes in early complement activation linked to measures of disease severity. Within these networks, C3 was the gene most strongly associated with ganglion cell/inner plexiform layer atrophy (P = 0.004) and C1QA and CR1 were top results in analysis of sustained low-contrast letter acuity loss. Namely, variant rs158772, linked to C1QA, and rs61822967, linked to CR1, were associated with 71% and 40% increases in risk of sustained LCLA loss, respectively, in meta-analysis pooling discovery and replication cohorts (rs158772: hazard ratio: 1.71; 95% confidence interval 1.30-2.25; P = 1.3 × 10-4; rs61822967: hazard ratio: 1.40; 95% confidence interval: 1.16-1.68; P = 4.1 × 10-4). In conclusion, early complement pathway gene variants were consistently associated with structural and functional measures of multiple sclerosis severity. These results from unbiased analyses are strongly supported by several prior reports that mechanistically implicated early complement factors in neurodegeneration.

A Fast and Flexible Framework for Network-Assisted Genomic Association

We present an accessible, fast, and customizable network propagation system for pathway boosting and interpretation of genome-wide association studies. This system-NAGA (Network Assisted Genomic Association)-taps the NDEx biological network resource to gain access to thousands of protein networks and select those most relevant and performative for a specific association study. The method works efficiently, completing genome-wide analysis in under 5 minutes on a modern laptop computer. We show that NAGA recovers many known disease genes from analysis of schizophrenia genetic data, and it substantially boosts associations with previously unappreciated genes such as amyloid beta precursor. On this and seven other gene-disease association tasks, NAGA outperforms conventional approaches in recovery of known disease genes and replicability of results. Protein interactions associated with disease are visualized and annotated in Cytoscape, which, in addition to standard programmatic interfaces, allows for downstream analysis.

Impact of polymorphisms within genes involved in regulating DNA methylation in patients with metastatic colorectal cancer enrolled in three independent, randomised, open-label clinical trials: a meta-analysis from TRIBE, MAVERICC and FIRE-3

BACKGROUND

CpG island DNA hypermethylation and global DNA hypomethylation are hallmark characteristics of colorectal cancer (CRC). Therefore, we aim to explore the effect of genetic variations within the genes that regulate the DNA methylation and demethylation pathways on outcomes in patients with metastatic CRC (mCRC) treated with first-line therapy and enrolled in three independent, randomised, open-label clinical trials.

METHODS

A total of 884 patients with mCRC enrolled in TRIBE, MAVERICC and FIRE-3 trials were included. Single-nucleotide polymorphisms (SNPs) within genes involved in DNA methylation and demethylation pathways were analysed. The prognostic value of each SNP across all treatment arms was quantified using the inverse-variance-weighted effect size, a meta-analysis approach implemented in the METASOFT software.

RESULTS

In the meta-analysis, DNMT3A rs11681717 was significantly associated with overall survival (hazard ratio = 1.26; 95% confidence interval [CI] 1.08-1.46; P = 0.002; false discovery rate [FDR] = 0.016), accounting for seven tests in the DNA methylation pathway. In addition, there was suggestive evidence of association for ten-eleven translocation (TET) genes variance with tumour response (TET1 rs3814177, odds ratio [OR] = 0.76, 95% CI 0.59-0.97, P = 0.025, FDR = 0.087; TET3 rs7560668, OR = 1.44; 95% CI 1.10-1.89; P = 0.009; FDR = 0.062).

CONCLUSIONS

We showed that polymorphisms within the genes responsible for the DNA methylation and demethylation machineries are correlated with outcomes in patients with mCRC who were enrolled in three independent, randomised, open-label, phase II/III clinical trials. In addition, we demonstrated the feasibility of a meta-analysis approach to identify stronger and more convincing association between gene polymorphisms and outcome, potentially leading the way to a new method of analysis for similar data set.

Network Analysis as a Grand Unifier in Biomedical Data Science

Biomedical data scientists study many types of networks, ranging from those formed by neurons to those created by molecular interactions. People often criticize these networks as uninterpretable diagrams termed hairballs; however, here we show that molecular biological networks can be interpreted in several straightforward ways. First, we can break down a network into smaller components, focusing on individual pathways and modules. Second, we can compute global statistics describing the network as a whole. Third, we can compare networks. These comparisons can be within the same context (e.g., between two gene regulatory networks) or cross-disciplinary (e.g., between regulatory networks and governmental hierarchies). The latter comparisons can transfer a formalism, such as that for Markov chains, from one context to another or relate our intuitions in a familiar setting (e.g., social networks) to the relatively unfamiliar molecular context. Finally, key aspects of molecular networks are dynamics and evolution, i.e., how they evolve over time and how genetic variants affect them. By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease.

Methylphenidate enhances neuronal differentiation and reduces proliferation concomitant to activation of Wnt signal transduction pathways

Methylphenidate (Ritalin) is the most commonly prescribed drug in the treatment of attention-deficit hyperactivity disorder. It is suggested that in vivo, methylphenidate treatment supports cortical maturation, however, the molecular and cellular mechanisms are not well understood. This study aimed to explore the potential effect of methylphenidate on cell proliferation and maturation in various cellular models, hypothesizing its interaction with the Wnt-signaling. The termination of cell proliferation concomitant to neuronal maturation following methylphenidate treatment was observed in all of the cell-models tested: murine neural stem-, rat PC12- and the human SH-SY5Y-cells. Inhibition of Wnt-signaling in SH-SY5Y cells with Dkk1 30 min before methylphenidate treatment suppressed neuronal differentiation but enhanced proliferation. The possible involvement of the dopamine-transporter in cell differentiation was discounted following the observation of opposing results after GBR-12909 treatment. Moreover, Wnt-activation via methylphenidate was confirmed in Wnt-luciferase-reporter assay. These findings reveal a new mechanism of action of methylphenidate that might explain long-term effects.

Incorporating networks in a probabilistic graphical model to find drivers for complex human diseases

Discovering genetic mechanisms driving complex diseases is a hard problem. Existing methods often lack power to identify the set of responsible genes. Protein-protein interaction networks have been shown to boost power when detecting gene-disease associations. We introduce a Bayesian framework, Conflux, to find disease associated genes from exome sequencing data using networks as a prior. There are two main advantages to using networks within a probabilistic graphical model. First, networks are noisy and incomplete, a substantial impediment to gene discovery. Incorporating networks into the structure of a probabilistic models for gene inference has less impact on the solution than relying on the noisy network structure directly. Second, using a Bayesian framework we can keep track of the uncertainty of each gene being associated with the phenotype rather than returning a fixed list of genes. We first show that using networks clearly improves gene detection compared to individual gene testing. We then show consistently improved performance of Conflux compared to the state-of-the-art diffusion network-based method Hotnet2 and a variety of other network and variant aggregation methods, using randomly generated and literature-reported gene sets. We test Hotnet2 and Conflux on several network configurations to reveal biases and patterns of false positives and false negatives in each case. Our experiments show that our novel Bayesian framework Conflux incorporates many of the advantages of the current state-of-the-art methods, while offering more flexibility and improved power in many gene-disease association scenarios.

Networks and pathway-based methods are commonly used to improve the power of gene detection in associations with complex human diseases. Network diffusion approaches have shown their effectiveness and superior performance in cancer studies. Still, there are many problems such as noise and missingness with currently available human networks that bias the results of gene detection. We propose a novel graphical model-based method Conflux that overcomes several of the pitfalls of the existing state-of-the-art approaches while building on their successes. Conflux integrates genotype data with networks directly, using diffusion-like methods, but only as part of a structure in a probabilistic model to reduce the negative effect of the noise in the networks. This Bayesian framework allows Conflux to keep track of the uncertainty in the gene list that is being associated with the disease and consequently rank the genes with respect to our confidence in the association. It also allows for the discovery of gene sets that are not fully supported by the network if they have enough support in the data. These improvements result in a flexible approach that improves the power in many gene-disease association scenarios while reducing the number of false positives reported.

Novel Gene and Network Associations Found for Acute Lymphoblastic Leukemia Using Case-Control and Family-Based Studies in Multiethnic Populations

Background: Acute lymphoblastic leukemia (ALL) is the most common childhood cancer, suggesting that germline variants influence ALL risk. Although multiple genome-wide association (GWA) studies have identified variants predisposing children to ALL, it remains unclear whether genetic heterogeneity affects ALL susceptibility and how interactions within and among genes containing ALL-associated variants influence ALL risk.Methods: Here, we jointly analyzed two published datasets of case-control GWA summary statistics along with germline data from ALL case-parent trios. We used the gene-level association method PEGASUS to identify genes with multiple variants associated with ALL. We then used PEGASUS gene scores as input to the network analysis algorithm HotNet2 to characterize the genomic architecture of ALL.Results: Using PEGASUS, we confirmed associations previously observed at genes such as ARID5B, IKZF1, CDKN2A/2B, and PIP4K2A, and we identified novel candidate gene associations. Using HotNet2, we uncovered significant gene subnetworks that may underlie inherited ALL risk: a subnetwork involved in B-cell differentiation containing the ALL-associated gene CEBPE, and a subnetwork of homeobox genes, including MEIS1Conclusions: Gene and network analysis uncovered loci associated with ALL that are missed by GWA studies, such as MEIS1 Furthermore, ALL-associated loci do not appear to interact directly with each other to influence ALL risk, and instead appear to influence leukemogenesis through multiple, complex pathways.Impact: We present a new pipeline for post hoc analysis of association studies that yields new insight into the etiology of ALL and can be applied in future studies to shed light on the genomic underpinnings of cancer. Cancer Epidemiol Biomarkers Prev; 26(10); 1531-9. ©2017 AACR.

Detecting gene subnetworks under selection in biological pathways

Advances in high throughput sequencing technologies have created a gap between data production and functional data analysis. Indeed, phenotypes result from interactions between numerous genes, but traditional methods treat loci independently, missing important knowledge brought by network-level emerging properties. Therefore, detecting selection acting on multiple genes affecting the evolution of complex traits remains challenging. In this context, gene network analysis provides a powerful framework to study the evolution of adaptive traits and facilitates the interpretation of genome-wide data. We developed a method to analyse gene networks that is suitable to evidence polygenic selection. The general idea is to search biological pathways for subnetworks of genes that directly interact with each other and that present unusual evolutionary features. Subnetwork search is a typical combinatorial optimization problem that we solve using a simulated annealing approach. We have applied our methodology to find signals of adaptation to high-altitude in human populations. We show that this adaptation has a clear polygenic basis and is influenced by many genetic components. Our approach, implemented in the R package signet, improves on gene-level classical tests for selection by identifying both new candidate genes and new biological processes involved in adaptation to altitude.

Network propagation: a universal amplifier of genetic associations

Biological networks are powerful resources for the discovery of genes and genetic modules that drive disease. Fundamental to network analysis is the concept that genes underlying the same phenotype tend to interact; this principle can be used to combine and to amplify signals from individual genes. Recently, numerous bioinformatic techniques have been proposed for genetic analysis using networks, based on random walks, information diffusion and electrical resistance. These approaches have been applied successfully to identify disease genes, genetic modules and drug targets. In fact, all these approaches are variations of a unifying mathematical machinery - network propagation - suggesting that it is a powerful data transformation method of broad utility in genetic research.