Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease

Non-coding and Loss-of-Function Coding Variants in TET2 are Associated with Multiple Neurodegenerative Diseases

We conducted genome sequencing to search for rare variation contributing to early-onset Alzheimer's disease (EOAD) and frontotemporal dementia (FTD). Discovery analysis was conducted on 435 cases and 671 controls of European ancestry. Burden testing for rare variation associated with disease was conducted using filters based on variant rarity (less than one in 10,000 or private), computational prediction of deleteriousness (CADD) (10 or 15 thresholds), and molecular function (protein loss-of-function [LoF] only, coding alteration only, or coding plus non-coding variants in experimentally predicted regulatory regions). Replication analysis was conducted on 16,434 independent cases and 15,587 independent controls. Rare variants in TET2 were enriched in the discovery combined EOAD and FTD cohort (p = 4.6 × 10-8, genome-wide corrected p = 0.0026). Most of these variants were canonical LoF or non-coding in predicted regulatory regions. This enrichment replicated across several cohorts of Alzheimer's disease (AD) and FTD (replication only p = 0.0029). The combined analysis odds ratio was 2.3 (95% confidence interval [CI] 1.6-3.4) for AD and FTD. The odds ratio for qualifying non-coding variants considered independently from coding variants was 3.7 (95% CI 1.7-9.4). For LoF variants, the combined odds ratio (for AD, FTD, and amyotrophic lateral sclerosis, which shares clinicopathological overlap with FTD) was 3.1 (95% CI 1.9-5.2). TET2 catalyzes DNA demethylation. Given well-defined changes in DNA methylation that occur during aging, rare variation in TET2 may confer risk for neurodegeneration by altering the homeostasis of key aging-related processes. Additionally, our study emphasizes the relevance of non-coding variation in genetic studies of complex disease.

Comprehensive functional annotation of susceptibility variants associated with asthma

Genome-wide association studies (GWAS) have identified hundreds of primarily non-coding disease-susceptibility variants that further need functional interpretation to prioritize and discriminate the disease-relevant variants. We present a comprehensive genome-wide non-coding variant prioritization scheme followed by validation using Pyrosequencing and TaqMan assays in asthma. We implemented a composite Functional Annotation Score (cFAS) to investigate over 32,000 variants consisting of 1525 GWAS-lead asthma-susceptibility variants and their LD proxies (r² ≥ 0.80). Functional annotation pipeline in cFAS revealed 274 variants with significant score at 1% false discovery rate. This study implicates a novel locus 4p16 (SLC26A1) with eQTL variant (rs11936407) and known loci in 17q12-21 and 5q22 which encode ORM1-like protein 3 (ORMDL3, rs406527, and rs12936231) and thymic stromal lymphopoietin (TSLP, rs3806932 and rs10073816) epithelial gene, respectively. Follow-up validation analysis through pyrosequencing of CpG sites in and nearby rs4065275 and rs11936407 showed genotype-dependent hypomethylation on asthma cases compared with healthy controls. Prioritized variants are enriched for asthma-specific histone modification associated with active chromatin (H3K4me1 and H3K27ac) in T cells, B cells, lung, and immune-related interferon gamma signaling pathways. Our findings, together with those from prior studies, suggest that SNPs can affect asthma by regulating enhancer activity, and our comprehensive bioinformatics and functional analysis could lead to biological insights into asthma pathogenesis.Graphic abstract.

An evaluation of noncoding genome annotation tools through enrichment analysis of 15 genome-wide association studies

Functionally annotating genetic variations is an essential yet challenging topic in human genetics research. As large consortia including ENCODE and Roadmap Epigenomics Project continue to generate high-throughput transcriptomic and epigenomic data, many computational frameworks have been developed to integrate these experimental data to predict functionality of genetic variations in both protein-coding and noncoding regions. Here, we compare a number of recently developed annotation frameworks for noncoding regions through enrichment analysis on genome-wide association studies (GWASs). We also compare several different strategies to quantify enrichment using GWAS summary statistics. Our analyses highlight the importance of jointly modeling context-specific annotations with genome-wide data in providing statistically powerful and biologically interpretable enrichment for complex disease associations. Our findings provide insights into when and how computational genome annotations may benefit future complex disease studies on the genome-wide scale.

The multiplex model of the genetics of Alzheimer's disease

Genes play a strong role in Alzheimer's disease (AD), with late-onset AD showing heritability of 58-79% and early-onset AD showing over 90%. Genetic association provides a robust platform to build our understanding of the etiology of this complex disease. Over 50 loci are now implicated for AD, suggesting that AD is a disease of multiple components, as supported by pathway analyses (immunity, endocytosis, cholesterol transport, ubiquitination, amyloid-β and tau processing). Over 50% of late-onset AD heritability has been captured, allowing researchers to calculate the accumulation of AD genetic risk through polygenic risk scores. A polygenic risk score predicts disease with up to 90% accuracy and is an exciting tool in our research armory that could allow selection of those with high polygenic risk scores for clinical trials and precision medicine. It could also allow cellular modelling of the combined risk. Here we propose the multiplex model as a new perspective from which to understand AD. The multiplex model reflects the combination of some, or all, of these model components (genetic and environmental), in a tissue-specific manner, to trigger or sustain a disease cascade, which ultimately results in the cell and synaptic loss observed in AD.

Enhancing α-secretase Processing for Alzheimer's Disease-A View on SFRP1

Amyloid β (Aβ) peptides generated via sequential β- and γ-secretase processing of the amyloid precursor protein (APP) are major etiopathological agents of Alzheimer's disease (AD). However, an initial APP cleavage by an α-secretase, such as the a disintegrin and metalloproteinase domain-containing protein ADAM10, precludes β-secretase cleavage and leads to APP processing that does not produce Aβ. The latter appears to underlie the disease symptom-attenuating effects of a multitude of experimental therapeutics in AD animal models. Recent work has indicated that an endogenous inhibitor of ADAM10, secreted-frizzled-related protein 1 (SFRP1), is elevated in human AD brains and associated with amyloid plaques in mouse AD models. Importantly, genetic or functional attenuation of SFRP1 lowered Aβ accumulation and improved AD-related histopathological and neurological traits. Given SFRP1's well-known activity in attenuating Wnt signaling, which is also commonly impaired in AD, SFRP1 appears to be a promising therapeutic target for AD. This idea, however, needs to be addressed with care because of cancer enhancement potentials resulting from a systemic loss of SFRP1 activity, as well as an upregulation of ADAM10 activity. In this focused review, I shall discuss α-secretase-effected APP processing in AD with a focus on SFRP1, and explore the contrasting perspectives arising from the recent findings.

Large-scale determination and characterization of cell type-specific regulatory elements in the human genome

Histone modifications have been widely elucidated to play vital roles in gene regulation and cell identity. The Roadmap Epigenomics Consortium generated a reference catalog of several key histone modifications across >100s of human cell types and tissues. Decoding these epigenomes into functional regulatory elements is a challenging task in computational biology. To this end, we adopted a differential chromatin modification analysis framework to comprehensively determine and characterize cell type-specific regulatory elements (CSREs) and their histone modification codes in the human epigenomes of five histone modifications across 127 tissues or cell types. The CSREs show significant relevance with cell type-specific biological functions and diseases and cell identity. Clustering of CSREs with their specificity signals reveals distinct histone codes, demonstrating the diversity of functional roles of CSREs within the same cell or tissue. Last but not least, dynamics of CSREs from close cell types or tissues can give a detailed view of developmental processes such as normal tissue development and cancer occurrence.

Comparative analyses of sperm DNA methylomes among human, mouse and cattle provide insights into epigenomic evolution and complex traits

Sperm DNA methylation is crucial for fertility and viability of offspring but epigenome evolution in mammals is largely understudied. By comparing sperm DNA methylomes and large-scale genome-wide association study (GWAS) signals between human and cattle, we aimed to examine the DNA methylome evolution and its associations with complex phenotypes in mammals. Our analysis revealed that genes with conserved non-methylated promoters (e.g., ANKS1A and WNT7A) among human and cattle were involved in common system and embryo development, and enriched for GWAS signals of body conformation traits in both species, while genes with conserved hypermethylated promoters (e.g., TCAP and CD80) were engaged in immune responses and highlighted by immune-related traits. On the other hand, genes with human-specific hypomethylated promoters (e.g., FOXP2 and HYDIN) were engaged in neuron system development and enriched for GWAS signals of brain-related traits, while genes with cattle-specific hypomethylated promoters (e.g., LDHB and DGAT2) mainly participated in lipid storage and metabolism. We validated our findings using sperm-retained nucleosome, preimplantation transcriptome, and adult tissue transcriptome data, as well as sequence evolutionary features, including motif binding sites, mutation rates, recombination rates and evolution signatures. In conclusion, our results demonstrate important roles of epigenome evolution in shaping the genetic architecture underlying complex phenotypes, hence enhance signal prioritization in GWAS and provide valuable information for human neurological disorders and livestock genetic improvement.

Genetic landscape of chronic obstructive pulmonary disease identifies heterogeneous cell-type and phenotype associations

Chronic obstructive pulmonary disease (COPD) is the leading cause of respiratory mortality worldwide. Genetic risk loci provide new insights into disease pathogenesis. We performed a genome-wide association study in 35,735 cases and 222,076 controls from the UK Biobank and additional studies from the International COPD Genetics Consortium. We identified 82 loci associated with P < 5 × 10-8; 47 of these were previously described in association with either COPD or population-based measures of lung function. Of the remaining 35 new loci, 13 were associated with lung function in 79,055 individuals from the SpiroMeta consortium. Using gene expression and regulation data, we identified functional enrichment of COPD risk loci in lung tissue, smooth muscle, and several lung cell types. We found 14 COPD loci shared with either asthma or pulmonary fibrosis. COPD genetic risk loci clustered into groups based on associations with quantitative imaging features and comorbidities. Our analyses provide further support for the genetic susceptibility and heterogeneity of COPD.

A statistical framework for cross-tissue transcriptome-wide association analysis

Transcriptome-wide association analysis is a powerful approach to studying the genetic architecture of complex traits. A key component of this approach is to build a model to impute gene expression levels from genotypes by using samples with matched genotypes and gene expression data in a given tissue. However, it is challenging to develop robust and accurate imputation models with a limited sample size for any single tissue. Here, we first introduce a multi-task learning method to jointly impute gene expression in 44 human tissues. Compared with single-tissue methods, our approach achieved an average of 39% improvement in imputation accuracy and generated effective imputation models for an average of 120% more genes. We describe a summary-statistic-based testing framework that combines multiple single-tissue associations into a powerful metric to quantify the overall gene-trait association. We applied our method, called UTMOST (unified test for molecular signatures), to multiple genome-wide-association results and demonstrate its advantages over single-tissue strategies.

Prioritizing Parkinson's disease genes using population-scale transcriptomic data

Genome-wide association studies (GWAS) have identified over 41 susceptibility loci associated with Parkinson's Disease (PD) but identifying putative causal genes and the underlying mechanisms remains challenging. Here, we leverage large-scale transcriptomic datasets to prioritize genes that are likely to affect PD by using a transcriptome-wide association study (TWAS) approach. Using this approach, we identify 66 gene associations whose predicted expression or splicing levels in dorsolateral prefrontal cortex (DLFPC) and peripheral monocytes are significantly associated with PD risk. We uncover many novel genes associated with PD but also novel mechanisms for known associations such as MAPT, for which we find that variation in exon 3 splicing explains the common genetic association. Genes identified in our analyses belong to the same or related pathways including lysosomal and innate immune function. Overall, our study provides a strong foundation for further mechanistic studies that will elucidate the molecular drivers of PD.

Leveraging Polygenic Functional Enrichment to Improve GWAS Power

Functional genomics data has the potential to increase GWAS power by identifying SNPs that have a higher prior probability of association. Here, we introduce a method that leverages polygenic functional enrichment to incorporate coding, conserved, regulatory, and LD-related genomic annotations into association analyses. We show via simulations with real genotypes that the method, functionally informed novel discovery of risk loci (FINDOR), correctly controls the false-positive rate at null loci and attains a 9%-38% increase in the number of independent associations detected at causal loci, depending on trait polygenicity and sample size. We applied FINDOR to 27 independent complex traits and diseases from the interim UK Biobank release (average N = 130K). Averaged across traits, we attained a 13% increase in genome-wide significant loci detected (including a 20% increase for disease traits) compared to unweighted raw p values that do not use functional data. We replicated the additional loci in independent UK Biobank and non-UK Biobank data, yielding a highly statistically significant replication slope (0.66-0.69) in each case. Finally, we applied FINDOR to the full UK Biobank release (average N = 416K), attaining smaller relative improvements (consistent with simulations) but larger absolute improvements, detecting an additional 583 GWAS loci. In conclusion, leveraging functional enrichment using our method robustly increases GWAS power.

Leveraging Polygenic Functional Enrichment to Improve GWAS Power

Functional genomics data has the potential to increase GWAS power by identifying SNPs that have a higher prior probability of association. Here, we introduce a method that leverages polygenic functional enrichment to incorporate coding, conserved, regulatory, and LD-related genomic annotations into association analyses. We show via simulations with real genotypes that the method, functionally informed novel discovery of risk loci (FINDOR), correctly controls the false-positive rate at null loci and attains a 9%-38% increase in the number of independent associations detected at causal loci, depending on trait polygenicity and sample size. We applied FINDOR to 27 independent complex traits and diseases from the interim UK Biobank release (average N = 130K). Averaged across traits, we attained a 13% increase in genome-wide significant loci detected (including a 20% increase for disease traits) compared to unweighted raw p values that do not use functional data. We replicated the additional loci in independent UK Biobank and non-UK Biobank data, yielding a highly statistically significant replication slope (0.66-0.69) in each case. Finally, we applied FINDOR to the full UK Biobank release (average N = 416K), attaining smaller relative improvements (consistent with simulations) but larger absolute improvements, detecting an additional 583 GWAS loci. In conclusion, leveraging functional enrichment using our method robustly increases GWAS power.

INFERNO: inferring the molecular mechanisms of noncoding genetic variants

The majority of variants identified by genome-wide association studies (GWAS) reside in the noncoding genome, affecting regulatory elements including transcriptional enhancers. However, characterizing their effects requires the integration of GWAS results with context-specific regulatory activity and linkage disequilibrium annotations to identify causal variants underlying noncoding association signals and the regulatory elements, tissue contexts, and target genes they affect. We propose INFERNO, a novel method which integrates hundreds of functional genomics datasets spanning enhancer activity, transcription factor binding sites, and expression quantitative trait loci with GWAS summary statistics. INFERNO includes novel statistical methods to quantify empirical enrichments of tissue-specific enhancer overlap and to identify co-regulatory networks of dysregulated long noncoding RNAs (lncRNAs). We applied INFERNO to two large GWAS studies. For schizophrenia (36,989 cases, 113,075 controls), INFERNO identified putatively causal variants affecting brain enhancers for known schizophrenia-related genes. For inflammatory bowel disease (IBD) (12,882 cases, 21,770 controls), INFERNO found enrichments of immune and digestive enhancers and lncRNAs involved in regulation of the adaptive immune response. In summary, INFERNO comprehensively infers the molecular mechanisms of causal noncoding variants, providing a sensitive hypothesis generation method for post-GWAS analysis. The software is available as an open source pipeline and a web server.

Deep-coverage whole genome sequences and blood lipids among 16,324 individuals

Large-scale deep-coverage whole-genome sequencing (WGS) is now feasible and offers potential advantages for locus discovery. We perform WGS in 16,324 participants from four ancestries at mean depth >29X and analyze genotypes with four quantitative traits—plasma total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and triglycerides. Common variant association yields known loci except for few variants previously poorly imputed. Rare coding variant association yields known Mendelian dyslipidemia genes but rare non-coding variant association detects no signals. A high 2M-SNP LDL-C polygenic score (top 5th percentile) confers similar effect size to a monogenic mutation (~30 mg/dl higher for each); however, among those with severe hypercholesterolemia, 23% have a high polygenic score and only 2% carry a monogenic mutation. At these sample sizes and for these phenotypes, the incremental value of WGS for discovery is limited but WGS permits simultaneous assessment of monogenic and polygenic models to severe hypercholesterolemia.

Common genetic variants associated with plasma lipids have been extensively studied for a better understanding of common diseases. Here, the authors use whole-genome sequencing of 16,324 individuals to analyze rare variant associations and to determine their monogenic and polygenic contribution to lipid traits.

The Post-GWAS Era: From Association to Function

During the past 12 years, genome-wide association studies (GWASs) have uncovered thousands of genetic variants that influence risk for complex human traits and diseases. Yet functional studies aimed at delineating the causal genetic variants and biological mechanisms underlying the observed statistical associations with disease risk have lagged. In this review, we highlight key advances in the field of functional genomics that may facilitate the derivation of biological meaning post-GWAS. We highlight the evidence suggesting that causal variants underlying disease risk often function through regulatory effects on the expression of target genes and that these expression effects might be modest and cell-type specific. We moreover discuss specific studies as proof-of-principle examples for current statistical, bioinformatic, and empirical bench-based approaches to downstream elucidation of GWAS-identified disease risk loci.

Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies

Genome-wide association studies (GWASs) have identified many disease associated loci, the majority of which have unknown biological functions. Understanding the mechanism underlying trait associations requires identifying trait-relevant tissues and investigating associations in a trait-specific fashion. Here, we extend the widely used linear mixed model to incorporate multiple SNP functional annotations from omics studies with GWAS summary statistics to facilitate the identification of trait-relevant tissues, with which to further construct powerful association tests. Specifically, we rely on a generalized estimating equation based algorithm for parameter inference, a mixture modeling framework for trait-tissue relevance classification, and a weighted sequence kernel association test constructed based on the identified trait-relevant tissues for powerful association analysis. We refer to our analytic procedure as the Scalable Multiple Annotation integration for trait-Relevant Tissue identification and usage (SMART). With extensive simulations, we show how our method can make use of multiple complementary annotations to improve the accuracy for identifying trait-relevant tissues. In addition, our procedure allows us to make use of the inferred trait-relevant tissues, for the first time, to construct more powerful SNP set tests. We apply our method for an in-depth analysis of 43 traits from 28 GWASs using tissue-specific annotations in 105 tissues derived from ENCODE and Roadmap. Our results reveal new trait-tissue relevance, pinpoint important annotations that are informative of trait-tissue relationship, and illustrate how we can use the inferred trait-relevant tissues to construct more powerful association tests in the Wellcome trust case control consortium study.

A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics

Despite the success of large-scale genome-wide association studies (GWASs) on complex traits, our understanding of their genetic architecture is far from complete. Jointly modeling multiple traits' genetic profiles has provided insights into the shared genetic basis of many complex traits. However, large-scale inference sets a high bar for both statistical power and biological interpretability. Here we introduce a principled framework to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics. Through theoretical and numerical analyses, we demonstrate that our method provides accurate covariance estimates, thereby enabling researchers to dissect both the shared and distinct genetic architecture across traits to better understand their etiologies. Among 50 complex traits with publicly accessible GWAS summary statistics (Ntotal≈ 4.5 million), we identified more than 170 pairs with statistically significant genetic covariance. In particular, we found strong genetic covariance between late-onset Alzheimer disease (LOAD) and amyotrophic lateral sclerosis (ALS), two major neurodegenerative diseases, in single-nucleotide polymorphisms (SNPs) with high minor allele frequencies and in SNPs located in the predicted functional genome. Joint analysis of LOAD, ALS, and other traits highlights LOAD's correlation with cognitive traits and hints at an autoimmune component for ALS.