Deciphering the Emerging Complexities of Molecular Mechanisms at GWAS Loci

Single-housing-induced islet epigenomic changes are related to polymorphisms in diabetic KK mice

A lack of social relationships is increasingly recognized as a type 2 diabetes (T2D) risk. To investigate the underlying mechanism, we used male KK mice, an inbred strain with spontaneous diabetes. Given the association between living alone and T2D risk in humans, we divided the non-diabetic mice into singly housed (KK-SH) and group-housed control mice. Around the onset of diabetes in KK-SH mice, we compared H3K27ac ChIP-Seq with RNA-Seq using pancreatic islets derived from each experimental group, revealing a positive correlation between single-housing-induced changes in H3K27ac and gene expression levels. In particular, single-housing-induced H3K27ac decreases revealed a significant association with islet cell functions and GWAS loci for T2D and related diseases, with significant enrichment of binding motifs for transcription factors representative of human diabetes. Although these H3K27ac regions were preferentially localized to a polymorphic genomic background, SNVs and indels did not cause sequence disruption of enriched transcription factor motifs in most of these elements. These results suggest alternative roles of genetic variants in environment-dependent epigenomic changes and provide insights into the complex mode of disease inheritance.

Identification of candidate causal cis -regulatory variants underlying electrocardiographic QT interval GWAS loci

Identifying causal variants among tens or hundreds of associated variants at each locus mapped by genome-wide association studies (GWAS) of complex traits is a challenge. As vast majority of GWAS variants are noncoding, sequence variation at cis -regulatory elements affecting transcriptional expression of specific genes is a widely accepted molecular hypothesis. Following this cis -regulatory hypothesis and combining it with the observation that nucleosome-free open chromatin is a universal hallmark of all types of cis -regulatory elements, we aimed to identify candidate causal regulatory variants underlying electrocardiographic QT interval GWAS loci. At a dozen loci, selected for higher effect sizes and a better understanding of the likely causal gene, we identified and included all common variants in high linkage disequilibrium with the GWAS variants as candidate variants. Using ENCODE DNase-seq and ATAC-seq from multiple human adult cardiac left ventricle tissue samples, we generated genome-wide maps of open chromatin regions marking putative regulatory elements. QT interval associated candidate variants were filtered for overlap with cardiac left ventricle open chromatin regions to identify candidate causal cis -regulatory variants, which were further assessed for colocalizing with a known cardiac GTEx expression quantitative trait locus variant as additional evidence for their causal role. Together, these efforts have generated a comprehensive set of candidate causal variants that are expected to be enriched for cis -regulatory potential and thereby, explaining the observed genetic associations.

Functional characterization of QT interval associated SCN5A enhancer variants identify combined additive effects

Several empirical and theoretical studies suggest presence of multiple enhancers per gene that collectively regulate gene expression, and that common sequence variation impacting on the activities of these enhancers is a major source of inter-individual variability in gene expression. However, for vast majority of genes, enhancers and the underlying regulatory variation remains unknown. Even for the genes with well-characterized enhancers, the nature of the combined effects from multiple enhancers and their variants, when known, on gene expression regulation remains unexplored. Here, we have evaluated the combined effects from five SCN5A enhancers and their regulatory variants that are known to collectively correlate with SCN5A cardiac expression and underlie QT interval association in the general population. Using small deletions centered at the regulatory variants in episomal reporter assays in a mouse cardiomyocyte cell line we demonstrate that the variants and their flanking sequences play critical role in individual enhancer activities, likely being a transcription factor (TF) binding site. By performing oligonucleotide-based pulldown assays on predicted TFs we identify the TFs likely driving allele-specific enhancer activities. Using all 32 possible allelic synthetic constructs in reporter assays, representing the five biallelic enhancers in tandem in their genomic order, we demonstrate combined additive effects on overall enhancer activities. Using transient enhancer assays in developing zebrafish embryos we demonstrate the four out the five enhancer elements act as enhancers in vivo . Together, these studies extend the previous findings to uncover the TFs driving the enhancer activities of QT interval associated SCN5A regulatory variants, reveal the additive effects from allelic combinations of these regulatory variants, and prove their potential to act as enhancers in vivo .

Genomic loci involved in sensing environmental cues and metabolism affect seasonal coat shedding in Bos taurus and Bos indicus cattle

Seasonal shedding of winter hair at the start of summer is well studied in wild and domesticated populations. However, the genetic influences on this trait and their interactions are poorly understood. We use data from 13,364 cattle with 36,899 repeated phenotypes to investigate the relationship between hair shedding and environmental variables, single nucleotide polymorphisms, and their interactions to understand quantitative differences in seasonal shedding. Using deregressed estimated breeding values from a repeated records model in a genome-wide association analysis (GWAA) and meta-analysis of year-specific GWAA gave remarkably similar results. These GWAA identified hundreds of variants associated with seasonal hair shedding. There were especially strong associations between chromosomes 5 and 23. Genotype-by-environment interaction GWAA identified 1,040 day length-by-genotype interaction associations and 17 apparent temperature-by-genotype interaction associations with hair shedding, highlighting the importance of day length on hair shedding. Accurate genomic predictions of hair shedding were created for the entire dataset, Angus, Hereford, Brangus, and multibreed datasets. Loci related to metabolism and light-sensing have a large influence on seasonal hair shedding. This is one of the largest genetic analyses of a phenological trait and provides insight into both agriculture production and basic science.

The limitations of phenotype prediction in metabolism

Phenotype prediction is at the center of many questions in biology. Prediction is often achieved by determining statistical associations between genetic and phenotypic variation, ignoring the exact processes that cause the phenotype. Here, we present a framework based on genome-scale metabolic reconstructions to reveal the mechanisms behind the associations. We calculated a polygenic score (PGS) that identifies a set of enzymes as predictors of growth, the phenotype. This set arises from the synergy of the functional mode of metabolism in a particular setting and its evolutionary history, and is suitable to infer the phenotype across a variety of conditions. We also find that there is optimal genetic variation for predictability and demonstrate how the linear PGS can still explain phenotypes generated by the underlying nonlinear biochemistry. Therefore, the explicit model interprets the black box statistical associations of the genotype-to-phenotype map and helps to discover what limits the prediction in metabolism.

Functional genomics identify causal variant underlying the protective CTSH locus for Alzheimer's disease

Alzheimer's disease (AD) is the most prevalent age-related neurodegenerative disease, which has a high heritability of up to 79%. Exploring the genetic basis is essential for understanding the pathogenic mechanisms underlying AD development. Recent genome-wide association studies (GWASs) reported an AD-associated signal in the Cathepsin H (CTSH) gene in European populations. However, the exact functional/causal variant(s), and the genetic regulating mechanism of CTSH in AD remain to be determined. In this study, we carried out a comprehensive study to characterize the role of CTSH variants in the pathogenesis of AD. We identified rs2289702 in CTSH as the most significant functional variant that is associated with a protective effect against AD. The genetic association between rs2289702 and AD was validated in independent cohorts of the Han Chinese population. The CTSH mRNA expression level was significantly increased in AD patients and AD animal models, and the protective allele T of rs2289702 was associated with a decreased expression level of CTSH through the disruption of the binding affinity of transcription factors. Human microglia cells with CTSH knockout showed a significantly increased phagocytosis of Aβ peptides. Our study identified CTSH as being involved in AD genetic susceptibility and uncovered the genetic regulating mechanism of CTSH in pathogenesis of AD.

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.

DeepGWAS: Enhance GWAS Signals for Neuropsychiatric Disorders via Deep Neural Network

Genetic dissection of neuropsychiatric disorders can potentially reveal novel therapeutic targets. While genome-wide association studies (GWAS) have tremendously advanced our understanding, we approach a sample size bottleneck (i.e., the number of cases needed to identify >90% of all loci is impractical). Therefore, computationally enhancing GWAS on existing samples may be particularly valuable. Here, we describe DeepGWAS, a deep neural network-based method to enhance GWAS by integrating GWAS results with linkage disequilibrium and brain-related functional annotations. DeepGWAS enhanced schizophrenia (SCZ) loci by ~3X when applied to the largest European GWAS, and 21.3% enhanced loci were validated by the latest multi-ancestry GWAS. Importantly, DeepGWAS models can be transferred to other neuropsychiatric disorders. Transferring SCZ-trained models to Alzheimer's disease and major depressive disorder, we observed 1.3-17.6X detected loci compared to standard GWAS, among which 27-40% were validated by other GWAS studies. We anticipate DeepGWAS to be a powerful tool in GWAS studies.

Incorporating genome-wide and transcriptome-wide association studies to identify genetic elements of longissimus dorsi muscle in Huaxi cattle

Locating the genetic variation of important livestock and poultry economic traits is essential for genetic improvement in breeding programs. Identifying the candidate genes for the productive ability of Huaxi cattle was one crucial element for practical breeding. Based on the genotype and phenotype data of 1,478 individuals and the RNA-seq data of 120 individuals contained in 1,478 individuals, we implemented genome-wide association studies (GWAS), transcriptome-wide association studies (TWAS), and Fisher's combined test (FCT) to identify the candidate genes for the carcass trait, the weight of longissimus dorsi muscle (LDM). The results indicated that GWAS, TWAS, and FCT identified seven candidate genes for LDM altogether: PENK was located by GWAS and FCT, PPAT was located by TWAS and FCT, and XKR4, MTMR3, FGFRL1, DHRS4, and LAP3 were only located by one of the methods. After functional analysis of these candidate genes and referring to the reported studies, we found that they were mainly functional in the progress of the development of the body and the growth of muscle cells. Combining advanced breeding techniques such as gene editing with our study will significantly accelerate the genetic improvement for the future breeding of Huaxi cattle.

Single nucleotide polymorphisms in the GFR-related gene and the SNP-SNP interactions on the risk of diabetic kidney disease in Chinese Han population

PURPOSE

Genetic susceptibility is an important pathogenic mechanism in diabetic kidney disease (DKD). However, the specific gene variant associated with DKD susceptibility remains unclear. Glomerular filtration rate (GFR), an important indicator for the process of DKD, has a heritable component. This study aimed to explore whether these GFR-related single nucleotide polymorphisms (SNPs) were associated with DKD.

METHODS

GFR-related SNPs were collected from the Phenotype-Genotype Integrator (PheGenI) database. SNPs for population cohort analysis were selected following the criteria of complete records of eQTL and MAF > 5% in the Chinese Han population. Totally 498 subjects participated, including166 patients with DKD, 166 patients with T2DM, and 166 controls. The genotypes of SNPs were determined using a Sequenom MassARRAY system. Plink software was employed to analyze the SNP-SNP interactions.

RESULTS

By screening the GFR-related SNPs recorded in the PheGenI database, four SNPs (rs1260326, rs17319721, rs35716097, and rs6420094) were finally selected to investigate the association with DKD. It was shown that one of the four SNPs was related to DKD. The G allele of SLC34A1 rs6420094 was associated with a decreased risk of DKD in DKD and T2DM groups (OR 0.716; P = 0.049). Genetic model analysis revealed that rs6420094 was a protective factor for DKD in T2DM in a dominant model and an additive model (P = 0.03; P = 0.032, respectively). Although rs17319721 was not associated with the risk of DKD, the SNP-SNP interactions between rs17319721 and rs6420094 predicted a significantly decreased risk of DKD (OR 0.464; P = 0.047).

CONCLUSION

SLC34A1 rs6420094 was associated with a decreased DKD risk in the Chinese Han population. SNP-SNP interaction between rs17319721 and rs6420094 was associated with a lower risk of DKD.

An integrative approach for identification of smoking-related genes involving bladder cancer

Tobacco smoking is one of the most important environmental risk factors involving bladder tumorigenesis. However, smoking-related genes in bladder carcinogenesis and corresponding genetic effects on bladder cancer risk remain unclear. Weighted correlation network analysis (WGCNA) underlying transcriptome of bladder cancer tissues was applied to identify smoking-related genes. The logistic regression model was utilized to estimate genetic effects of single nucleotide polymorphisms (SNPs) in smoking-related genes on bladder cancer risk in the Chinese and European populations with a total of 6510 cases and 6569 controls, as well as the interaction with smoking status. Transcriptome of cells and tissues was used to profile the expression pattern of candidate genes and their genetic variants. Our results demonstrated that a total of 24 SNPs in 14 smoking-related genes were associated with the risk of bladder cancer, of which rs9348451 in CDKAL1 exhibited an interaction with smoking status (OR_interaction = 1.38, P_interaction = 1.08 × 10^-2) and tobacco smoking might combine with CDKAL1 rs9348451 to increase the risk of bladder cancer (P_trend = 4.27 × 10^-4). Moreover, rs9348451 was associated with CDKAL1 expression in bladder cancer, especially in smokers (P < 0.001). Besides, CDKAL1 was upregulated in bladder cancer compared to normal adjacent tissues, as well as upregulated via treatment of cigarette smoke extracts. This study highlights the important role of nurture and nature, as well as their interaction on tumorigenesis, which provides a new way to decipher the etiology of bladder cancer with smoking status.

Immuno-Modulatory Effects of Intervertebral Disc Cells

Low back pain is a highly prevalent, chronic, and costly medical condition predominantly triggered by intervertebral disc degeneration (IDD). IDD is often caused by structural and biochemical changes in intervertebral discs (IVD) that prompt a pathologic shift from an anabolic to catabolic state, affecting extracellular matrix (ECM) production, enzyme generation, cytokine and chemokine production, neurotrophic and angiogenic factor production. The IVD is an immune-privileged organ. However, during degeneration immune cells and inflammatory factors can infiltrate through defects in the cartilage endplate and annulus fibrosus fissures, further accelerating the catabolic environment. Remarkably, though, catabolic ECM disruption also occurs in the absence of immune cell infiltration, largely due to native disc cell production of catabolic enzymes and cytokines. An unbalanced metabolism could be induced by many different factors, including a harsh microenvironment, biomechanical cues, genetics, and infection. The complex, multifactorial nature of IDD brings the challenge of identifying key factors which initiate the degenerative cascade, eventually leading to back pain. These factors are often investigated through methods including animal models, 3D cell culture, bioreactors, and computational models. However, the crosstalk between the IVD, immune system, and shifted metabolism is frequently misconstrued, often with the assumption that the presence of cytokines and chemokines is synonymous to inflammation or an immune response, which is not true for the intact disc. Therefore, this review will tackle immunomodulatory and IVD cell roles in IDD, clarifying the differences between cellular involvements and implications for therapeutic development and assessing models used to explore inflammatory or catabolic IVD environments.

Integration of eQTL Analysis and GWAS Highlights Regulation Networks in Cotton under Stress Condition

The genus Gossypium is one of the most economically important crops in the world. Here, we used RNA-seq to quantify gene expression in a collection of G. arboreum seedlings and performed eGWAS on 28,382 expressed genes. We identified a total of 30,089 eQTLs in 10,485 genes, of which >90% were trans-regulate target genes. Using luciferase assays, we confirmed that different cis-eQTL haplotypes could affect promoter activity. We found ~6600 genes associated with ~1300 eQTL hotspots. Moreover, hotspot 309 regulates the expression of 325 genes with roles in stem length, fresh weight, seed germination rate, and genes related to cell wall biosynthesis and salt stress. Transcriptome-wide association study (TWAS) identified 19 candidate genes associated with the cotton growth and salt stress response. The variation in gene expression across the population played an essential role in population differentiation. Only a small number of the differentially expressed genes between South China, the Yangtze River region, and the Yellow River region sites were located in different chromosomal regions. The eQTLs found across the duplicated gene pairs showed conservative cis- or trans- regulation and that the expression levels of gene pairs were correlated. This study provides new insights into the evolution of gene expression regulation in cotton, and identifies eQTLs in stress-related genes for use in breeding improved cotton varieties.

Discovery and Functional Characterization of Two Regulatory Variants Underlying Lupus Susceptibility at 2p13.1

Genome-wide association studies have identified 2p13.1 as a prominent susceptibility locus for systemic lupus erythematosus (SLE)—a complex, multisystem autoimmune disease. However, the identity of underlying causal variant (s) and molecular mechanisms for increasing disease susceptibility are poorly understood. Using meta-analysis (cases = 10,252, controls = 21,604) followed by conditional analysis, bioinformatic annotation, and eQTL and 3D-chromatin interaction analyses, we computationally prioritized potential functional variants and subsequently experimentally validated their effects. Ethnicity-specific meta-analysis revealed striking allele frequency differences between Asian and European ancestries, but with similar odds ratios. We identified 20 genome-wide significant (p < 5 × 10−8) variants, and conditional analysis pinpointed two potential functional variants, rs6705628 and rs2272165, likely to explain the association. The two SNPs are near DGUOK, mitochondrial deoxyguanosine kinase, and its associated antisense RNA DGUOK-AS1. Using luciferase reporter gene assays, we found significant cell type- and allele-specific promoter activity at rs6705628 and enhancer activity at rs2272165. This is supported by ChIP-qPCR showing allele-specific binding with three histone marks (H3K27ac, H3K4me3, and H3K4me1), RNA polymerase II (Pol II), transcriptional coactivator p300, CCCTC-binding factor (CTCF), and transcription factor ARID3A. Transcriptome data across 28 immune cell types from Asians showed both SNPs are cell-type-specific but only in B-cells. Splicing QTLs showed strong regulation of DGUOK-AS1. Genotype-specific DGOUK protein levels are supported by Western blots. Promoter capture Hi-C data revealed long-range chromatin interactions between rs2272165 and several nearby promoters, including DGUOK. Taken together, we provide mechanistic insights into how two noncoding variants underlie SLE risk at the 2p13.1 locus.

Common Polymorphism That Protects From Cardiovascular Disease Increases Fibronectin Processing and Secretion

BACKGROUND

Fibronectin (FN1) is an essential regulator of homodynamic processes and tissue remodeling that have been proposed to contribute to atherosclerosis. Moreover, recent large-scale genome-wide association studies (GWAS) have linked common genetic variants within the FN1 gene to coronary artery disease risk.

METHODS

Public databases were analyzed by 2-Sample Mendelian Randomization. Expression constructs encoding short FN1 reporter constructs and full-length plasma FN1 variants were introduced in various cell models. Secreted and cellular levels were then analyzed and quantified by SDS-PAGE and fluorescence microscopy. Mass spectrometry and glycosylation analyses were performed to probe possible posttranscriptional differences.

RESULTS

Bioinformatic analyses revealed that common coronary artery disease risk single nucleotide polymorphisms in the FN1 locus associate with circulating levels of FN1 and that higher FN1 (fibronectin 1) protein levels in plasma are linked to lower coronary artery disease risk. The coronary artery disease-associated FN1 locus encompasses a common polymorphism that translates a L15Q variant situated within the FN1 signal peptide. Introduction of FN1 reporter constructs, differing at position 15, revealed differences in secretion, with the FN1 Q15 variant being less well secreted. Moreover, the L15Q polymorphism was found to alter glycosylation in some cell models but not in human plasma.

CONCLUSIONS

In addition to providing novel functional evidence implicating FN1 in cardioprotection, these findings demonstrate that a common variant within a secretion signal peptide regulates protein function.

Modelling the genetic aetiology of complex disease: human-mouse conservation of noncoding features and disease-associated loci

Understanding the genetic aetiology of loci associated with a disease is crucial for developing preventative measures and effective treatments. Mouse models are used extensively to understand human pathobiology and mechanistic functions of disease-associated loci. However, the utility of mouse models is limited in part by evolutionary divergence in transcription regulation for pathways of interest. Here, we summarize the alignment of genomic (exonic and multi-cell regulatory) annotations alongside Mendelian and complex disease-associated variant sites between humans and mice. Our results highlight the importance of understanding evolutionary divergence in transcription regulation when interpreting functional studies using mice as models for human disease variants.

Genetic mapping reveals complex architecture and candidate genes involved in common bean response to Meloidogyne incognita infection

Root-knot nematodes (RKNs), particularly Meloidogyne incognita, are among the most damaging and prevalent agricultural pathogens due to their ability to infect roots of almost all crops. The best strategy for their control is through the use of resistant cultivars. However, laborious phenotyping procedures make it difficult to assess nematode resistance in breeding programs. For common bean, this task is especially challenging because little has been done to discover resistance genes or markers to assist selection. We performed genome-wide association studies and quantitative trait loci mapping to explore the genetic architecture and genomic regions underlying the resistance to M. incognita and to identify candidate resistance genes. Phenotypic data were collected by a high-throughput assay, and the number of egg masses and the root-galling index were evaluated. Complex genetic architecture and independent genomic regions were associated with each trait. Single nucleotide polymorphisms on chromosomes Pv06, Pv07, Pv08, and Pv11 were associated with the number of egg masses, and SNPs on Pv01, Pv02, Pv05, and Pv10 were associated with root-galling. A total of 216 candidate genes were identified, including 14 resistance gene analogs and five differentially expressed in a previous RNA sequencing analysis. Histochemical analysis indicated that reactive oxygen species might play a role in the resistance response. Our findings open new perspectives to improve selection efficiency for RKN resistance, and the candidate genes are valuable targets for functional investigation and gene editing approaches.

Comparison of the choice of animals for re-sequencing in two maternal pig lines

Next-generation sequencing is a promising approach for the detection of causal variants within previously identified quantitative trait loci. Because of the costs of re-sequencing experiments, this application is currently mainly restricted to subsets of animals from already genotyped populations. Imputation from a lower to a higher marker density could represent a useful complementary approach. An analysis of the literature shows that several strategies are available to select animals for re-sequencing. This study demonstrates an animal selection workflow under practical conditions. Our approach considers different data sources and limited resources such as budget and availability of sampling material. The workflow combines previously described approaches and makes use of genotype and pedigree information from a Landrace and Large White population. Genotypes were phased and haplotypes were accurately estimated with AlphaPhase. Then, AlphaSeqOpt was used to optimize selection of animals for re-sequencing, reflecting the existing diversity of haplotypes. AlphaSeqOpt and ENDOG were used to select individuals based on pedigree information and by taking into account key animals that represent the genetic diversity of the populations. After the best selection criteria were determined, a subset of 57 animals was selected for subsequent re-sequencing. In order to evaluate and assess the advantage of this procedure, imputation accuracy was assessed by setting a set of single nucleotide polymorphism (SNP) chip genotypes to missing. Accuracy values were compared to those of alternative selection scenarios and the results showed the clear benefits of a targeted selection within this practical-driven approach. Especially imputation of low-frequency markers benefits from the combined approach described here. Accuracy was increased by up to 12% compared to a randomized or exclusively haplotype-based selection of sequencing candidates.

Genetic and Clinical Factors Associated with Opioid Response in Chinese Han Patients with Cancer Pain: An Exploratory Cross-Sectional Study

Introduction

Studies have shown that genetic variation and environmental factors are associated with individual differences in therapeutic efficacy and side effects of opioids. However, the focus of these studies has been on a single factor of single-nucleotide polymorphisms (SNPs) or haplotypes, for which results have rarely been validated. For complex traits, such as cancer pain and opioid response, interactions between multiple genetic variation and environmental factors need to be considered to explain the opioid individual differences.

Methods

We conducted an exploratory two-stage cross-sectional study with 1027 Chinese patients who were taking strong opioid medications for their cancer pain, and genotyped 110 SNPs to explore the association of SNPs, haplotypes, gene–gene and gene–environment interactions with opioid dose, pain relief, and opioid-induced constipation.

Results

Due to the failure to meet Benjamini–Hochberg criteria in the discovery stage or to be validated in replication stage, no association was found between SNPs, haplotypes, paired SNP–SNP interactions or multi-dimensional gene–gene interactions and opioid response. However, for gene–environment interactions, optimal models have been constructed in all phenotypes of opioid response.

Conclusions

This study reveals for the first time that construction of multidimensional gene–environment interactions enables better interpretations of the effect of genetic variation and environmental factors on the opioid response in patients with cancer pain.

Trial registration

Chictr.org.cn, identifier, ChiCTR2000033576.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40122-022-00353-5.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Recent innovations and in-depth aspects of post-genome wide association study (Post-GWAS) to understand the genetic basis of complex phenotypes

In the past decade, the high throughput and low cost of sequencing/genotyping approaches have led to the accumulation of a large amount of data from genome-wide association studies (GWASs). The first aim of this review is to highlight how post-GWAS analysis can be used make sense of the obtained associations. Novel directions for integrating GWAS results with other resources, such as somatic mutation, metabolite-transcript, and transcriptomic data, are also discussed; these approaches can help us move beyond each individual data point and provide valuable information about complex trait genetics. In addition, cross-phenotype association tests, when the loci detected by GWASs have significant associations with multiple traits, are reviewed to provide biologically informative results for use in real-time applications. This review also discusses the challenges of identifying interactions between genetic mutations (epistasis) and mutations of loci affecting more than one trait (pleiotropy) as underlying causes of cross-phenotype associations; these challenges can be overcome using post-GWAS analysis. Genetic similarities between phenotypes that can be revealed using post-GWAS analysis are also discussed. In summary, different methodologies of post-GWAS analysis are now available, enhancing the value of information obtained from GWAS results, and facilitating application in both humans and nonhuman species. However, precise methods still need to be developed to overcome challenges in the field and uncover the genetic underpinnings of complex traits.

Non-coding regulatory elements: Potential roles in disease and the case of epilepsy

Non-coding DNA (ncDNA) refers to the portion of the genome that does not code for proteins and accounts for the greatest physical proportion of the human genome. ncDNA includes sequences that are transcribed into RNA molecules, such as ribosomal RNAs (rRNAs), microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and un-transcribed sequences that have regulatory functions, including gene promoters and enhancers. Variation in non-coding regions of the genome have an established role in human disease, with growing evidence from many areas, including several cancers, Parkinson's disease and autism. Here, we review the features and functions of the regulatory elements that are present in the non-coding genome and the role that these regions have in human disease. We then review the existing research in epilepsy and emphasise the potential value of further exploring non-coding regulatory elements in epilepsy. In addition, we outline the most widely used techniques for recognising regulatory elements throughout the genome, current methodologies for investigating variation and the main challenges associated with research in the field of non-coding DNA.

A large Canadian cohort provides insights into the genetic architecture of human hair colour

Hair colour is a polygenic phenotype that results from differences in the amount and ratio of melanins located in the hair bulb. Genome-wide association studies (GWAS) have identified many loci involved in the pigmentation pathway affecting hair colour. However, most of the associated loci overlap non-protein coding regions and many of the molecular mechanisms underlying pigmentation variation are still not understood. Here, we conduct GWAS meta-analyses of hair colour in a Canadian cohort of 12,741 individuals of European ancestry. By performing fine-mapping analyses we identify candidate causal variants in pigmentation loci associated with blonde, red and brown hair colour. Additionally, we observe colocalization of several GWAS hits with expression and methylation quantitative trait loci (QTLs) of cultured melanocytes. Finally, transcriptome-wide association studies (TWAS) further nominate the expression of EDNRB and CDK10 as significantly associated with hair colour. Our results provide insights on the mechanisms regulating pigmentation biology in humans.

Multi-Modal Data Analysis for Alzheimer's Disease Diagnosis: An Ensemble Model Using Imagery and Genetic Features

Alzheimer's disease (AD) is a devastating neurological disorder primarily affecting the elderly. An estimated 6.2 million Americans age 65 and older are suffering from Alzheimer's dementia today. Brain magnetic resonance imaging (MRI) is widely used for the clinical diagnosis of AD. In the meanwhile, medical researchers have identified 40 risk locus using single-nucleotide polymorphisms (SNPs) information from Genome-wide association study (GWAS) in the past decades. However, existing studies usually treat MRI and GWAS separately. For instance, convolutional neural networks are often trained using MRI for AD diagnosis. GWAS and SNPs are frequently used to identify genomic traits. In this study, we propose a multi-modal AD diagnosis neural network that uses both MRIs and SNPs. The proposed method demonstrates a novel way to use GWAS findings by directly including SNPs in predictive models. We test the proposed methods on the Alzheimer's Disease Neuroimaging Initiative dataset. The evaluation results show that the proposed method improves the model performance on AD diagnosis and achieves 93.5% AUC and 96.1% AP, respectively, when patients have both MRI and SNP data. We believe this work brings exciting new insights to GWAS applications and sheds light on future research directions.

Chromatin accessibility and gene expression during adipocyte differentiation identify context-dependent effects at cardiometabolic GWAS loci

Chromatin accessibility and gene expression in relevant cell contexts can guide identification of regulatory elements and mechanisms at genome-wide association study (GWAS) loci. To identify regulatory elements that display differential activity across adipocyte differentiation, we performed ATAC-seq and RNA-seq in a human cell model of preadipocytes and adipocytes at days 4 and 14 of differentiation. For comparison, we created a consensus map of ATAC-seq peaks in 11 human subcutaneous adipose tissue samples. We identified 58,387 context-dependent chromatin accessibility peaks and 3,090 context-dependent genes between all timepoint comparisons (log2 fold change>1, FDR<5%) with 15,919 adipocyte- and 18,244 preadipocyte-dependent peaks. Adipocyte-dependent peaks showed increased overlap (60.1%) with Roadmap Epigenomics adipocyte nuclei enhancers compared to preadipocyte-dependent peaks (11.5%). We linked context-dependent peaks to genes based on adipocyte promoter capture Hi-C data, overlap with adipose eQTL variants, and context-dependent gene expression. Of 16,167 context-dependent peaks linked to a gene, 5,145 were linked by two or more strategies to 1,670 genes. Among GWAS loci for cardiometabolic traits, adipocyte-dependent peaks, but not preadipocyte-dependent peaks, showed significant enrichment (LD score regression P<0.005) for waist-to-hip ratio and modest enrichment (P < 0.05) for HDL-cholesterol. We identified 659 peaks linked to 503 genes by two or more approaches and overlapping a GWAS signal, suggesting a regulatory mechanism at these loci. To identify variants that may alter chromatin accessibility between timepoints, we identified 582 variants in 454 context-dependent peaks that demonstrated allelic imbalance in accessibility (FDR<5%), of which 55 peaks also overlapped GWAS variants. At one GWAS locus for palmitoleic acid, rs603424 was located in an adipocyte-dependent peak linked to SCD and exhibited allelic differences in transcriptional activity in adipocytes (P = 0.003) but not preadipocytes (P = 0.09). These results demonstrate that context-dependent peaks and genes can guide discovery of regulatory variants at GWAS loci and aid identification of regulatory mechanisms.

Cardiovascular and metabolic diseases are widespread, and an increased understanding of genetic mechanisms behind these diseases could improve treatment. Chromatin accessibility and gene expression in relevant cell contexts can guide identification of regulatory elements and genetic mechanisms for disease traits. A relevant context for cardiovascular and metabolic disease traits is adipocyte differentiation. To identify regulatory elements and genes that display differences in activity during adipocyte differentiation, we profiled chromatin accessibility and gene expression in a human cell model of preadipocytes and adipocytes. We identified chromatin regions that change accessibility during differentiation and predicted genes they may affect. We also linked these chromatin regions to genetic variants associated with risk of disease. At one genomic region linked to fatty acids, a chromatin region more accessible in adipocytes linked to a fatty acid synthesis gene and exhibited allelic differences in transcriptional activity in adipocytes but not preadipocytes. These results demonstrate that chromatin regions and genes that change during cell context can guide discovery of regulatory variants and aid identification of disease mechanisms.

Identification of novel drug targets for Alzheimer's disease by integrating genetics and proteomes from brain and blood

Genome-wide association studies (GWASs) have discovered numerous risk genes for Alzheimer's disease (AD), but how these genes confer AD risk is challenging to decipher. To efficiently transform genetic associations into drug targets for AD, we employed an integrative analytical pipeline using proteomes in the brain and blood by systematically applying proteome-wide association study (PWAS), Mendelian randomization (MR) and Bayesian colocalization. Collectively, we identified the brain protein abundance of 7 genes (ACE, ICA1L, TOM1L2, SNX32, EPHX2, CTSH, and RTFDC1) are causal in AD (P < 0.05/proteins identified for PWAS and MR; PPH4 >80% for Bayesian colocalization). The proteins encoded by these genes were mainly expressed on the surface of glutamatergic neurons and astrocytes. Of them, ACE with its protein abundance was also identified in significant association with AD on the blood-based studies and showed significance at the transcriptomic level. SNX32 was also found to be associated with AD at the blood transcriptomic level. Collectively, our current study results on genetic, proteomic, and transcriptomic approaches has identified compelling genes, which may provide important leads to design future functional studies and potential drug targets for AD.

Genetic dissection of complex traits using hierarchical biological knowledge

Despite the growing constellation of genetic loci linked to common traits, these loci have yet to account for most heritable variation, and most act through poorly understood mechanisms. Recent machine learning (ML) systems have used hierarchical biological knowledge to associate genetic mutations with phenotypic outcomes, yielding substantial predictive power and mechanistic insight. Here, we use an ontology-guided ML system to map single nucleotide variants (SNVs) focusing on 6 classic phenotypic traits in natural yeast populations. The 29 identified loci are largely novel and account for ~17% of the phenotypic variance, versus <3% for standard genetic analysis. Representative results show that sensitivity to hydroxyurea is linked to SNVs in two alternative purine biosynthesis pathways, and that sensitivity to copper arises through failure to detoxify reactive oxygen species in fatty acid metabolism. This work demonstrates a knowledge-based approach to amplifying and interpreting signals in population genetic studies.

Genome-wide association studies (GWAS) have identified many important loci for common diseases and other traits. However, the loci identified by these studies are almost always many steps away from an understanding of underlying biological mechanisms. Here we develop an approach using hierarchical biological knowledge to identify genes and pathways responsible for phenotypic traits. Variants identified by the new method could explain a substantially greater fraction of heritability than previously reported. Moreover, we identified mechanistic pathways by which each causal variant affects cellular function. For example, we find that sensitivity to hydroxyurea is tied to genetic variants in two alternative purine biosynthesis pathways, and that sensitivity to copper arises through failure to detoxify reactive oxygen species in fatty acid metabolism. The new approach is a potentially transformative concept for understanding the genetic drivers of phenotypic variance, with potential applications in understanding traits in biomedicine and agriculture.

Identification of Parkinson's Disease-Causing Genes via Omics Data

Parkinson's disease (PD) is the second most frequent neurogenic disease after Alzheimer's disease. The clinical manifestations include mostly motor disorders, such as bradykinesia, myotonia, and static tremors. Since the cause of this pathological features remain unclear, there is currently no radical treatment for PD. Environmental and genetic factors are thought to contribute to the pathology of PD. To identify the genetic factors, some studies employed the Genome-Wide Association Studies (GWAS) method and detected certain genes closely related to PD. However, the functions of these gene mutants in the development of PD are unknown. Combining GWAS and expression Quantitative Trait Loci (eQTL) analysis, the biological meaning of mutation could be explained to some extent. Therefore, the present investigation used Summary data-based Mendelian Randomization (SMR) analysis to integrate of two PD GWAS datasets and four eQTL datasets with the objective of identifying casual genes. Using this strategy, we found six Single Nucleotide Polymorphism (SNP) loci which could cause the development of PD through altering the susceptibility gene expression, and three risk genes: Synuclein Alpha (SNCA), Mitochondrial Poly(A) Polymerase (MTPAP), and RP11-305E6.4. We proved the accuracy of results through case studies and inferred the functions of these genes in PD. Overall, this study provides insights into the genetic mechanism behind PD, which is crucial for the study of the development of this disease and its diagnosis and treatment.

Natural Variation in Crops: Realized Understanding, Continuing Promise

Crops feed the world's population and shape human civilization. The improvement of crop productivity has been ongoing for almost 10,000 years and has evolved from an experience-based to a knowledge-driven practice over the past three decades. Natural alleles and their reshuffling are long-standing genetic changes that affect how crops respond to various environmental conditions and agricultural practices. Decoding the genetic basis of natural variation is central to understanding crop evolution and, in turn, improving crop breeding. Here, we review current advances in the approaches used to map the causal alleles of natural variation, provide refined insights into the genetics and evolution of natural variation, and outline how this knowledge promises to drive the development of sustainable agriculture under the dome of emerging technologies.

TreeMap: a structured approach to fine mapping of eQTL variants

Motivation

Expression quantitative trait loci (eQTL) harbor genetic variants modulating gene transcription. Fine mapping of regulatory variants at these loci is a daunting task due to the juxtaposition of causal and linked variants at a locus as well as the likelihood of interactions among multiple variants. This problem is exacerbated in genes with multiple cis-acting eQTL, where superimposed effects of adjacent loci further distort the association signals.

Results

We developed a novel algorithm, TreeMap, that identifies putative causal variants in cis-eQTL accounting for multisite effects and genetic linkage at a locus. Guided by the hierarchical structure of linkage disequilibrium, TreeMap performs an organized search for individual and multiple causal variants. Via extensive simulations, we show that TreeMap detects co-regulating variants more accurately than current methods. Furthermore, its high computational efficiency enables genome-wide analysis of long-range eQTL. We applied TreeMap to GTEx data of brain hippocampus samples and transverse colon samples to search for eQTL in gene bodies and in 4 Mbps gene-flanking regions, discovering numerous distal eQTL. Furthermore, we found concordant distal eQTL that were present in both brain and colon samples, implying long-range regulation of gene expression.

Availability and implementation

TreeMap is available as an R package enabled for parallel processing at https://github.com/liliulab/treemap.

Supplementary information

Supplementary data are available at Bioinformatics online.

Accelerated discovery of functional genomic variation in pigs

The genotype-phenotype link is a major research topic in the life sciences but remains highly complex to disentangle. Part of the complexity arises from the number of genes contributing to the observed phenotype. Despite the vast increase of molecular data, pinpointing the causal variant underlying a phenotype of interest is still challenging. In this study, we present an approach to map causal variation and molecular pathways underlying important phenotypes in pigs. We prioritize variation by utilizing and integrating predicted variant impact scores (pCADD), functional genomic information, and associated phenotypes in other mammalian species. We demonstrate the efficacy of our approach by reporting known and novel causal variants, of which many affect non-coding sequences. Our approach allows the disentangling of the biology behind important phenotypes by accelerating the discovery of novel causal variants and molecular mechanisms affecting important phenotypes in pigs. This information on molecular mechanisms could be applicable in other mammalian species, including humans.

Advances in protein-protein interaction network analysis for Parkinson's disease

Protein-protein interactions (PPIs) are a key component of the subcellular molecular networks which enable cells to function. Due to their importance in homeostasis, alterations to the networks can be detrimental, leading to cellular dysfunction and ultimately disease states. Parkinson's disease (PD) is a progressive neurodegenerative condition with multifactorial aetiology, spanning genetic variation and environmental modifiers. At a molecular and systems level, the characterisation of PD is the focus of extensive research, largely due to an unmet need for disease modifying therapies. PPI network analysis approaches are a valuable strategy to accelerate our understanding of the molecular crosstalk and biological processes underlying PD pathogenesis, especially due to the complex nature of this disease. In this review, we describe the utility of PPI network approaches in modelling complex systems, focusing on previous work in PD research. We discuss four principal strategies for using PPI network approaches: to infer PD related cellular functions, pathways and novel genes; to support genomics studies; to study the interactome of single PD related genes; and to compare the molecular basis of PD to other neurodegenerative disorders. This is an evolving area of research which is likely to further expand as omics data generation and availability increase. These approaches complement and bridge-the-gap between genetics and functional research to inform future investigations. In this review we outline several limitations that require consideration, acknowledging that ongoing challenges in this field continue to be addressed and the refinement of these approaches will facilitate further advances using PPI network analysis for understanding complex diseases.

Integrative genomic analysis of blood pressure and related phenotypes in rats

Despite remarkable progress made in human genome-wide association studies, there remains a substantial gap between statistical evidence for genetic associations and functional comprehension of the underlying mechanisms governing these associations. As a means of bridging this gap, we performed genomic analysis of blood pressure (BP) and related phenotypes in spontaneously hypertensive rats (SHR) and their substrain, stroke-prone SHR (SHRSP), both of which are unique genetic models of severe hypertension and cardiovascular complications. By integrating whole-genome sequencing, transcriptome profiling, genome-wide linkage scans (maximum n=1415), fine congenic mapping (maximum n=8704), pharmacological intervention and comparative analysis with transcriptome-wide association study (TWAS) datasets, we searched causal genes and causal pathways for the tested traits. The overall results validated the polygenic architecture of elevated BP compared with a non-hypertensive control strain, Wistar Kyoto rats (WKY); e.g. inter-strain BP differences between SHRSP and WKY could be largely explained by an aggregate of BP changes in seven SHRSP-derived consomic strains. We identified 26 potential target genes, including rat homologs of human TWAS loci, for the tested traits. In this study, we re-discovered 18 genes that had previously been determined to contribute to hypertension or cardiovascular phenotypes. Notably, five of these genes belong to the kallikrein-kinin/renin-angiotensin systems (KKS/RAS), in which the most prominent differential expression between hypertensive and non-hypertensive alleles could be detected in rat Klk1 paralogs. In combination with a pharmacological intervention, we provide in vivo experimental evidence supporting the presence of key disease pathways, such as KKS/RAS, in a rat polygenic hypertension model.

Triangulating molecular evidence to prioritize candidate causal genes at established atopic dermatitis loci

Genome-wide association studies for atopic dermatitis (AD) have identified 25 reproducible loci. We attempt to prioritize candidate causal genes at these loci using extensive molecular resources compiled into a bioinformatics pipeline. We identified a list of 103 molecular resources for AD aetiology, including expression, protein and DNA methylation QTL datasets in skin or immune-relevant tissues which were tested for overlap with GWAS signals. This was combined with functional annotation using regulatory variant prediction, and features such as promoter-enhancer interactions, expression studies and variant fine-mapping. For each gene at each locus, we condensed the evidence into a prioritization score. Across the investigated loci, we detected significant enrichment of genes with adaptive immune regulatory function and epidermal barrier formation among the top prioritized genes. At 8 loci, we were able to prioritize a single candidate gene (IL6R, ADO, PRR5L, IL7R, ETS1, INPP5D, MDM1, TRAF3). In addition, at 6 of the 25 loci, our analysis prioritizes less familiar candidates (SLC22A5, IL2RA, MDM1, DEXI, ADO, STMN3). Our analysis provides support for previously implicated genes at several AD GWAS loci, as well as evidence for plausible additional candidates at others, which may represent potential targets for drug discovery.

Neonatal diabetes mutations disrupt a chromatin pioneering function that activates the human insulin gene

Despite the central role of chromosomal context in gene transcription, human noncoding DNA variants are generally studied outside of their genomic location. This limits our understanding of disease-causing regulatory variants. INS promoter mutations cause recessive neonatal diabetes. We show that all INS promoter point mutations in 60 patients disrupt a CC dinucleotide, whereas none affect other elements important for episomal promoter function. To model CC mutations, we humanized an ∼3.1-kb region of the mouse Ins2 gene. This recapitulated developmental chromatin states and cell-specific transcription. A CC mutant allele, however, abrogated active chromatin formation during pancreas development. A search for transcription factors acting through this element revealed that another neonatal diabetes gene product, GLIS3, has a pioneer-like ability to derepress INS chromatin, which is hampered by the CC mutation. Our in vivo analysis, therefore, connects two human genetic defects in an essential mechanism for developmental activation of the INS gene.

Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer's dementia

Transcriptome-wide association studies (TWAS) have been widely used to integrate transcriptomic and genetic data to study complex human diseases. Within a test dataset lacking transcriptomic data, traditional two-stage TWAS methods first impute gene expression by creating a weighted sum that aggregates SNPs with their corresponding cis-eQTL effects on reference transcriptome. Traditional TWAS methods then employ a linear regression model to assess the association between imputed gene expression and test phenotype, thereby assuming the effect of a cis-eQTL SNP on test phenotype is a linear function of the eQTL's estimated effect on reference transcriptome. To increase TWAS robustness to this assumption, we propose a novel Variance-Component TWAS procedure (VC-TWAS) that assumes the effects of cis-eQTL SNPs on phenotype are random (with variance proportional to corresponding reference cis-eQTL effects) rather than fixed. VC-TWAS is applicable to both continuous and dichotomous phenotypes, as well as individual-level and summary-level GWAS data. Using simulated data, we show VC-TWAS is more powerful than traditional TWAS methods based on a two-stage Burden test, especially when eQTL genetic effects on test phenotype are no longer a linear function of their eQTL genetic effects on reference transcriptome. We further applied VC-TWAS to both individual-level (N = ~3.4K) and summary-level (N = ~54K) GWAS data to study Alzheimer's dementia (AD). With the individual-level data, we detected 13 significant risk genes including 6 known GWAS risk genes such as TOMM40 that were missed by traditional TWAS methods. With the summary-level data, we detected 57 significant risk genes considering only cis-SNPs and 71 significant genes considering both cis- and trans- SNPs, which also validated our findings with the individual-level GWAS data. Our VC-TWAS method is implemented in the TIGAR tool for public use.

A catalog of GWAS fine-mapping efforts in autoimmune disease

Genome-wide association studies (GWASs) have enabled unbiased identification of genetic loci contributing to common complex diseases. Because GWAS loci often harbor many variants and genes, it remains a major challenge to move from GWASs’ statistical associations to the identification of causal variants and genes that underlie these association signals. Researchers have applied many statistical and functional fine-mapping strategies to prioritize genetic variants and genes as potential candidates. There is no gold standard in fine-mapping approaches, but consistent results across different approaches can improve confidence in the fine-mapping findings. Here, we combined text mining with a systematic review and formed a catalog of 85 studies with evidence of fine mapping for at least one autoimmune GWAS locus. Across all fine-mapping studies, we compiled 230 GWAS loci with allelic heterogeneity estimates and predictions of causal variants and trait-relevant genes. These 230 loci included 455 combinations of locus-by-disease association signals with 15 autoimmune diseases. Using these estimates, we assessed the probability of mediating disease risk associations across genes in GWAS loci and identified robust signals of causal disease biology. We predict that this comprehensive catalog of GWAS fine-mapping efforts in autoimmune disease will greatly help distill the plethora of information in the field and inform therapeutic strategies.

Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation

Genome-wide association studies (GWAS) have uncovered thousands of genetic variants that influence risk for human diseases and traits. Yet understanding the mechanisms by which these genetic variants, mainly noncoding, have an impact on associated diseases and traits remains a significant hurdle. In this review, we discuss emerging experimental approaches that are being applied for functional studies of causal variants and translational advances from GWAS findings to disease prevention and treatment. We highlight the use of genome editing technologies in GWAS functional studies to modify genomic sequences, with proof-of-principle examples. We discuss the challenges in interrogating causal variants, points for consideration in experimental design and interpretation of GWAS locus mechanisms, and the potential for novel therapeutic opportunities. With the accumulation of knowledge of functional genetics, therapeutic genome editing based on GWAS discoveries will become increasingly feasible.

New Technologies to Study Functional Genomics of Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is the most common cause of irreversible vision loss in people over 50 years old in developed countries. Currently, we still lack a comprehensive understanding of the genetic factors contributing to AMD, which is critical to identify effective therapeutic targets to improve treatment outcomes for AMD patients. Here we discuss the latest technologies that can facilitate the identification and functional study of putative genes in AMD pathology. We review improved genomic methods to identify novel AMD genes, advances in single cell transcriptomics to profile gene expression in specific retinal cell types, and summarize recent development of in vitro models for studying AMD using induced pluripotent stem cells, organoids and biomaterials, as well as new molecular technologies using CRISPR/Cas that could facilitate functional studies of AMD-associated genes.

Fine genetic mapping of the chromosome 11q23.3 region in a Han Chinese population: insights into the apolipoprotein genes underlying the blood lipid-lipoprotein variances

The unusual chromosome 11q23.3 harboring the apolipoprotein (APO) gene cluster has been well documented for its essential roles in plasma lipid-related traits and atherosclerotic cardiovascular diseases. However, its genetic architecture and the potential biological mechanisms underlying complex phenotypes have not been well assessed. We conducted a study for this target region in a Han Chinese population through a stepwise forward framework based on massive parallel sequencing, association analyses, genetic fine mapping, and functional interpretation. The present study identified new meaningful genetic associations that were not simply determined by statistical significance. In addition to the APOA5 gene, we found robust evidence of the genetic commitments of APOC3 and APOA1 to blood lipids. Several variants with high confidence were prioritized along with the potential biological mechanism interpretations in the wake of adaptive fine-mapping analyses. rs2849174 in the APOC3 enhancer was discovered with an unrivaled posterior probability of causality for triglyceride levels and could mediate APOC3 expression through enhancer activity modulated by a combination of histone modifications and transcription factor accessibility. Similarly, multiple lines of evidence converged in favor of rs3741297 as a causal variant influencing high-density lipoprotein cholesterol. Our findings provided novel insights into this genomic locus in the Chinese population.

Combining structure and genomics to understand antimicrobial resistance

Antimicrobials against bacterial, viral and parasitic pathogens have transformed human and animal health. Nevertheless, their widespread use (and misuse) has led to the emergence of antimicrobial resistance (AMR) which poses a potentially catastrophic threat to public health and animal husbandry. There are several routes, both intrinsic and acquired, by which AMR can develop. One major route is through non-synonymous single nucleotide polymorphisms (nsSNPs) in coding regions. Large scale genomic studies using high-throughput sequencing data have provided powerful new ways to rapidly detect and respond to such genetic mutations linked to AMR. However, these studies are limited in their mechanistic insight. Computational tools can rapidly and inexpensively evaluate the effect of mutations on protein function and evolution. Subsequent insights can then inform experimental studies, and direct existing or new computational methods. Here we review a range of sequence and structure-based computational tools, focussing on tools successfully used to investigate mutational effect on drug targets in clinically important pathogens, particularly Mycobacterium tuberculosis. Combining genomic results with the biophysical effects of mutations can help reveal the molecular basis and consequences of resistance development. Furthermore, we summarise how the application of such a mechanistic understanding of drug resistance can be applied to limit the impact of AMR.

Omics research in vascular calcification

Vascular calcification (VC), the pathological process of hydroxyapatite mineral deposition in the vascular system, is closely associated with aging, atherosclerotic plaque formation, cardiovascular disease (CVD) and diabetes mellitus (DM). Studies have shown that VC is related to cellular phenotypic changes, extracellular vesicles, disordered calcium and phosphate homeostasis, and an imbalance between inducers and inhibitors of VC. Unfortunately, there is currently no effective preventive or targeted treatment for pathologic condition. The rapid evolution of omics technology (genomics, epigenomics, transcriptomics, proteomics and metabolomics) has provided a novel approach for elucidation of pathophysiologic mechanisms in general and those associated with VC specifically. Here, we review articles published over the last twenty years and focus on the current state, challenges, limitations and future of omics in VC research and clinical practice. Highlighting potential targets based on omics technology will improve our understanding of this pathologic condition and assist in the development of potential treatment options for VC related disease.

Development of a genetic evaluation for hair shedding in American Angus cattle to improve thermotolerance

BACKGROUND

Heat stress and fescue toxicosis caused by ingesting tall fescue infected with the endophytic fungus Epichloë coenophiala represent two of the most prevalent stressors to beef cattle in the United States and cost the beef industry millions of dollars each year. The rate at which a beef cow sheds her winter coat early in the summer is an indicator of adaptation to heat and an economically relevant trait in temperate or subtropical parts of the world. Furthermore, research suggests that early-summer hair shedding may reflect tolerance to fescue toxicosis, since vasoconstriction induced by fescue toxicosis limits the ability of an animal to shed its winter coat. Both heat stress and fescue toxicosis reduce profitability partly via indirect maternal effects on calf weaning weight. Here, we developed parameters for routine genetic evaluation of hair shedding score in American Angus cattle, and identified genomic loci associated with variation in hair shedding score via genome-wide association analysis (GWAA).

RESULTS

Hair shedding score was moderately heritable (h2 = 0.34 to 0.40), with different repeatability estimates between cattle grazing versus not grazing endophyte-infected tall fescue. Our results suggest modestly negative genetic and phenotypic correlations between a dam's hair shedding score (lower score is earlier shedding) and the weaning weight of her calf, which is one metric of performance. Together, these results indicate that economic gains can be made by using hair shedding score breeding values to select for heat-tolerant cattle. GWAA identified 176 variants significant at FDR < 0.05. Functional enrichment analyses using genes that were located within 50 kb of these variants identified pathways involved in keratin formation, prolactin signalling, host-virus interaction, and other biological processes.

CONCLUSIONS

This work contributes to a continuing trend in the development of genetic evaluations for environmental adaptation. Our results will aid beef cattle producers in selecting more sustainable and climate-adapted cattle, as well as enable the development of similar routine genetic evaluations in other breeds.

Omics research in vascular calcification

Vascular calcification (VC), the pathological process of hydroxyapatite mineral deposition in the vascular system, is closely associated with aging, atherosclerotic plaque formation, cardiovascular disease (CVD) and diabetes mellitus (DM). Studies have shown that VC is related to cellular phenotypic changes, extracellular vesicles, disordered calcium phosphate homeostasis and an imbalance between inducers and inhibitors of VC. Unfortunately, there is currently no effective preventive or targeted treatment for this disorder. Recently, the evolution of omics technology (genomics, epigenomics, transcriptomics, proteomics and metabolomics) has paved the way for elucidation of complex biochemical processes and, as such, may provide new insight on VC. Accordingly, we conducted a review of articles published over the last twenty years and herein focus on current and future potential of omics technology in clarifying mechanisms of this disease process. Identification of new biomarkers will provide additional tools in characterizing this pathology and will further assist in the development of potential therapeutic targets.

Chromatin interactions in differentiating keratinocytes reveal novel atopic dermatitis- and psoriasis-associated genes

BACKGROUND

Hundreds of variants associated with atopic dermatitis (AD) and psoriasis, 2 common inflammatory skin disorders, have previously been discovered through genome-wide association studies (GWASs). The majority of these variants are in noncoding regions, and their target genes remain largely unclear.

OBJECTIVE

We sought to understand the effects of these noncoding variants on the development of AD and psoriasis by linking them to the genes that they regulate.

METHODS

We constructed genomic 3-dimensional maps of human keratinocytes during differentiation by using targeted chromosome conformation capture (Capture Hi-C) targeting more than 20,000 promoters and 214 GWAS variants and combined these data with transcriptome and epigenomic data sets. We validated our results with reporter assays, clustered regularly interspaced short palindromic repeats activation, and examination of patient gene expression from previous studies.

RESULTS

We identified 118 target genes of 82 AD and psoriasis GWAS variants. Differential expression of 58 of the 118 target genes (49%) occurred in either AD or psoriatic lesions, many of which were not previously linked to any skin disease. We highlighted the genes AFG1L, CLINT1, ADO, LINC00302, and RP1-140J1.1 and provided further evidence for their potential roles in AD and psoriasis.

CONCLUSIONS

Our work focused on skin barrier pathology through investigation of the interaction profile of GWAS variants during keratinocyte differentiation. We have provided a catalogue of candidate genes that could modulate the risk of AD and psoriasis. Given that only 35% of the target genes are the gene nearest to the known GWAS variants, we expect that our work will contribute to the discovery of novel pathways involved in AD and psoriasis.

Refining Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder Genetic Loci by Integrating Summary Data From Genome-wide Association, Gene Expression, and DNA Methylation Studies

BACKGROUND

Recent genome-wide association studies (GWASs) identified the first genetic loci associated with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). The next step is to use these results to increase our understanding of the biological mechanisms involved. Most of the identified variants likely influence gene regulation. The aim of the current study is to shed light on the mechanisms underlying the genetic signals and prioritize genes by integrating GWAS results with gene expression and DNA methylation (DNAm) levels.

METHODS

We applied summary-data-based Mendelian randomization to integrate ADHD and ASD GWAS data with fetal brain expression and methylation quantitative trait loci, given the early onset of these disorders. We also analyzed expression and methylation quantitative trait loci datasets of adult brain and blood, as these provide increased statistical power. We subsequently used summary-data-based Mendelian randomization to investigate if the same variant influences both DNAm and gene expression levels.

RESULTS

We identified multiple gene expression and DNAm levels in fetal brain at chromosomes 1 and 17 that were associated with ADHD and ASD, respectively, through pleiotropy at shared genetic variants. The analyses in brain and blood showed additional associated gene expression and DNAm levels at the same and additional loci, likely because of increased statistical power. Several of the associated genes have not been identified in ADHD and ASD GWASs before.

CONCLUSIONS

Our findings identified the genetic variants associated with ADHD and ASD that likely act through gene regulation. This facilitates prioritization of candidate genes for functional follow-up studies.

Identification and Characterization of a Transcribed Distal Enhancer Involved in Cardiac Kcnh2 Regulation

The human ether-a-go-go-related gene KCNH2 encodes the voltage-gated potassium channel underlying I_Kr, a current critical for the repolarization phase of the cardiac action potential. Mutations in KCNH2 that cause a reduction of the repolarizing current can result in cardiac arrhythmias associated with long-QT syndrome. Here, we investigate the regulation of KCNH2 and identify multiple active enhancers. A transcribed enhancer ∼85 kbp downstream of Kcnh2 physically contacts the promoters of two Kcnh2 isoforms in a cardiac-specific manner in vivo. Knockdown of its ncRNA transcript results in reduced expression of Kcnh2b and two neighboring mRNAs, Nos3 and Abcb8, in vitro. Genomic deletion of the enhancer, including the ncRNA transcription start site, from the mouse genome causes a modest downregulation of both Kcnh2a and Kcnh2b in the ventricles. These findings establish that the regulation of Kcnh2a and Kcnh2b is governed by a complex regulatory landscape that involves multiple partially redundantly acting enhancers.

Adiponectin GWAS loci harboring extensive allelic heterogeneity exhibit distinct molecular consequences

Loci identified in genome-wide association studies (GWAS) can include multiple distinct association signals. We sought to identify the molecular basis of multiple association signals for adiponectin, a hormone involved in glucose regulation secreted almost exclusively from adipose tissue, identified in the Metabolic Syndrome in Men (METSIM) study. With GWAS data for 9,262 men, four loci were significantly associated with adiponectin: ADIPOQ, CDH13, IRS1, and PBRM1. We performed stepwise conditional analyses to identify distinct association signals, a subset of which are also nearly independent (lead variant pairwise r²<0.01). Two loci exhibited allelic heterogeneity, ADIPOQ and CDH13. Of seven association signals at the ADIPOQ locus, two signals colocalized with adipose tissue expression quantitative trait loci (eQTLs) for three transcripts: trait-increasing alleles at one signal were associated with increased ADIPOQ and LINC02043, while trait-increasing alleles at the other signal were associated with decreased ADIPOQ-AS1. In reporter assays, adiponectin-increasing alleles at two signals showed corresponding directions of effect on transcriptional activity. Putative mechanisms for the seven ADIPOQ signals include a missense variant (ADIPOQ G90S), a splice variant, a promoter variant, and four enhancer variants. Of two association signals at the CDH13 locus, the first signal consisted of promoter variants, including the lead adipose tissue eQTL variant for CDH13, while a second signal included a distal intron 1 enhancer variant that showed ~2-fold allelic differences in transcriptional reporter activity. Fine-mapping and experimental validation demonstrated that multiple, distinct association signals at these loci can influence multiple transcripts through multiple molecular mechanisms.

Many DNA variants affect common human traits, and distinct variants can have different effects on the function or expression level of the same gene. We identified variants associated with levels of adiponectin, a hormone involved in glucose regulation. Among these variants, we specifically studied the sets of variants located near two genes, ADIPOQ and CDH13, to determine how the variants affect gene expression or function. We focused on sets of variants that can be inherited together but are not always inherited together. Of the variants associated with adiponectin and located near ADIPOQ, one set were also associated with higher expression levels of the protein-coding ADIPOQ gene and a nearby non-coding gene, a second set of variants were associated with lower levels of the ADIPOQ-AS1 antisense gene, and additional variants changed the amino acid sequence or size of the adiponectin protein. Our examples show the benefits of identifying multiple sets of trait-associated variants in the same DNA region. These variants explain more trait variation, help identify genes that affect the trait, and guide studies of gene regulation and biological processes.