The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases

Genome-wide association study and network analysis of in vitro transformation in Populus trichocarpa support key roles of diverse phytohormone pathways and cross talk

Wide variation in amenability to transformation and regeneration (TR) among many plant species and genotypes presents a challenge to the use of genetic engineering in research and breeding. To help understand the causes of this variation, we performed association mapping and network analysis using a population of 1204 wild trees of Populus trichocarpa (black cottonwood). To enable precise and high-throughput phenotyping of callus and shoot TR, we developed a computer vision system that cross-referenced complementary red, green, and blue (RGB) and fluorescent-hyperspectral images. We performed association mapping using single-marker and combined variant methods, followed by statistical tests for epistasis and integration of published multi-omic datasets to identify likely regulatory hubs. We report 409 candidate genes implicated by associations within 5 kb of coding sequences, and epistasis tests implicated 81 of these candidate genes as regulators of one another. Gene ontology terms related to protein-protein interactions and transcriptional regulation are overrepresented, among others. In addition to auxin and cytokinin pathways long established as critical to TR, our results highlight the importance of stress and wounding pathways. Potential regulatory hubs of signaling within and across these pathways include GROWTH REGULATORY FACTOR 1 (GRF1), PHOSPHATIDYLINOSITOL 4-KINASE β1 (PI-4Kβ1), and OBF-BINDING PROTEIN 1 (OBP1).

Fibrinaloid Microclots and Atrial Fibrillation

Atrial fibrillation (AF) is a comorbidity of a variety of other chronic, inflammatory diseases for which fibrinaloid microclots are a known accompaniment (and in some cases, a cause, with a mechanistic basis). Clots are, of course, a well-known consequence of atrial fibrillation. We here ask the question whether the fibrinaloid microclots seen in plasma or serum may in fact also be a cause of (or contributor to) the development of AF. We consider known 'risk factors' for AF, and in particular, exogenous stimuli such as infection and air pollution by particulates, both of which are known to cause AF. The external accompaniments of both bacterial (lipopolysaccharide and lipoteichoic acids) and viral (SARS-CoV-2 spike protein) infections are known to stimulate fibrinaloid microclots when added in vitro, and fibrinaloid microclots, as with other amyloid proteins, can be cytotoxic, both by inducing hypoxia/reperfusion and by other means. Strokes and thromboembolisms are also common consequences of AF. Consequently, taking a systems approach, we review the considerable evidence in detail, which leads us to suggest that it is likely that microclots may well have an aetiological role in the development of AF. This has significant mechanistic and therapeutic implications.

Gene expression variation underlying tissue-specific responses to copper stress in Drosophila melanogaster

Copper is one of a handful of biologically necessary heavy metals that is also a common environmental pollutant. Under normal conditions, copper ions are required for many key physiological processes. However, in excess, copper results in cell and tissue damage ranging in severity from temporary injury to permanent neurological damage. Because of its biological relevance, and because many conserved copper-responsive genes respond to nonessential heavy metal pollutants, copper resistance in Drosophila melanogaster is a useful model system with which to investigate the genetic control of the heavy metal stress response. Because heavy metal toxicity has the potential to differently impact specific tissues, we genetically characterized the control of the gene expression response to copper stress in a tissue-specific manner in this study. We assessed the copper stress response in head and gut tissue of 96 inbred strains from the Drosophila Synthetic Population Resource using a combination of differential expression analysis and expression quantitative trait locus mapping. Differential expression analysis revealed clear patterns of tissue-specific expression. Tissue and treatment specific responses to copper stress were also detected using expression quantitative trait locus mapping. Expression quantitative trait locus associated with MtnA, Mdr49, Mdr50, and Sod3 exhibited both genotype-by-tissue and genotype-by-treatment effects on gene expression under copper stress, illuminating tissue- and treatment-specific patterns of gene expression control. Together, our data build a nuanced description of the roles and interactions between allelic and expression variation in copper-responsive genes, provide valuable insight into the genomic architecture of susceptibility to metal toxicity, and highlight candidate genes for future functional characterization.

Mapping the functional impact of non-coding regulatory elements in primary T cells through single-cell CRISPR screens

BACKGROUND

Drug targets with genetic evidence are expected to increase clinical success by at least twofold. Yet, translating disease-associated genetic variants into functional knowledge remains a fundamental challenge of drug discovery. A key issue is that the vast majority of complex disease associations cannot be cleanly mapped to a gene. Immune disease-associated variants are enriched within regulatory elements found in T-cell-specific open chromatin regions.

RESULTS

To identify genes and molecular programs modulated by these regulatory elements, we develop a CRISPRi-based single-cell functional screening approach in primary human T cells. Our pipeline enables the interrogation of transcriptomic changes induced by the perturbation of regulatory elements at scale. We first optimize an efficient CRISPRi protocol in primary CD4+ T cells via CROPseq vectors. Subsequently, we perform a screen targeting 45 non-coding regulatory elements and 35 transcription start sites and profile approximately 250,000 T -cell single-cell transcriptomes. We develop a bespoke analytical pipeline for element-to-gene (E2G) mapping and demonstrate that our method can identify both previously annotated and novel E2G links. Lastly, we integrate genetic association data for immune-related traits and demonstrate how our platform can aid in the identification of effector genes for GWAS loci.

CONCLUSIONS

We describe "primary T cell crisprQTL" - a scalable, single-cell functional genomics approach for mapping regulatory elements to genes in primary human T cells. We show how this framework can facilitate the interrogation of immune disease GWAS hits and propose that the combination of experimental and QTL-based techniques is likely to address the variant-to-function problem.

Association of Three Genetic Loci with Molar Incisor Hypomineralization in Polish Children

BACKGROUND

Molar incisor hypomineralization (MIH) is a qualitative, demarcated enamel defect of hypomineralization affecting one to four first permanent molars, often with incisor involvement. Its etiology is complex. However, evidence suggests the influence of genetic factors, potentially including the single nucleotide polymorphisms (SNPs) rs2889956, rs4811117 and rs13058467, which were previously linked to MIH in a genome-wide association study of German children. The aim was to replicate analyses of possible associations between the SNPs and molar incisor hypomineralization in Polish children.

METHODS

The final study group consisted of 778 children aged 126-168 months old. Saliva samples were taken, and genomic DNA was extracted and genotyped using beadchip microarrays.

RESULTS

Among the 778 subjects, there were 68 (8.7%) subjects with MIH and 710 (91.3%) subjects without MIH. There were no significant differences in distributions in age, sex, or the frequency of caries in permanent dentition between the MIH and non-MIH groups. The rs2889956, rs4811117, and rs13058467 genotype distributions in the studied group conformed to the expected Hardy-Weinberg equilibria, and there were no significant differences in the distributions of their alleles or genotypes between the MIH and non-MIH groups.

CONCLUSION

Our replication study did not confirm highly significant associations between the single nucleotide polymorphisms rs2889956, rs4811117, and rs13058467 with molar incisor hypomineralization in Polish children.

Minimum entropy framework identifies a novel class of genomic functional elements and reveals regulatory mechanisms at human disease loci

We introduce CoRE-BED, a framework trained using 19 epigenomic features in 33 major cell and tissue types to predict cell-type-specific regulatory function. CoRE-BED identifies nine functional classes de-novo, capturing both known and new regulatory categories. Notably, we describe a previously undercharacterized class that we term Development Associated Elements (DAEs), which are highly enriched in cell types with elevated regenerative potential and distinguished by the dual presence of either H3K4me2 and H3K9ac (an epigenetic signature associated with kinetochore assembly) or H3K79me3 and H4K20me1 (a signature associated with transcriptional pause release). Unlike bivalent promoters, which represent a transitory state between active and silenced promoters, DAEs transition directly to or from a non-functional state during stem cell differentiation and are proximal to highly expressed genes. CoRE-BED's interpretability facilitates causal inference and functional prioritization. Across 70 complex traits, distal insulators account for the largest mean proportion of SNP heritability (~49%) captured by the GWAS. Collectively, our results demonstrate the value of exploring non-conventional ways of regulatory classification that enrich for trait heritability, to complement existing approaches for cis-regulatory prediction.

Epigenetic context predicts gene expression variation and reproductive traits across genetically identical individuals

In recent decades, genome-wide association studies (GWAS) have been the major approach to understand the biological basis of individual differences in traits and diseases. However, GWAS approaches have proven to have limited predictive power to explain individual differences, particularly for complex traits and diseases in which environmental factors play a substantial role in their etiology. Indeed, individual differences persist even in genetically identical individuals, although fully separating genetic and environmental causation is difficult or impossible in most organisms. To understand the basis of individual differences in the absence of genetic differences, we measured two quantitative reproductive traits in 180 genetically identical young adult Caenorhabditis elegans roundworms in a shared environment and performed single-individual transcriptomics on each worm. We identified hundreds of genes for which expression variation was strongly associated with reproductive traits, some of which depended on prior environmental experience and some of which was random. Multiple small sets of genes together were highly predictive of reproductive traits across individuals, explaining on average over half and over a quarter of variation in the two traits. We manipulated mRNA levels of predictive genes using RNA interference to identify a set of causal genes, demonstrating the utility of this approach for both prediction and understanding underlying biology. Finally, we found that the chromatin environment of predictive genes was enriched for H3K27 trimethylation, suggesting that individual gene expression differences underlying critical traits may be driven in part by chromatin structure. Together, this work shows that individual differences in gene expression that arise independently of underlying genetic differences are both predictive and causal in shaping reproductive traits at levels that equal or exceed genetic variation.

Machine learning reveals genetic modifiers of the immune microenvironment of cancer

Heritability in the immune tumor microenvironment (iTME) has been widely observed yet remains largely uncharacterized. Here, we developed a machine learning approach to map iTME modifiers within loci from genome-wide association studies (GWASs) for breast cancer (BrCa) incidence. A random forest model was trained on a positive set of immune-oncology (I-O) targets, and then used to assign I-O target probability scores to 1,362 candidate genes in linkage disequilibrium with 155 BrCa GWAS loci. Cluster analysis of the most probable candidates revealed two subfamilies of genes related to effector functions and adaptive immune responses, suggesting that iTME modifiers impact multiple aspects of anticancer immunity. Two of the top ranking BrCa candidates, LSP1 and TLR1, were orthogonally validated as iTME modifiers using BrCa patient biopsies and comparative mapping studies, respectively. Collectively, these data demonstrate a robust and flexible framework for functionally fine-mapping GWAS risk loci to identify translatable therapeutic targets.

Implementing Core Genes and an Omnigenic Model for Behaviour Traits Prediction in Genomics

A high number of genome variants are associated with complex traits, mainly due to genome-wide association studies (GWAS). Using polygenic risk scores (PRSs) is a widely accepted method for calculating an individual's complex trait prognosis using such data. Unlike monogenic traits, the practical implementation of complex traits by applying this method still falls behind. Calculating PRSs from all GWAS data has limited practical usability in behaviour traits due to statistical noise and the small effect size from a high number of genome variants involved. From a behaviour traits perspective, complex traits are explored using the concept of core genes from an omnigenic model, aiming to employ a simplified calculation version. Simplification may reduce the accuracy compared to a complete PRS encompassing all trait-associated variants. Integrating genome data with datasets from various disciplines, such as IT and psychology, could lead to better complex trait prediction. This review elucidates the significance of clear biological pathways in understanding behaviour traits. Specifically, it highlights the essential role of genes related to hormones, enzymes, and neurotransmitters as robust core genes in shaping these traits. Significant variations in core genes are prominently observed in behaviour traits such as stress response, impulsivity, and substance use.

Zebrafish regulatory genomic resources for disease modelling and regeneration

In the past decades, the zebrafish has become a disease model with increasing popularity owing to its advantages that include fast development, easy genetic manipulation, simplicity for imaging, and sharing conserved disease-associated genes and pathways with those of human. In parallel, studies of disease mechanisms are increasingly focusing on non-coding mutations, which require genome annotation maps of regulatory elements, such as enhancers and promoters. In line with this, genomic resources for zebrafish research are expanding, producing a variety of genomic data that help in defining regulatory elements and their conservation between zebrafish and humans. Here, we discuss recent developments in generating functional annotation maps for regulatory elements of the zebrafish genome and how this can be applied to human diseases. We highlight community-driven developments, such as DANIO-CODE, in generating a centralised and standardised catalogue of zebrafish genomics data and functional annotations; consider the advantages and limitations of current annotation maps; and offer considerations for interpreting and integrating existing maps with comparative genomics tools. We also discuss the need for developing standardised genomics protocols and bioinformatic pipelines and provide suggestions for the development of analysis and visualisation tools that will integrate various multiomic bulk sequencing data together with fast-expanding data on single-cell methods, such as single-cell assay for transposase-accessible chromatin with sequencing. Such integration tools are essential to exploit the multiomic chromatin characterisation offered by bulk genomics together with the cell-type resolution offered by emerging single-cell methods. Together, these advances will build an expansive toolkit for interrogating the mechanisms of human disease in zebrafish.

Gene expression variation underlying tissue-specific responses to copper stress in Drosophila melanogaster

Copper is one of a handful of biologically necessary heavy metals that is also a common environmental pollutant. Under normal conditions, copper ions are required for many key physiological processes. However, in excess, copper quickly results in cell and tissue damage that can range in severity from temporary injury to permanent neurological damage. Because of its biological relevance, and because many conserved copper-responsive genes also respond to other non-essential heavy metal pollutants, copper resistance in Drosophila melanogaster is a useful model system with which to investigate the genetic control of the response to heavy metal stress. Because heavy metal toxicity has the potential to differently impact specific tissues, we genetically characterized the control of the gene expression response to copper stress in a tissue-specific manner in this study. We assessed the copper stress response in head and gut tissue of 96 inbred strains from the Drosophila Synthetic Population Resource (DSPR) using a combination of differential expression analysis and expression quantitative trait locus (eQTL) mapping. Differential expression analysis revealed clear patterns of tissue-specific expression, primarily driven by a more pronounced gene expression response in gut tissue. eQTL mapping of gene expression under control and copper conditions as well as for the change in gene expression following copper exposure (copper response eQTL) revealed hundreds of genes with tissue-specific local cis-eQTL and many distant trans-eQTL. eQTL associated with MtnA, Mdr49, Mdr50, and Sod3 exhibited genotype by environment effects on gene expression under copper stress, illuminating several tissue- and treatment-specific patterns of gene expression control. Together, our data build a nuanced description of the roles and interactions between allelic and expression variation in copper-responsive genes, provide valuable insight into the genomic architecture of susceptibility to metal toxicity, and highlight many candidate genes for future functional characterization.

From GWAS to signal validation: An approach for estimating genetic effects while preserving genomic context

Validating associations between genotypic and phenotypic variation remains a challenge, despite advancements in association studies. Common approaches for signal validation rely on gene-level perturbations, such as loss-of-function mutations or RNAi, which test the effect of genetic modifications usually not observed in nature. CRISPR-based methods can validate associations at the SNP level, but have significant drawbacks, including resulting off-target effects and being both time-consuming and expensive. Both approaches usually modify the genome of a single genetic background, limiting the generalizability of experiments. To address these challenges, we present a simple, low-cost experimental scheme for validating genetic associations at the SNP level in outbred populations. The approach involves genotyping live outbred individuals at a focal SNP, crossing homozygous individuals with the same genotype at that locus, and contrasting phenotypes across resulting synthetic outbred populations. We tested this method in Drosophila melanogaster, measuring the longevity effects of a polymorphism at a naturally-segregating cis-eQTL for the midway gene. Our results demonstrate the utility of this method in SNP-level validation of naturally occurring genetic variation regulating complex traits. This method provides a bridge between the statistical discovery of genotype-phenotype associations and their validation in the natural context of heterogeneous genomic contexts.

Association of the rs3039851 Insertion/Deletion in the Gene PPP3R1, Which Encodes the Regulatory Calcineurin Subunit B Type 1, with Left Ventricular Mass in Polish Full-Term Newborns

BACKGROUND

The five base-pair (bp) insertion/deletion (rs3039851) polymorphism in the PPP3R1 gene, which encodes calcineurin subunit B type 1, has been found to be associated with left ventricular hypertrophy (LVH) in hypertensive patients and in athletes. The aim of this study is to analyze the possible association between PPP3R1:rs3039851 polymorphism and left ventricular mass (LVM) in full-term healthy newborns.

METHODS

The study group consisted of 162 consecutive, full-term, healthy newborns. Two-dimensional M-mode echocardiography was used to assess LVM. The PPP3R1:rs3039851 polymorphism was identified by PCR-RFLP in genomic DNA extracted from cord blood leukocytes.

RESULTS

No significant differences were found between newborns homozygous for the reference allele (5I/5I, n = 135) and newborns carrying at least one 5D allele (n = 27) for LVM standardized for body mass, body length or body surface area (LVM/BM, LVM/BL or LVM/BSA, respectively). However, the frequency of PPP3R1:rs3039851 genotypes with a 5D allele (5I/5D + 5D/5D) among newborns with the largest LVM/BM or LVM/BSA (upper tertile) was statistically significantly higher compared with the prevalence in individuals with the lowest values of both indices (lower tertile).

CONCLUSIONS

Our results suggest that the PPP3R1:rs3039851 polymorphism may contribute to subtle variation in left ventricular mass at birth.

Epigenetics as a mediator of genetic risk in osteoarthritis: role during development, homeostasis, aging, and disease progression

The identification of genomic loci that are associated with osteoarthritis (OA) has provided a starting point for understanding how genetic variation activates catabolic processes in the joint. However, genetic variants can only alter gene expression and cellular function when the epigenetic environment is permissive to these effects. In this review, we provide examples of how epigenetic shifts at distinct life stages can alter the risk for OA, which we posit is critical for the proper interpretation of genome-wide association studies (GWAS). During development, intensive work on the growth and differentiation factor 5 (GDF5) locus has revealed the importance of tissue-specific enhancer activity in controlling both joint development and the subsequent risk for OA. During homeostasis in adults, underlying genetic risk factors may help establish beneficial or catabolic "set points" that dictate tissue function, with a strong cumulative effect on OA risk. During aging, methylation changes and the reorganization of chromatin can "unmask" the effects of genetic variants. The destructive function of variants that alter aging would only mediate effects after reproductive competence and thus avoid any evolutionary selection pressure, as consistent with larger frameworks of biological aging and its relationship to disease. A similar "unmasking" may occur during OA progression, which is supported by the finding of distinct expression quantitative trait loci (eQTLs) in chondrocytes depending on the degree of tissue degradation. Finally, we propose that massively parallel reporter assays (MPRAs) will be a valuable tool to test the function of putative OA GWAS variants in chondrocytes from different life stages.

Functional variation in the non-coding genome: molecular implications for food security

The growing world population, in combination with the anticipated effects of climate change, is pressuring food security. Plants display an impressive arsenal of cellular mechanisms conferring resilience to adverse environmental conditions, and humans rely on these mechanisms for stable food production. The elucidation of the molecular basis of the mechanisms used by plants to achieve resilience promises knowledge-based approaches to enhance food security. DNA sequence polymorphisms can reveal genomic regions that are linked to beneficial traits of plants. However, our ability to interpret how a given DNA sequence polymorphism confers a fitness advantage at the molecular level often remains poor. A key factor is that these polymorphisms largely localize to the enigmatic non-coding genome. Here, we review the functional impact of sequence variations in the non-coding genome on plant biology in the context of crop breeding and agricultural traits. We focus on examples of non-coding with particularly convincing functional support. Our survey combines findings that are consistent with the view that the non-coding genome contributes to cellular mechanisms assisting many plant traits. Understanding how DNA sequence polymorphisms in the non-coding genome shape plant traits at the molecular level offers a largely unexplored reservoir of solutions to address future challenges in plant growth and resilience.

Proteogenomic links to human metabolic diseases

Studying the plasma proteome as the intermediate layer between the genome and the phenome has the potential to identify new disease processes. Here, we conducted a cis-focused proteogenomic analysis of 2,923 plasma proteins measured in 1,180 individuals using antibody-based assays. We (1) identify 256 unreported protein quantitative trait loci (pQTL); (2) demonstrate shared genetic regulation of 224 cis-pQTLs with 575 specific health outcomes, revealing examples for notable metabolic diseases (such as gastrin-releasing peptide as a potential therapeutic target for type 2 diabetes); (3) improve causal gene assignment at 40% (n = 192) of overlapping risk loci; and (4) observe convergence of phenotypic consequences of cis-pQTLs and rare loss-of-function gene burden for 12 proteins, such as TIMD4 for lipoprotein metabolism. Our findings demonstrate the value of integrating complementary proteomic technologies with genomics even at moderate scale to identify new mediators of metabolic diseases with the potential for therapeutic interventions.

Аpplication of massive parallel reporter analysis in biotechnology and medicine

The development and functioning of an organism relies on tissue-specific gene programs. Genome regulatory elements play a key role in the regulation of such programs, and disruptions in their function can lead to the development of various pathologies, including cancers, malformations and autoimmune diseases. The emergence of high-throughput genomic studies has led to massively parallel reporter analysis (MPRA) methods, which allow the functional verification and identification of regulatory elements on a genome-wide scale. Initially MPRA was used as a tool to investigate fundamental aspects of epigenetics, but the approach also has great potential for clinical and practical biotechnology. Currently, MPRA is used for validation of clinically significant mutations, identification of tissue-specific regulatory elements, search for the most promising loci for transgene integration, and is an indispensable tool for creating highly efficient expression systems, the range of application of which extends from approaches for protein development and design of next-generation therapeutic antibody superproducers to gene therapy. In this review, the main principles and areas of practical application of high-throughput reporter assays will be discussed.

Genotype-environment associations to reveal the molecular basis of environmental adaptation

A fundamental goal in plant biology is to identify and understand the variation underlying plants' adaptation to their environment. Climate change has given new urgency to this goal, as society aims to accelerate adaptation of ecologically important plant species, endangered plant species, and crops to hotter, less predictable climates. In the pre-genomic era, identifying adaptive alleles was painstaking work, leveraging genetics, molecular biology, physiology, and ecology. Now, the rise of genomics and new computational approaches may facilitate this research. Genotype-environment associations (GEAs) use statistical associations between allele frequency and environment of origin to test the hypothesis that allelic variation at a given gene is adapted to local environments. Researchers may scan the genome for GEAs to generate hypotheses on adaptive genetic variants (environmental genome-wide association studies). Despite the rapid adoption of these methods, many important questions remain about the interpretation of GEA findings, which arise from fundamental unanswered questions on the genetic architecture of adaptation and limitations inherent to association-based analyses. We outline strategies to ground GEAs in the underlying hypotheses of genetic architecture and better test GEA-generated hypotheses using genetics and ecophysiology. We provide recommendations for new users who seek to learn about the molecular basis of adaptation. When combined with a rigorous hypothesis testing framework, GEAs may facilitate our understanding of the molecular basis of climate adaptation for plant improvement.

Advanced genomics and clinical phenotypes in psoriatic arthritis

Psoriatic Arthritis (PsA) is a complex polygenic inflammatory disease showing a variable musculoskeletal involvement in patients with skin psoriasis. PsA coexist in 25-40 % of patients with the dermatological manifestations, but PsA may also predate the appearance of psoriasis. Nonetheless, the immunopathogenesis of psoriasis and PsA manifest significant similarities, with a major role of the individual susceptibility in both cases. Genome wide association studies (GWAS) identified several genes/loci associated with the risk to develop PsA, both dependent and independent of psoriasis. The major challenge is thus represented by the need to translate the identification of functional polymorphisms and other genetics findings into biological mechanisms along with the identification of novel putative drug targets. A functional genomics approach aims to increase GWAS power and recent evidence supports the use of a multilayer process, including eQTL, methylome, chromatin conformation analysis and genome editing to discover novel genes that can be affected by disease-associated variants, such as PsA. The available data have considered PsA as a unique homogeneous clinical entity while the clinical experience supports a wide variability of skin and joint manifestations coexisting in diverse patients with different mechanisms underlying the musculoskeletal and dermatological domains. A better discrimination of the patient features is encouraged by the limited data on functional genomics. We provide herein a review of the latest findings on PsA functional genomics highlighting the exciting developments in the field and how these might lead to a better understanding of gene regulation underpinning disease mechanisms and ultimately refine clinical phenotyping.