From genetic associations to genes: methods, applications, and challenges.

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

Transcriptome-Wide Association Study Reveals Potentially Candidate Genes Responsible for Milk Production Traits in Buffalo.

Identifying key causal genes is critical for unraveling the genetic basis of complex economic traits, yet it remains a formidable challenge. The advent of large-scale sequencing data and computational algorithms, such as transcriptome-wide association studies (TWASs), offers a promising avenue for identifying potential causal genes. In this study, we harnessed the power of TWAS to identify genes potentially responsible for milk production traits, including daily milk yield (MY), fat percentage (FP), and protein percentage (PP), within a cohort of 100 buffaloes. Our approach began by generating the genotype and expression profiles for these 100 buffaloes through whole-genome resequencing and RNA sequencing, respectively. Through comprehensive genome-wide association studies (GWAS), we pinpointed a total of seven and four single nucleotide polymorphisms (SNPs) significantly associated with MY and FP traits, respectively. By using TWAS, we identified 55, 71, and 101 genes as significant signals for MY, FP, and PP traits, respectively. To delve deeper, we conducted protein-protein interaction (PPI) analysis, revealing the categorization of these genes into distinct PPI networks. Interestingly, several TWAS-identified genes within the PPI network played a vital role in milk performance. These findings open new avenues for identifying potentially causal genes underlying important traits, thereby offering invaluable insights for genomics and breeding in buffalo populations.

Discovery of genetic loci and causal genes for seed germination via deep re-sequencing in soybean.

High seed germination is crucial for mechanical sowing, seedling establishment, growth potential, multiple resistances, and the formation of yield and quality. However, few genetic loci and candidate genes conferring seed germination were explored in soybean at present. In view of this, a natural population containing 199 accessions was assessed for the germination potential (GP) and germination rate (GR) and also was re-sequenced at the average sequencing depth of 18.4 × for each accession. In total, 5,665,469 SNPs were obtained for association analysis, and 470 SNPs in 55 loci on 18 chromosomes were identified to associate with seed germination. Of them, 85 SNPs on chromosomes 1, 10, and 14 were associated with mean value and BLUP value for GP and GR, simultaneously. Moreover, 324 SNPs (68.9% of the total) in four loci were located on chromosome 14 for seed germination, of which 11 SNPs were located in the exons, 30 in introns, 17 in 5'UTR or 3'UTR, and 46 in upstream or downstream. Based on these, 131 candidate genes flanking the associated SNPs were analyzed for gene annotation, SNP mutation, and RNA expression, and three causal genes, Glyma.14G069800 (RNA-binding protein), Glyma.14G071400 (bZIP transcription factor), and Glyma.17G033200 (nucleic acid-binding protein), were screened out and might be responsible for the seed germination. The closely associated SNPs and causal genes provided an important resource and dissecting of genetic basis for seed germination improvement in soybean. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-022-01316-6.

Genetic loci and causal genes for seed fatty acids accumulation across multiple environments and genetic backgrounds in soybean.

Soybean is a major oil crop in the world, and fatty acids are the predominant components for oil bio-synthesis and catabolism metabolisms and also are the most important energy resources for organisms. In view of this, two recombinant inbred line (RIL) populations (ZL-RIL and ZQ-RIL) and one natural population were evaluated for five individual seed fatty acid contents (palmitic acid, stearic acid, oleic acid, linoleic acid, and linolenic acid) under four different environments, simultaneously. In total, sixteen additive QTL clusters were identified in ZL-RIL population, and fifteen were stably expressed across multiple environments or had pleiotropic effects on various fatty acid contents. Furthermore, five and five of these 16 QTL clusters were verified in ZQ-RIL population and natural population, respectively. Among these consistent and stable QTL clusters, one QTL cluster controlling fatty acid on chromosome 5 with pleiotropic effect was identified under all of the environments in ZL-RIL and ZQ-RIL populations and also was validated in the natural population. Meanwhile, another stable QTL cluster was detected on chromosome 9 with pleiotropic effect under multiple environments in ZL-RIL population and was further verified by the natural population. More importantly, some causal genes, such as the genes on chromosome 9, involving in the fatty acid catabolism process were found in these stable QTL clusters, and some of them, such as Gm09G042000, Gm09G041500, and Gm09G047200 on chromosome 9, showed different expressions in ZL-RIL parents (Zheng92116 and Liaodou14) based on the transcriptome sequencing analysis at different seed developmental stages. Thus, the study results provided insights into the genetic basis and molecular markers for regulating seed fatty acid contents in soybean breeding program. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-021-01227-y.

Clinical and molecular findings in a cohort of 152 Brazilian severe early onset inherited retinal dystrophy patients.

Leber congenital amaurosis (LCA) and early-onset retinal dystrophy (EORD) are severe inherited retinal dystrophy that can cause deep blindness childhood. They represent 5% of all retinal dystrophies in the world population and about 10% in Brazil. Clinical findings and molecular basis of syndromic and nonsyndromic LCA/EORD in a Brazilian sample (152 patients/137 families) were studied. In this population, 15 genes were found to be related to the phenotype, 38 new variants were detected and four new complex alleles were discovered. Among 123 variants found, the most common were CEP290: c.2991+1655A>G, CRB1: p.Cys948Tyr, and RPGRIP1: exon10-18 deletion.

Using functional genomics to advance the understanding of psoriatic arthritis.

Psoriatic arthritis (PsA) is a complex disease where susceptibility is determined by genetic and environmental risk factors. Clinically, PsA involves inflammation of the joints and the skin, and, if left untreated, results in irreversible joint damage. There is currently no cure and the few treatments available to alleviate symptoms do not work in all patients. Over the past decade, genome-wide association studies (GWAS) have uncovered a large number of disease-associated loci but translating these findings into functional mechanisms and novel targets for therapeutic use is not straightforward. Most variants have been predicted to affect primarily long-range regulatory regions such as enhancers. There is now compelling evidence to support the use of chromatin conformation analysis methods to discover novel genes that can be affected by disease-associated variants. Here, we will review the studies published in the field that have given us a novel understanding of gene regulation in the context of functional genomics and how this relates to the study of PsA and its underlying disease mechanism.

Refined mapping of autoimmune disease associated genetic variants with gene expression suggests an important role for non-coding RNAs.

Genome-wide association and fine-mapping studies in 14 autoimmune diseases (AID) have implicated more than 250 loci in one or more of these diseases. As more than 90% of AID-associated SNPs are intergenic or intronic, pinpointing the causal genes is challenging. We performed a systematic analysis to link 460 SNPs that are associated with 14 AID to causal genes using transcriptomic data from 629 blood samples. We were able to link 71 (39%) of the AID-SNPs to two or more nearby genes, providing evidence that for part of the AID loci multiple causal genes exist. While 54 of the AID loci are shared by one or more AID, 17% of them do not share candidate causal genes. In addition to finding novel genes such as ULK3, we also implicate novel disease mechanisms and pathways like autophagy in celiac disease pathogenesis. Furthermore, 42 of the AID SNPs specifically affected the expression of 53 non-coding RNA genes. To further understand how the non-coding genome contributes to AID, the SNPs were linked to functional regulatory elements, which suggest a model where AID genes are regulated by network of chromatin looping/non-coding RNAs interactions. The looping model also explains how a causal candidate gene is not necessarily the gene closest to the AID SNP, which was the case in nearly 50% of cases.

Transcriptomic Analysis Reveals Sixteen Potential Genes Associated with the Successful Differentiation of Antibody-Secreting Cells through the Utilization of Unfolded Protein Response Mechanisms in Robust Responders to the Influenza Vaccine.

The seasonal influenza vaccine remains one of the vital recommended infection control measures for the elderly with chronic illnesses. We investigated the immunogenicity of a single dose of influenza vaccine in 123 seronegative participants and classified them into four distinct groups, determined by the promptness of vaccine response, the longevity of humoral immunity, and the likelihood of exhibiting cross-reactivity. Subsequently, we used transcriptional profiling and differential gene expression analysis to identify potential genes directly associated with the robust response to the vaccine. The group of exemplary vaccine responders differentially expressed 16 genes, namely: MZB1, MYDGF, TXNDC5, TXNDC11, HSP90B1, FKBP11, PDIA5, PRDX4, CD38, SDC1, TNFRSF17, TNFRSF13B, PAX5, POU2AF1, IRF4, and XBP1. Our findings point out a list of expressed proteins that are related to B cell proliferation, unfolded protein response, and cellular haemostasis, as well as a linkage of these expressions to the survival of long-lived plasma cells.

Transcriptome-wide association study by different approaches reveals candidate causal genes for cannabis use disorder.

Cannabis is one of the most commonly used psychoactive substances, which could induce moderate-severe cannabis use disorders (CUD). Here, a tissue-specific transcriptome-wide association study (TWAS) of CUD was performed by FUSION and S-PrediXcan, utilizing a genome-wide association study (GWAS) dataset of CUD (including 43,380 cases and 141,385 controls of European ancestry) and gene expression reference data from 17 different brain-related and non-brain related tissues, with totally 26 TWAS-associated genes were identified, including CADM2 (P = 2.13 × 10-17), SRR (P = 8.09 × 10-9) and TUFM (P = 1.24 × 10-8). Fine-mapping of causal gene sets (FOCUS) was used to prioritize genes with strong evidence for causality, and SRR, CADM2-AS1, and SH2B1 were prioritized with a posterior probability of 0.973, 0.951, and 0.788, respectively. Furthermore, gene ontology (GO) and pathway enrichment analysis on CUD-associated genes were performed, including cytosol, protein binding, nucleoplasm, metabolic pathways, and herpes simplex virus 1 infection. These findings could provide new insights for understanding the mechanism of CUD.

A unifying statistical framework to discover disease genes from GWASs.

Genome-wide association studies (GWASs) identify genomic loci associated with complex traits, but it remains a challenge to identify the genes affected by causal genetic variants in these loci. Attempts to solve this challenge are frustrated by a number of compounding problems. Here, we show how to combine solutions to these problems into a unified mathematical framework. From this synthesis, it becomes possible to compute the probability that each gene in the genome is affected by a causal variant, given a particular trait, without making assumptions about the relevant cell types or tissues. We validate each component of the framework individually and in combination. When applied to large GWASs of human disease, the resulting paradigm can rediscover the majority of well-known disease genes. Moreover, it establishes human genetics support for many genes previously implicated only by clinical or preclinical evidence, and it uncovers a plethora of novel disease genes with compelling biological rationale.

Causal Inference of Genetic Variants and Genes in Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a fatal progressive multisystem disorder with limited therapeutic options. Although genome-wide association studies (GWASs) have revealed multiple ALS susceptibility loci, the exact identities of causal variants, genes, cell types, tissues, and their functional roles in the development of ALS remain largely unknown. Here, we reported a comprehensive post-GWAS analysis of the recent large ALS GWAS (n = 80,610), including functional mapping and annotation (FUMA), transcriptome-wide association study (TWAS), colocalization (COLOC), and summary data-based Mendelian randomization analyses (SMR) in extensive multi-omics datasets. Gene property analysis highlighted inhibitory neuron 6, oligodendrocytes, and GABAergic neurons (Gad1/Gad2) as functional cell types of ALS and confirmed cerebellum and cerebellar hemisphere as functional tissues of ALS. Functional annotation detected the presence of multiple deleterious variants at three loci (9p21.2, 12q13.3, and 12q14.2) and highlighted a list of SNPs that are potentially functional. TWAS, COLOC, and SMR identified 43 genes at 24 loci, including 23 novel genes and 10 novel loci, showing significant evidence of causality. Integrating multiple lines of evidence, we further proposed that rs2453555 at 9p21.2 and rs229243 at 14q12 functionally contribute to the development of ALS by regulating the expression of C9orf72 in pituitary and SCFD1 in skeletal muscle, respectively. Together, these results advance our understanding of the biological etiology of ALS, feed into new therapies, and provide a guide for subsequent functional experiments.

Translational Analysis of Moderate to Severe Asthma GWAS Signals Into Candidate Causal Genes and Their Functional, Tissue-Dependent and Disease-Related Associations.

Asthma affects more than 300 million people globally and is both under diagnosed and under treated. The most recent and largest genome-wide association study investigating moderate to severe asthma to date was carried out in 2019 and identified 25 independent signals. However, as new and in-depth downstream databases become available, the translational analysis of these signals into target genes and pathways is timely. In this study, unique (U-BIOPRED) and publicly available datasets (HaploReg, Open Target Genetics and GTEx) were investigated for the 25 GWAS signals to identify 37 candidate causal genes. Additional traits associated with these signals were identified through PheWAS using the UK Biobank resource, with asthma and eosinophilic traits amongst the strongest associated. Gene expression omnibus dataset examination identified 13 candidate genes with altered expression profiles in the airways and blood of asthmatic subjects, including MUC5AC and STAT6. Gene expression analysis through publicly available datasets highlighted lung tissue cell specific expression, with both MUC5AC and SLC22A4 genes showing enriched expression in ciliated cells. Gene enrichment pathway and interaction analysis highlighted the dominance of the HLA-DQA1/A2/B1/B2 gene cluster across many immunological diseases including asthma, type I diabetes, and rheumatoid arthritis. Interaction and prediction analyses found IL33 and IL18R1 to be key co-localization partners for other genes, predicted that CD274 forms co-expression relationships with 13 other genes, including the HLA-DQA1/A2/B1/B2 gene cluster and that MUC5AC and IL37 are co-expressed. Drug interaction analysis revealed that 11 of the candidate genes have an interaction with available therapeutics. This study provides significant insight into these GWAS signals in the context of cell expression, function, and disease relationship with the view of informing future research and drug development efforts for moderate-severe asthma.

QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants.

Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. While it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as the training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. The average precision was 0.027 for Arabidopsis and 0.029 for rice. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. In addition, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.

The Integrated Landscape of Biological Candidate Causal Genes in Coronary Artery Disease.

BACKGROUND: Genome-wide association studies (GWASs) have identified more than 150 genetic loci that demonstrate robust association with coronary artery disease (CAD). In contrast to the success of GWAS, the translation from statistical signals to biological mechanism and exploration of causal genes for drug development remain difficult, owing to the complexity of gene regulatory and linkage disequilibrium patterns. We aim to prioritize the plausible causal genes for CAD at a genome-wide level. METHODS: We integrated the latest GWAS summary statistics with other omics data from different layers and utilized eight different computational methods to predict CAD potential causal genes. The prioritized candidate genes were further characterized by pathway enrichment analysis, tissue-specific expression analysis, and pathway crosstalk analysis. RESULTS: Our analysis identified 55 high-confidence causal genes for CAD, among which 15 genes (LPL, COL4A2, PLG, CDKN2B, COL4A1, FES, FLT1, FN1, IL6R, LPA, PCSK9, PSRC1, SMAD3, SWAP70, and VAMP8) ranked the highest priority because of consistent evidence from different data-driven approaches. GO analysis showed that these plausible causal genes were enriched in lipid metabolic and extracellular regions. Tissue-specific enrichment analysis revealed that these genes were significantly overexpressed in adipose and liver tissues. Further, KEGG and crosstalk analysis also revealed several key pathways involved in the pathogenesis of CAD. CONCLUSION: Our study delineated the landscape of CAD potential causal genes and highlighted several biological processes involved in CAD pathogenesis. Further studies and experimental validations of these genes may shed light on mechanistic insights into CAD development and provide potential drug targets for future therapeutics.

Update on genetic predisposition to colorectal cancer and polyposis.

The present article summarizes recent developments in the characterization of genetic predisposition to colorectal cancer (CRC). The main themes covered include new hereditary CRC and polyposis syndromes, non-CRC hereditary cancer genes found mutated in CRC patients, strategies used to identify novel causal genes, and review of candidate genes that have been proposed to predispose to CRC and/or colonic polyposis. We provide an overview of newly described genes and syndromes associated with predisposition to CRC and polyposis, including: polymerase proofreading-associated polyposis, NTHL1-associated polyposis, mismatch repair gene biallelic inactivation-related adenomatous polyposis (including MSH3- and MLH3-associated polyposes), GREM1-associated mixed polyposis, RNF43-associated serrated polyposis, and RPS20 mutations as a rare cause of hereditary nonpolyposis CRC. The implementation of next generation sequencing approaches for genetic testing has exposed the presence of pathogenic germline variants in genes associated with hereditary cancer syndromes not traditionally linked to CRC, which may have an impact on genetic testing, counseling and surveillance. The identification of new hereditary CRC and polyposis genes has not deemed an easy endeavor, even though known CRC-related genes explain a small proportion of the estimated familial risk. Whole-genome sequencing may offer a technology for increasing this proportion, particularly if applied on pedigree data allowing linkage type of analysis. The final section critically surveys the large number of candidate genes that have been recently proposed for CRC predisposition.

Genetic screening in early-onset dementia patients with unclear phenotype: relevance for clinical diagnosis.

In a prospective study of dementia in Flanders (Belgium), we observed a substantial fraction of early-onset dementia patients who did not fulfill the criteria for a specific dementia subtype, leaving the patients without a precise clinical diagnosis. We selected 211 of these patients for genetic testing of causal genes linked to neurodegenerative brain diseases. In this group, the onset or inclusion age was 59.9 ± 8.2 years and 27.4% had a positive family history. We used a panel of 16 major genes linked to Alzheimer's disease, frontotemporal dementia, amyotrophic lateral sclerosis, Parkinson's disease, and prion diseases. In addition, we tested for the presence of a pathogenic C9orf72 repeat expansion. We identified 13 rare variants in 15 patients, including a carrier of variants in 2 different genes. Six patients (2.84%), carried a mutation in a Mendelian causal gene, that is, APP, MAPT, SOD1, TBK1, and C9orf72. In the other 7 patients, 7 variants were of uncertain significance, including a frameshift mutation in PSEN2, p.G359Lfs*74, in 2 patients sharing a common haplotype, and in LRRK2, p.L2063fs*. Expression studies showed reduced PSEN2 and a near complete loss of LRRK2, in lymphoblast cells or brain material of these patients. Overall, our study underscores the relevance of genetic testing of known causal genes in early-onset patients with symptomatology of neurodegenerative dementia but an unclear clinical diagnosis. A positive genetic result can help to obtain a precise diagnosis as well as a better understanding of the presence of multiple affected relatives in the family.

The genetics of dementia with Lewy bodies.

Dementia with Lewy bodies (DLB), the most common non-AD neurodegenerative disease has in the past several decades attracted the attention of the neurological scientific community due to its highly negative impact on the quality of life of both the affected individuals and those caring for them. The strong hereditary component in related conditions such as PD and AD and the description of a number of DLB families suggest that genetic factors may play a role in the pathogenesis of DLB. This chapter focuses on currently proposed causal and risk genes and their role in the pathophysiology of DLB, discusses the feasibility of genetic therapy and genetic testing in the diagnostic and treatment of DLB and provides directions for future research. While no single mutation is specific enough to support its regular use in the diagnosis/treatment of DLB, identification of combinations of causative gene or single-gene point mutations and risk genes interfering with the pathogenesis of DLB may help elucidate the genetic mechanisms involved in DLB and inform development of gene-specific therapies.

Leber congenital amaurosis, from darkness to light: An ode to Irene Maumenee.

This article is dedicated to Irene Hussels Maumenee, Professor of Human Genetics and Ophthalmology, Johns Hopkins' Wilmer Eye Institute, Ocular Genetics Fellowship director in 1994-1995. Leber congenital amaurosis (LCA) has almost come full circle, from a profound and molecularly uncharacterized form of congenital retinal blindness to one in which a large number of causative genes and disease pathways are known, and the world's first human retinal disease to be treated by gene therapy. Dr. Maumenee's insights, efforts, and leadership have contributed significantly to this remarkable scientific journey. In this manuscript, we present a short summary of the known LCA genes, LCA disease subtypes, and emerging treatment options. Our manuscript consolidates previous knowledge with current findings in an attempt to provide a more comprehensive understanding of LCA.

Genetical Genomics of Behavior: A Novel Chicken Genomic Model for Anxiety Behavior.

The identification of genetic variants responsible for behavioral variation is an enduring goal in biology, with wide-scale ramifications, ranging from medical research to evolutionary theory on personality syndromes. Here, we use for the first time a large-scale genetical genomics analysis in the brains of chickens to identify genes affecting anxiety as measured by an open field test. We combine quantitative trait locus (QTL) analysis in 572 individuals and expression QTL (eQTL) analysis in 129 individuals from an advanced intercross between domestic chickens and Red Junglefowl. We identify 10 putative quantitative trait genes affecting anxiety behavior. These genes were tested for an association in the mouse Heterogeneous Stock anxiety (open field) data set and human GWAS data sets for bipolar disorder, major depressive disorder, and schizophrenia. Although comparisons between species are complex, associations were observed for four of the candidate genes in mice and three of the candidate genes in humans. Using a multimodel approach we have therefore identified a number of putative quantitative trait genes affecting anxiety behavior, principally in chickens but also with some potentially translational effects as well. This study demonstrates that chickens are an excellent model organism for the genetic dissection of behavior.