Causal Variants Research Articles

Differential Gene Expression Associated with Idiopathic Epilepsy in Belgian Shepherd Dogs

Background: Idiopathic epilepsy (IE) disproportionately affects Belgian shepherd dogs and although genomic risk markers have been identified previously in the breed, causative variants have not been described. Methods: The current study analyzed differences in RNA expression associated with IE and with a previously identified IE risk haplotype on canine chromosome (CFA) 14 using a transcriptomics RNA-seq approach. Results: MFSD2A and a likely pseudogene of RPL19, both of which are genes implicated in seizure activity, were upregulated in dogs with IE. Genes in the interferon signaling pathway were downregulated in Belgian shepherds with IE. The CFA14 risk haplotype was associated with upregulation of CLIC1, ACE2, and PIGN and downregulation of EPDR1, all known to be involved with epilepsy or the Wnt/β-catenin signaling pathway. Conclusions: These results highlight the value of assessing gene expression in canine IE research to uncover genomic contributory factors.

A noncoding variant confers pancreatic differentiation defect and contributes to diabetes susceptibility by recruiting RXRA.

Human genetics analysis has identified many noncoding SNPs associated with diabetic traits, but whether and how these variants contribute to diabetes is largely unknown. Here, we focus on a noncoding variant, rs6048205, and report that the risk-G variant impairs the generation of PDX1+/NKX6-1+ pancreatic progenitor cells and further results in the abnormal decrease of functional β cells during pancreatic differentiation. Mechanistically, this risk-G variant greatly enhances RXRA binding and over-activates FOXA2 transcription, specifically in the pancreatic progenitor stage, which in turn represses NKX6-1 expression. Consistently, inducible FOXA2 overexpression could phenocopy the differentiation defect. More importantly, mice carrying risk-G exhibit abnormal pancreatic islet architecture and are more sensitive to streptozotocin or a high-fat diet to develop into diabetes eventually. This study not only identifies a causal noncoding variant in diabetes susceptibility but also dissects the underlying gain-of-function mechanism by recruiting stage-specific factors.

Allele frequency impacts the cross-ancestry portability of gene expression prediction in lymphoblastoid cell lines.

Population-level genetic studies are overwhelmingly biased toward European ancestries. Transferring genetic predictions from European ancestries to other ancestries results in a substantial loss of accuracy. Yet, it remains unclear how much various genetic factors, such as causal effect differences, linkage disequilibrium (LD) differences, or allele frequency differences, contribute to the loss of prediction accuracy across ancestries. In this study, we used gene expression levels in lymphoblastoid cell lines to understand how much each genetic factor contributes to lowered portability of gene expression prediction from European to African ancestries. We found that cis-genetic effects on gene expression are highly similar between European and African individuals. However, we found that allele frequency differences of causal variants have a striking impact on prediction portability. For example, portability is reduced by more than 32% when the causal cis-variant is common (minor allele frequency, MAF >5%) in European samples (training population) but is rarer (MAF <5%) in African samples (prediction population). While large allele frequency differences can decrease portability through increasing LD differences, we also determined that causal allele frequency can significantly impact portability when the impact from LD is substantially controlled. This observation suggests that improving statistical fine-mapping alone does not overcome the loss of portability resulting from differences in causal allele frequency. We conclude that causal cis-eQTL effects are highly similar in European and African individuals, and allele frequency differences have a large impact on the accuracy of gene expression prediction.

EPCO-58. INTEGRATIVE PROTEOGENOMIC ANALYSES REVEAL INSIGHTS INTO SUBTYPE-SPECIFIC GLIOMA RISK

Abstract Gliomas are heterogeneous brain tumors with prognostically-significant subtypes and few risk factors. IDH-wildtype glioblastoma, the most common subtype, carries a dismal prognosis. Using the largest GWAS dataset with histological (11,304 cases, 304,523 controls) and molecular (3,418 cases, 8,156 controls) subtypes, we analyzed the plasma and brain proteome to uncover proteins associated with glioma susceptibility. Using protein quantitative trait loci (pQTL) for 1,776 proteins in brain tissue, we identified 21 candidate proteins, including 9 associations with high probability (&amp;gt;70%) of colocalization (shared causal genetic variants for glioma risk and protein abundance). We confirmed known risk genes such as EGFR (p=1.9×10-35) and D2HGDH (p=1.1×10-5). We also identified novel risk proteins, including ENPP6, a choline phosphodiesterase overexpressed in glioma, for IDH-wildtype glioblastoma (OR=0.62, p=7.6×10-5), and galectin-3, a lectin encoded by a tumor fitness gene in CRISPR screens of glioma proliferation and a clinical drug target, for glioma overall (OR=1.37, p=1.3×10-5). Analyses of the plasma proteome using pQTLs for 1,362 proteins from 3 cohorts (N=88,838) identified 9 colocalized associations in whole blood. Most risk signals were subtype-specific, such as CRELD1 (p=2.5×10-5) for triple-negative glioblastoma. We found three candidate risk proteins for IDH-mutant 1p19q-intact glioma: immunoglobulin glycoprotein BCAM (p=9.6×10-8), neurotrophin receptor SorCS2 (p=3.6×10-5) and immune checkpoint PD-1 (p=1.1×10-5). Interestingly, genetically-predicted PD-1 levels had a larger effect on IDH-mutant 1p19q-intact (OR=2.75, 95% CI=1.61-4.69) than IDH-wildtype (OR=0.73, 95% CI=0.51-1.05) tumors, suggesting germline differences in immune checkpoint mechanisms among subtypes. For all colocalized candidates, genetically-predicted protein abundance was correlated with cis-regulated transcriptional activity in brain tissue. For example, genetically-predicted SORCS2 gene expression in brain tissue was associated with plasma SorCS2 levels (Z=16.1, p=1.1×10-68). Our analysis identified candidate proteins involved in immune response and neuronal proliferation, which may enable prioritization of drug targets and provide insight into disease mechanisms for glioma.

Prediction of causal genes at GWAS loci with pleiotropic gene regulatory effects using sets of correlated instrumental variables.

Multivariate Mendelian randomization (MVMR) is a statistical technique that uses sets of genetic instruments to estimate the direct causal effects of multiple exposures on an outcome of interest. At genomic loci with pleiotropic gene regulatory effects, that is, loci where the same genetic variants are associated to multiple nearby genes, MVMR can potentially be used to predict candidate causal genes. However, consensus in the field dictates that the genetic instruments in MVMR must be independent (not in linkage disequilibrium), which is usually not possible when considering a group of candidate genes from the same locus. Here we used causal inference theory to show that MVMR with correlated instruments satisfies the instrumental set condition. This is a classical result by Brito and Pearl (2002) for structural equation models that guarantees the identifiability of individual causal effects in situations where multiple exposures collectively, but not individually, separate a set of instrumental variables from an outcome variable. Extensive simulations confirmed the validity and usefulness of these theoretical results. Importantly, the causal effect estimates remained unbiased and their variance small even when instruments are highly correlated, while bias introduced by horizontal pleiotropy or LD matrix sampling error was comparable to standard MR. We applied MVMR with correlated instrumental variable sets at genome-wide significant loci for coronary artery disease (CAD) risk using expression Quantitative Trait Loci (eQTL) data from seven vascular and metabolic tissues in the STARNET study. Our method predicts causal genes at twelve loci, each associated with multiple colocated genes in multiple tissues. We confirm causal roles for PHACTR1 and ADAMTS7 in arterial tissues, among others. However, the extensive degree of regulatory pleiotropy across tissues and the limited number of causal variants in each locus still require that MVMR is run on a tissue-by-tissue basis, and testing all gene-tissue pairs with cis-eQTL associations at a given locus in a single model to predict causal gene-tissue combinations remains infeasible. Our results show that within tissues, MVMR with dependent, as opposed to independent, sets of instrumental variables significantly expands the scope for predicting causal genes in disease risk loci with pleiotropic regulatory effects. However, considering risk loci with regulatory pleiotropy that also spans across tissues remains an unsolved problem.

Coding and non-coding variants in the ciliopathy gene CFAP410 cause early-onset non-syndromic retinal degeneration

Inherited retinal degenerations are blinding genetic disorders characterized by high genetic and phenotypic heterogeneity. In this retrospective study, we describe sixteen families with early-onset non-syndromic retinal degenerations in which affected probands carried rare bi-allelic variants in CFAP410, a ciliary gene previously associated with recessive Jeune syndrome. We detected twelve variants, eight of which were novel, including c.373+91A>G, which led to aberrant splicing. To our knowledge this is the first likely pathogenic deep-intronic variant identified in this gene. Analysis of all reported and novel CFAP410 variants revealed no clear correlation between the severity of the CFAP410-associated phenotypes and the identified causal variants. This is supported by the fact that the frequently encountered missense variant p.(Arg73Pro), often found in syndromic cases, was also associated with non-syndromic retinal degeneration. This study expands the current knowledge of CFAP410-associated ciliopathy by enriching its mutational landscape and supports its association with non-syndromic retinal degeneration.

RNA-First Approach Identifies Deep Intronic PHEX Variants in X-Linked Hypophosphatemic Rickets.

Up to 20% of patients with X-linked hypophosphatemic rickets (XLH) have no causative variant identified on routine molecular diagnostic testing. To identify intronic variants causing PHEX mis-splicing in patients with XLH. The metabolic bone clinic of a paediatric orthopedic hospital. Four patients (age 6 to 12 years; 3 girls) with clinically diagnosed XLH and no PHEX variant on routine testing. RNA and DNA sequence analysis of PHEX. Urine-derived cells were cultured, and mRNA was extracted and transcribed to cDNA. PHEX cDNA was amplified by PCR, followed by sequencing of PCR products. Sequencing of PHEX intronic DNA regions was performed to identify variants causing mis-splicing observed on RNA analysis. PHEX mis-splicing was identified in 3 of the 4 participants, and an intronic variant was identified in all 3 cases. In a 12-year-old boy, transcript analysis showed skipping of PHEX exon 13, while sequencing of PHEX intronic regions revealed a de novo 18 bp deletion in intron 13. In a 7-year-old girl, a pseudoexon in PHEX intron 17 was found, associated with a de novo deep intronic variant (c.1768+173A>G) that activated a cryptic splice donor site. Finally, an 84 bp pseudoexon in PHEX intron 21 caused by a recurrent de novo deep intronic variant (c.2147+1197A>G) was identified in an 11-year-old girl. Analysis of RNA from urine-derived cells combined with sequencing of PHEX introns can identify deep intronic variants in individuals with XLH without a detectable PHEX variant in routine exon-centric molecular diagnosis.

Developmental and Epileptic Encephalopathies: Need for Bridging the Gaps Between Clinical Syndromes and Underlying Genetic Etiologies.

Advancement in genetic testing has become increasingly important in diagnosing and managing developmental and epileptic encephalopathies (DEEs), a group of rare neurodevelopmental disorders characterized by early-onset seizures, developmental delay, and electroencephalographic (EEG) abnormalities. These early epileptic encephalopathies are often described as various syndromes as per their clearly defined, relatively uniform, and distinct clinical phenotypes with consistent EEG and/or neuroimaging findings. Finding the underlying molecular mechanisms can cause a definitive change in the management strategy. With the evolving overlapping phenotypes, advent of technologies, and discovery of new genes, it is exceedingly becoming challenging to correctly characterise these disorders and plan subsequent evidence-based management. Cytogenetic microarray (CMA) and next generation sequencing (NGS) with improved data analysis pipe-lines and algorithms have revamped the diagnostic yield dramatically. However, as of now, there is a big lacuna in step-wise evaluation guideline or consensus on integration of genetic testing results with management plan. Understanding the genetic etiologies of such syndromes timely has three major implications: (1) Knowing the outcome of such a syndrome, (2) Therapeutic implications including licensing of drugs for certain forms (e.g. genetic syndromes involving ion channels) and (3) Genetic counseling, prenatal testing and choosing reproductive options in future pregnancies in such families. The focus of this review is to provide an understanding of different types of causative variants and their step-wise genetic testing approach; the most pressing clinical need and to develop an optimal diagnostic pathway for this group of patients.

17α-hydroxylase/17,20-lyase deficiency (17-OHD): A Meta-analysis of Reported Cases.

Homozygous pathogenic variants in the CYP17A1 gene result in defective activity of the steroidogenic enzymes 17α-hydroxylase/17,20-lyase resulting in the clinical syndrome 17-OHD characterised by hypertension, hypokalaemia, and disorders of sexual development. Pathogenic variants of CYP17A1 lead to complete or partial loss of enzymatic activity and clinical presentations of varying severity. This study aimed to examine relationships between CYP17A1 genotype and clinical presentation in a global cohort. We searched PubMed and Scopus for case reports and cohort studies reporting clinical data on patients with 17-OHD published between 1988 and 2022. Of 451 studies, 178 met inclusion criteria comprising a total of 465 patients. We pooled patient data and examined associations between causative variants and their clinical presentations. There were 465 unique patients with a mean age of 18·9 (9·0) years, 52·5% (n=244) were XY and 6·4% (n=29) were phenotypically male. Homozygous variants were seen in 48·0% (n=223) of patients. Common clinical presentations were hypertension (57·0%, n=256), hypokalaemia (45·4% n=211), primary amenorrhoea (38·3%, n=178), cryptorchidism (15·3%, n=71), and atypical genitalia (14·2%, n=66). Frequently occurring variants included p.Y329Kfs (n=86), p.D487_F489del (n=44) and p.W406R (n=39). More severe variants, such as p.Y329Kfs, were associated with hypocortisolism (p<0·05), combined hypokalaemia and hypertension (p<0·01), and DSD (p<0·01). 17-OHD is a rare, frequently misdiagnosed disease. Male patients are typically diagnosed earlier due to genital dysplasia associated with less severe variants, while female patients are typically diagnosed later due to primary amenorrhoea and hypertension. Patients presenting with DSD and hypertension should be investigated for 17-OHD.

Fine-mapping and molecular characterisation of primary sclerosing cholangitis genetic risk loci

Genome-wide association studies of primary sclerosing cholangitis have identified 23 susceptibility loci. The majority of these loci reside in non-coding regions of the genome and are thought to exert their effect by perturbing the regulation of nearby genes. Here, we aim to identify these genes to improve the biological understanding of primary sclerosing cholangitis, and nominate potential drug targets. We first build an eQTL map for six primary sclerosing cholangitis-relevant T-cell subsets obtained from the peripheral blood of primary sclerosing cholangitis and ulcerative colitis patients. These maps identify 10,459 unique eGenes, 87% of which are shared across all six primary sclerosing cholangitis T-cell types. We then search for colocalisations between primary sclerosing cholangitis loci and eQTLs and undertake Bayesian fine-mapping to identify disease-causing variants. In this work, colocalisation analyses nominate likely primary sclerosing cholangitis effector genes and biological mechanisms at five non-coding (UBASH3A, PRKD2, ETS2 and AP003774.1/CCDC88B) and one coding (SH2B3) primary sclerosing cholangitis loci. Through fine-mapping we identify likely causal variants for a third of all primary sclerosing cholangitis-associated loci, including two to single variant resolution.

Molecular analysis of inherited disorders of cornification in polish patients show novel variants and functional data and provokes questions on the significance of secondary findings.

The Mendelian Disorders of Cornification (MeDOC) comprise a large number of disorders that present with either localised (palmoplantar keratoderma, PPK) or generalised (ichthyoses) signs. The MeDOC are highly heterogenic in terms of genetics and phenotype. Consequently, diagnostic process is challenging and before implementation of the next generation sequencing, was mostly symptomatic, not causal, which limited research on those diseases. The aim of the study was to genetically characterise a cohort of 265 Polish patients with MeDOC and to get insight into the skin lesions using transcriptome and lipid profile analyses. We detected causal variants in 85% (226/265) patients. In addition to the primary gene defect, a pathogenic variant in another gene involved in MeDOC pathology was identified in 23 cases. We found 150 distinct variants in 33 genes, including 32 novel and 16 recurrent (present in > 5 alleles). In 43 alleles large rearrangements were detected, including deletions in the STS, SPINK5, CERS3 and recurrent duplication of exons 10-14 in TGM1. The RNA analysis using samples collected from 18 MeDOC patients and 22 controls identified 1377 differentially expressed genes - DEG. The gene ontology analysis revealed that 114 biological processes were upregulated in the MeDOC group, including i.e. epithelial cell differentiation, lipid metabolic process; homeostasis; regulation of water loss via skin; peptide cross-linking. The DEG between TGM1 and ALOX12B patients, showed that RNA profile is highly similar, though fatty acid profile in epidermal scrapings of those patients showed differences e.g. for the very long chain fatty acids (VLCFAs; FAs ≥ C20), the very long-chain monounsaturated fatty acids (VLC-MUFAs, FAs ≥ C20:1) and the n6 polyunsaturated fatty acids (n6 PUFAs). Our results show that NGS-based analysis is an effective MeDOC diagnostic tool. The Polish MeDOC patients are heterogenic, however recurrent variants are present. The novel variants and high number of TGM1 and SPINK5 copy number variations give further insight into molecular pathology of MeDOC. We show that secondary variants in MeDOC-related genes are present in a significant group of patients, which should be further investigated in the context of phenotype modifiers. Finally, we provide novel RNA and lipid data that characterise molecularly MeDOC epidermis.

Genetic background of high myopia in children.

High myopia is a significant risk factor for irreversible vision loss and can occur in isolation or as a component of various syndromes. However, the genetic basis of early-onset high myopia remains poorly understood. We aimed to identify the causative genetic variants for high myopia in a cohort of Slovenian children. The study included children referred to a tertiary paediatric ophthalmology centre at the University Eye Clinic in Ljubljana between 2010 and 2022. The participants met the following inclusion criteria: age ≤ 15 years and high myopia ≤-5.0 D before the age of 10 years. Genetic analysis included exome sequencing and/or molecular karyotyping. Participants were categorized based on clinical presentation: high myopia with systemic involvement, high myopia with ocular involvement, and isolated high myopia. Genetic analysis of 36 probands revealed a genetic cause of high myopia in 22 (61.1%) children. Among those with systemic involvement (50.0%), genetic causes were identified in 13 out of 18 children, with Stickler's and Pitt-Hopkins being the most common syndromes. Among cases of high myopia with ocular involvement (38.9%), a genetic cause was found in 8 out of 14 probands, including (likely) pathogenic variants in genes related to retinal dystrophies (CACNA1F, RPGR, RP2, NDP). The non-syndromic ARR3- associated high myopia was identified in the isolated high myopia group. A genetic cause of high myopia was identified in 61.1% of children tested, demonstrating the value of genetic testing in this population for diagnosis and proactive counseling.

Evaluation of Bayesian Linear Regression models for gene set prioritization in complex diseases.

Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights into the model's performance under various scenarios, highlighting the impact of factors such as the number of causal genes, proportions of causal variants, heritability, and disease prevalence. Comparative analyses with MAGMA (Multi-marker Analysis of GenoMic Annotation) demonstrate BLR's superior performance, especially in highly overlapped gene sets. Application of both single-trait and multi-trait BLR models to real data, specifically GWAS summary data for type 2 diabetes (T2D) and related phenotypes, identifies significant associations with T2D-related pathways. Furthermore, comparison between single- and multi-trait BLR analyses highlights the superior performance of the multi-trait approach in identifying associated pathways, showcasing increased statistical power when analyzing multiple traits jointly. Additionally, enrichment analysis with integrated data from various public resources supports our results, confirming significant enrichment of diabetes-related genes within the top T2D pathways resulting from the multi-trait analysis. The BLR model's ability to handle diverse genomic features, perform regularization, conduct variable selection, and integrate information from multiple traits, genders, and ancestries demonstrates its utility in understanding the genetic architecture of complex traits. Our study provides insights into the potential of the BLR model to prioritize gene sets, offering a flexible framework applicable to various datasets. This model presents opportunities for advancing personalized medicine by exploring the genetic underpinnings of multifactorial traits.

A Catalogue of Structural Variation across Ancestrally Diverse Asian Genomes

Structural variants (SVs) are significant contributors to inter-individual genetic variation associated with traits and diseases. Current SV studies using whole-genome sequencing (WGS) have a largely Eurocentric composition, with little known about SV diversity in other ancestries, particularly from Asia. Here, we present a WGS catalogue of 73,035 SVs from 8392 Singaporeans of East Asian, Southeast Asian and South Asian ancestries, of which ~65% (47,770 SVs) are novel. We show that Asian populations can be stratified by their global SV patterns and identified 42,239 novel SVs that are specific to Asian populations. 52% of these novel SVs are restricted to one of the three major ancestry groups studied (Indian, Chinese or Malay). We uncovered SVs affecting major clinically actionable loci. Lastly, by identifying SVs in linkage disequilibrium with single-nucleotide variants, we demonstrate the utility of our SV catalogue in the fine-mapping of Asian GWAS variants and identification of potential causative variants. These results augment our knowledge of structural variation across human populations, thereby reducing current ancestry biases in global references of genetic variation afflicting equity, diversity and inclusion in genetic research.

Concealed cardiomyopathy as an emerging cause of sudden cardiac arrest and sudden cardiac death.

The inherited cardiomyopathies exhibit a broad spectrum of disease, with some patients remaining asymptomatic throughout life, while, for others, the first symptom of disease is sudden cardiac death at a young age. The risk of malignant ventricular arrhythmia in these conditions has traditionally been linked to the degree of structural myocardial abnormalities and functional impairment. However, recent advances in genetic testing and knowledge of the genetic basis of the diseases have led to the identification of concealed cardiomyopathy, in which sudden cardiac arrest or sudden cardiac death occurs in the absence of observable clinical features of cardiomyopathy, with a diagnosis being made only after the identification of a causative genetic variant. Increased awareness of concealed cardiomyopathy, a better understanding of mechanisms of arrhythmia and identification of risk modulators will be vital to improve care for families with concealed cardiomyopathy.

Attraction or differentiation: diachronic changes in the causative alternation of Chinese change of state verbs

Abstract This study examines the interplay of attraction and differentiation through the diachronic encoding of causative alternations in Chinese. A corpus-based analysis is conducted to profile the use of two Change of State verbs (COS verbs), pò ‘break’ and kāi ‘open’, focusing on their argument structure constructions. The analysis yields two main insights: (i) In Chinese, there are four pairs of causative alternations. The first pair, CA1, involving the alternation between NP1+COS+NP2 and NP2+COS, serves as the source for two diachronic trajectories. In the first trajectory, NP1+COS+NP2 is replaced by NP1+V+COS+NP2, and forms CA2 with NP2+COS. Subsequently, NP2+COS transitions into NP2+V+COS, and constitutes CA3 with NP1+V+COS+NP2. In the second trajectory, NP1+COS+V+NP2 and NP2+COS+V emerge and form CA4. (ii) Evidence of attraction is found in the mutual influence between the causative variants NP1+V+COS+NP2 and NP1+COS+V+NP2, and in the modeling effect of the causative variant NP1+V+COS+NP2 on the inchoative variant NP2+V+COS. Differentiation is supported by the anchoring force of a larger constructional network, including the transitive/intransitive opposition and compound constructions. This study advances the theoretical understanding of the intertwinement of attraction and differentiation by elucidating the diachronic evolution of causative alternations in Chinese.

GWAS for identification of genomic regions and candidate genes in vegetable crops.

Genome-wide association Studies (GWAS), initially developed for human genetics, have been highly effective in plant research, particularly for vegetable crops. GWAS is a robust tool for identifying genes associated with key traits such as yield, nutritional value, disease resistance, adaptability, and bioactive compound biosynthesis. Unlike traditional methods, GWAS does not require prior biological knowledge and can accurately pinpoint loci, minimizing false positives. The process involves developing a diverse panel, rigorous phenotyping and genotyping, and sophisticated statistical analysis using various models and software tools. By scanning the entire genome, GWAS identifies specific loci or single nucleotide polymorphisms (SNPs) linked to target traits. When a causal SNP variant is not directly genotyped, GWAS identifies SNPs in linkage disequilibrium (LD) with the causal variant, mapping the genetic interval. The method begins with careful panel selection, phenotyping, and genotyping, controlling for environmental effects and utilizing Best Linear Unbiased Prediction (BLUP). High-correlation, high-heritability traits are prioritized. Various genotyping methods address confounders like population structure and kinship. Bonferroni correction (BC) prevents false positives, and significant associations are shown in Manhattan plots. Candidate genes are identified through LD analysis and fine mapping, followed by functional validation. GWAS offers critical insights for enhancing vegetable crop breeding efficiency and precision, driving breakthroughs through advanced methods.

Cardiomyopathies in 100,000 genomes project: interval evaluation improves diagnostic yield and informs strategies for ongoing gene discovery

BackgroundCardiomyopathies are clinically important conditions, with a strong genetic component. National genomic initiatives such as 100,000 Genome Project (100KGP) provide opportunity to study these rare conditions at scale beyond conventional research studies.MethodsWe present the clinical and molecular characteristics of the 100KGP cohort, comparing paediatric and adult probands with diverse cardiomyopathies. We assessed the diagnostic yield and spectrum of genetic aetiologies across clinical presentations. We re-analysed existing genomic data using an updated analytical strategy (revised gene panels; unbiased analyses of de novo variants; and improved variant prioritisation strategies) to identify new causative variants in genetically unsolved children.ResultsWe identified 1918 individuals (1563 probands, 355 relatives) with cardiomyopathy (CM) in 100KGP. Probands, comprising 273 children and 1290 adults, were enrolled under > 55 different recruitment categories. Paediatric probands had higher rates of co-existing congenital heart disease (12%) compared to adults (0.9%). Diagnostic yield following 100KGP’s initial analysis was significantly higher for children (19%) than for adults (11%) with 11% of diagnoses overall made in genes not on the existing UK paediatric or syndromic CM panel. Our re-analysis of paediatric probands yields a potential diagnosis in 40%, identifying new probable or possible diagnoses in 49 previously unsolved paediatric cases. Structural and intronic variants accounted for 11% of all potential diagnoses in children while de novo variants were identified in 17%.Conclusions100KGP demonstrates the benefit of genome sequencing over a standalone panel in CM. Re-analysis of paediatric CM probands allowed a significant uplift in diagnostic yield, emphasising the importance of iterative re-evaluation in genomic studies. Despite these efforts, many children with CM remain without a genetic diagnosis, highlighting the need for better gene-disease relationship curation and ongoing data sharing. The 100KGP CM cohort is likely to be useful for further gene discovery, but heterogeneous ascertainment and key technical limitations must be understood and addressed.

Prognostic role of gene variants in sudden cardiac death risk associated to paediatric hypertrophic cardiomyopathy

Abstract Introduction Genetic testing plays an important role in diagnosis of hypertrophic cardiomyopathy (HCM), being sarcomeric genes commonly involved, particularly MYBPC3. However, arrhythmic risk related to genotype remains unclear in adults and almost unexplored in children. Hence, current sudden cardiac death (SCD) risk calculators do not include genetic information. Purpose To analyse risk of arrhythmic events associated with causal variants in MYBPC3 respecting to other genes in a paediatric cohort with non-syndromic HCM from a cardiomyopathy referral centre. Methods This retrospective study included patients with non-syndromic HCM 0-18 years old between 2009-2022. Demographic, clinical, and genetic data were collected. Genetic variants pathogenicity followed American College of Medical Genetics criteria. A positive genetic test was considered if a likely pathogenic or pathogenic variant in HCM related genes was found. Considered outcomes were SCD or a major arrhythmic cardiac event (MACE) within 5 years follow-up: recovered SCD, appropriate ICD therapy, or sustained ventricular tachycardia. Some SCD-risk estimation models (in 5 years) as HCM Risk-SCD model for patients &amp;gt;=15 y.o and/or 55 kg, ESC Guidelines paediatric algorithm, and HCM Risk-Kids model were applied. Results 77 patients were diagnosed with non-syndromic HCM. A causal variant was found in 45/73 (62%). No significant differences in cardiac phenotype were found related to genes. 10/77 patients (13%) presented a MACE in 5 years follow-up. A positive genetic study was significantly associated with having an event (p=0.011). Most patients with a positive genetic test harboured MYBPC3 variants (21/45, 47%), however only 2 patients who suffered a MACE had a causal variant in MYBPC3. Those with causal variants in genes other than MYBPC3 presented more and earlier events (p=0.022). Risk of having a MACE during follow-up was higher when presenting causal variants in genes other than MYBPC3: Hazard Ratio 18.5 (95% CI 2.31, 148), p=0.006. Applying HCM Risk-SCD calculator, model had better predictive discrimination when variable "causal variant in gene other than MYBPC3" was added in multivariate analysis, with high-risk category &amp;gt;=6% (C-statistic =0.931 vs 0.844) and intermediate-risk category 4-6% (C-statistic =0.845 vs 0.514). Same occurred with ESC Guidelines algorithm, adding that variable to 2 risk factors category (C-statistic =0.881 vs 0.742) or 1 risk factor (C-statistic =0.844 vs 0.635). HCM Risk-Kids also improved its predictive discrimination adding the genetic value to high-risk category &amp;gt;=6% (C-statistic =0.896 vs 0.796) and intermediate-risk (C-statistic =0.821 vs 0.506). Conclusions Genotype may have prognostic value in SCD risk in HCM in children. Our results suggests that causal genetic variants in genes other than MYBPC3 can lead to higher risk of SCD. Genetic information might be considered for future models of arrhythmic risk in children with HCM.

Multivariate Bayesian variable selection for multi-trait genetic fine mapping

Abstract Genome-wide association studies (GWAS) have identified thousands of single-nucleotide polymorphisms (SNPs) associated with complex traits, but determining the underlying causal variants remains challenging. Fine mapping aims to pinpoint the potentially causal variants from a large number of correlated SNPs possibly with group structure in GWAS-enriched genomic regions using variable selection approaches. In multi-trait fine mapping, we are interested in identifying the causal variants for multiple related traits. Existing multivariate variable selection methods for fine mapping select variables for all responses without considering the possible heterogeneity across different responses. Here, we develop a novel multivariate Bayesian variable selection method for multi-trait fine mapping to select causal variants from a large number of grouped SNPs that target at multiple correlated and possibly heterogeneous traits. Our new method is featured by its selection at multiple levels, incorporation of prior biological knowledge to guide selection and identification of best subset of traits the variants target at. We showed the advantage of our method over existing methods via comprehensive simulations that mimic typical fine-mapping settings and a real-world fine-mapping example in UK Biobank, where we identified critical causal variants potentially targeting at different subsets of addictive behaviours and risk factors.