Genetic associations of protein-coding variants in human disease

SPLENDID incorporates continuous genetic ancestry in biobank-scale data to improve polygenic risk prediction across diverse populations

Polygenic risk scores are widely used in disease risk stratification, but their accuracy varies across diverse populations. Recent methods large-scale leverage multi-ancestry data to improve accuracy in under-represented populations but require labelling individuals by ancestry for prediction. This poses challenges for practical use, as clinical practices are typically not based on ancestry. We propose SPLENDID, a novel penalized regression framework for diverse biobank-scale data. Our method utilizes ancestry principal component interactions to model genetic ancestry as a continuum within a single prediction model for all ancestries, eliminating the need for discrete labels. In extensive simulations and analyses of 9 traits from the All of Us Research Program (N=224,364) and UK Biobank (N=340,140), SPLENDID significantly outperformed existing methods in prediction accuracy and model sparsity. By directly incorporating continuous genetic ancestry in model training, SPLENDID stands as a valuable tool for robust risk prediction across diverse populations and fairer clinical implementation.

INDELpred: Improving the prediction and interpretation of indel pathogenicity within the clinical genome

Small insertions and deletions (indels) are critical yet challenging genetic variations with significant clinical implications. However, the identification of pathogenic indels from neutral variants in clinical contexts remains an understudied problem. Here, we developed INDELpred, a machine-learning-based predictive model for discerning pathogenic from benign indels. INDELpred was established based on key features, including allele frequency, indel length, function-based features, and gene-based features. A set of comprehensive evaluation analyses demonstrated that INDELpred exhibited superior performance over competing methods in terms of computational efficiency and prediction accuracy. Importantly, INDELpred highlighted the crucial role of function-based features in identifying pathogenic indels, with a clear interpretability of the features in understanding the disease-causing variants. We envisage INDELpred as a desirable tool for the detection of pathogenic indels within large-scale genomic datasets, thereby enhancing the precision of genetic diagnoses in clinical settings.

Assessment of the Impact of Preanalytical DNA Integrity on the Genome Data Quality

Many molecular approaches have been employed for the quality control (QC) of biobanked DNA samples. Since 2003, the National Biobank of Korea (NBK) has provided various studies with over half a million quality-controlled genomic DNA samples using conventional agarose gel electrophoresis and spectrophotometry. We assessed the postanalytical genomic data quality of DNA samples (n = 41) with a different range of the DNA quality index such as genomic quality number (GQN) for developing an evidence-based best practice for DNA quality criteria. We examined the quality indices of three different platforms, including single nucleotide polymorphism arrays, methylation arrays, and next-generation sequencing, using the same DNA samples (n = 41) of different quality, ranging from 4.0 to 10.0 values of the GQN. Our data analysis revealed that higher GQN value and/or double-stranded DNA concentration resulted in higher quality genomic data. In addition, all the analyzed DNA samples successfully generated good-quality genomic data. This study provides a guide for the QC of biobanked DNA samples for genomic analysis platforms.

Genetics of type-1 diabetes

Type-1 diabetes is a multifactorial disease characterized by genetic and environmental factors that contribute to its development and progression. Despite progress in the management of type-1 diabetes, the final goal of curing the disease is yet to be achieved. To establish effective methods for the prevention, intervention, and cure of the disease, the molecular mechanisms and pathways involved in its development and progression should be clarified. One effective approach is to identify genes responsible for disease susceptibility and apply information obtained from the function of genes in disease etiology for the protection, intervention, and cure of type-1 diabetes. In this review, we discuss the genetic basis of type-1 diabetes, along with prospects for its prevention, intervention, and cure for type-1 diabetes.

Rare coding variant analysis for human diseases across biobanks and ancestries

Large-scale sequencing has enabled unparalleled opportunities to investigate the role of rare coding variation in human phenotypic variability. Here, we present a pan-ancestry analysis of sequencing data from three large biobanks, including the All of Us research program. Using mixed-effects models, we performed gene-based rare variant testing for 601 diseases across 748,879 individuals, including 155,236 with ancestry dissimilar to European. We identified 363 significant associations, which highlighted core genes for the human disease phenome and identified potential novel associations, including UBR3 for cardiometabolic disease and YLPM1 for psychiatric disease. Pan-ancestry burden testing represented an inclusive and useful approach for discovery in diverse datasets, although we also highlight the importance of ancestry-specific sensitivity analyses in this setting. Finally, we found that effect sizes for rare protein-disrupting variants were concordant between samples similar to European ancestry and other genetic ancestries (βDeming = 0.7-1.0). Our results have implications for multi-ancestry and cross-biobank approaches in sequencing association studies for human disease.

Genetic and causal relationship between chronic gastrointestinal diseases and erectile dysfunction: a Mendelian randomization study

Background

Studies based on observations have indicated potential associations between chronic gastrointestinal diseases and an increased risk of erectile dysfunction (ED). However, the causality of these connections remains ambiguous.

Methods

Summary data for chronic gastrointestinal diseases were extracted from public data. Summary data on ED were extracted from three distinct sources. The genetic correlations between chronic gastrointestinal diseases and ED were explored using linkage disequilibrium score regression (LDSC). The causal associations between chronic gastrointestinal diseases and ED were evaluated using Mendelian randomization (MR) analysis, followed by a meta-analysis to determine the ultimate causal effect.

Results

The LDSC results suggested significant genetic correlations between Crohn's disease (CD) and ED. Inflammatory bowel disease (IBD), ulcerative colitis (UC), and liver cirrhosis (LC) were found to have potential genetic correlations with ED. The combined multiple MR results indicate that IBD and CD have significant causal relationships with ED, while colorectal cancer (CRC) may have a potential causal effect on ED.

Conclusion

This research provided evidence supporting a causal association between IBD, CD, CRC, and ED. The impact of chronic gastrointestinal diseases on ED warrants greater attention in clinical practice.

Large-scale whole-exome sequencing analyses identified protein-coding variants associated with immune-mediated diseases in 350,770 adults

The genetic contribution of protein-coding variants to immune-mediated diseases (IMDs) remains underexplored. Through whole exome sequencing of 40 IMDs in 350,770 UK Biobank participants, we identified 162 unique genes in 35 IMDs, among which 124 were novel genes. Several genes, including FLG which is associated with atopic dermatitis and asthma, showed converging evidence from both rare and common variants. 91 genes exerted significant effects on longitudinal outcomes (interquartile range of Hazard Ratio: 1.12-5.89). Mendelian randomization identified five causal genes, of which four were approved drug targets (CDSN, DDR1, LTA, and IL18BP). Proteomic analysis indicated that mutations associated with specific IMDs might also affect protein expression in other IMDs. For example, DXO (celiac disease-related gene) and PSMB9 (alopecia areata-related gene) could modulate CDSN (autoimmune hypothyroidism-, psoriasis-, asthma-, and Graves' disease-related gene) expression. Identified genes predominantly impact immune and biochemical processes, and can be clustered into pathways of immune-related, urate metabolism, and antigen processing. Our findings identified protein-coding variants which are the key to IMDs pathogenesis and provided new insights into tailored innovative therapies.

Distinct roles of COMPASS subunits to Drosophila heart development

The multiprotein complexes known as the complex of proteins associated with Set1 (COMPASS) play a crucial role in the methylation of histone 3 lysine 4 (H3K4). In Drosophila, the COMPASS series complexes comprise core subunits Set1, Trx, and Trr, which share several common subunits such as ash2, Dpy30-L1, Rbbp5, and wds, alongside their unique subunits: Wdr82 for Set1/COMPASS, Mnn1 for Trx/COMPASS-like, and Ptip for Trr/COMPASS-like. Our research has shown that flies deficient in any of these common or unique subunits exhibited high lethality at eclosion (the emergence of adult flies from their pupal cases) and significantly shortened lifespans of the few adults that do emerge. Silencing these common or unique subunits led to severe heart morphological and functional defects. Moreover, specifically silencing the unique subunits of the COMPASS series complexes, Wdr82, Mnn1, and Ptip, in the heart results in decreased levels of H3K4 monomethylation and dimethylation, consistent with effects observed from silencing the core subunits Set1, Trx, and Trr. These findings underscore the critical roles of each subunit of the COMPASS series complexes in regulating histone methylation during heart development and provide valuable insights into their potential involvement in congenital heart diseases, thereby informing ongoing research in heart disease.

ProtVar: mapping and contextualizing human missense variation

Genomic variation can impact normal biological function in complex ways and so understanding variant effects requires a broad range of data to be coherently assimilated. Whilst the volume of human variant data and relevant annotations has increased, the corresponding increase in the breadth of participating fields, standards and versioning mean that moving between genomic, coding, protein and structure positions is increasingly complex. In turn this makes investigating variants in diverse formats and assimilating annotations from different resources challenging. ProtVar addresses these issues to facilitate the contextualization and interpretation of human missense variation with unparalleled flexibility and ease of accessibility for use by the broadest range of researchers. By precalculating all possible variants in the human proteome it offers near instantaneous mapping between all relevant data types. It also combines data and analyses from a plethora of resources to bring together genomic, protein sequence and function annotations as well as structural insights and predictions to better understand the likely effect of missense variation in humans. It is offered as an intuitive web server https://www.ebi.ac.uk/protvar where data can be explored and downloaded, and can be accessed programmatically via an API.

ITPR1 variant-induced autosomal dominant hereditary spastic paraplegia in a Chinese family

Hereditary spastic paraplegia (HSP) is a rare neurodegenerative disease prominently characterized by slowly progressive lower limb weakness and spasticity. The significant genotypic and phenotypic heterogeneity of this disease makes its accurate diagnosis challenging. In this study, we identified the NM_001168272: c.2714A > G (chr3.hg19: g.4716912A > G, N905S) variant in the ITPR1 gene in a three-generation Chinese family with multiple individuals affected by HSP, which we believed to be associated with HSP pathogenesis. To confirm, we performed whole exome sequencing, copy number variant assays, dynamic mutation analysis of the entire family, and protein structure prediction. The variant identified in this study was in the coupling domain, and this is the first corroborated report assigning ITPR1 variants to HSP. These findings expand the clinical and genetic spectrum of HSP and provide important data for its genetic analysis and diagnosis.

Severity of effect considerations regarding the use of mutation as a toxicological endpoint for risk assessment: A report from the 8th International Workshop on Genotoxicity Testing (IWGT)

Exposure levels without appreciable human health risk may be determined by dividing a point of departure on a dose-response curve (e.g., benchmark dose) by a composite adjustment factor (AF). An "effect severity" AF (ESAF) is employed in some regulatory contexts. An ESAF of 10 may be incorporated in the derivation of a health-based guidance value (HBGV) when a "severe" toxicological endpoint, such as teratogenicity, irreversible reproductive effects, neurotoxicity, or cancer was observed in the reference study. Although mutation data have been used historically for hazard identification, this endpoint is suitable for quantitative dose-response modeling and risk assessment. As part of the 8th International Workshops on Genotoxicity Testing, a sub-group of the Quantitative Analysis Work Group (WG) explored how the concept of effect severity could be applied to mutation. To approach this question, the WG reviewed the prevailing regulatory guidance on how an ESAF is incorporated into risk assessments, evaluated current knowledge of associations between germline or somatic mutation and severe disease risk, and mined available data on the fraction of human germline mutations expected to cause severe disease. Based on this review and given that mutations are irreversible and some cause severe human disease, in regulatory settings where an ESAF is used, a majority of the WG recommends applying an ESAF value between 2 and 10 when deriving a HBGV from mutation data. This recommendation may need to be revisited in the future if direct measurement of disease-causing mutations by error-corrected next generation sequencing clarifies selection of ESAF values.

Multi-organ imaging-derived polygenic indexes for brain and body health

The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.

Whole genome sequencing of mouse lines divergently selected for fatness (FLI) and leanness (FHI) revealed several genetic variants as candidates for novel obesity genes

BACKGROUND

Analysing genomes of animal model organisms is widely used for understanding the genetic basis of complex traits and diseases, such as obesity, for which only a few mouse models exist, however, without their lean counterparts.

OBJECTIVE

To analyse genetic differences in the unique mouse models of polygenic obesity (Fat line) and leanness (Lean line) originating from the same base population and established by divergent selection over more than 60 generations.

METHODS

Genetic variability was analysed using WGS. Variants were identified with GATK and annotated with Ensembl VEP. g.Profiler, WebGestalt, and KEGG were used for GO and pathway enrichment analysis. miRNA seed regions were obtained with miRPathDB 2.0, LncRRIsearch was used to predict targets of identified lncRNAs, and genes influencing adipose tissue amount were searched using the IMPC database.

RESULTS

WGS analysis revealed 6.3 million SNPs, 1.3 million were new. Thousands of potentially impactful SNPs were identified, including within 24 genes related to adipose tissue amount. SNP density was highest in pseudogenes and regulatory RNAs. The Lean line carries SNP rs248726381 in the seed region of mmu-miR-3086-3p, which may affect fatty acid metabolism. KEGG analysis showed deleterious missense variants in immune response and diabetes genes, with food perception pathways being most enriched. Gene prioritisation considering SNP GERP scores, variant consequences, and allele comparison with other mouse lines identified seven novel obesity candidate genes: 4930441H08Rik, Aff3, Fam237b, Gm36633, Pced1a, Tecrl, and Zfp536.

CONCLUSION

WGS revealed many genetic differences between the lines that accumulated over the selection period, including variants with potential negative impacts on gene function. Given the increasing availability of mouse strains and genetic polymorphism catalogues, the study is a valuable resource for researchers to study obesity.

Refining the impact of genetic evidence on clinical success

The cost of drug discovery and development is driven primarily by failure1, with only about 10% of clinical programmes eventually receiving approval2-4. We previously estimated that human genetic evidence doubles the success rate from clinical development to approval5. In this study we leverage the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure. We estimate the probability of success for drug mechanisms with genetic support is 2.6 times greater than those without. This relative success varies among therapy areas and development phases, and improves with increasing confidence in the causal gene, but is largely unaffected by genetic effect size, minor allele frequency or year of discovery. These results indicate we are far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.

Bioinformatics pipeline for the systematic mining genomic and proteomic variation linked to rare diseases: The example of monogenic diabetes

Monogenic diabetes is characterized as a group of diseases caused by rare variants in single genes. Like for other rare diseases, multiple genes have been linked to monogenic diabetes with different measures of pathogenicity, but the information on the genes and variants is not unified among different resources, making it challenging to process them informatically. We have developed an automated pipeline for collecting and harmonizing data on genetic variants linked to monogenic diabetes. Furthermore, we have translated variant genetic sequences into protein sequences accounting for all protein isoforms and their variants. This allows researchers to consolidate information on variant genes and proteins linked to monogenic diabetes and facilitates their study using proteomics or structural biology. Our open and flexible implementation using Jupyter notebooks enables tailoring and modifying the pipeline and its application to other rare diseases.

Association between gut microbiota and male infertility: a two-sample Mendelian randomization study

Previous research has confirmed the significant association between gut microbiota (GM) and male infertility (MI), but the causality between them remains unclear. This study aims to investigate the causal relationship between GM and MI using Mendelian randomization (MR) and provide supplementary information for the optimization of future randomized controlled trials (RCTs). Instrumental variables for 211 GM taxa were obtained from genome-wide association studies (GWAS), and inverse variance weighted (IVW) method was used as the main analysis method for two-sample MR analysis to assess the impact of GM on the risk of MI. Four methods were used to test for horizontal pleiotropy and heterogeneity of MR results to ensure the reliability of the MR findings. A total of 50 single-nucleotide polymorphisms (SNPs) closely related to GM were included, and ultimately identified 1 family and 4 general are causally associated with MI. Among them, Anaerotruncus (OR = 1.96, 95% CI 1.31-3.40, P = 0.016) is significantly associated with increased MI risk. Furthermore, we used four MR methods to evaluate the causality, and the results supported these findings. The leave-one-out analysis showed stable results with no instrumental variables exerting strong influence on the results. The causal direction indicated a positive effect, and the effects of heterogeneity and horizontal pleiotropy on the estimation of causal effect were minimized. We confirmed a causal relationship between GM taxa and MI, providing new insights into the mechanisms underlying GM-mediated MI.

Proteome-wide Mendelian randomization identifies causal plasma proteins in lung cancer

Plasma proteins are promising biomarkers and potential drug targets in lung cancer. To evaluate the causal association between plasma proteins and lung cancer, we performed proteome-wide Mendelian randomization meta-analysis (PW-MR-meta) based on lung cancer genome-wide association studies (GWASs), protein quantitative trait loci (pQTLs) of 4,719 plasma proteins in deCODE and 4,775 in Fenland. Further, causal-protein risk score (CPRS) was developed based on causal proteins and validated in the UK Biobank. 270 plasma proteins were identified using PW-MR meta-analysis, including 39 robust causal proteins (both FDR-q < 0.05) and 78 moderate causal proteins (FDR-q < 0.05 in one and p < 0.05 in another). The CPRS had satisfactory performance in risk stratification for lung cancer (top 10% CPRS:Hazard ratio (HR) (95%CI):4.33(2.65-7.06)). The CPRS [AUC (95%CI): 65.93 (62.91-68.78)] outperformed the traditional polygenic risk score (PRS) [AUC (95%CI): 55.71(52.67-58.59)]. Our findings offer further insight into the genetic architecture of plasma proteins for lung cancer susceptibility.

Association of clonal haematopoiesis with severe postoperative complications in patients undergoing radical oesophagectomy

BACKGROUND

Clonal haematopoiesis (CH) is an age-associated clonal expansion of blood cells driven by leukaemia-associated somatic mutations. Although CH has been reported to be a risk factor for leukaemia and a number of non-haematopoietic diseases, its role in perioperative medicine remains unexplored.

METHODS

This was a single-centre, prospective, observational study. Patients undergoing radical oesophagectomy were enrolled, and peripheral blood samples were collected for DNA sequencing. Patients with haematopoietic somatic mutations (variant allele frequencies ≥1%) in the DNMT3A gene, TET2 gene, or both were defined as CH carriers. The primary outcome was the incidence of severe postoperative complications (Clavien-Dindo classification ≥3). The secondary outcomes included the major types of postoperative complications, mortality, and other common perioperative variables.

RESULTS

Clonal haematopoiesis was found in 21.2% (33/156) of the patients (mean age: 66 yr [range: 26-79 yr]; 83% males). Some 14/33 (42.4%) patients with CH had severe postoperative complications, compared with patients without CH carriers (28/123 [22.8%]; P=0.024). Multivariable logistic regression analysis showed that CH was associated with an increased risk of developing severe postoperative complications (odds ratio, 3.63; 95% confidence interval, 1.37-9.66; P=0.010). Among the major postoperative complications, the incidence of pulmonary complications was significantly higher in the patients with CH than in those without CH (15 in 33 [45.5%] vs 30 in 123 [24.4%], P=0.018).

CONCLUSIONS

Clonal haematopoiesis was associated with a higher incidence of severe postoperative complications in patients undergoing radical oesophagectomy, suggesting that clonal haematopoiesis can play an important role in perioperative medicine.

CLINICAL TRIAL REGISTRATION

ChiCTR2100044175 (Chinese Clinical Trial Registry, http://www.chictr.org.cn/showproj.aspx?proj=123193).

Whole genome sequencing in clinical practice

Whole genome sequencing (WGS) is becoming the preferred method for molecular genetic diagnosis of rare and unknown diseases and for identification of actionable cancer drivers. Compared to other molecular genetic methods, WGS captures most genomic variation and eliminates the need for sequential genetic testing. Whereas, the laboratory requirements are similar to conventional molecular genetics, the amount of data is large and WGS requires a comprehensive computational and storage infrastructure in order to facilitate data processing within a clinically relevant timeframe. The output of a single WGS analyses is roughly 5 MIO variants and data interpretation involves specialized staff collaborating with the clinical specialists in order to provide standard of care reports. Although the field is continuously refining the standards for variant classification, there are still unresolved issues associated with the clinical application. The review provides an overview of WGS in clinical practice - describing the technology and current applications as well as challenges connected with data processing, interpretation and clinical reporting.

Potential Causal Association between Plasma Metabolites, Immunophenotypes, and Female Reproductive Disorders: A Two-Sample Mendelian Randomization Analysis

BACKGROUND

While extensive research highlighted the involvement of metabolism and immune cells in female reproductive diseases, causality remains unestablished.

METHODS

Instrumental variables for 486 circulating metabolites (N = 7824) and 731 immunophenotypes (N = 3757) were derived from a genome-wide association study (GWAS) meta-analysis. FinnGen contributed data on 14 female reproductive disorders. A bidirectional two-sample Mendelian randomization study was performed to determine the relationships between exposures and outcomes. The robustness of results, potential heterogeneity, and horizontal pleiotropy were examined through sensitivity analysis.

RESULTS

High levels of mannose were found to be causally associated with increased risks of gestational diabetes (GDM) (OR [95% CI], 6.02 [2.85-12.73], p = 2.55 × 10-6). A genetically predicted elevation in the relative count of circulating CD28-CD25++CD8+ T cells was causally related to increased female infertility risk (OR [95% CI], 1.26 [1.14-1.40], p = 1.07 × 10-5), whereas a high absolute count of NKT cells reduced the risk of ectopic pregnancy (OR [95% CI], 0.87 [0.82-0.93], p = 5.94 × 10-6). These results remained consistent in sensitivity analyses.

CONCLUSIONS

Our study supports mannose as a promising GDM biomarker and intervention target by integrating metabolomics and genomics.

Advancing athletic assessment by integrating conventional methods with cutting-edge biomedical technologies for comprehensive performance, wellness, and longevity insights

In modern athlete assessment, the integration of conventional biochemical and ergophysiologic monitoring with innovative methods like telomere analysis, genotyping/phenotypic profiling, and metabolomics has the potential to offer a comprehensive understanding of athletes' performance and potential longevity. Telomeres provide insights into cellular functioning, aging, and adaptation and elucidate the effects of training on cellular health. Genotype/phenotype analysis explores genetic variations associated with athletic performance, injury predisposition, and recovery needs, enabling personalization of training plans and interventions. Metabolomics especially focusing on low-molecular weight metabolites, reveal metabolic pathways and responses to exercise. Biochemical tests assess key biomarkers related to energy metabolism, inflammation, and recovery. Essential elements depict the micronutrient status of the individual, which is critical for optimal performance. Echocardiography provides detailed monitoring of cardiac structure and function, while burnout testing evaluates psychological stress, fatigue, and readiness for optimal performance. By integrating this scientific testing battery, a multidimensional understanding of athlete health status can be achieved, leading to personalized interventions in training, nutrition, supplementation, injury prevention, and mental wellness support. This scientifically rigorous approach hereby presented holds significant potential for improving athletic performance and longevity through evidence-based, individualized interventions, contributing to advances in the field of sports performance optimization.

High-dimensional phenotyping to define the genetic basis of cellular morphology

The morphology of cells is dynamic and mediated by genetic and environmental factors. Characterizing how genetic variation impacts cell morphology can provide an important link between disease association and cellular function. Here, we combine genomic sequencing and high-content imaging approaches on iPSCs from 297 unique donors to investigate the relationship between genetic variants and cellular morphology to map what we term cell morphological quantitative trait loci (cmQTLs). We identify novel associations between rare protein altering variants in WASF2, TSPAN15, and PRLR with several morphological traits related to cell shape, nucleic granularity, and mitochondrial distribution. Knockdown of these genes by CRISPRi confirms their role in cell morphology. Analysis of common variants yields one significant association and nominate over 300 variants with suggestive evidence (P < 10-6) of association with one or more morphology traits. We then use these data to make predictions about sample size requirements for increasing discovery in cellular genetic studies. We conclude that, similar to molecular phenotypes, morphological profiling can yield insight about the function of genes and variants.

RAVAR: a curated repository for rare variant-trait associations

Rare variants contribute significantly to the genetic causes of complex traits, as they can have much larger effects than common variants and account for much of the missing heritability in genome-wide association studies. The emergence of UK Biobank scale datasets and accurate gene-level rare variant-trait association testing methods have dramatically increased the number of rare variant associations that have been detected. However, no systematic collection of these associations has been carried out to date, especially at the gene level. To address the issue, we present the Rare Variant Association Repository (RAVAR), a comprehensive collection of rare variant associations. RAVAR includes 95 047 high-quality rare variant associations (76186 gene-level and 18 861 variant-level associations) for 4429 reported traits which are manually curated from 245 publications. RAVAR is the first resource to collect and curate published rare variant associations in an interactive web interface with integrated visualization, search, and download features. Detailed gene and SNP information are provided for each association, and users can conveniently search for related studies by exploring the EFO tree structure and interactive Manhattan plots. RAVAR could vastly improve the accessibility of rare variant studies. RAVAR is freely available for all users without login requirement at http://www.ravar.bio.

TTD: Therapeutic Target Database describing target druggability information

Target discovery is one of the essential steps in modern drug development, and the identification of promising targets is fundamental for developing first-in-class drug. A variety of methods have emerged for target assessment based on druggability analysis, which refers to the likelihood of a target being effectively modulated by drug-like agents. In the therapeutic target database (TTD), nine categories of established druggability characteristics were thus collected for 426 successful, 1014 clinical trial, 212 preclinical/patented, and 1479 literature-reported targets via systematic review. These characteristic categories were classified into three distinct perspectives: molecular interaction/regulation, human system profile and cell-based expression variation. With the rapid progression of technology and concerted effort in drug discovery, TTD and other databases were highly expected to facilitate the explorations of druggability characteristics for the discovery and validation of innovative drug target. TTD is now freely accessible at: https://idrblab.org/ttd/.

Identification of Novel Loci and Cross-Disorder Pleiotropy Through Multi-Ancestry Genome-Wide Analysis of Alcohol Use Disorder in Over One Million Individuals

Alcohol use disorder (AUD) is highly heritable and burdensome worldwide. Genome-wide association studies (GWASs) can provide new evidence regarding the aetiology of AUD. We report a multi-ancestry GWASs across diverse ancestries focusing on a narrow AUD phenotype, using novel statistical tools in a total sample of 1,041,450 individuals [102,079 cases; European, 75,583; African, 20,689 (mostly African-American); Hispanic American, 3,449; East Asian, 2,254; South Asian, 104; descent]. Cross-ancestry functional analyses were performed with European and African samples. Thirty-seven genome-wide significant loci were identified, of which seven were novel for AUD and six for other alcohol phenotypes. Loci were mapped to genes enriched for brain regions relevant for AUD (striatum, hypothalamus, and prefrontal cortex) and potential drug targets (GABAergic, dopaminergic and serotonergic neurons). African-specific analysis yielded a unique pattern of immune-related gene sets. Polygenic overlap and positive genetic correlations showed extensive shared genetic architecture between AUD and both mental and general medical phenotypes, suggesting they are not only complications of alcohol use but also share genetic liability with AUD. Leveraging a cross-ancestry approach allowed identification of novel genetic loci for AUD and underscores the value of multi-ancestry genetic studies. These findings advance our understanding of AUD risk and clinically-relevant comorbidities.

Polygenic risk alters the penetrance of monogenic kidney disease

Chronic kidney disease (CKD) is determined by an interplay of monogenic, polygenic, and environmental risks. Autosomal dominant polycystic kidney disease (ADPKD) and COL4A-associated nephropathy (COL4A-AN) represent the most common forms of monogenic kidney diseases. These disorders have incomplete penetrance and variable expressivity, and we hypothesize that polygenic factors explain some of this variability. By combining SNP array, exome/genome sequence, and electronic health record data from the UK Biobank and All-of-Us cohorts, we demonstrate that the genome-wide polygenic score (GPS) significantly predicts CKD among ADPKD monogenic variant carriers. Compared to the middle tertile of the GPS for noncarriers, ADPKD variant carriers in the top tertile have a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile have only a 3-fold increased risk of CKD. Similarly, the GPS significantly predicts CKD in COL4A-AN carriers. The carriers in the top tertile of the GPS have a 2.5-fold higher risk of CKD, while the risk for carriers in the bottom tertile is not different from the average population risk. These results suggest that accounting for polygenic risk improves risk stratification in monogenic kidney disease.

Central Adiposity Increases Risk of Kidney Stone Disease through Effects on Serum Calcium Concentrations

SIGNIFICANCE STATEMENT

Kidney stone disease is a common disorder with poorly understood pathophysiology. Observational and genetic studies indicate that adiposity is associated with an increased risk of kidney stone disease. However, the relative contribution of general and central adipose depots and the mechanisms by which effects of adiposity on kidney stone disease are mediated have not been defined. Using conventional and genetic epidemiological techniques, we demonstrate that general and central adiposity are independently associated with kidney stone disease. In addition, one mechanism by which central adiposity increases risk of kidney stone disease is by increasing serum calcium concentration. Therapies targeting adipose depots may affect calcium homeostasis and help to prevent kidney stone disease.

BACKGROUND

Kidney stone disease affects approximately 10% of individuals in their lifetime and is frequently recurrent. The disease is linked to obesity, but the mechanisms mediating this association are uncertain.

METHODS

Associations of adiposity and incident kidney stone disease were assessed in the UK Biobank over a mean of 11.6 years/person. Genome-wide association studies and Mendelian randomization (MR) analyses were undertaken in the UK Biobank, FinnGen, and in meta-analyzed cohorts to identify factors that affect kidney stone disease risk.

RESULTS

Observational analyses on UK Biobank data demonstrated that increasing central and general adiposity is independently associated with incident kidney stone formation. Multivariable MR, using meta-analyzed UK Biobank and FinnGen data, established that risk of kidney stone disease increases by approximately 21% per one standard deviation increase in body mass index (BMI, a marker of general adiposity) independent of waist-to-hip ratio (WHR, a marker of central adiposity) and approximately 24% per one standard deviation increase of WHR independent of BMI. Genetic analyses indicate that higher WHR, but not higher BMI, increases risk of kidney stone disease by elevating adjusted serum calcium concentrations (β=0.12 mmol/L); WHR mediates 12%-15% of its effect on kidney stone risk in this way.

CONCLUSIONS

Our study indicates that visceral adipose depots elevate serum calcium concentrations, resulting in increased risk of kidney stone disease. These findings highlight the importance of weight loss in individuals with recurrent kidney stones and suggest that therapies targeting adipose depots may affect calcium homeostasis and contribute to prevention of kidney stone disease.

Missense variants in SORT1 are associated with LDL-C in an Amish population

Common noncoding variants at the human 1p13.3 locus associated with SORT1 expression are among those most strongly associated with low-density lipoprotein cholesterol (LDL-C) in human genome-wide association studies. However, validation studies in mice and cell lines have produced variable results regarding the directionality of the effect of SORT1 on LDL-C. This, together with the fact that the 1p13.3 variants are associated with expression of several genes, has raised the question of whether SORT1 is the causal gene at this locus. Using whole exome sequencing in members of an Amish population, we identified coding variants in SORT1 that are associated with increased (rs141749679, K302E) and decreased (rs149456022, Q225H) LDL-C. Further, analysis of plasma lipoprotein particle subclasses by ion mobility in a subset of rs141749679 (K302E) carriers revealed higher levels of large LDL particles compared to noncarriers. In contrast to the effect of these variants in the Amish, the sortilin K302E mutation introduced into a C57BL/6J mouse via CRISPR/Cas9 resulted in decreased non-high-density lipoprotein cholesterol, and the sortilin Q225H mutation did not alter cholesterol levels in mice. This is indicative of different effects of these mutations on cholesterol metabolism in the two species. To our knowledge, this is the first evidence that naturally occurring coding variants in SORT1 are associated with LDL-C, thus supporting SORT1 as the gene responsible for the association of the 1p13.3 locus with LDL-C.

Zebrafish as a Model for Osteoporosis: Functional Validations of Genome-Wide Association Studies

PURPOSE OF REVIEW

GWAS, as a largely correlational analysis, requires in vitro or in vivo validation. Zebrafish (Danio rerio) have many advantages for studying the genetics of human diseases. Since gene editing in zebrafish has been highly valuable for studying embryonic skeletal developmental processes that are prenatally or perinatally lethal in mammalian models, we are reviewing pros and cons of this model.

RECENT FINDINGS

The true power for the use of zebrafish is the ease by which the genome can be edited, especially using the CRISPR/Cas9 system. Gene editing, followed by phenotyping, for complex traits such as BMD, is beneficial, but the major physiological differences between the fish and mammals must be considered. Like mammals, zebrafish do have main bone cells; thus, both in vivo stem cell analyses and in vivo imaging are doable. Yet, the "long" bones of fish are peculiar, and their bone cavities do not contain bone marrow. Partial duplication of the zebrafish genome should be taken into account. Overall, small fish toolkit can provide unmatched opportunities for genetic modifications and morphological investigation as a follow-up to human-first discovery.

Identification of genetic associations and functional SNPs of bovine KLF6 gene on milk production traits in Chinese holstein

BACKGROUND

Our previous research identified the Kruppel like factor 6 (KLF6) gene as a prospective candidate for milk production traits in dairy cattle. The expression of KLF6 in the livers of Holstein cows during the peak of lactation was significantly higher than that during the dry and early lactation periods. Notably, it plays an essential role in activating peroxisome proliferator-activated receptor α (PPARα) signaling pathways. The primary aim of this study was to further substantiate whether the KLF6 gene has significant genetic effects on milk traits in dairy cattle.

RESULTS

Through direct sequencing of PCR products with pooled DNA, we totally identified 12 single nucleotide polymorphisms (SNPs) within the KLF6 gene. The set of SNPs encompasses 7 located in 5' flanking region, 2 located in exon 2 and 3 located in 3' untranslated region (UTR). Of these, the g.44601035G > A is a missense mutation that resulting in the replacement of arginine (CGG) with glutamine (CAG), consequently leading to alterations in the secondary structure of the KLF6 protein, as predicted by SOPMA. The remaining 7 regulatory SNPs significantly impacted the transcriptional activity of KLF6 following mutation (P < 0.005), manifesting as changes in transcription factor binding sites. Additionally, 4 SNPs located in both the UTR and exons were predicted to influence the secondary structure of KLF6 mRNA using the RNAfold web server. Furthermore, we performed the genotype-phenotype association analysis using SAS 9.2 which found all the 12 SNPs were significantly correlated to milk yield, fat yield, fat percentage, protein yield and protein percentage within both the first and second lactations (P < 0.0001 ~ 0.0441). Also, with Haploview 4.2 software, we found the 12 SNPs linked closely and formed a haplotype block, which was strongly associated with five milk traits (P < 0.0001 ~ 0.0203).

CONCLUSIONS

In summary, our study represented the KLF6 gene has significant impacts on milk yield and composition traits in dairy cattle. Among the identified SNPs, 7 were implicated in modulating milk traits by impacting transcriptional activity, 4 by altering mRNA secondary structure, and 1 by affecting the protein secondary structure of KLF6. These findings provided valuable molecular insights for genomic selection program of dairy cattle.

Circulating Inflammatory Cytokines and Female Reproductive Diseases: A Mendelian Randomization Analysis

CONTEXT

Extensive studies have provided considerable evidence suggesting the role of inflammation in the development of female reproductive diseases. However, causality has not been established.

OBJECTIVE

To explore whether genetically determined circulating levels of cytokines are causally associated with female reproductive diseases and discover potential novel drug targets for these diseases.

METHODS

Instrumental variables (IVs) for 47 circulating cytokines were obtained from a genome-wide association study (GWAS) meta-analysis of 31 112 European individuals. Protein quantitative trait loci and expression quantitative trait loci close to genes served as our IVs. Summary data of 9 female reproductive diseases were mainly derived from GWAS meta-analysis of the UK biobank and FinnGen. We elevated the association using the Wald ratio or inverse variance-weighted Mendelian randomization (MR) with subsequent assessments for MR assumptions in several sensitivity and colocalization analyses. We consider a false discovery rate <0.05 as statistical significance in MR analyses. Replication studies were conducted for further validation, and phenome-wide association studies were designed to explore potential side effects.

RESULTS

Our results indicated that high levels of macrophage colony-stimulating factor (MCSF), growth-regulated oncogene-alpha (GROα), and soluble intercellular adhesion molecule-1 were associated with increased risks of endometriosis, female infertility, and pre-eclampsia, respectively. High platelet-derived growth factor-BB (PDGF-BB) levels that reduced the risk of ovarian aging were also supported. Replication analysis supported the relationship between GROα and female infertility, and between MCSF and endometriosis.

CONCLUSION

We identified 4 correlated pairs that implied potential protein drug targets. Notably, we preferred highlighting the value of PDGF-BB as a drug target for ovarian aging, and MCSF as a drug target for endometriosis.

The role of admixture in the rare variant contribution to inflammatory bowel disease

BACKGROUND

Identification of rare variants involved in complex, polygenic diseases like Crohn's disease (CD) has accelerated with the introduction of whole exome/genome sequencing association studies. Rare variants can be used in both diagnostic and therapeutic assessments; however, since they are likely to be restricted to specific ancestry groups, their contributions to risk assessment need to be evaluated outside the discovery population. Prior studies implied that the three known rare variants in NOD2 are absent in West African and Asian populations and only contribute in African Americans via admixture.

METHODS

Whole genome sequencing (WGS) data from 3418 African American individuals, 1774 inflammatory bowel disease (IBD) cases, and 1644 controls were used to assess odds ratios and allele frequencies (AF), as well as haplotype-specific ancestral origins of European-derived CD variants discovered in a large exome-wide association study. Local and global ancestry was performed to assess the contribution of admixture to IBD contrasting European and African American cohorts.

RESULTS

Twenty-five rare variants associated with CD in European discovery cohorts are typically five-fold lower frequency in African Americans. Correspondingly, where comparisons could be made, the rare variants were found to have a predicted four-fold reduced burden for IBD in African Americans, when compared to European individuals. Almost all of the rare CD European variants were found on European haplotypes in the African American cohort, implying that they contribute to disease risk in African Americans primarily due to recent admixture. In addition, proportion of European ancestry correlates the number of rare CD European variants each African American individual carry, as well as their polygenic risk of disease. Similar findings were observed for 23 mutations affecting 10 other common complex diseases for which the rare variants were discovered in European cohorts.

CONCLUSIONS

European-derived Crohn's disease rare variants are even more rare in African Americans and contribute to disease risk mainly due to admixture, which needs to be accounted for when performing cross-ancestry genetic assessments.

Urinary stone disease: closing the heritability gap by challenging conventional Mendelian inheritance

Urinary stone disease is based on gene-environment interaction with an almost 50% heritability. Despite all efforts from exome-sequencing and genome-wide association studies, the genetic factors making up for observed heritability have been incompletely characterized. The study by Sadeghi-Alavijeh et al. leverages the invaluable resources of the 100,000 Genomes Project and the UK Biobank to identify heterozygous rare variants in the phosphate transporter SLC34A3 as a significant factor of urinary stone disease, challenging the traditional concept of Mendelian inheritance.

Rare variants in the sodium-dependent phosphate transporter gene SLC34A3 explain missing heritability of urinary stone disease

Urinary stone disease (USD) is a major health burden affecting over 10% of the United Kingdom population. While stone disease is associated with lifestyle, genetic factors also strongly contribute. Common genetic variants at multiple loci from genome-wide association studies account for 5% of the estimated 45% heritability of the disorder. Here, we investigated the extent to which rare genetic variation contributes to the unexplained heritability of USD. Among participants of the United Kingdom 100,000-genome project, 374 unrelated individuals were identified and assigned diagnostic codes indicative of USD. Whole genome gene-based rare variant testing and polygenic risk scoring against a control population of 24,930 ancestry-matched controls was performed. We observed (and replicated in an independent dataset) exome-wide significant enrichment of monoallelic rare, predicted damaging variants in the SLC34A3 gene for a sodium-dependent phosphate transporter that were present in 5% cases compared with 1.6% of controls. This gene was previously associated with autosomal recessive disease. The effect on USD risk of having a qualifying SLC34A3 variant was greater than that of a standard deviation increase in polygenic risk derived from GWAS. Addition of the rare qualifying variants in SLC34A3 to a linear model including polygenic score increased the liability-adjusted heritability from 5.1% to 14.2% in the discovery cohort. We conclude that rare variants in SLC34A3 represent an important genetic risk factor for USD, with effect size intermediate between the fully penetrant rare variants linked with Mendelian disorders and common variants associated with USD. Thus, our findings explain some of the heritability unexplained by prior common variant genome-wide association studies.

Large-scale exome array summary statistics resources for glycemic traits to aid effector gene prioritization

Background

Genome-wide association studies for glycemic traits have identified hundreds of loci associated with these biomarkers of glucose homeostasis. Despite this success, the challenge remains to link variant associations to genes, and underlying biological pathways.

Methods

To identify coding variant associations which may pinpoint effector genes at both novel and previously established genome-wide association loci, we performed meta-analyses of exome-array studies for four glycemic traits: glycated hemoglobin (HbA1c, up to 144,060 participants), fasting glucose (FG, up to 129,665 participants), fasting insulin (FI, up to 104,140) and 2hr glucose post-oral glucose challenge (2hGlu, up to 57,878). In addition, we performed network and pathway analyses.

Results

Single-variant and gene-based association analyses identified coding variant associations at more than 60 genes, which when combined with other datasets may be useful to nominate effector genes. Network and pathway analyses identified pathways related to insulin secretion, zinc transport and fatty acid metabolism. HbA1c associations were strongly enriched in pathways related to blood cell biology.

Conclusions

Our results provided novel glycemic trait associations and highlighted pathways implicated in glycemic regulation. Exome-array summary statistic results are being made available to the scientific community to enable further discoveries.

Gene expression and RNA splicing explain large proportions of the heritability for complex traits in cattle

Many quantitative trait loci (QTLs) are in non-coding regions. Therefore, QTLs are assumed to affect gene regulation. Gene expression and RNA splicing are primary steps of transcription, so DNA variants changing gene expression (eVariants) or RNA splicing (sVariants) are expected to significantly affect phenotypes. We quantify the contribution of eVariants and sVariants detected from 16 tissues (n = 4,725) to 37 traits of ∼120,000 cattle (average magnitude of genetic correlation between traits = 0.13). Analyzed in Bayesian mixture models, averaged across 37 traits, cis and trans eVariants and sVariants detected from 16 tissues jointly explain 69.2% (SE = 0.5%) of heritability, 44% more than expected from the same number of random variants. This 69.2% includes an average of 24% from trans e-/sVariants (14% more than expected). Averaged across 56 lipidomic traits, multi-tissue cis and trans e-/sVariants also explain 71.5% (SE = 0.3%) of heritability, demonstrating the essential role of proximal and distal regulatory variants in shaping mammalian phenotypes.

Causal association between inflammatory bowel disease and 32 site-specific extracolonic cancers: a Mendelian randomization study

BACKGROUND

The risk of extracolonic cancer is increased in inflammatory bowel disease (IBD) patients, but it is not clear whether there is a causal relationship. We aimed to systematically estimate the causal relationship between IBD and extracolonic cancers.

METHODS

Independent genetic variants strongly associated with IBD were extracted as instrumental variables from genome-wide association study (GWAS) conducted by the International IBD Genetics Consortium including 12,882 IBD patients, 5956 Crohn's disease (CD) patients, and 6968 ulcerative colitis (UC) patients. Three sources of cancer GWAS were selected as outcome data. Two-sample Mendelian randomization (MR) analysis was conducted to assess the causal effects of IBD on 32 extracolonic cancers. The meta-analysis was applied to assess the combined causal effect with multiple MR results.

RESULTS

IBD, CD, and UC have potential causal associations with oral cavity cancer (IBD: OR = 1.180, 95% CI: 1.059 to 1.316, P = 0.003; CD: OR = 1.112, 95% CI: 1.008 to 1.227, P = 0.034; UC: OR = 1.158, 95% CI: 1.041 to 1.288, P = 0.007). Meta-analysis showed a significant positive causal relationship between IBD and breast cancer (OR = 1.059; 95% CI: 1.033 to 1.086; P < 0.0001) as well as a potential causal relationship between CD and breast cancer (OR = 1.029; 95% CI: 1.002 to 1.055; P = 0.032) based on combining multiple MR results.

CONCLUSIONS

This comprehensive MR analysis suggested that genetically predicted IBD, as well as its subtypes, may be a risk factor in the development of oral cavity and breast cancer.

Precise Differentiation of Wobble-Type Allele via Ratiometric Design of a Ligase Chain Reaction-Based Electrochemical Biosensor for CYP2C19*2 Genotyping of Clinical Samples

Due to the comparable stability between the perfect-base pair and the wobble-base pair, a precise differentiation of the wobble-type allele has remained a challenge, often leading to false results. Herein, we proposed a ligase chain reaction (LCR)-based ratiometric electrochemical DNA sensor, namely, R-eLCR, for a precise typing of the wobble-type allele, in which the traditionally recognized "negative" signal of wobble-base pair-mediated amplification was fully utilized as a "positive" one and a ratiometric readout mode was employed to ameliorated the underlying potential external influence and improved its detection accuracy in the typing of the wobble-type allele. The results showed that the ratio between current of methylene blue (IMB) and current of ferrocene (IFc) was partitioned in three regions and three types of wobble-type allele were thus precisely differentiated (AA homozygote: IMB/IFc > 2; GG homozygote: IMB/IFc < 1; GA heterozygote: 1 < IMB/IFc < 2); the proposed R-eLCR successfully discriminated the three types of CYP2C19*2 allele in nine cases of human whole blood samples, which was consistent with those of the sequencing method. These results evidence that the proposed R-eLCR can serve as an accurate and robust alternative for the identification of wobble-type allele, which lays a solid foundation and holds great potential for precision medicine.

Somatic gain-of-function mutations in BUD13 promote oncogenesis by disrupting Fbw7 function

Somatic mutations occurring on key enzymes are extensively studied and targeted therapies are developed with clinical promises. However, context-dependent enzyme function through distinct substrates complicated targeting a given enzyme. Here, we develop an algorithm to elucidate a new class of somatic mutations occurring on enzyme-recognizing motifs that cancer may hijack to facilitate tumorigenesis. We validate BUD13-R156C and -R230Q mutations evading RSK3-mediated phosphorylation with enhanced oncogenicity in promoting colon cancer growth. Further mechanistic studies reveal BUD13 as an endogenous Fbw7 inhibitor that stabilizes Fbw7 oncogenic substrates, while cancerous BUD13-R156C or -R230Q interferes with Fbw7Cul1 complex formation. We also find this BUD13 regulation plays a critical role in responding to mTOR inhibition, which can be used to guide therapy selections. We hope our studies reveal the landscape of enzyme-recognizing motif mutations with a publicly available resource and provide novel insights for somatic mutations cancer hijacks to promote tumorigenesis with the potential for patient stratification and cancer treatment.

Ensembled best subset selection using summary statistics for polygenic risk prediction

Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, yet existing methods face a tradeoff between predictive power and computational efficiency. We introduce ALL-Sum, a fast and scalable PRS method that combines an efficient summary statistic-based L 0 L 2 penalized regression algorithm with an ensembling step that aggregates estimates from different tuning parameters for improved prediction performance. In extensive large-scale simulations across a wide range of polygenicity and genome-wide association studies (GWAS) sample sizes, ALL-Sum consistently outperforms popular alternative methods in terms of prediction accuracy, runtime, and memory usage. We analyze 27 published GWAS summary statistics for 11 complex traits from 9 reputable data sources, including the Global Lipids Genetics Consortium, Breast Cancer Association Consortium, and FinnGen, evaluated using individual-level UKBB data. ALL-Sum achieves the highest accuracy for most traits, particularly for GWAS with large sample sizes. We provide ALL-Sum as a user-friendly command-line software with pre-computed reference data for streamlined user-end analysis.

A FinnGen pilot clinical recall study for Alzheimer's disease

Successful development of novel therapies requires that clinical trials are conducted in patient cohorts with the highest benefit-to-risk ratio. Population-based biobanks with comprehensive health and genetic data from large numbers of individuals hold promise to facilitate identification of trial participants, particularly when interventions need to start while symptoms are still mild, such as for Alzheimer's disease (AD). This study describes a process for clinical recall studies from FinnGen. We demonstrate the feasibility to systematically ascertain customized clinical data from FinnGen participants with ICD10 diagnosis of AD or mild cognitive disorder (MCD) in a single-center cross-sectional study testing blood-based biomarkers and cognitive functioning in-person, computer-based and remote. As a result, 19% (27/140) of a pre-specified FinnGen subcohort were successfully recalled and completed the study. Hospital records largely validated registry entries. For 8/12 MCD patients, other reasons than AD were identified as underlying diagnosis. Cognitive measures correlated across platforms, with highest consistencies for dementia screening (r = 0.818) and semantic fluency (r = 0.764), respectively, for in-person versus telephone-administered tests. Glial fibrillary acidic protein (GFAP) (p < 0.002) and phosphorylated-tau 181 (pTau-181) (p < 0.020) most reliably differentiated AD from MCD participants. We conclude that informative, customized clinical recall studies from FinnGen are feasible.

A Large-Scale Exome-Wide Association Study Identifies Novel Germline Mutations in Lung Cancer

Rationale: Genome-wide association studies have identified common variants of lung cancer. However, the contribution of rare exome-wide variants, especially protein-coding variants, to cancers remains largely unexplored. Objectives: To evaluate the role of human exomes in genetic predisposition to lung cancer. Methods: We performed exome-wide association studies to detect the association of exomes with lung cancer in 30,312 patients and 652,902 control subjects. A scalable and accurate implementation of a generalized mixed model was used to detect the association signals for loss-of-function, missense, and synonymous variants and gene-level sets. Furthermore, we performed association and Bayesian colocalization analyses to evaluate their relationships with intermediate exposures. Measurements and Main Results: We systematically analyzed 216,739 single-nucleotide variants in the human exome. The loss-of-function variants exhibited the most notable effects on lung cancer risk. We identified four novel variants, including two missense variants (rs202197044TET3 [Pmeta (P values of meta-analysis) = 3.60 × 10-8] and rs202187871POT1 [Pmeta = 2.21 × 10-8]) and two synonymous variants (rs7447927TMEM173 [Pmeta = 1.32 × 10-9] and rs140624366ATRN [Pmeta = 2.97 × 10-9]). rs202197044TET3 was significantly associated with emphysema (odds ratio, 3.55; Pfdr = 0.015), whereas rs7447927POT1 was strongly associated with telomere length (β = 1.08; Pfdr (FDR corrected P value) = 3.76 × 10-53). Functional evidence of expression of quantitative trait loci, splicing quantitative trait loci, and isoform expression was found for the four novel genes. Gene-level association tests identified several novel genes, including POT1 (protection of telomeres 1), RTEL1, BSG, and ZNF232. Conclusions: Our findings provide insights into the genetic architecture of human exomes and their role in lung cancer predisposition.

Polygenic prediction of preeclampsia and gestational hypertension

Preeclampsia and gestational hypertension are common pregnancy complications associated with adverse maternal and child outcomes. Current tools for prediction, prevention and treatment are limited. Here we tested the association of maternal DNA sequence variants with preeclampsia in 20,064 cases and 703,117 control individuals and with gestational hypertension in 11,027 cases and 412,788 control individuals across discovery and follow-up cohorts using multi-ancestry meta-analysis. Altogether, we identified 18 independent loci associated with preeclampsia/eclampsia and/or gestational hypertension, 12 of which are new (for example, MTHFR-CLCN6, WNT3A, NPR3, PGR and RGL3), including two loci (PLCE1 and FURIN) identified in the multitrait analysis. Identified loci highlight the role of natriuretic peptide signaling, angiogenesis, renal glomerular function, trophoblast development and immune dysregulation. We derived genome-wide polygenic risk scores that predicted preeclampsia/eclampsia and gestational hypertension in external cohorts, independent of clinical risk factors, and reclassified eligibility for low-dose aspirin to prevent preeclampsia. Collectively, these findings provide mechanistic insights into the hypertensive disorders of pregnancy and have the potential to advance pregnancy risk stratification.

The impact of rare protein coding genetic variation on adult cognitive function

Compelling evidence suggests that human cognitive function is strongly influenced by genetics. Here, we conduct a large-scale exome study to examine whether rare protein-coding variants impact cognitive function in the adult population (n = 485,930). We identify eight genes (ADGRB2, KDM5B, GIGYF1, ANKRD12, SLC8A1, RC3H2, CACNA1A and BCAS3) that are associated with adult cognitive function through rare coding variants with large effects. Rare genetic architecture for cognitive function partially overlaps with that of neurodevelopmental disorders. In the case of KDM5B we show how the genetic dosage of one of these genes may determine the variability of cognitive, behavioral and molecular traits in mice and humans. We further provide evidence that rare and common variants overlap in association signals and contribute additively to cognitive function. Our study introduces the relevance of rare coding variants for cognitive function and unveils high-impact monogenic contributions to how cognitive function is distributed in the normal adult population.

Polygenic risk affects the penetrance of monogenic kidney disease

Background

Chronic kidney disease (CKD) is a genetically complex disease determined by an interplay of monogenic, polygenic, and environmental risks. Most forms of monogenic kidney diseases have incomplete penetrance and variable expressivity. It is presently unknown if some of the variability in penetrance can be attributed to polygenic factors.

Methods

Using the UK Biobank (N=469,835 participants) and the All of Us (N=98,622 participants) datasets, we examined two most common forms of monogenic kidney disorders, autosomal dominant polycystic kidney disease (ADPKD) caused by deleterious variants in the PKD1 or PKD2 genes, and COL4A-associated nephropathy (COL4A-AN caused by deleterious variants in COL4A3, COL4A4, or COL4A5 genes). We used the eMERGE-III electronic CKD phenotype to define cases (estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m2 or kidney failure) and controls (eGFR >90 mL/min/1.73m2 in the absence of kidney disease diagnoses). The effects of the genome-wide polygenic score (GPS) for CKD were tested in monogenic variant carriers and non-carriers using logistic regression controlling for age, sex, diabetes, and genetic ancestry.

Results

As expected, the carriers of known pathogenic and rare predicted loss-of-function variants in PKD1 or PKD2 had a high risk of CKD (ORmeta=17.1, 95% CI: 11.1-26.4, P=1.8E-37). The GPS was comparably predictive of CKD in both ADPKD variant carriers (ORmeta=2.28 per SD, 95%CI: 1.55-3.37, P=2.6E-05) and non-carriers (ORmeta=1.72 per SD, 95% CI=1.69-1.76, P< E-300) independent of age, sex, diabetes, and genetic ancestry. Compared to the middle tertile of the GPS distribution for non-carriers, ADPKD variant carriers in the top tertile had a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile had only a 3-fold increased risk of CKD. Similarly, the GPS was predictive of CKD in both COL4-AN variant carriers (ORmeta=1.78, 95% CI=1.22-2.58, P=2.38E-03) and non-carriers (ORmeta=1.70, 95%CI: 1.68-1.73 P

Conclusions

Variable penetrance of kidney disease in ADPKD and COL4-AN is partially explained by differences in polygenic risk profiles. Accounting for polygenic factors has the potential to improve risk stratification in monogenic kidney disease and may have implications for genetic counseling.

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Key Words

• APOL1
• GWAS
• chronic kidney disease (CKD)
• polygenic risk score (PRS)

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MESH Headings Collapse

Grants

• OT2 OD026556 NIH HHS
• U2C OD023196 NIH HHS
• OT2 OD026551 NIH HHS
• U24 OD023121 NIH HHS
• OT2 OD026552 NIH HHS
• OT2 OD026549 NIH HHS
• OT2 OD025337 NIH HHS
• OT2 OD025277 NIH HHS
• OT2 OD026555 NIH HHS
• OT2 OD026550 NIH HHS
• OT2 OD026553 NIH HHS
• OT2 OD023205 NIH HHS
• U01 HG008680 NHGRI NIH HHS
• OT2 OD025276 NIH HHS
• OT2 OD026557 NIH HHS
• U24 OD023163 NIH HHS
• OT2 OD023206 NIH HHS
• R01 LM013061 NLM NIH HHS
• U24 OD023176 NIH HHS
• OT2 OD026548 NIH HHS
• OT2 OD025315 NIH HHS
• K25 DK128563 NIDDK NIH HHS
• OT2 OD026554 NIH HHS
• UL1 TR001873 NCATS NIH HHS

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Affiliation(s)

Atlas Khan Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
Ning Shang Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
Jordan G. Nestor Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
Chunhua Weng Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
George Hripcsak Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
Peter C. Harris Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota 55905, USA
Ali G. Gharavi Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
Krzysztof Kiryluk Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY

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Clinical and Functional Assessment of Digenicity in Renal Phosphate Wasting

Apart from increased fluid intake, patients with kidney stone disease (KSD) due to renal phosphate wasting require specific metaphylaxis. NaPi2a, NaPi2c, and NHERF1 regulate plasma phosphate concentration by reabsorbing phosphate in proximal kidney tubules and have been found altered in monogenic hypophosphatemia with a risk of KSD. In this study, we aimed at assessing the combined genetic alterations impacting NaPi2a, NaPi2c, and NHERF1. Therefore, we screened our hereditary KSD registry for cases of oligo- and digenicity, conducted reverse phenotyping, and undertook functional studies. As a result, we identified three patients from two families with digenic alterations in NaPi2a, NaPi2c, and NHERF1. In family 1, the index patient, who presented with severe renal calcifications and a bone mineralization disorder, carried digenic alterations affecting both NaPi transporter 2a and 2c. Functional analysis confirmed an additive genetic effect. In family 2, the index patient presented with kidney function decline, distinct musculature-related symptoms, and intracellular ATP depletion. Genetically, this individual was found to harbor variants in both NaPi2c and NHERF1 pointing towards genetic interaction. In summary, digenicity and gene dosage are likely to impact the severity of renal phosphate wasting and should be taken into account in terms of metaphylaxis through phosphate substitution.

Modeling Nonalcoholic Fatty Liver Disease in the Dish Using Human-Specific Platforms: Strategies and Limitations

Nonalcoholic fatty liver disease (NAFLD) is a chronic liver disease affecting multiple cell types of the human liver. The high prevalence of NAFLD and the lack of approved therapies increase the demand for reliable models for the preclinical discovery of drug targets. In the last decade, multiple proof-of-principle studies have demonstrated human-specific NAFLD modeling in the dish. These systems have included technologies based on human induced pluripotent stem cell derivatives, liver tissue section cultures, intrahepatic cholangiocyte organoids, and liver-on-a-chip. These platforms differ in functional maturity, multicellularity, scalability, and spatial organization. Identifying an appropriate model for a specific NAFLD-related research question is challenging. Therefore, we review different platforms for their strengths and limitations in modeling NAFLD. To define the fidelity of the current human in vitro NAFLD models in depth, we define disease hallmarks within the NAFLD spectrum that range from steatosis to severe fibroinflammatory tissue injury. We discuss how the most common methods are efficacious in modeling genetic contributions and aspects of the early NAFLD-related tissue response. We also highlight the shortcoming of current models to recapitulate the complexity of inter-organ crosstalk and the chronic process of liver fibrosis-to-cirrhosis that usually takes decades in patients. Importantly, we provide methodological overviews and discuss implementation hurdles (eg, reproducibility or costs) to help choose the most appropriate NAFLD model for the individual research focus: hepatocyte injury, ductular reaction, cellular crosstalk, or other applications. In sum, we highlight current strategies and deficiencies to model NAFLD in the dish and propose a framework for the next generation of human-specific investigations.

Polygenic architecture of rare coding variation across 394,783 exomes

Both common and rare genetic variants influence complex traits and common diseases. Genome-wide association studies have identified thousands of common-variant associations, and more recently, large-scale exome sequencing studies have identified rare-variant associations in hundreds of genes1-3. However, rare-variant genetic architecture is not well characterized, and the relationship between common-variant and rare-variant architecture is unclear4. Here we quantify the heritability explained by the gene-wise burden of rare coding variants across 22 common traits and diseases in 394,783 UK Biobank exomes5. Rare coding variants (allele frequency < 1 × 10-3) explain 1.3% (s.e. = 0.03%) of phenotypic variance on average-much less than common variants-and most burden heritability is explained by ultrarare loss-of-function variants (allele frequency < 1 × 10-5). Common and rare variants implicate the same cell types, with similar enrichments, and they have pleiotropic effects on the same pairs of traits, with similar genetic correlations. They partially colocalize at individual genes and loci, but not to the same extent: burden heritability is strongly concentrated in significant genes, while common-variant heritability is more polygenic, and burden heritability is also more strongly concentrated in constrained genes. Finally, we find that burden heritability for schizophrenia and bipolar disorder6,7 is approximately 2%. Our results indicate that rare coding variants will implicate a tractable number of large-effect genes, that common and rare associations are mechanistically convergent, and that rare coding variants will contribute only modestly to missing heritability and population risk stratification.

CACNA1D-Related Channelopathies: From Hypertension to Autism

Tightly controlled Ca²⁺ influx through voltage-gated Ca²⁺ channels (Cavs) is indispensable for proper physiological function. Thus, it is not surprising that Cav loss and/or gain of function have been implicated in human pathology. Deficiency of Cav1.3 L-type Ca²⁺ channels (LTCCs) causes deafness and bradycardia, whereas several genetic variants of CACNA1D, the gene encoding the pore-forming α1 subunit of Cav1.3, have been linked to various disease phenotypes, such as hypertension, congenital hypoglycemia, or autism. These variants include not only common polymorphisms associated with an increased disease risk, but also rare de novo missense variants conferring high risk. This review provides a concise summary of disease-associated CACNA1D variants, whereas the main focus lies on de novo germline variants found in individuals with a neurodevelopmental disorder of variable severity. Electrophysiological recordings revealed activity-enhancing gating changes induced by these de novo variants, and tools to predict their pathogenicity and to study the resulting pathophysiological consequences will be discussed. Despite the low number of affected patients, potential phenotype-genotype correlations and factors that could impact the severity of symptoms will be covered. Since increased channel activity is assumed as the disease-underlying mechanism, pharmacological inhibition could be a treatment option. In the absence of Cav1.3-selective blockers, dihydropyridine LTCC inhibitors clinically approved for the treatment of hypertension may be used for personalized off-label trials. Findings from in vitro studies and treatment attempts in some of the patients seem promising as outlined. Taken together, due to advances in diagnostic sequencing techniques the number of reported CACNA1D variants in human diseases is constantly rising. Evidence from in silico, in vitro, and in vivo disease models can help to predict the pathogenic potential of such variants and to guide diagnosis and treatment in the clinical practice when confronted with patients harboring CACNA1D variants.

Maximizing treatment efficacy through patient stratification in neuropathic pain trials

Treatment of neuropathic pain remains inadequate despite the elucidation of multiple pathophysiological mechanisms and the development of promising therapeutic compounds. The lack of success in translating knowledge into clinical practice has discouraged pharmaceutical companies from investing in pain medicine; however, new patient stratification approaches could help bridge the translation gap and develop individualized therapeutic approaches. As we highlight in this article, subgrouping of patients according to sensory profiles and other baseline characteristics could aid the prediction of treatment success. Furthermore, novel outcome measures have been developed for patients with neuropathic pain. The extent to which sensory profiles and outcome measures can be employed in routine clinical practice and clinical trials and across distinct neuropathic pain aetiologies is yet to be determined. Improvements in animal models, drawing on our knowledge of human pain, and robust public-private partnerships will be needed to pave the way to innovative and effective pain medicine in the future.