scholarly journals Identification of rare loss of function variation regulating body fat distribution

2021 ◽  
Author(s):  
Mine Koprulu ◽  
Yajie Zhao ◽  
Eleanor Wheeler ◽  
Liang Dong ◽  
Nuno Rocha ◽  
...  
Keyword(s):  
Large Scale ◽  
Fat Distribution ◽  
Therapeutic Benefit ◽  
Specific Gene ◽  
Loss Of Function ◽  
Genetic Studies ◽  
Dual Approach ◽  
Genome Wide ◽  
Beneficial Impact ◽  
Coding Variants

Biological and translational insights from large-scale, array-based genetic studies of fat distribution, a key determinant of metabolic health, have been limited by the difficulty in linking identified predominantly non-coding variants to specific gene targets. Rare coding variant analyses provide greater confidence that a specific gene is involved, but do not necessarily indicate whether gain or loss of function would be of most therapeutic benefit. Here we use a dual approach that combines the power of genome-wide analysis of array-based rare, non-synonymous variants in 184,246 individuals of UK Biobank with exome-sequence-based rare loss of function gene burden testing. The data indicates that loss-of-function (LoF) of four genes (PLIN1, INSR, ACVR1C and PDE3B) is associated with a beneficial impact on WHRadjBMI and increased gluteofemoral fat mass, whereas PLIN4 LoF adversely affects these parameters. This study robustly implicates these genes in the regulation of fat distribution, providing new and in some cases somewhat counter-intuitive insight into the potential consequences of targeting these molecules therapeutically.

scholarly journals Identification of rare loss of function genetic variation regulating body fat distribution

2021 ◽  
Author(s):  
Mine Koprulu ◽  
Yajie Zhao ◽  
Eleanor Wheeler ◽  
Liang Dong ◽  
Nuno Rocha ◽  
...  
Keyword(s):  
Large Scale ◽  
Hdl Cholesterol ◽  
Fat Distribution ◽  
Therapeutic Benefit ◽  
Specific Gene ◽  
Loss Of Function ◽  
Genome Wide ◽  
Beneficial Impact ◽  
Coding Variants

Abstract Context Biological and translational insights from large-scale, array-based genetic studies of fat distribution, a key determinant of metabolic health, have been limited by the difficulty in linking predominantly non-coding variants to specific gene targets. Rare coding variant analyses provide greater confidence that a specific gene is involved, but do not necessarily indicate whether gain or loss-of-function (LoF) would be of most therapeutic benefit. Objective, Design and Setting To identify genes/proteins involved in determining fat distribution, we combined the power of genome-wide analysis of array-based rare, non-synonymous variants in 450,562 individuals of UK Biobank with exome-sequence-based rare loss of function gene burden testing in 184,246 individuals. Results The data indicates that loss-of-function of four genes (PLIN1 [LoF variants, p=5.86×10 -7], INSR [LoF variants, p=6.21×10 -7], ACVR1C [LoF + Moderate impact variants, p=1.68×10 -7; Moderate impact variants, p=4.57×10 -7] and PDE3B [LoF variants, p=1.41×10 -6]) is associated with a beneficial impact on WHRadjBMI and increased gluteofemoral fat mass, whereas LoF of PLIN4 [LoF variants, p=5.86×10 -7] adversely affects these parameters. Phenotypic follow-up suggests that LoF of PLIN1, PDE3B and ACVR1C favourably affects metabolic phenotypes (e.g. triglyceride [TG] and HDL cholesterol concentrations) and reduces the risk of cardiovascular disease, whereas PLIN4 LoF has adverse health consequences. INSR LoF is associated with lower TG and HDL levels but may increase the risk of type 2 diabetes. Conclusion This study robustly implicates these genes in the regulation of fat distribution, providing new and in some cases somewhat counter-intuitive insight into the potential consequences of targeting these molecules therapeutically.

scholarly journals Genome-Wide Identification and Characterization of MLO Gene Family in Octoploid Strawberry (Fragaria ×ananassa)

2020 ◽  
Author(s):  
Ronald R. Tapia ◽  
Christopher R. Barbey ◽  
Saket Chandra ◽  
Kevin M. Folta ◽  
Vance M. Whitaker ◽  
...  
Keyword(s):  
Gene Family ◽  
Genomic Structure ◽  
Resistance Locus ◽  
Fragaria Ananassa ◽  
Loss Of Function ◽  
Genetic Studies ◽  
Genome Wide ◽  
Segregating Population

AbstractPowdery mildew (PM) caused by Podosphaera aphanis is a major fungal disease in cultivated strawberry. Mildew Resistance Locus O (MLO) is a gene family described for having conserved seven-transmembrane domains. Induced loss-of-function in specific MLO genes can confer durable and broad resistance against PM pathogens. However, the underlying biological role of MLO genes in strawberry is still unknown. In the present study, the genomic structure of MLO genes were characterized in both diploid (Fragaria vesca) and octoploid strawberry (Fragaria ×ananassa), and the potential sources of MLO-mediated susceptibility were identified. Twenty MLO-like sequences were identified in F. vesca, with sixty-eight in F. ×ananassa. Phylogenetic analysis divides strawberry MLO genes into eight different clades, in which three FveMLO and ten FaMLO genes were grouped together with the functionally known MLO susceptibility. Out of ten FaMLO genes, FaMLO17-2 and FaMLO17-3 showed the highest similarity to the known susceptibility MLO proteins. Gene expression analysis of FaMLO genes was conducted using a multi-parental segregating population. Three expression quantitative trait loci (eQTL) were substantially associated with MLO transcript levels in mature fruits, suggesting discrete genetic control of susceptibility. These results are a critical first step in understanding allele function of MLO genes, and are necessary for further genetic studies of PM resistance in cultivated strawberry.

scholarly journals Population-scale analysis of common and rare genetic variation associated with hearing loss in adults

2021 ◽  
Author(s):  
Kavita Praveen ◽  
Lee Dobbyn ◽  
Lauren Gurski ◽  
Ariane H. Ayer ◽  
Jeffrey Staples ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Exome Sequencing ◽  
Large Scale ◽  
Meta Analysis ◽  
Adult Population ◽  
Loss Of Function ◽  
Common Coding ◽  
A Genome ◽  
Increased Risk ◽  
Coding Variants

ABSTRACTUnderstanding the genetic underpinnings of disabling hearing loss, which affects ∼466 million people worldwide, can provide avenues for new therapeutic target development. We performed a genome-wide association meta-analysis of hearing loss with 125,749 cases and 469,497 controls across five cohorts, including UK Biobank, Geisinger DiscovEHR, the Malmö Diet and Cancer Study, Mount Sinai’s BioMe Personalized Medicine Cohort, and FinnGen. We identified 53 loci affecting hearing loss risk, 15 of which are novel, including common coding variants in COL9A3 and TMPRSS3. Through exome-sequencing of 108,415 cases and 329,581 controls from the same cohorts, we identified hearing loss associations with burden of rare coding variants in FSCN2 (odds ratio [OR] = 1.14, P = 1.9 × 10−15) and burden of predicted loss-of-function variants in KLHDC7B (OR = 2.14, P = 5.2 × 10−30). We also observed single-variant and gene-burden associations with 11 genes known to cause Mendelian forms of hearing loss, including an increased risk in heterozygous carriers of mutations in the autosomal recessive hearing loss genes GJB2 (Gly12fs; OR = 1.21, P = 4.2 × 10−11) and SLC26A5 (gene burden; OR = 1.96, P = 2.8 × 10−17). Our results suggest that loss of KLHDC7B function increases risk for hearing loss, and show that Mendelian hearing loss genes contribute to the burden of hearing loss in the adult population, suggesting a shared etiology between common and rare forms of hearing loss. This work illustrates the potential of large-scale exome sequencing to elucidate the genetic architecture of common traits in which risk is modulated by both common and rare variation.

scholarly journals Biobank-scale inference of ancestral recombination graphs enables genealogy-based mixed model association of complex traits

2021 ◽  
Author(s):  
Brian C Zhang ◽  
Arjun Biddanda ◽  
Pier Francesco Palamara
Keyword(s):  
Complex Traits ◽  
Large Scale ◽  
Mixed Model ◽  
Large Population ◽  
Complex Trait ◽  
Genotype Imputation ◽  
Loss Of Function ◽  
Genome Wide ◽  
Wide Range ◽  
Ancestral Recombination Graphs

Accurate inference of gene genealogies from genetic data has the potential to facilitate a wide range of analyses. We introduce a method for accurately inferring biobank-scale genome-wide genealogies from sequencing or genotyping array data, as well as strategies to utilize genealogies within linear mixed models to perform association and other complex trait analyses. We use these new methods to build genome-wide genealogies using genotyping data for 337,464 UK Biobank individuals and to detect associations in 7 complex traits. Genealogy-based association detects more rare and ultra-rare signals (N = 133, frequency range 0.0004% - 0.1%) than genotype imputation from ~65,000 sequenced haplotypes (N = 65). In a subset of 138,039 exome sequencing samples, these associations strongly tag (average r = 0.72) underlying sequencing variants, which are enriched for missense (2.3×) and loss-of-function (4.5×) variation. Inferred genealogies also capture additional association signals in higher frequency variants. These results demonstrate that large-scale inference of gene genealogies may be leveraged in the analysis of complex traits, complementing approaches that require the availability of large, population-specific sequencing panels.

Aetiology and pathogenesis

2018 ◽  
Author(s):  
Joanna E. Parkes ◽  
Simon Rothwell ◽  
Janine A. Lamb
Keyword(s):  
Immune Responses ◽  
Large Scale ◽  
Adaptive Immune Response ◽  
Infectious Agents ◽  
Inflammatory Myopathies ◽  
Genetic Studies ◽  
Adaptive Immune ◽  
Histocompatibility Complex ◽  
Genome Wide ◽  
Models Of Disease

The aetiology and pathogenesis of idiopathic inflammatory myopathies (IIM) is poorly understood; IIM are thought to result from exposure to environmental factors in genetically susceptible individuals. Both innate and adaptive immune responses are involved in IIM, and there is increasing evidence that non-inflammatory mechanisms play an important role in disease pathology. Several environmental risk factors, including infectious agents, ultraviolet radiation, cigarette smoking, and exposure to statins, have been implicated. Genetic studies have identified the major histocompatibility complex as the most strongly associated region, while recent large scale genome-wide studies have implicated genes that commonly regulate the adaptive immune response, which overlap with other seropositive autoimmune diseases. Integrating data across these various fields should facilitate refined models of disease pathogenesis.

Genetic contributions to anxiety disorders: where we are and where we are heading

2021 ◽  
pp. 1-16
Author(s):  
Helga Ask ◽  
Rosa Cheesman ◽  
Eshim S. Jami ◽  
Daniel F. Levey ◽  
Kirstin L. Purves ◽  
...  
Keyword(s):  
Anxiety Disorders ◽  
Psychiatric Disorders ◽  
Large Scale ◽  
Current Knowledge ◽  
Association Studies ◽  
Twin Studies ◽  
Future Research ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Anxiety disorders are among the most common psychiatric disorders worldwide. They often onset early in life, with symptoms and consequences that can persist for decades. This makes anxiety disorders some of the most debilitating and costly disorders of our time. Although much is known about the synaptic and circuit mechanisms of fear and anxiety, research on the underlying genetics has lagged behind that of other psychiatric disorders. However, alongside the formation of the Psychiatric Genomic Consortium Anxiety workgroup, progress is rapidly advancing, offering opportunities for future research. Here we review current knowledge about the genetics of anxiety across the lifespan from genetically informative designs (i.e. twin studies and molecular genetics). We include studies of specific anxiety disorders (e.g. panic disorder, generalised anxiety disorder) as well as those using dimensional measures of trait anxiety. We particularly address findings from large-scale genome-wide association studies and show how such discoveries may provide opportunities for translation into improved or new therapeutics for affected individuals. Finally, we describe how discoveries in anxiety genetics open the door to numerous new research possibilities, such as the investigation of specific gene–environment interactions and the disentangling of causal associations with related traits and disorders. We discuss how the field of anxiety genetics is expected to move forward. In addition to the obvious need for larger sample sizes in genome-wide studies, we highlight the need for studies among young people, focusing on specific underlying dimensional traits or components of anxiety.

scholarly journals A genome-wide CRISPR screen identifies the glycosylation enzyme DPM1 as a modifier of DPAGT1 deficiency and ER stress

2021 ◽  
Author(s):  
Hans M. Dalton ◽  
Raghuvir Viswanatha ◽  
Ricky Brathwaite ◽  
Jae Sophia Zuno ◽  
Stephanie E. Mohr ◽  
...  
Keyword(s):  
Er Stress ◽  
Therapeutic Targets ◽  
Therapeutic Benefit ◽  
Loss Of Function ◽  
Gene Encoding ◽  
Fructose Metabolism ◽  
Genome Wide ◽  
A Genome ◽  
Crispr Screen

AbstractPartial loss-of-function mutations in glycosylation pathways underlie a set of rare diseases called Congenital Disorders of Glycosylation (CDGs). In particular, DPAGT1-CDG is caused by mutations in the gene encoding the first step in N-glycosylation, DPAGT1, and this disorder currently lacks effective therapies. To identify potential therapeutic targets for DPAGT1-CDG, we performed CRISPR knockout screens in Drosophila cells for genes associated with better survival and glycoprotein levels under DPAGT1 inhibition. We identified hundreds of candidate genes that may be of therapeutic benefit. Intriguingly, inhibition of the mannosyltransferase Dpm1, or its downstream glycosylation pathways, could rescue two in vivo models of DPAGT1 inhibition and ER stress, even though impairment of these pathways alone usually cause CDGs. While both in vivo models ostensibly cause ER stress (through DPAGT1 inhibition or a misfolded protein), we found a novel difference in fructose metabolism that may indicate glycolysis as a modulator of DPAGT1-CDG. Our results provide new therapeutic targets for DPAGT1-CDG, include the unique finding of Dpm1-related pathways rescuing DPAGT1 inhibition, and reveal a novel interaction between fructose metabolism and ER stress.

scholarly journals Identifying Lethal Dependencies with HUGE Predictive Power from Large-Scale Functional Genomic Screens

2021 ◽  
Author(s):  
Fernando Carazo ◽  
Edurne San Jose Eneriz ◽  
Marian Gimeno ◽  
Leire Garate ◽  
Estibaliz Miranda ◽  
...  
Keyword(s):  
Statistical Power ◽  
Large Scale ◽  
Synthetic Lethality ◽  
Functional Genomic ◽  
Loss Of Function ◽  
Mutational Status ◽  
Genome Wide ◽  
Targeted Treatments ◽  
Functional Screens

Recent functional genomic screens -such as CRISPR-Cas9 or RNAi screening- have fostered a new wave of targeted treatments based on the concept of synthetic lethality. These approaches identified LEthal Dependencies (LEDs) by estimating the effect of genetic events on cell viability. The multiple-hypothesis problem related to a large number of gene knockouts limits the statistical power of these studies. Here, we show that predictions of LEDs from functional screens can be dramatically improved by incorporating the <HUb effect in Genetic Essentiality> (HUGE) of gene alterations. We analyze three recent genome-wide loss-of-function screens -Project Score, CERES score, and DEMETER score- identifying LEDs with 75 times larger statistical power than using state-of-the-art methods. HUGE shows an increased enrichment in a recent harmonized knowledgebase of clinical interpretations of somatic genomic variants in cancer (with an AUROC up to 0.87). Our approach is effective even in tumors with large genetic heterogeneity such as acute myeloid leukemia, where we identified LEDs not recalled by previous pipelines, including FLT3-mutant genotypes sensitive to FLT3 inhibitors. Interestingly, in-vitro validations confirm lethal dependencies of either NRAS or PTPN11 depending on the NRAS mutational status. HUGE will hopefully help discover novel genetic dependencies amenable for precision-targeted therapies in cancer.

scholarly journals Rare Coding Variants Associated with Electrocardiographic Intervals Identify Monogenic Arrhythmia Susceptibility Genes: A Multi-ancestry Analysis

2021 ◽  
Author(s):  
Seung Hoan Choi ◽  
Sean J. Jurgens ◽  
Christopher M. Haggerty ◽  
Amelia W. Hall ◽  
Jennifer L. Halford ◽  
...  
Keyword(s):  
Large Scale ◽  
Rare Variants ◽  
Sequence Data ◽  
Susceptibility Genes ◽  
P Wave ◽  
Cardiac Conduction ◽  
Incomplete Penetrance ◽  
Loss Of Function ◽  
Electrocardiographic Intervals ◽  
Coding Variants

Background - Alterations in electrocardiographic (ECG) intervals are well-known markers for arrhythmia and sudden cardiac death (SCD) risk. While the genetics of arrhythmia syndromes have been studied, relations between ECG intervals and rare genetic variation at a population level are poorly understood. Methods - Using a discovery sample of 29,000 individuals with whole-genome sequencing from TOPMed and replication in nearly 100,000 with whole-exome sequencing from the UK Biobank and MyCode, we examined associations between low-frequency and rare coding variants with 5 routinely measured ECG traits (RR, P-wave, PR, and QRS intervals and corrected QT interval [QTc]). Results - We found that rare variants associated with population-based ECG intervals identify established monogenic SCD genes ( KCNQ1 , KCNH2 , SCN5A ), a controversial monogenic SCD gene ( KCNE1 ), and novel genes ( PAM , MFGE8 ) involved in cardiac conduction. Loss-of-function and pathogenic SCN5A variants, carried by 0.1% of individuals, were associated with a nearly 6-fold increased odds of first-degree atrioventricular block ( P =8.4x10 -5 ). Similar variants in KCNQ1 and KCNH2 (0.2% of individuals) were associated with a 23-fold increased odds of marked QTc prolongation ( P =4x10 -25 ), a marker of SCD risk. Incomplete penetrance of such deleterious variation was common as over 70% of carriers had normal ECG intervals. Conclusions - Our findings indicate that large-scale high-depth sequence data and ECG analysis identifies monogenic arrhythmia susceptibility genes and rare variants with large effects. Known pathogenic variation in conventional arrhythmia and SCD genes exhibited incomplete penetrance and accounted for only a small fraction of marked ECG interval prolongation.

scholarly journals Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Ning Shang ◽  
Atlas Khan ◽  
Fernanda Polubriaginof ◽  
Francesca Zanoni ◽  
Karla Mehl ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Medical Records ◽  
Large Scale ◽  
Association Studies ◽  
Clinical Care ◽  
Medical Systems ◽  
Genetic Studies ◽  
Genome Wide ◽  
Independent Case

AbstractChronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational and genetic studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods to automatically place patients on the staging grid of albuminuria by glomerular filtration rate (“A-by-G” grid). We manually validated the algorithm by 451 chart reviews across three medical systems, demonstrating overall positive predictive value of 95% for CKD cases and 97% for healthy controls. Independent case-control validation using 2350 patient records demonstrated diagnostic specificity of 97% and sensitivity of 87%. Application of the phenotype to 1.3 million patients demonstrated that over 80% of CKD cases are undetected using ICD codes alone. We also demonstrated several large-scale applications of the phenotype, including identifying stage-specific kidney disease comorbidities, in silico estimation of kidney trait heritability in thousands of pedigrees reconstructed from medical records, and biobank-based multicenter genome-wide and phenome-wide association studies.

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