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> Disorders > Cell or Molecular Dysfunction > Haploinsufficiency

Haploinsufficiency

Haploinsufficiency is a genetic condition where an individual has only one functional copy of a gene, rather than the usual two.
This can lead to a reduced expression of the gene product, resulting in a variety of health implications.
Researchers studying haploinsufficiency often need to identify the most relevant protocols and products to enhance the accuracy of their investigations.
PubCompare.ai's AI-driven protocol optimization can streamline this process, helping researchers easily locate and compare protocols from literature, pre-prinis, and patents to determine the best approch for their specific needs.
Improving research outcomes in haploinsuffciency studies.

Most cited protocols related to «Haploinsufficiency»

1
Estimating Prevalence of Developmental Disorders
We estimated the birth prevalence of monoallelic developmental disorders by using the germline mutation model. We calculated the expected cumulative germline mutation rate of truncating DNMs in 238 haploinsufficient DD-associated genes. We scaled this upwards based on the composition of excess DNMs in the DDD cohort using the ratio of excess DNMs (n=1816) to DNMs within dominant haploinsufficient DD-associated genes (n=412). Around 10% of DDs are caused by de novo CNVs44 (link),45 (link), which are underrepresented in our cohort as a result of prior genetic testing. If included, the excess DNM in our cohort would increase by 21%, therefore we scaled the prevalence estimate upwards by this factor.
Mothers aged 29.9 and fathers aged 29.5 have children with 77 DNMs per genome on average21 (link). We calculated the mean number of DNMs expected under different combinations of parental ages, given our estimates of the extra DNMs per year from older mothers and fathers. We scaled the prevalence to different combinations of parental ages using the ratio of expected mutations at a given age combination to the number expected at the mean cohort parental ages.
To estimate the annual number of live births with developmental disorders caused by DNMs, we obtained country population sizes, birth rates, age at first birth46 , and calculated global birth rate (18.58 live births/1000 individuals) and age at first birth (22.62 years), weighted by population size. We calculated the mean age when giving birth (26.57 years) given a total fertility rate of 2.45 children per mother47 , and a mean interpregnancy interval of 29 months48 . We calculated the number of live births given our estimate of DD prevalence caused by DNMs at this age (0.00288), the global population size (7.4 billion individuals) and the global birth rate.
Prevalence and architecture of de novo mutations in developmental disorders. (2017). Nature, 542(7642), 433-438.
Publication 2017
Child Childbirth Developmental Disabilities Fathers Genes Genes, Dominant Genome Germ-Line Mutation Germ Line Haploinsufficiency Mothers Mutation
2
Evaluating Haploinsufficiency in Neurodevelopmental Disorders
We identified 150 autosomal dominant haploinsufficient genes that affect neurodevelopment within our curated developmental disorder gene set. Genes affecting neurodevelopment were identified where the affected organs included the brain, or where HPO phenotypes linked to defects in the gene included either an abnormality of brain morphology (HP:0012443) or cognitive impairment (HP:0100543) term.
The 150 genes were classified for ease of clinical recognition of the syndrome from gene defects by two consultant clinical geneticists. Genes were rated from 1 (least recognisable) to 5 (most recognisable). Categories 1 and 2 contained 5 and 22 genes respectively, and so were combined in later analyses. The remaining categories had more than 33 genes per category. The ratio of observed loss-of-function DNMs to expected loss-of-function DNMs was calculated for each recognisability category, along with 95% confidence intervals from a Poisson distribution given observed counts.
We estimated the likelihood of obtaining the observed number of PTV DNMs under two models. Our first model assumed no haploinsufficiency, and mutation counts were expected to follow baseline mutation rates. Our second model assumed fully penetrant haploinsufficiency, and scaled the baseline PTV mutation expectations by the observed PTV enrichment in our known haploinsufficient neurodevelopmental genes, stratified by clinical recognisability into low (containing genes with our “low”, “mild” and “moderate” labels) and high categories. We calculated the likelihoods of both models per gene as the Poisson probability of obtaining the observed number of PTVs, given the expected mutation rates. We computed the Akaike’s Information Criterion for each model and ranked them by the difference between model 1 and model 2 (ΔAIC).
Prevalence and architecture of de novo mutations in developmental disorders. (2017). Nature, 542(7642), 433-438.
Publication 2017
Brain Congenital Abnormality Consultant Disorders, Cognitive Genes Haploinsufficiency Multiple Pterygium Syndrome, Autosomal Dominant Mutation Neurodevelopmental Disorders Phenotype Syndrome
3
Evaluating Haploinsufficiency in Neurodevelopmental Disorders
We identified 150 autosomal dominant haploinsufficient genes that affect neurodevelopment within our curated developmental disorder gene set. Genes affecting neurodevelopment were identified where the affected organs included the brain, or where HPO phenotypes linked to defects in the gene included either an abnormality of brain morphology (HP:0012443) or cognitive impairment (HP:0100543) term.
The 150 genes were classified for ease of clinical recognition of the syndrome from gene defects by two consultant clinical geneticists. Genes were rated from 1 (least recognisable) to 5 (most recognisable). Categories 1 and 2 contained 5 and 22 genes respectively, and so were combined in later analyses. The remaining categories had more than 33 genes per category. The ratio of observed loss-of-function DNMs to expected loss-of-function DNMs was calculated for each recognisability category, along with 95% confidence intervals from a Poisson distribution given observed counts.
We estimated the likelihood of obtaining the observed number of PTV DNMs under two models. Our first model assumed no haploinsufficiency, and mutation counts were expected to follow baseline mutation rates. Our second model assumed fully penetrant haploinsufficiency, and scaled the baseline PTV mutation expectations by the observed PTV enrichment in our known haploinsufficient neurodevelopmental genes, stratified by clinical recognisability into low (containing genes with our “low”, “mild” and “moderate” labels) and high categories. We calculated the likelihoods of both models per gene as the Poisson probability of obtaining the observed number of PTVs, given the expected mutation rates. We computed the Akaike’s Information Criterion for each model and ranked them by the difference between model 1 and model 2 (ΔAIC).
Prevalence and architecture of de novo mutations in developmental disorders. (2017). Nature, 542(7642), 433-438.
Publication 2017
Brain Congenital Abnormality Consultant Disorders, Cognitive Genes Haploinsufficiency Multiple Pterygium Syndrome, Autosomal Dominant Mutation Neurodevelopmental Disorders Phenotype Syndrome
4
Cataloging Genes Linked to Autism and ID

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Publication 2014
Brain Fragile X Mental Retardation Protein Genes Haploinsufficiency Homo sapiens Mammals Phenotype Post-Synaptic Density
5
Assessing Genetic Intolerance Across Diverse Gene Lists
In addition to the primary OMIM gene lists, we assessed the behaviour of the residual variation intolerance score within four alternatively derived lists of interest. Two lists were derived from the Mouse Genome Informatics (MGI) database (last accessed 3rd December 2012, http://www.informatics.jax.org/), and a third was the combination of overlapping entries between OMIM “haploinsufficient” and OMIM “de novo” lists (n = 108). The first MGI-derived list focused on “lethality” genes (n = 91), which represent human orthologs, with public CCDS transcript(s), where mouse knockouts have resulted in embryonic [MP:0008762], prenatal, [MP:0002080] or perinatal [MP:0002081] lethality. The second list focused on “seizure” genes (n = 95), which represent human orthologs, with public CCDS transcript(s), where mouse knockouts have resulted in a phenotype with a seizure presentation (MP:0002064). Gene lists are available in Dataset S1. While we do not expect all the mouse knockout “lethality” and “seizure” genes to have identical consequence in humans, they are comparable proxies that are expected to be enriched for genes that when disrupted could have comparable phenotypes.
A fourth list comprised of genes considered “essential” in a recent paper by Georgi et al. (2013) [21] (link). Of the 2,472 “essential” genes, 2,288 (92.6%) had an available RVIS score. The remaining 7.4% of “essential” genes were unavailable due to having either less than 70% of the gene assessed within the NHLBI-ESP, as described in earlier methods, or not matching a public CCDS Release 9 transcript.
Petrovski S., Wang Q., Heinzen E.L., Allen A.S, & Goldstein D.B. (2013). Genic Intolerance to Functional Variation and the Interpretation of Personal Genomes. PLoS Genetics, 9(8), e1003709.
Publication 2013
Embryo Genes Genes, Essential Genes, Lethal Genome Haploinsufficiency Homo sapiens Mice, Laboratory Phenotype Seizures

Most recents protocols related to «Haploinsufficiency»

1
Evaluating Neurological Disease Genes
We accessed 299 known haploinsufficient (HI) genes from Dang et al. (2008) (link). We retained 49 HI genes that are related to neurodegenerative and/or neurodevelopmental diseases (i.e., Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, SA, multiple system atrophy, epilepsy, autism spectrum disorder, and schizophrenia) (Supplementary Table 6). These 49 genes were used as a positive control for evaluating the strictness measure.
Liu H., Guan L., Deng M., Bolund L., Kristiansen K., Zhang J., Luo Y, & Zhang Z. (2023). Integrative genetic and single cell RNA sequencing analysis provides new clues to the amyotrophic lateral sclerosis neurodegeneration. Frontiers in Neuroscience, 17, 1116087.
Publication 2023
Autism Spectrum Disorders Epilepsy Genes Haploinsufficiency Huntington Disease Multiple System Atrophy Neurodevelopmental Disorders Schizophrenia
2
Predicting Disease-Causing Variant Combinations
Prediction of potentially disease-causing combinations was performed using VarCoPP [70 (link), 109 (link)] on an in-house cluster. VarCoPP is designed to process alleles in pairs to prioritize disease-causing combinations. This classifier, trained on digenic cases contained in the digenic disease database (DIDA) [110 (link)], uses 11 features at the variant (e.g., CADD raw scores), gene (e.g., haploinsufficiency) and gene-pair level (e.g., biological distance). Specifically, 500 random forest predictors constitute VarCoPP, where each individual predictor classifies a given variant combination. Two scores are assigned to each combination, the classification score CS (i.e., median probability calculated over all the pathogenic probabilities provided by the ensemble of predictors) and the support score SS (i.e., percentage of the 500 predictors that deem the combination pathogenic). Thresholds are defined with regard to these two scores to create confidence zones. We considered bi-locus variant combinations that fells in the 99% confidence zone (CS ≥ 0.74; SS = 100%). These combinations were further inspected using the ORVAL plateform (https://orval.isquare.be) [70 (link)], which incorporates VarCoPP [109 (link)].
Jacquemin V., Versbraegen N., Duerinckx S., Massart A., Soblet J., Perazzolo C., Deconinck N., Brischoux-Boucher E., De Leener A., Revencu N., Janssens S., Moorgat S., Blaumeiser B., Avela K., Touraine R., Abou Jaoude I., Keymolen K., Saugier-Veber P., Lenaerts T., Abramowicz M, & Pirson I. (2023). Congenital hydrocephalus: new Mendelian mutations and evidence for oligogenic inheritance. Human Genomics, 17, 16.
Publication 2023
Alleles Biopharmaceuticals Conditioning, Psychology Genes Haploinsufficiency pathogenesis
3
Human Gene Damage Index Analysis
The human gene database GeneCards was employed for obtainment of human gene damage index (GDI) and residual variation tolerance score of PRKCI gene. The haploinsufficiency scores, loss of function observed to expected (o:e) ratio, and loss of function intolerance probability (pLI) scores were retrieved from DECIPHER and gnomAD [29 (link)].
Shah H., Khan K., Badshah Y., Mahmood Ashraf N., Shabbir M., Trembley J.H., Afsar T., Abusharha A, & Razak S. (2023). Investigation of UTR Variants by Computational Approaches Reveal Their Functional Significance in PRKCI Gene Regulation. Genes, 14(2), 247.
Publication 2023
Genetic Diversity Haploinsufficiency Homo sapiens Immune Tolerance PRKCI protein, human
4
Genetic Factors Influencing Care in Osteogenesis Imperfecta
The characteristics of the cohort (age and Sillence classification) were described for the total cohort as well as for the different genetic groups: haploinsufficiency, DN missense p.other, DN COL1A1 missense glycine, DN COL1A2 missense glycine, DN inframe deletions or insertions, recessive. The data is presented as absolute numbers and percentages. For age, the median and the 25th and 75th percentiles were reported.
In addition, the characteristics of hospital admissions, DTCs, and outpatient clinic visits are reported in absolute numbers and percentages. The yearly average number of hospital admissions, DTCs, outpatient clinic visits, and X-rays was reported for the cohort and for the different genetic groups using the mean, median, and standard deviation (SD). The average number of admissions specified per medical specialty per year was reported per 100 patient years. Generalized linear regression analyses were performed to estimate the effects of different pathogenic variants on the number of hospital admissions, number of DTCs, number of outpatient clinic visits, and number of X-rays (on average per year). As age could possibly serve as an effect modifier, when statistically significant, the patients’ mean age during follow-up was added to the model. p-values were adjusted for multiple testing using Tukey’s honest significance difference (HSD) test. A p-value of ≤0.05 was deemed statistically significant. The average number of drug prescriptions in 2017 was reported for the cohort as a whole as well as for seven age categories: 0 to 14, 15 to 24, 25 to 34, 35 to 44, 45 to 64, 65 to 74, and 75 years and older. The group of patients who were 75 years and older was analyzed but not described due to the low patient number. As group numbers would be too low, it was not possible to subdivide according to genetic group based on age.
When possible, health care data was compared to the general Dutch population using public data from the CBS (“www.cbs.nl (last accessed on 1 October 2022)”). Data on the number of hospital admissions, the number of DTCs, and the proportion of people using medication were available for the total Dutch population. Incidence rate ratios (IRR) were calculated for both hospital admissions and DTCs by genetic group for the OI population compared to the total Dutch population. The proportion of patients using medication in the OI cohort was compared to the proportion of people using medication in the total Dutch population based on the aforementioned age categories. The data is reported as percentages.
In order to ensure patient confidentiality, information regarding patient groups lower than 10 is not shown in the results. Groups with fewer than 10 patients are only shown when they cannot be directly traced back to single patients. Descriptive analyses were performed using IBM SPSS Statistics for Windows version 25 (IBM Corporation, Armonk, NY, USA), and for the generalized linear regression analysis, Rstudio v3.6.2 (RStudio: Integrated Development for R., PBC, Boston, MA, USA) was used.
Storoni S., Verdonk S.J., Zhytnik L., Pals G., Treurniet S., Elting M.W., Sakkers R.J., van den Aardweg J.G., Eekhoff E.M, & Micha D. (2023). From Genetics to Clinical Implications: A Study of 675 Dutch Osteogenesis Imperfecta Patients. Biomolecules, 13(2), 281.
Publication 2023
Clinic Visits COL1A2 protein, human Ditiocarb Gene Deletion Glycine Haploinsufficiency Insertion Mutation pathogenesis Patients Pharmaceutical Preparations X-Rays, Diagnostic
5
Genetically Diagnosed Osteogenesis Imperfecta Variants
During the period between December 1991 and April 2021, all patients genetically diagnosed with OI at the national reference center for the molecular diagnosis of OI at the Amsterdam UMC were eligible for inclusion in this study. The pathogenic OI gene variants were identified in COL1A1, COL1A2, CRTAP, TMEM38B, IFITM5, CREB3L1, FKBP10, PLOD2, SP7, SERPINF1, P3H1, BMP1, and PPIB. [15 (link)] Pathogenic variants in SERPINH1 and KDELR2 were excluded since these variants had been found in the research setting and were not yet included in the diagnostic database. Based on the type of the pathogenic gene variant, patients were divided into six groups. The groups were haploinsufficiency (HI), dominant negative missense variants in an amino acid other than glycine in either the COL1A1 or COL1A2 gene (DN missense p.other), dominant negative missense variants of glycine substitution in the COL1A1 gene (DN COL1A1 missense glycine), dominant negative variants of glycine substitution in the COL1A2 gene (DN COL1A2 missense glycine), dominant negative inframe deletions or insertions in the COL1A1 or COL1A2 gene (DN inframe deletions or insertions) and recessive variants (recessive). These groups were determined based on the expected effects of the variants [9 (link),17 (link),24 (link),25 (link)]. In addition to the patients’ genetic variant, the Sillence classification was provided as evaluated by the referring physician. The patients with OI type V (IFITM5) were excluded; due to the low patient number (<10 patients), this group could not be described.
Storoni S., Verdonk S.J., Zhytnik L., Pals G., Treurniet S., Elting M.W., Sakkers R.J., van den Aardweg J.G., Eekhoff E.M, & Micha D. (2023). From Genetics to Clinical Implications: A Study of 675 Dutch Osteogenesis Imperfecta Patients. Biomolecules, 13(2), 281.
Publication 2023
Amino Acids BMP1 protein, human COL1A2 protein, human CREB3L1 protein, human Diagnosis Gene Deletion Genes Genetic Diversity Glycine Haploinsufficiency Insertion Mutation Missense Mutation Molecular Diagnostics pathogenesis Patients Physicians PPIB protein, human

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More about "Haploinsufficiency"

Haploinsufficiency is a genetic condition where an individual has only one functional copy of a gene, rather than the usual two.
This can lead to a reduced expression of the gene product, resulting in a variety of health implications.
Researchers studying haploinsufficiency often need to identify the most relevant protocols and products to enhance the accuracy of their investigations.
PubCompare.ai's AI-driven protocol optimization can streamline this process, helping researchers easily locate and compare protocols from literature, pre-prints, and patents to determine the best approach for their specific needs.
Improving research outcomes in haploinsufficiency studies is crucial, and PubCompare.ai's platform can assist in this endeavor.
Haploinsufficiency is related to other genetic conditions, such as those involving the PDGFRA gene (C57BL/6J line, B6.129S4‐Pdgfratm11(EGFP)Sor/J) and the NES gene (B6.Cg-Tg(Nes-cre)1Kln/J).
The use of tamoxifen, LPS, and sildenafil may also be relevant in haploinsufficiency research.
Ingenuity Pathway Analysis and the GoldenGate Genotyping Assay are tools that can be utilized to better understand the pathways and genetic markers involved in haploinsufficiency.
Additionally, the use of human AB serum may be beneficial in certain experimental setups.
By incorporating these insights and tools, researchers can enhance the accuracy and outcomes of their haploinsufficiency studies, leading to improved understanding and potential treatments for affected individuals.
PubCompare.ai's AI-driven protocol optimization can be a valuable resource in this process.