Littérature scientifique sur le sujet « Genome-Wide polygenic score »
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Articles de revues sur le sujet "Genome-Wide polygenic score"
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Forrest, Iain S., Kumardeep Chaudhary, Ishan Paranjpe, Ha My T. Vy, Carla Marquez-Luna, Ghislain Rocheleau, Aparna Saha et al. « Genome-wide polygenic risk score for retinopathy of type 2 diabetes ». Human Molecular Genetics 30, no 10 (10 mars 2021) : 952–60. http://dx.doi.org/10.1093/hmg/ddab067.
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Abstract Diabetic retinopathy (DR) is a common consequence in type 2 diabetes (T2D) and a leading cause of blindness in working-age adults. Yet, its genetic predisposition is largely unknown. Here, we examined the polygenic architecture underlying DR by deriving and assessing a genome-wide polygenic risk score (PRS) for DR. We evaluated the PRS in 6079 individuals with T2D of European, Hispanic, African and other ancestries from a large-scale multi-ethnic biobank. Main outcomes were PRS association with DR diagnosis, symptoms and complications, and time to diagnosis, and transferability to non-European ancestries. We observed that PRS was significantly associated with DR. A standard deviation increase in PRS was accompanied by an adjusted odds ratio (OR) of 1.12 [95% confidence interval (CI) 1.04–1.20; P = 0.001] for DR diagnosis. When stratified by ancestry, PRS was associated with the highest OR in European ancestry (OR = 1.22, 95% CI 1.02–1.41; P = 0.049), followed by African (OR = 1.15, 95% CI 1.03–1.28; P = 0.028) and Hispanic ancestries (OR = 1.10, 95% CI 1.00–1.10; P = 0.050). Individuals in the top PRS decile had a 1.8-fold elevated risk for DR versus the bottom decile (P = 0.002). Among individuals without DR diagnosis, the top PRS decile had more DR symptoms than the bottom decile (P = 0.008). The PRS was associated with retinal hemorrhage (OR = 1.44, 95% CI 1.03–2.02; P = 0.03) and earlier DR presentation (10% probability of DR by 4 years in the top PRS decile versus 8 years in the bottom decile). These results establish the significant polygenic underpinnings of DR and indicate the need for more diverse ancestries in biobanks to develop multi-ancestral PRS.
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Dauber, Andrew, Yan Meng, Laura Audi, Sailaja Vedantam, Benjamin Weaver, Antonio Carrascosa, Kerstin Albertsson-Wikland et al. « A Genome-Wide Pharmacogenetic Study of Growth Hormone Responsiveness ». Journal of Clinical Endocrinology & ; Metabolism 105, no 10 (11 juillet 2020) : 3203–14. http://dx.doi.org/10.1210/clinem/dgaa443.
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Abstract Context Individual patients vary in their response to growth hormone (GH). No large-scale genome-wide studies have looked for genetic predictors of GH responsiveness. Objective To identify genetic variants associated with GH responsiveness. Design Genome-wide association study (GWAS). Setting Cohorts from multiple academic centers and a clinical trial. Patients A total of 614 individuals from 5 short stature cohorts receiving GH: 297 with idiopathic short stature, 276 with isolated GH deficiency, and 65 born small for gestational age. Intervention Association of more than 2 million variants was tested. Main Outcome Measures Primary analysis: individual single nucleotide polymorphism (SNP) association with first-year change in height standard deviation scores. Secondary analyses: SNP associations in clinical subgroups adjusted for clinical variables; association of polygenic score calculated from 697 genome-wide significant height SNPs with GH responsiveness. Results No common variant associations reached genome-wide significance in the primary analysis. The strongest suggestive signals were found near the B4GALT4 and TBCE genes. After meta-analysis including replication data, signals at several loci reached or retained genome-wide significance in secondary analyses, including variants near ST3GAL6. There was no significant association with variants previously reported to be associated with GH response nor with a polygenic predicted height score. Conclusions We performed the largest GWAS of GH responsiveness to date. We identified 2 loci with a suggestive effect on GH responsiveness in our primary analysis and several genome-wide significant associations in secondary analyses that require further replication. Our results are consistent with a polygenic component to GH responsiveness, likely distinct from the genetic regulators of adult height.
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Thomas, Minta, Lori C. Sakoda, Michael Hoffmeister, Elisabeth A. Rosenthal, Jeffrey K. Lee, Franzel J. B. van Duijnhoven, Elizabeth A. Platz et al. « Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk ». American Journal of Human Genetics 107, no 3 (septembre 2020) : 432–44. http://dx.doi.org/10.1016/j.ajhg.2020.07.006.
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Choi, Shing Wan, Judit García-González, Yunfeng Ruan, Hei Man Wu, Christian Porras, Jessica Johnson, Clive J. Hoggart et Paul F. O’Reilly. « PRSet : Pathway-based polygenic risk score analyses and software ». PLOS Genetics 19, no 2 (7 février 2023) : e1010624. http://dx.doi.org/10.1371/journal.pgen.1010624.
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Polygenic risk scores (PRSs) have been among the leading advances in biomedicine in recent years. As a proxy of genetic liability, PRSs are utilised across multiple fields and applications. While numerous statistical and machine learning methods have been developed to optimise their predictive accuracy, these typically distil genetic liability to a single number based on aggregation of an individual’s genome-wide risk alleles. This results in a key loss of information about an individual’s genetic profile, which could be critical given the functional sub-structure of the genome and the heterogeneity of complex disease. In this manuscript, we introduce a ‘pathway polygenic’ paradigm of disease risk, in which multiple genetic liabilities underlie complex diseases, rather than a single genome-wide liability. We describe a method and accompanying software, PRSet, for computing and analysing pathway-based PRSs, in which polygenic scores are calculated across genomic pathways for each individual. We evaluate the potential of pathway PRSs in two distinct ways, creating two major sections: (1) In the first section, we benchmark PRSet as a pathway enrichment tool, evaluating its capacity to capture GWAS signal in pathways. We find that for target sample sizes of >10,000 individuals, pathway PRSs have similar power for evaluating pathway enrichment as leading methods MAGMA and LD score regression, with the distinct advantage of providing individual-level estimates of genetic liability for each pathway–opening up a range of pathway-based PRS applications, (2) In the second section, we evaluate the performance of pathway PRSs for disease stratification. We show that using a supervised disease stratification approach, pathway PRSs (computed by PRSet) outperform two standard genome-wide PRSs (computed by C+T and lassosum) for classifying disease subtypes in 20 of 21 scenarios tested. As the definition and functional annotation of pathways becomes increasingly refined, we expect pathway PRSs to offer key insights into the heterogeneity of complex disease and treatment response, to generate biologically tractable therapeutic targets from polygenic signal, and, ultimately, to provide a powerful path to precision medicine.
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Curtis, David. « Clinical relevance of genome‐wide polygenic score may be less than claimed ». Annals of Human Genetics 83, no 4 (25 mars 2019) : 274–77. http://dx.doi.org/10.1111/ahg.12302.
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Lang, M., T. Leménager, F. Streit, M. Fauth-Bühler, J. Frank, D. Juraeva, S. H. Witt et al. « Genome-wide association study of pathological gambling ». European Psychiatry 36 (août 2016) : 38–46. http://dx.doi.org/10.1016/j.eurpsy.2016.04.001.
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AbstractBackgroundPathological gambling is a behavioural addiction with negative economic, social, and psychological consequences. Identification of contributing genes and pathways may improve understanding of aetiology and facilitate therapy and prevention. Here, we report the first genome-wide association study of pathological gambling. Our aims were to identify pathways involved in pathological gambling, and examine whether there is a genetic overlap between pathological gambling and alcohol dependence.MethodsFour hundred and forty-five individuals with a diagnosis of pathological gambling according to the Diagnostic and Statistical Manual of Mental Disorders were recruited in Germany, and 986 controls were drawn from a German general population sample. A genome-wide association study of pathological gambling comprising single marker, gene-based, and pathway analyses, was performed. Polygenic risk scores were generated using data from a German genome-wide association study of alcohol dependence.ResultsNo genome-wide significant association with pathological gambling was found for single markers or genes. Pathways for Huntington's disease (P-value = 6.63 × 10−3); 5′-adenosine monophosphate-activated protein kinase signalling (P-value = 9.57 × 10−3); and apoptosis (P-value = 1.75 × 10−2) were significant. Polygenic risk score analysis of the alcohol dependence dataset yielded a one-sided nominal significant P-value in subjects with pathological gambling, irrespective of comorbid alcohol dependence status.ConclusionsThe present results accord with previous quantitative formal genetic studies which showed genetic overlap between non-substance- and substance-related addictions. Furthermore, pathway analysis suggests shared pathology between Huntington's disease and pathological gambling. This finding is consistent with previous imaging studies.
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Pain, Oliver, Alexandra C. Gillett, Jehannine C. Austin, Lasse Folkersen et Cathryn M. Lewis. « A tool for translating polygenic scores onto the absolute scale using summary statistics ». European Journal of Human Genetics 30, no 3 (4 janvier 2022) : 339–48. http://dx.doi.org/10.1038/s41431-021-01028-z.
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AbstractThere is growing interest in the clinical application of polygenic scores as their predictive utility increases for a range of health-related phenotypes. However, providing polygenic score predictions on the absolute scale is an important step for their safe interpretation. We have developed a method to convert polygenic scores to the absolute scale for binary and normally distributed phenotypes. This method uses summary statistics, requiring only the area-under-the-ROC curve (AUC) or variance explained (R2) by the polygenic score, and the prevalence of binary phenotypes, or mean and standard deviation of normally distributed phenotypes. Polygenic scores are converted using normal distribution theory. We also evaluate methods for estimating polygenic score AUC/R2 from genome-wide association study (GWAS) summary statistics alone. We validate the absolute risk conversion and AUC/R2 estimation using data for eight binary and three continuous phenotypes in the UK Biobank sample. When the AUC/R2 of the polygenic score is known, the observed and estimated absolute values were highly concordant. Estimates of AUC/R2 from the lassosum pseudovalidation method were most similar to the observed AUC/R2 values, though estimated values deviated substantially from the observed for autoimmune disorders. This study enables accurate interpretation of polygenic scores using only summary statistics, providing a useful tool for educational and clinical purposes. Furthermore, we have created interactive webtools implementing the conversion to the absolute (https://opain.github.io/GenoPred/PRS_to_Abs_tool.html). Several further barriers must be addressed before clinical implementation of polygenic scores, such as ensuring target individuals are well represented by the GWAS sample.
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Paranjpe, Ishan, Noah Tsao, Renae Judy, Manish Paranjpe, Kumardeep Chaudhary, Derek Klarin, Iain Forrest et al. « Derivation and validation of genome-wide polygenic score for urinary tract stone diagnosis ». Kidney International 98, no 5 (novembre 2020) : 1323–30. http://dx.doi.org/10.1016/j.kint.2020.04.055.
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Sexton, Corinne E., Mark T. W. Ebbert, Ryan H. Miller, Meganne Ferrel, Jo Ann T. Tschanz, Christopher D. Corcoran, Alzheimer’s Disease Neuroimaging Initiative, Perry G. Ridge et John S. K. Kauwe. « Common DNA Variants Accurately Rank an Individual of Extreme Height ». International Journal of Genomics 2018 (4 septembre 2018) : 1–7. http://dx.doi.org/10.1155/2018/5121540.
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Polygenic scores (or genetic risk scores) quantify the aggregate of small effects from many common genetic loci that have been associated with a trait through genome-wide association. Polygenic scores were first used successfully in schizophrenia and have since been applied to multiple phenotypes including multiple sclerosis, rheumatoid arthritis, and height. Because human height is an easily-measured and complex polygenic trait, polygenic height scores provide exciting insights into the predictability of aggregate common variant effect on the phenotype. Shawn Bradley is an extremely tall former professional basketball player from Brigham Young University and the National Basketball Association (NBA), measuring 2.29 meters (7′6″, 99.99999th percentile for height) tall, with no known medical conditions. Here, we present a case where a rare combination of common SNPs in one individual results in an extremely high polygenic height score that is correlated with an extreme phenotype. While polygenic scores are not clinically significant in the average case, our findings suggest that for extreme phenotypes, polygenic scores may be more successful for the prediction of individuals.
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Belsky, Daniel W., Benjamin W. Domingue, Robbee Wedow, Louise Arseneault, Jason D. Boardman, Avshalom Caspi, Dalton Conley et al. « Genetic analysis of social-class mobility in five longitudinal studies ». Proceedings of the National Academy of Sciences 115, no 31 (9 juillet 2018) : E7275—E7284. http://dx.doi.org/10.1073/pnas.1801238115.
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A summary genetic measure, called a “polygenic score,” derived from a genome-wide association study (GWAS) of education can modestly predict a person’s educational and economic success. This prediction could signal a biological mechanism: Education-linked genetics could encode characteristics that help people get ahead in life. Alternatively, prediction could reflect social history: People from well-off families might stay well-off for social reasons, and these families might also look alike genetically. A key test to distinguish biological mechanism from social history is if people with higher education polygenic scores tend to climb the social ladder beyond their parents’ position. Upward mobility would indicate education-linked genetics encodes characteristics that foster success. We tested if education-linked polygenic scores predicted social mobility in >20,000 individuals in five longitudinal studies in the United States, Britain, and New Zealand. Participants with higher polygenic scores achieved more education and career success and accumulated more wealth. However, they also tended to come from better-off families. In the key test, participants with higher polygenic scores tended to be upwardly mobile compared with their parents. Moreover, in sibling-difference analysis, the sibling with the higher polygenic score was more upwardly mobile. Thus, education GWAS discoveries are not mere correlates of privilege; they influence social mobility within a life. Additional analyses revealed that a mother’s polygenic score predicted her child’s attainment over and above the child’s own polygenic score, suggesting parents’ genetics can also affect their children’s attainment through environmental pathways. Education GWAS discoveries affect socioeconomic attainment through influence on individuals’ family-of-origin environments and their social mobility.
Thèses sur le sujet "Genome-Wide polygenic score"
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Kawaguchi, Takahisa. « Risk estimation model for nonalcoholic fatty liver disease in the Japanese using multiple genetic markers ». Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263348.
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Privé, Florian. « Genetic risk score based on statistical learning Efficient analysis of large-scale genome-wide data with two R packages : bigstatsr and bigsnpr Efficient implementation of penalized regression for genetic risk prediction Making the most of Clumping and Thresholding for polygenic scores ». Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAS024.
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Le génotypage devient de moins en moins cher, rendant les données de génotypes disponibles pour des millions d’individus. Par ailleurs, l’imputation permet d’obtenir l’information génotypique pour des millions de positions de l’ADN, capturant l’essentiel de la variation génétique du génome humain. Compte tenu de la richesse des données et du fait que de nombreux traits et maladies sont héréditaires (par exemple, la génétique peut expliquer 80% de la variation de la taille dans la population), il est envisagé d’utiliser des modèles prédictifs basés sur l’information génétique dans le cadre d’une médecine personnalisée.Au cours de ma thèse, je me suis concentré sur l’amélioration de la capacité prédictive des modèles polygéniques. Les modèles prédictifs faisant partie d’une analyse statistique plus large des jeux de données, j’ai développé des outils permettant l’analyse exploratoire de grands jeux de données, constitués de deux packages R/C++ décrits dans la première partie de ma thèse. Ensuite, j’ai développé une implémentation efficace de larégression pénalisée pour construire des modèles polygéniques basés sur des centaines de milliers d’individus génotypés. Enfin, j’ai amélioré la méthode appelée “clumpingand thresholding”, qui est la méthode polygénique la plus largement utilisée et qui estbasée sur des statistiques résumées plus largement accessibles par rapport aux données individuelles.Dans l’ensemble, j’ai appliqué de nombreux concepts d’apprentissage statistique aux données génétiques. J’ai utilisé du “extreme gradient boosting” pour imputer des variants génotypés, du “feature engineering” pour capturer des effets récessifs et dominants dans une régression pénalisée, et du “parameter tuning” et des “stacked regres-sions” pour améliorer les modèles polygéniques prédictifs. L’apprentissage statistique n’est pour l’instant pas très utilisé en génétique humaine et ma thèse est une tentative pour changer celaGenotyping is becoming cheaper, making genotype data available for millions of indi-viduals. Moreover, imputation enables to get genotype information at millions of locicapturing most of the genetic variation in the human genome. Given such large data andthe fact that many traits and diseases are heritable (e.g. 80% of the variation of heightin the population can be explained by genetics), it is envisioned that predictive modelsbased on genetic information will be part of a personalized medicine.In my thesis work, I focused on improving predictive ability of polygenic models.Because prediction modeling is part of a larger statistical analysis of datasets, I de-veloped tools to allow flexible exploratory analyses of large datasets, which consist intwo R/C++ packages described in the first part of my thesis. Then, I developed someefficient implementation of penalized regression to build polygenic models based onhundreds of thousands of genotyped individuals. Finally, I improved the “clumping andthresholding” method, which is the most widely used polygenic method and is based onsummary statistics that are widely available as compared to individual-level data.Overall, I applied many concepts of statistical learning to genetic data. I used ex-treme gradient boosting for imputing genotyped variants, feature engineering to cap-ture recessive and dominant effects in penalized regression, and parameter tuning andstacked regressions to improve polygenic prediction. Statistical learning is not widelyused in human genetics and my thesis is an attempt to change that
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Shen, Hanyang, Bizu Gelaye, Hailiang Huang, Marta B. Rondon, Sixto Sanchez et Laramie E. Duncan. « Polygenic prediction and GWAS of depression, PTSD, and suicidal ideation/self-harm in a Peruvian cohort ». Springer Nature, 2020. http://hdl.handle.net/10757/652459.
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Genome-wide approaches including polygenic risk scores (PRSs) are now widely used in medical research; however, few studies have been conducted in low- and middle-income countries (LMICs), especially in South America. This study was designed to test the transferability of psychiatric PRSs to individuals with different ancestral and cultural backgrounds and to provide genome-wide association study (GWAS) results for psychiatric outcomes in this sample. The PrOMIS cohort (N = 3308) was recruited from prenatal care clinics at the Instituto Nacional Materno Perinatal (INMP) in Lima, Peru. Three major psychiatric outcomes (depression, PTSD, and suicidal ideation and/or self-harm) were scored by interviewers using valid Spanish questionnaires. Illumina Multi-Ethnic Global chip was used for genotyping. Standard procedures for PRSs and GWAS were used along with extra steps to rule out confounding due to ancestry. Depression PRSs significantly predicted depression, PTSD, and suicidal ideation/self-harm and explained up to 0.6% of phenotypic variation (minimum p = 3.9 × 10−6). The associations were robust to sensitivity analyses using more homogeneous subgroups of participants and alternative choices of principal components. Successful polygenic prediction of three psychiatric phenotypes in this Peruvian cohort suggests that genetic influences on depression, PTSD, and suicidal ideation/self-harm are at least partially shared across global populations. These PRS and GWAS results from this large Peruvian cohort advance genetic research (and the potential for improved treatments) for diverse global populations.National Institutes of Health
Revisión por pares
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Meijsen, Joeri Jeroen. « Combining genome-wide association studies, polygenic risk scores and SNP-SNP interactions to investigate the genomic architecture of human complex diseases : more than the sum of its parts ». Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33094.
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Major Depressive Disorder is a devastating psychiatric illness with a complex genetic and environmental component that affects 10% of the UK population. Previous studies have shown that that individuals with depression show poorer performance on measures of cognitive domains such as memory, attention, language and executive functioning. A major risk factor for depression is a higher level of neuroticism, which has been shown to be associated with depression throughout life. Understanding cognitive performance in depression and neuroticism could lead to a better understanding of the aetiology of depression. The first aim of this thesis focused on assessing phenotypic and genetic differences in cognitive performance between healthy controls and depressed individuals and also between single episode and recurrent depression. A second aim was determining the capability of two decision-tree based methods to detect simulated gene-gene interactions. The third aim was to develop a novel statistical methodology for simultaneously analysing single SNP, additive and interacting genetic components associated with neuroticism using machine leaning. To assess the phenotypic and genetic differences in depression, 7,012 unrelated Generation Scotland participants (of which 1,042 were clinically diagnosed with depression) were analysed. Significant differences in cognitive performance were observed in two domains: processing speed and vocabulary. Individuals with recurrent depression showed lower processing speed scores compared to both controls and individuals with single episode depression. Higher vocabulary scores were observed in depressed individuals compared to controls and in individuals with recurrent depression compared to controls. These significant differences could not be tied to significant single locus associations. Derived polygenic scores using the large CHARGE processing speed GWAS explained up to 1% of variation in processing speed performance among individuals with single episode and recurrent depression. Two greedy non-parametric decision-tree based methods - C5.0 and logic regression - were applied to simulated gene-gene interaction data from Generation Scotland. Several gene-gene interactions were simulated under multiple scenarios (e.g. size, strength of association levels and the presence of a polygenic component) to assess the power and type I error. C5.0 was found to have an increased power with a conservative type I error using simulated data. C5.0 was applied to years of education as a proxy of educational attainment in 6,765 Generation Scotland participants. Multiple interacting loci were detected that were associated with years of education, some most notably located in genes known to be associated with reading and spelling (RCAN3) and neurodevelopmental traits (NPAS3). C5.0 was incorporated in a novel methodology called Machine-learning for Additive and Interaction Combined Analysis (MAICA). MAICA allows for a simultaneous analysis of single locus, polygenic components, and gene-gene interaction risk factors by means of a machine learning implementation. MAICA was applied on neuroticism scores in both Generation Scotland and UK Biobank. The MAICA model in Generation Scotland included 151 single loci and 11 gene-gene interaction sets, and explained ~6.5% of variation in neuroticism scores. Applying the same model to UK Biobank did not lead to a statistically significant prediction of neuroticism scores. The results presented in this thesis showed that individuals with depression performed significantly lower on the processing speed tests but higher on vocabulary test and that 1% of variation in processing speed can be explained by using a large processing speed GWAS. Evidence was provided that C5.0 had increased power and acceptable type I error rates versus logic regression when epistatic models exist - even with a strong underlying polygenic component, and that MAICA is an efficient tool to assess single locus, polygenic and epistatic components simultaneously. MAICA is open-source, and will provide a useful tool for other researchers of complex human traits who are interested in exploring the relative contributions of these different genomic architectures.
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Lind, Mackenzie J. « Sleep disturbances and depression : the role of genes and trauma ». VCU Scholars Compass, 2017. http://scholarscompass.vcu.edu/etd/4858.
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Sleep disturbances and insomnia are prevalent, with around 33% of adults indicating that they experience at least one main symptom of insomnia, and bidirectional relationships exist with common psychopathology, particularly major depressive disorder (MDD). However, genetic and environmental (e.g., traumatic event exposure) contributions to the etiology of these phenotypes are not yet well understood. A genetically informative sample of approximately 12,000 Han Chinese women aged 30-60 (50% with recurrent MDD) was used to address several gaps within the sleep literature. Sleep disturbances were assessed in all individuals using a general item addressing sleeplessness (GS). A sleep within depression sum score (SDS) was also created in MDD cases, combining information from the GS and two insomnia items within MDD. A total of 11 traumatic events were assessed and additional information on childhood sexual abuse (CSA) was also obtained. First, factor analyses were conducted to determine trauma factor structure. The best-fit solution included 3 factors: interpersonal, child interpersonal, and non-assaultive, and composite variables were constructed accordingly. A series of hierarchical regressions were run to examine differential effects of trauma type and timing on sleeplessness. All traumatic events predicted sleeplessness at similar magnitudes, although population models indicated that childhood interpersonal trauma may be particularly potent. An association between CSA and sleeplessness was also replicated. A series of genetic analyses demonstrated that the single nucleotide polymorphism-based heritability of sleep phenotypes did not differ significantly from zero. Further, association analyses did not identify any genome-wide significant loci. However, using a liberal false discovery rate threshold of 0.5, two genes of interest, KCNK9 and ALDH1A2, emerged for the SDS. Polygenic risk score (PRS) analyses demonstrated genetic overlap between the SDS in MDD cases and GS in MDD controls, with PRSs explaining 0.2-0.3% of the variance. A final combined model of both genetic and environmental risk indicated that both PRS and traumatic events were significant predictors of sleeplessness. While genetic results should be interpreted with caution given the lack of heritability, additional research into the genetic and environmental contributions to insomnia, utilizing more standardized phenotypes and properly ascertained samples, is clearly warranted.
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Huang, Xiufeng. « Immunogenetics of acute anterior uveitis and comparison to ankylosing spondylitis ». Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/213839/1/Xiufeng_Huang_Thesis.pdf.
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This thesis comprehensively applies genome-wide association study, Mendelian randomization analyses, and cytokine proteomics to investigate the genetic basis and immunopathogenic mechanisms of acute anterior uveitis (AAU). Multiple susceptibility loci, environmental risk factors, and potential biomarkers are identified, and a polygenic risk score for AAU developed. These findings provide novel insights into the immunogenetics in AAU, and contribute to clinical translational studies.
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Charmet, Romain. « Étude des interactions entre la fonction rénale, la thrombose artérielle et la thrombose veineuse par une approche génétique Novel risk genes identified in a genome‑wide association study for coronary artery disease in patients with type 1 diabetes Association of impaired renal function with venous thrombosis : A genetic risk score approach ». Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS392.
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L'objet de cette thèse est l'étude de la génétique des coronaropathies dans le contexte du diabète de type I et de la génétique de la thrombose veineuse ainsi que de l'interaction entre ces facteurs génétiques et la fonction rénale. Le but de ces travaux et d'apporter de nouveaux éléments permettant de caractériser le rôle de la fonction rénale dans le développement des maladies cardiovasculaires. À l'aide d'une approche GWAS je cherche à identifier des marqueurs génétiques associés au risque de coronaropathie dans un groupe cas-témoin composé de patients atteints de diabète de type I et d'étudier l'effet de l'interaction entre ces marqueurs génétiques et la néphropathie diabétique sur le risque de coronaropathie. Ensuite, j'étudie le rôle de la fonction rénale dans le risque de thrombose veineuse en rassemblant des marqueurs génétiques de la fonction rénale dans un score polygénique. Ces travaux ont permis de mettre en évidence des gènes candidats associés au risque de coronaropathie chez les patients diabétiques de type I qui sont impliqués dans le développement du rein et la fonction rénale et l'association entre le score polygénique et le risque de thrombose veineuse a permis de renforcer l'hypothèse de la fonction rénale en tant que facteur de risque de la thrombose veineuse. Les résultats obtenus dans ces travaux doivent être confirmés mais dans l'ensemble ils montrent que la fonction rénale joue un rôle dans le développement des maladies cardiovasculairesThis thesis deals with the study of genetic factors of coronary artery diseases as complications of type I diabetes, genetics factors of venous thrombosis and the interactions between these genetic factors and renal function. This work aims to add new pieces of knowledge concerning the role of renal function in the development of cardiovascular diseases. From a GWAS approach I aim to identify genetic markers associated with coronary artery disease within a group of type I diabetic patients a study the effect of the interaction between these markers and diabetic nephropathy. Then, I will study the link between renal function and venous thrombosis using a polygenetic risk score composed from genetic markers of renal function. This work highlighted new candidate genes associated with coronary artery disease among type I diabetic patients that are involved in kidney development and renal function. The association between the polygenetic risk score and renal function strengthened the hypothesis of renal function as a risk factor for venous thrombosis. The obtained results must be confirmed but they globally demonstrate that renal function plays a role in the development of cardiovascular diseases
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Shih, Yu-Hsuan, et 施又瑄. « A Genome-wide Association Study of Neurocognitive Impairments in Schizophrenia : Polygenic Score Approach ». Thesis, 2014. http://ndltd.ncl.edu.tw/handle/70148708696391474501.
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碩士國立臺灣大學
流行病學與預防醫學研究所
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Schizophrenia is considered to be genetically and phenotypically heterogeneous. The high heritability of neurocognitive impairments found in patients with schizophrenia suggests that these impairments may serve as an endophenotype resulting from a set of underlying genes in a mode of polygenic effects. In this study, a total of 165 schizophrenia patients were selected and these subjects were subjected to genotyping for 642,832 single nucleotide polymorphisms (SNPs). After quality control, 564,110 SNPs were left. We then performed principal component analysis (PCA) on 14 indices of the Continuous Performance Test (CPT) and the Wisconsin Card Sorting Test (WCST). Two orthogonal principal components (PC1 and PC2) identified with PCA explained 36.11% and 18.30% of the total variance, respectively. Then the two PCs were investigated for association with SNPs. Because of small sample size in this study, however, we were unable to detect SNPs associated with neurocognitive performance in schizophrenia with genome-wide significance. For exploring the polygenic effect, polygenic score analysis was conducted on 240,579 LD-based pruning SNPs (r2 > 0.5), which were imputed using MaCH and MiniMac. The sample was first randomly divided into a learning set (n = 83) and a test set (n = 82). Then the effect size (β) for the results of association test in the learning set was used to develop a linear model to generate scores in the test set. The sustained attention demonstrated a significant association (p = 0.03) with the PC1 polygenic score at a threshold of p = 0.0001 (25 markers), For the executive function, when polygenic scores in the test set was calculated based on the results of the best 0.1% of SNPs (219 markers) in the association study of learning set.
Livres sur le sujet "Genome-Wide polygenic score"
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Kan, Carol, et Ma-Li Wong. Genetics. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198789284.003.0004.
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An association between type 2 diabetes mellitus (T2DM) and depression has been reported in epidemiological studies. Finding a genetic overlap between T2DM and depression will provide evidence to support a common biological pathway to both disorders. Genetic correlations observed from twin studies indicate that a small magnitude of the variance in liability can be attributed to genetic factors. However, no genetic overlap has been observed between T2DM and depression in genome-wide association studies using both the polygenic score and the linkage disequilibrium score regression approaches. Clarifying the shared heritability between these two complex traits is an important next step towards better therapy and treatment. Another area that needs to be explored is gene–environment interaction, since genotypes can affect an individual’s responses to the environment and environment can differentially affect genotypes expression.
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Langley, Kate. ADHD genetics. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198739258.003.0003.
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This chapter reviews the evidence suggesting that there is a strong genetic component to ADHD and the efforts to identify the specific genetic factors that might be involved. It discusses the different types of genetic contributions, from common to rare variants, and the evidence that these are involved in the aetiology of the disorder. An overview of the methodological strategies employed, including genome-wide association studies (GWAS), polygenic risk score, and copy number variant (CNV) analyses, is undertaken, as well as discussion of the strengths and pitfalls of such work. The contradictory findings in the field and controversies that arise as a result are also explored. Finally, this chapter considers how the heritability of ADHD and specific genetic factors involved need to be examined in the context of clinical factors such as comorbidity and how these factors affect investigations into the genetics of ADHD.
Chapitres de livres sur le sujet "Genome-Wide polygenic score"
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Nurnberger, John I. « General genetics of bipolar disorder ». Dans The Bipolar Brain, 200–222. Oxford University Press, 2022. http://dx.doi.org/10.1093/med/9780197574522.003.0011.
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It has been known for centuries that major mood disorders, including bipolar disorder, cluster in families. The heritability of bipolar disorder appears to be about 85%. Some of that heritability can now be assigned to specific common genetic variants identified in genome-wide association studies and specific rare variants identified in sequencing studies. Some key areas for ongoing investigation include calcium channel–related genes, variants related to synaptic transmission, and markers of neuronal growth and development. Genetic counseling is now based on empirical risk figures from family studies but may in the future be aided by genetic measures such as polygenic risk scores and/or screening for rare variants.
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Halldorsdottir, Thorhildur, et Hildur Ýr Hilmarsdottir. « Genetic Risk Factors of Depression ». Dans Depression, sous la direction de Sonia Israel, David Benrimoh, Sylvanne Daniels et Gustavo Turecki, 33–50. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780190929565.003.0003.
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Research on the genetic underpinnings of depression has rapidly advanced in the past decade. This field of research provides a promising avenue toward improving the diagnosis of, prevention of, and treatment for this devastating disorder. The goal of this chapter is to review the main genetic and gene-by-environment interaction findings on depression. We first describe family and twin studies used to empirically study the familial aggregation of depression. Second, we provide a review of the genome-wide association studies (GWAS) published to date. Building on GWAS findings, we will discuss the use of polygenic risk scores in predicting depression. We also review the most robust candidate gene studies and gene-by-environment interaction studies. Finally, we discuss the clinical implications of the findings and promising strategies for making further progress within this field.
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Allendorf, Fred W., W. Chris Funk, Sally N. Aitken, Margaret Byrne et Gordon Luikart. « Quantitative Genetics ». Dans Conservation and the Genomics of Populations, 223–52. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780198856566.003.0011.
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Most phenotypic traits are the product of many genes as well as environmental effects, and the resulting phenotypic variation is quantitative rather than qualitative. The extent to which traits are under genetic control is termed heritability, and can be estimated by analyzing the phenotypic similarity of related individuals. Quantitative genetic approaches can be used to estimate population differentiation. Selection on quantitative traits produces changes in phenotypes as a function of the heritability, the intensity of selection, and the amount of phenotypic variation within a population. Human activities, such as size-limited harvesting and habitat degradation, can impose selection on natural populations and result in changes in phenotypes, and genetic drift in small populations can erode quantitative genetic variation. Genome-wide association studies can identify genes and markers associated with quantitative trait variation that can then be used to predict phenotypes from polygenic scores.
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Abdallah, Sarah B., et Thomas V. Fernandez. « Genetic Susceptibility in Tourette Syndrome ». Dans Tourette Syndrome, sous la direction de Sarah B. Abdallah et Thomas V. Fernandez, 125–36. 2e éd. Oxford University Press, 2022. http://dx.doi.org/10.1093/med/9780197543214.003.0009.
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Abstract Early twin and family studies point to a significant genetic contribution to Tourette syndrome (TS). Informed by early segregation analyses in TS families pointing to a single-gene autosomal dominant inheritance pattern with partial penetrance, initial efforts at gene discovery in TS utilized parametric linkage analysis in large multigenerational families but failed to identify a single specific genetic locus. Later segregation analyses supported the current characterization of TS as a complex, genetically heterogeneous disorder. Nonparametric linkage analyses have yet to identify common TS risk alleles. Candidate gene association studies in TS have not yielded significant reproducible findings. Genome-wide association studies (GWAS) have proven valuable for identifying and replicating loci for common complex traits and disorders across the medical field. A GWAS meta-analysis of almost 5,000 cases identified a significant locus in FLT3. Polygenic risk scores, calculated from the most recent TS GWAS summary statistics, have shown correlation with tic severity and affected status in independent samples. There has been an increasing effort to evaluate the contribution of rare allele variants toward TS. Recent studies have found a greater burden of rare copy number variants in TS cases; the largest study to date identified NRXN1 and CNTN6 as TS risk genes. Identifying rare de novo single nucleotide variants and indels in more than 800 parent–child trios has so far identified six likely TS risk genes (WWC1, CELSR3, OPA1, NIPBL, FN1, and FBN2), which present an enriched function for cell polarity.
Actes de conférences sur le sujet "Genome-Wide polygenic score"
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Thomas, Minta, Lori C. Sakoda, Jeffrey K. Lee, Mark A. Jenkins, Andrea Burnett-Hartman, Heather Hampel, Elisabeth A. Rosenthal et al. « Abstract 881 : Benchmarking genome-wide polygenic risk score development techniques in colorectal cancer risk prediction ». Dans Proceedings : AACR Annual Meeting 2021 ; April 10-15, 2021 and May 17-21, 2021 ; Philadelphia, PA. American Association for Cancer Research, 2021. http://dx.doi.org/10.1158/1538-7445.am2021-881.
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Darst, Burcu F., Ravi K. Madduri, Alexis A. Rodriguez, Xin Sheng, Rosalind A. Eeles, Zsofia Kote-Jarai, John M. Gaziano, Amy C. Justice, David V. Conti et Christopher A. Haiman. « Abstract PO-163 : Genome-wide polygenic risk score of prostate cancer in African and European ancestry men ». Dans Abstracts : AACR Virtual Conference : 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved ; October 6-8, 2021. American Association for Cancer Research, 2022. http://dx.doi.org/10.1158/1538-7755.disp21-po-163.
Texte intégralRapports d'organisations sur le sujet "Genome-Wide polygenic score"
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Zeng, Yi, Huashuai Chen, Xiaomin Liu, Rui Ye, Enjun Xie, Zhihua Chen, Jiehua Lu et al. Sex differences in genetic associations with longevity in Han Chinese : sex-stratified genome-wide association study and polygenic risk score analysis. Rostock : Max Planck Institute for Demographic Research, février 2017. http://dx.doi.org/10.4054/mpidr-wp-2017-004.
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Sela, Hanan, Eduard Akhunov et Brian J. Steffenson. Population genomics, linkage disequilibrium and association mapping of stripe rust resistance genes in wild emmer wheat, Triticum turgidum ssp. dicoccoides. United States Department of Agriculture, janvier 2014. http://dx.doi.org/10.32747/2014.7598170.bard.
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The primary goals of this project were: (1) development of a genetically characterized association panel of wild emmer for high resolution analysis of the genetic basis of complex traits; (2) characterization and mapping of genes and QTL for seedling and adult plant resistance to stripe rust in wild emmer populations; (3) characterization of LD patterns along wild emmer chromosomes; (4) elucidation of the multi-locus genetic structure of wild emmer populations and its correlation with geo-climatic variables at the collection sites. Introduction In recent years, Stripe (yellow) rust (Yr) caused by Pucciniastriiformis f. sp. tritici(PST) has become a major threat to wheat crops in many parts of the world. New races have overcome most of the known resistances. It is essential, therefore, that the search for new genes will continue, followed by their mapping by molecular markers and introgression into the elite varieties by marker-assisted selection (MAS). The reservoir of genes for disease and pest resistance in wild emmer wheat (Triticumdicoccoides) is an important resource that must be made available to wheat breeders. The majority of resistance genes that were introgressed so far in cultivated wheat are resistance (R) genes. These genes, though confering near-immunity from the seedling stage, are often overcome by the pathogen in a short period after being deployed over vast production areas. On the other hand, adult-plant resistance (APR) is usually more durable since it is, in many cases, polygenic and confers partial resistance that may put less selective pressure on the pathogen. In this project, we have screened a collection of 480 wild emmer accessions originating from Israel for APR and seedling resistance to PST. Seedling resistance was tested against one Israeli and 3 North American PST isolates. APR was tested on accessions that did not have seedling resistance. The APR screen was conducted in two fields in Israel and in one field in the USA over 3 years for a total of 11 replicates. We have found about 20 accessions that have moderate stripe rust APR with infection type (IT<5), and about 20 additional accessions that have novel seedling resistance (IT<3). We have genotyped the collection using genotyping by sequencing (GBS) and the 90K SNP chip array. GBS yielded a total 341K SNP that were filtered to 150K informative SNP. The 90K assay resulted in 11K informative SNP. We have conducted a genome-wide association scan (GWAS) and found one significant locus on 6BL ( -log p >5). Two novel loci were found for seedling resistance. Further investigation of the 6BL locus and the effect of Yr36 showed that the 6BL locus and the Yr36 have additive effect and that the presence of favorable alleles of both loci results in reduction of 2 grades in the IT score. To identify alleles conferring adaption to extreme climatic conditions, we have associated the patterns of genomic variation in wild emmer with historic climate data from the accessions’ collection sites. The analysis of population stratification revealed four genetically distinct groups of wild emmer accessions coinciding with their geographic distribution. Partitioning of genomic variance showed that geographic location and climate together explain 43% of SNPs among emmer accessions with 19% of SNPs affected by climatic factors. The top three bioclimatic factors driving SNP distribution were temperature seasonality, precipitation seasonality, and isothermality. Association mapping approaches revealed 57 SNPs associated with these bio-climatic variables. Out of 21 unique genomic regions controlling heading date variation, 10 (~50%) overlapped with SNPs showing significant association with at least one of the three bioclimatic variables. This result suggests that a substantial part of the genomic variation associated with local adaptation in wild emmer is driven by selection acting on loci regulating flowering. Conclusions: Wild emmer can serve as a good source for novel APR and seedling R genes for stripe rust resistance. APR for stripe rust is a complex trait conferred by several loci that may have an additive effect. GWAS is feasible in the wild emmer population, however, its detection power is limited. A panel of wild emmer tagged with more than 150K SNP is available for further GWAS of important traits. The insights gained by the bioclimatic-gentic associations should be taken into consideration when planning conservation strategies.