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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 (March 10, 2021): 952–60. http://dx.doi.org/10.1093/hmg/ddab067.
Повний текст джерелаАнотація:
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 (July 11, 2020): 3203–14. http://dx.doi.org/10.1210/clinem/dgaa443.
Повний текст джерелаАнотація:
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 (September 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, and Paul F. O’Reilly. "PRSet: Pathway-based polygenic risk score analyses and software." PLOS Genetics 19, no. 2 (February 7, 2023): e1010624. http://dx.doi.org/10.1371/journal.pgen.1010624.
Повний текст джерелаАнотація:
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 (March 25, 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 (August 2016): 38–46. http://dx.doi.org/10.1016/j.eurpsy.2016.04.001.
Повний текст джерелаАнотація:
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, and Cathryn M. Lewis. "A tool for translating polygenic scores onto the absolute scale using summary statistics." European Journal of Human Genetics 30, no. 3 (January 4, 2022): 339–48. http://dx.doi.org/10.1038/s41431-021-01028-z.
Повний текст джерелаАнотація:
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 (November 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, and John S. K. Kauwe. "Common DNA Variants Accurately Rank an Individual of Extreme Height." International Journal of Genomics 2018 (September 4, 2018): 1–7. http://dx.doi.org/10.1155/2018/5121540.
Повний текст джерелаАнотація:
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 (July 9, 2018): E7275—E7284. http://dx.doi.org/10.1073/pnas.1801238115.
Повний текст джерелаАнотація:
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.
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Ritchie, Stuart J., W. David Hill, Riccardo E. Marioni, Gail Davies, Saskia P. Hagenaars, Sarah E. Harris, Simon R. Cox, et al. "Polygenic predictors of age-related decline in cognitive ability." Molecular Psychiatry 25, no. 10 (February 13, 2019): 2584–98. http://dx.doi.org/10.1038/s41380-019-0372-x.
Повний текст джерелаАнотація:
AbstractPolygenic scores can be used to distil the knowledge gained in genome-wide association studies for prediction of health, lifestyle, and psychological factors in independent samples. In this preregistered study, we used fourteen polygenic scores to predict variation in cognitive ability level at age 70, and cognitive change from age 70 to age 79, in the longitudinal Lothian Birth Cohort 1936 study. The polygenic scores were created for phenotypes that have been suggested as risk or protective factors for cognitive ageing. Cognitive abilities within older age were indexed using a latent general factor estimated from thirteen varied cognitive tests taken at four waves, each three years apart (initialn = 1091 age 70; finaln = 550 age 79). The general factor indexed over two-thirds of the variance in longitudinal cognitive change. We ran additional analyses using an age-11 intelligence test to index cognitive change from age 11 to age 70. Several polygenic scores were associated with the level of cognitive ability at age-70 baseline (range of standardizedβ-values = –0.178 to 0.302), and the polygenic score for education was associated with cognitive change from childhood to age 70 (standardizedβ = 0.100). No polygenic scores were statistically significantly associated with variation in cognitive change between ages 70 and 79, and effect sizes were small. However,APOEe4 status made a significant prediction of the rate of cognitive decline from age 70 to 79 (standardizedβ = –0.319 for carriers vs. non-carriers). The results suggest that the predictive validity for cognitive ageing of polygenic scores derived from genome-wide association study summary statistics is not yet on a par withAPOEe4, a better-established predictor.
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Carlson, Maryn O., Daniel P. Rice, Jeremy J. Berg, and Matthias Steinrücken. "Polygenic score accuracy in ancient samples: Quantifying the effects of allelic turnover." PLOS Genetics 18, no. 5 (May 6, 2022): e1010170. http://dx.doi.org/10.1371/journal.pgen.1010170.
Повний текст джерелаАнотація:
Polygenic scores link the genotypes of ancient individuals to their phenotypes, which are often unobservable, offering a tantalizing opportunity to reconstruct complex trait evolution. In practice, however, interpretation of ancient polygenic scores is subject to numerous assumptions. For one, the genome-wide association (GWA) studies from which polygenic scores are derived, can only estimate effect sizes for loci segregating in contemporary populations. Therefore, a GWA study may not correctly identify all loci relevant to trait variation in the ancient population. In addition, the frequencies of trait-associated loci may have changed in the intervening years. Here, we devise a theoretical framework to quantify the effect of this allelic turnover on the statistical properties of polygenic scores as functions of population genetic dynamics, trait architecture, power to detect significant loci, and the age of the ancient sample. We model the allele frequencies of loci underlying trait variation using the Wright-Fisher diffusion, and employ the spectral representation of its transition density to find analytical expressions for several error metrics, including the expected sample correlation between the polygenic scores of ancient individuals and their true phenotypes, referred to as polygenic score accuracy. Our theory also applies to a two-population scenario and demonstrates that allelic turnover alone may explain a substantial percentage of the reduced accuracy observed in cross-population predictions, akin to those performed in human genetics. Finally, we use simulations to explore the effects of recent directional selection, a bias-inducing process, on the statistics of interest. We find that even in the presence of bias, weak selection induces minimal deviations from our neutral expectations for the decay of polygenic score accuracy. By quantifying the limitations of polygenic scores in an explicit evolutionary context, our work lays the foundation for the development of more sophisticated statistical procedures to analyze both temporally and geographically resolved polygenic scores.
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Weiss, Alexander, Bart M. L. Baselmans, Edith Hofer, Jingyun Yang, Aysu Okbay, Penelope A. Lind, Mike B. Miller, et al. "Personality Polygenes, Positive Affect, and Life Satisfaction." Twin Research and Human Genetics 19, no. 5 (August 22, 2016): 407–17. http://dx.doi.org/10.1017/thg.2016.65.
Повний текст джерелаАнотація:
Approximately half of the variation in wellbeing measures overlaps with variation in personality traits. Studies of non-human primate pedigrees and human twins suggest that this is due to common genetic influences. We tested whether personality polygenic scores for the NEO Five-Factor Inventory (NEO-FFI) domains and for item response theory (IRT) derived extraversion and neuroticism scores predict variance in wellbeing measures. Polygenic scores were based on published genome-wide association (GWA) results in over 17,000 individuals for the NEO-FFI and in over 63,000 for the IRT extraversion and neuroticism traits. The NEO-FFI polygenic scores were used to predict life satisfaction in 7 cohorts, positive affect in 12 cohorts, and general wellbeing in 1 cohort (maximalN= 46,508). Meta-analysis of these results showed no significant association between NEO-FFI personality polygenic scores and the wellbeing measures. IRT extraversion and neuroticism polygenic scores were used to predict life satisfaction and positive affect in almost 37,000 individuals from UK Biobank. Significant positive associations (effect sizes <0.05%) were observed between the extraversion polygenic score and wellbeing measures, and a negative association was observed between the polygenic neuroticism score and life satisfaction. Furthermore, using GWA data, genetic correlations of -0.49 and -0.55 were estimated between neuroticism with life satisfaction and positive affect, respectively. The moderate genetic correlation between neuroticism and wellbeing is in line with twin research showing that genetic influences on wellbeing are also shared with other independent personality domains.
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Elliott, Maxwell L., Daniel W. Belsky, Kevin Anderson, David L. Corcoran, Tian Ge, Annchen Knodt, Joseph A. Prinz, et al. "A Polygenic Score for Higher Educational Attainment is Associated with Larger Brains." Cerebral Cortex 29, no. 8 (September 12, 2018): 3496–504. http://dx.doi.org/10.1093/cercor/bhy219.
Повний текст джерелаАнотація:
Abstract People who score higher on intelligence tests tend to have larger brains. Twin studies suggest the same genetic factors influence both brain size and intelligence. This has led to the hypothesis that genetics influence intelligence partly by contributing to the development of larger brains. We tested this hypothesis using four large imaging genetics studies (combined N = 7965) with polygenic scores derived from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. We conducted meta-analysis to test associations among participants’ genetics, total brain volume (i.e., brain size), and cognitive test performance. Consistent with previous findings, participants with higher polygenic scores achieved higher scores on cognitive tests, as did participants with larger brains. Participants with higher polygenic scores also had larger brains. We found some evidence that brain size partly mediated associations between participants’ education polygenic scores and their cognitive test performance. Effect sizes were larger in the population-based samples than in the convenience-based samples. Recruitment and retention of population-representative samples should be a priority for neuroscience research. Findings suggest promise for studies integrating GWAS discoveries with brain imaging to understand neurobiology linking genetics with cognitive performance.
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Schmitz, Judith, Filippo Abbondanza, and Silvia Paracchini. "Genome‐wide association study and polygenic risk score analysis for hearing measures in children." American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 186, no. 5 (July 2021): 318–28. http://dx.doi.org/10.1002/ajmg.b.32873.
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Geraghty, Robert M., Ian Wilson, and John A. Sayer. "Regarding “Derivation and validation of genome-wide polygenic score for urinary tract stone diagnosis”." Kidney International 98, no. 5 (November 2020): 1347. http://dx.doi.org/10.1016/j.kint.2020.08.016.
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Hachiya, Tsuyoshi, Jun Hata, Yoichiro Hirakawa, Daigo Yoshida, Yoshihiko Furuta, Takanari Kitazono, Atsushi Shimizu, and Toshiharu Ninomiya. "Genome-Wide Polygenic Score and the Risk of Ischemic Stroke in a Prospective Cohort." Stroke 51, no. 3 (March 2020): 759–65. http://dx.doi.org/10.1161/strokeaha.119.027520.
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Wang, Minxian, Ramesh Menon, Sanghamitra Mishra, Aniruddh P. Patel, Mark Chaffin, Deepak Tanneeru, Manjari Deshmukh, et al. "Validation of a Genome-Wide Polygenic Score for Coronary Artery Disease in South Asians." Journal of the American College of Cardiology 76, no. 6 (August 2020): 703–14. http://dx.doi.org/10.1016/j.jacc.2020.06.024.
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Hsiao, Yu-Jer, Hao-Kai Chuang, Sheng-Chu Chi, Yung-Yu Wang, Pin-Hsuan Chiang, Pai-Chi Teng, Tung-Mei Kuang, et al. "Genome-Wide Polygenic Risk Score for Predicting High Risk Glaucoma Individuals of Han Chinese Ancestry." Journal of Personalized Medicine 11, no. 11 (November 9, 2021): 1169. http://dx.doi.org/10.3390/jpm11111169.
Повний текст джерелаАнотація:
Glaucoma is a progressive and irreversible blindness-causing disease. However, the underlying genetic factors and molecular mechanisms remain poorly understood. Previous genome-wide association studies (GWAS) have made tremendous progress on the SNP-based disease association and characterization. However, most of them were conducted for Europeans. Since differential genetic characteristics among ethnic groups were evident in glaucoma, it is worthwhile to complete its genetic landscape from the larger cohorts of Asian individuals. Here, we present a GWAS based on the Taiwan Biobank. Among 1013 glaucoma patients and 36,562 controls, we identified a total of 138 independent glaucoma-associated SNPs at the significance level of p < 1 × 10−5. After clumping genetically linked SNPs (LD clumping), 134 independent SNPs with p < 10−4 were recruited to construct a Polygenic Risk Score (PRS). The model achieved an area under the receiver operating characteristic curve (AUC) of 0.8387 (95% CI = [0.8269–0.8506]), and those within the top PRS quantile had a 45.48-fold increased risk of glaucoma compared with those within the lowest quantile. The PRS model was validated with an independent cohort that achieved an AUC of 0.7283, thereby showing the effectiveness of our polygenic risk score in predicting individuals in the Han Chinese population with higher glaucoma risks.
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Limonova, A. S., A. I. Ershova, A. V. Kiseleva, V. E. Ramensky, Yu V. Vyatkin, V. A. Kutsenko, A. N. Meshkov, and O. M. Drapkina. "Assessment of polygenic risk of hypertension." Cardiovascular Therapy and Prevention 21, no. 12 (January 19, 2023): 3464. http://dx.doi.org/10.15829/1728-8800-2022-3464.
Повний текст джерелаАнотація:
Hypertension (HTN) is a leading risk factor for the development of cardiovascular diseases. In recent decades, the rapid development of genetic tests, in particular genome-wide association study (GWAS), has made it possible to identify hundreds of nucleotide sequence variants associated with the development of HTN. One approach to improve the predictive power of genetic testing is to combine information about many nucleotide sequence variants into a single risk assessment system, often referred to as a genetic risk score. Within the framework of this review, the most significant publications on the study of the genetic risk score for HTN will be considered, and the features of their development and application will be discussed.
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Park, Young-Min. "Circadian Rhythm and Polygenic Risk Scores." Chronobiology in Medicine 4, no. 1 (March 31, 2022): 21–23. http://dx.doi.org/10.33069/cim.2022.0007.
Повний текст джерелаАнотація:
Genome-wide association study (GWAS) have led to paradigm shift in the study of psychiatric illnesses. However, GWAS has certain limitations. Thus, investigators have invented the concept of polygenic risk using GWAS. The current study aimed to review the papers on the association between polygenic risk score (PRS) and circadian rhythm. Circadian preferences are indicative of endogenous circadian rhythms. Some studies have revealed that the PRS for circadian preferences is associated with sleep timing, median sleep time, and the risk of bipolar disorder. Moreover, another study revealed that the PRS for diabetes, Alzheimer’s disease, and mood disorders was associated with sleep patterns, wakefulness after sleep onset, and circadian rhythm. In conclusion, the PRS is an anticipated biomarker for predicting diagnosis, treatment response, and clinical outcomes. Thus, PRS will play many roles in personalized medicine soon.
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Lin, Ying-Ju, Chi-Fung Cheng, Chung-Hsing Wang, Wen-Miin Liang, Chih-Hsin Tang, Li-Ping Tsai, Chien-Hsiun Chen, et al. "Genetic Architecture Associated With Familial Short Stature." Journal of Clinical Endocrinology & Metabolism 105, no. 6 (March 14, 2020): 1801–13. http://dx.doi.org/10.1210/clinem/dgaa131.
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Abstract Context Human height is an inheritable, polygenic trait under complex and multilocus genetic regulation. Familial short stature (FSS; also called genetic short stature) is the most common type of short stature and is insufficiently known. Objective To investigate the FSS genetic profile and develop a polygenic risk predisposition score for FSS risk prediction. Design and Setting The FSS participant group of Han Chinese ancestry was diagnosed by pediatric endocrinologists in Taiwan. Patients and Interventions The genetic profiles of 1163 participants with FSS were identified by using a bootstrapping subsampling and genome-wide association studies (GWAS) method. Main Outcome Measures Genetic profile, polygenic risk predisposition score for risk prediction. Results Ten novel genetic single nucleotide polymorphisms (SNPs) and 9 reported GWAS human height-related SNPs were identified for FSS risk. These 10 novel SNPs served as a polygenic risk predisposition score for FSS risk prediction (area under the curve: 0.940 in the testing group). This FSS polygenic risk predisposition score was also associated with the height reduction regression tendency in the general population. Conclusion A polygenic risk predisposition score composed of 10 genetic SNPs is useful for FSS risk prediction and the height reduction tendency. Thus, it might contribute to FSS risk in the Han Chinese population from Taiwan.
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El Charif, Omar, Heather E. Wheeler, Matthew Trendowski, Eric R. Gamazon, Shirin Ardeshirrouhanifard, Darren R. Feldman, Robert James Hamilton, et al. "Genome-wide association study (GWAS) of chemotherapy-induced Raynaud's phenomenon (RP) to reveal shared pathways with cardiovascular disease (CVD)." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e18162-e18162. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e18162.
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e18162 Background: RP is an adverse drug reaction characterized by reduced blood flow to the extremities causing pain and sensations of cold. Few studies have examined the genetic basis for RP, although family studies suggest a heritable component to primary RP. Methods: Eligible testicular cancer survivors (TCS) were < 55 y at diagnosis, treated with first line cisplatin-based chemotherapy, and completed questionnaires. Genotyping with standard quality control and imputation were performed. A case-control RP phenotype was derived from patient-reported outcomes and associations were computed by logistic regression. GWAS used cumulative bleomycin dose and 10 genetic principal components as covariates. Gene set enrichment analysis (GSEA) utilized genes ranked by the most significant GWAS SNP in/within 20 kilobases. A polygenic risk score for CVD derived from four prior independent GWAS (Khera et al. NEJM 2016) was assessed for association with RP. Results: Of 749 patients (median age 38 y, median time since chemotherapy 5 y), 38% reported RP. Bleomycin dose was the most significant predictor of RP (OR100 mg/m2 = 1.25, p < 0.0001). Number of years smoking also correlated with RP (ORyear = 1.05, p = 0.002). Age and hypertension showed no significant correlation with RP. GSEA revealed several significant pathways (FDR q < 0.1), including “ cellular response to VEGF stimulus” (q = 0.05) and “ cardiac muscle cell action potential” (q = 0.09). We hypothesized that RP may share genetic architecture with CVD. Deriving a polygenic risk score from genome-wide significant SNPs in prior CVD GWAS (n = 4260-22,389), we showed nearly significant case-control differences in CVD polygenic risk score (two-tailed t-test, p = 0.053). RP frequency significantly increased with polygenic risk score quartile (OR = 1.19, p = 0.008). Conclusions: Over one third of TCS report RP, with greater frequency among bleomycin-treated patients and smokers. Implicated genetic pathways include ones established in CVD. Although shared genetic risk between chemotherapy-induced RP and CVD may be possible, further investigation is required. Primary RP has been inconsistently linked with CVD.
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Fawns-Ritchie, Chloe, Gail Davies, Saskia P. Hagenaars, and Ian J. Deary. "Genetic Contributions to Health Literacy." Twin Research and Human Genetics 22, no. 03 (June 2019): 131–39. http://dx.doi.org/10.1017/thg.2019.28.
Повний текст джерелаАнотація:
AbstractHigher health literacy is associated with higher cognitive function and better health. Despite its wide use in medical research, no study has investigated the genetic contributions to health literacy. Using 5783 English Longitudinal Study of Ageing (ELSA) participants (mean age = 65.49, SD = 9.55) who had genotyping data and had completed a health literacy test at wave 2 (2004–2005), we carried out a genome-wide association study (GWAS) of health literacy. We estimated the proportion of variance in health literacy explained by all common single nucleotide polymorphisms (SNPs). Polygenic profile scores were calculated using summary statistics from GWAS of 21 cognitive and health measures. Logistic regression was used to test whether polygenic scores for cognitive and health-related traits were associated with having adequate, compared to limited, health literacy. No SNPs achieved genome-wide significance for association with health literacy. The proportion of variance in health literacy accounted for by common SNPs was 8.5% (SE = 7.2%). Greater odds of having adequate health literacy were associated with a 1 standard deviation higher polygenic score for general cognitive ability [OR = 1.34, 95% CI (1.26, 1.42)], verbal-numerical reasoning [OR = 1.30, 95% CI (1.23, 1.39)], and years of schooling [OR = 1.29, 95% CI (1.21, 1.36)]. Reduced odds of having adequate health literacy were associated with higher polygenic profiles for poorer self-rated health [OR = 0.92, 95% CI (0.87, 0.98)] and schizophrenia [OR = 0.91, 95% CI (0.85, 0.96)). The well-documented associations between health literacy, cognitive function and health may partly be due to shared genetic etiology. Larger studies are required to obtain accurate estimates of SNP-based heritability and to discover specific health literacy-associated genetic variants.
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Hindy, George, Krishna G. Aragam, Kenney Ng, Mark Chaffin, Luca A. Lotta, Aris Baras, Isabel Drake, et al. "Genome-Wide Polygenic Score, Clinical Risk Factors, and Long-Term Trajectories of Coronary Artery Disease." Arteriosclerosis, Thrombosis, and Vascular Biology 40, no. 11 (November 2020): 2738–46. http://dx.doi.org/10.1161/atvbaha.120.314856.
Повний текст джерелаАнотація:
Objective: To determine the relationship of a genome-wide polygenic score for coronary artery disease (GPS CAD ) with lifetime trajectories of CAD risk, directly compare its predictive capacity to traditional risk factors, and assess its interplay with the Pooled Cohort Equations (PCE) clinical risk estimator. Approach and Results: We studied GPS CAD in 28 556 middle-aged participants of the Malmö Diet and Cancer Study, of whom 4122 (14.4%) developed CAD over a median follow-up of 21.3 years. A pronounced gradient in lifetime risk of CAD was observed—16% for those in the lowest GPS CAD decile to 48% in the highest. We evaluated the discriminative capacity of the GPS CAD —as assessed by change in the C-statistic from a baseline model including age and sex—among 5685 individuals with PCE risk estimates available. The increment for the GPS CAD (+0.045, P <0.001) was higher than for any of 11 traditional risk factors (range +0.007 to +0.032). Minimal correlation was observed between GPS CAD and 10-year risk defined by the PCE ( r =0.03), and addition of GPS CAD improved the C-statistic of the PCE model by 0.026. A significant gradient in lifetime risk was observed for the GPS CAD , even among individuals within a given PCE clinical risk stratum. We replicated key findings—noting strikingly consistent results—in 325 003 participants of the UK Biobank. Conclusions: GPS CAD —a risk estimator available from birth—stratifies individuals into varying trajectories of clinical risk for CAD. Implementation of GPS CAD may enable identification of high-risk individuals early in life, decades in advance of manifest risk factors or disease.
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Wertz, J., A. Caspi, D. W. Belsky, A. L. Beckley, L. Arseneault, J. C. Barnes, D. L. Corcoran, et al. "Genetics and Crime: Integrating New Genomic Discoveries Into Psychological Research About Antisocial Behavior." Psychological Science 29, no. 5 (March 7, 2018): 791–803. http://dx.doi.org/10.1177/0956797617744542.
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Drawing on psychological and sociological theories of crime causation, we tested the hypothesis that genetic risk for low educational attainment (assessed via a genome-wide polygenic score) is associated with criminal offending. We further tested hypotheses of how polygenic risk relates to the development of antisocial behavior from childhood through adulthood. Across the Dunedin and Environmental Risk (E-Risk) birth cohorts of individuals growing up 20 years and 20,000 kilometers apart, education polygenic scores predicted risk of a criminal record with modest effects. Polygenic risk manifested during primary schooling in lower cognitive abilities, lower self-control, academic difficulties, and truancy, and it was associated with a life-course-persistent pattern of antisocial behavior that onsets in childhood and persists into adulthood. Crime is central in the nature-nurture debate, and findings reported here demonstrate how molecular-genetic discoveries can be incorporated into established theories of antisocial behavior. They also suggest that improving school experiences might prevent genetic influences on crime from unfolding.
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Canzian, Federico, Chiara Piredda, Angelica Macauda, Daria Zawirska, Niels Frost Andersen, Arnon Nagler, Jan Maciej Zaucha, et al. "A polygenic risk score for multiple myeloma risk prediction." European Journal of Human Genetics 30, no. 4 (November 30, 2021): 474–79. http://dx.doi.org/10.1038/s41431-021-00986-8.
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AbstractThere is overwhelming epidemiologic evidence that the risk of multiple myeloma (MM) has a solid genetic background. Genome-wide association studies (GWAS) have identified 23 risk loci that contribute to the genetic susceptibility of MM, but have low individual penetrance. Combining the SNPs in a polygenic risk score (PRS) is a possible approach to improve their usefulness. Using 2361 MM cases and 1415 controls from the International Multiple Myeloma rESEarch (IMMEnSE) consortium, we computed a weighted and an unweighted PRS. We observed associations with MM risk with OR = 3.44, 95% CI 2.53–4.69, p = 3.55 × 10−15 for the highest vs. lowest quintile of the weighted score, and OR = 3.18, 95% CI 2.1 = 34–4.33, p = 1.62 × 10−13 for the highest vs. lowest quintile of the unweighted score. We found a convincing association of a PRS generated with 23 SNPs and risk of MM. Our work provides additional validation of previously discovered MM risk variants and of their combination into a PRS, which is a first step towards the use of genetics for risk stratification in the general population.
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Luciano, Michelle, Riccardo E. Marioni, Maria Valdés Hernández, Susana Muñoz Maniega, Iona F. Hamilton, Natalie A. Royle, Ganesh Chauhan, et al. "Structural Brain MRI Trait Polygenic Score Prediction of Cognitive Abilities." Twin Research and Human Genetics 18, no. 6 (October 2, 2015): 738–45. http://dx.doi.org/10.1017/thg.2015.71.
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Structural brain magnetic resonance imaging (MRI) traits share part of their genetic variance with cognitive traits. Here, we use genetic association results from large meta-analytic studies of genome-wide association (GWA) for brain infarcts (BI), white matter hyperintensities, intracranial, hippocampal, and total brain volumes to estimate polygenic scores for these traits in three Scottish samples: Generation Scotland: Scottish Family Health Study (GS:SFHS), and the Lothian Birth Cohorts of 1936 (LBC1936) and 1921 (LBC1921). These five brain MRI trait polygenic scores were then used to: (1) predict corresponding MRI traits in the LBC1936 (numbers ranged 573 to 630 across traits), and (2) predict cognitive traits in all three cohorts (in 8,115–8,250 persons). In the LBC1936, all MRI phenotypic traits were correlated with at least one cognitive measure, and polygenic prediction of MRI traits was observed for intracranial volume. Meta-analysis of the correlations between MRI polygenic scores and cognitive traits revealed a significant negative correlation (maximal r = 0.08) between the HV polygenic score and measures of global cognitive ability collected in childhood and in old age in the Lothian Birth Cohorts. The lack of association to a related general cognitive measure when including the GS:SFHS points to either type 1 error or the importance of using prediction samples that closely match the demographics of the GWA samples from which prediction is based. Ideally, these analyses should be repeated in larger samples with data on both MRI and cognition, and using MRI GWA results from even larger meta-analysis studies.
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Lu, Xiangfeng, Xiaoge Niu, Chong Shen, Fangchao Liu, Zhongying Liu, Keyong Huang, Laiyuan Wang, et al. "Development and Validation of a Polygenic Risk Score for Stroke in the Chinese Population." Neurology 97, no. 6 (May 24, 2021): e619-e628. http://dx.doi.org/10.1212/wnl.0000000000012263.
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ObjectiveTo construct a polygenic risk score (PRS) for stroke and evaluate its utility in risk stratification and primary prevention for stroke.MethodsUsing a meta-analytic approach and large genome-wide association results for stroke and stroke-related traits in East Asians, we generated a combined PRS (metaPRS) by incorporating 534 genetic variants in a training set of 2,872 patients with stroke and 2,494 controls. We then validated its association with incident stroke using Cox regression models in large Chinese population-based prospective cohorts comprising 41,006 individuals.ResultsDuring a total of 367,750 person-years (mean follow-up 9.0 years), 1,227 participants developed stroke before age 80 years. Individuals with high polygenic risk had an about 2-fold higher risk of incident stroke compared with those with low polygenic risk (hazard ratio [HR] 1.99, 95% confidence interval [CI] 1.66–2.38), with the lifetime risk of stroke being 25.2% (95% CI 22.5%–27.7%) and 13.6% (95% CI 11.6%–15.5%), respectively. Individuals with both high polygenic risk and family history displayed lifetime risk as high as 41.1% (95% CI 31.4%–49.5%). Individuals with high polygenic risk achieved greater benefits in terms of absolute risk reductions from adherence to ideal fasting blood glucose and total cholesterol than those with low polygenic risk. Maintaining favorable cardiovascular health (CVH) profile could substantially mitigate the increased risk conferred by high polygenic risk to the level of low polygenic risk (from 34.6% to 13.2%).ConclusionsOur metaPRS has great potential for risk stratification of stroke and identification of individuals who may benefit more from maintaining ideal CVH.Classification of EvidenceThis study provides Class I evidence that metaPRS is predictive of stroke risk.
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Bralten, Janita, Nina R. Mota, Cornelius J. H. M. Klemann, Ward De Witte, Emma Laing, David A. Collier, Hilde de Kluiver, et al. "Genetic underpinnings of sociability in the general population." Neuropsychopharmacology 46, no. 9 (May 30, 2021): 1627–34. http://dx.doi.org/10.1038/s41386-021-01044-z.
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AbstractLevels of sociability are continuously distributed in the general population, and decreased sociability represents an early manifestation of several brain disorders. Here, we investigated the genetic underpinnings of sociability in the population. We performed a genome-wide association study (GWAS) of a sociability score based on four social functioning-related self-report questions from 342,461 adults in the UK Biobank. Subsequently we performed gene-wide and functional follow-up analyses. Robustness analyses were performed in the form of GWAS split-half validation analyses, as well as analyses excluding neuropsychiatric cases. Using genetic correlation analyses as well as polygenic risk score analyses we investigated genetic links of our sociability score to brain disorders and social behavior outcomes. Individuals with autism spectrum disorders, bipolar disorder, depression, and schizophrenia had a lower sociability score. The score was significantly heritable (SNP h2 of 6%). We identified 18 independent loci and 56 gene-wide significant genes, including genes like ARNTL, DRD2, and ELAVL2. Many associated variants are thought to have deleterious effects on gene products and our results were robust. The sociability score showed negative genetic correlations with autism spectrum, disorders, depression, schizophrenia, and two sociability-related traits—loneliness and social anxiety—but not with bipolar disorder or Alzheimer’s disease. Polygenic risk scores of our sociability GWAS were associated with social behavior outcomes within individuals with bipolar disorder and with major depressive disorder. Variation in population sociability scores has a genetic component, which is relevant to several psychiatric disorders. Our findings provide clues towards biological pathways underlying sociability.
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Testori, Alessandro, Zalman Vaksman, Sharon J. Diskin, Hakon Hakonarson, Mario Capasso, Achille Iolascon, John M. Maris, and Marcella Devoto. "Genetic Analysis in African American Children Supports Ancestry-Specific Neuroblastoma Susceptibility." Cancer Epidemiology, Biomarkers & Prevention 31, no. 4 (February 7, 2022): 870–75. http://dx.doi.org/10.1158/1055-9965.epi-21-0782.
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Abstract Background: Neuroblastoma is rarer in African American (AA) children compared with American children of European descent. AA children affected with neuroblastoma, however, more frequently develop the high-risk form of the disease. Methods: We have genotyped an AA cohort of 629 neuroblastoma cases (254 high-risk) and 2,990 controls to investigate genetic susceptibility to neuroblastoma in AAs. Results: We confirmed the known neuroblastoma susceptibility gene BARD1 at genome-wide significance in the subset of high-risk cases. We also estimated local admixture across the autosomal genome in the AA cases and controls and detected a signal at 4q31.22 where cases show an increase in European ancestry. A region at 17p13.1 showed increased African ancestry in the subgroup of high-risk cases with respect to intermediate- and low-risk cases. Using results from our published European American (EA) genome-wide association study (GWAS), we found that a polygenic score that included all independent SNPs showed a highly significant association (P value = 1.8 × 10−73) and explained 19% of disease risk variance in an independent EA cohort. In contrast, the best fit polygenic score (P value = 3.2 × 10−11) in AAs included only 22 independent SNPs with association P value < 2.75 × 10−6 in the EA GWAS, and explained 2% of neuroblastoma risk variance. The significance of the polygenic score dropped rapidly with inclusion of additional SNPs. Conclusions: These findings suggest that several common variants contribute to risk of neuroblastoma in an ancestry-specific fashion. Impact: This work supports the need for GWAS to be performed in populations of all races and ethnicities.
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Musliner, K. L., F. Seifuddin, J. A. Judy, M. Pirooznia, F. S. Goes, and P. P. Zandi. "Polygenic risk, stressful life events and depressive symptoms in older adults: a polygenic score analysis." Psychological Medicine 45, no. 8 (December 9, 2014): 1709–20. http://dx.doi.org/10.1017/s0033291714002839.
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Background.Previous studies suggest that the relationship between genetic risk and depression may be moderated by stressful life events (SLEs). The goal of this study was to assess whether SLEs moderate the association between polygenic risk of major depressive disorder (MDD) and depressive symptoms in older adults.Method.We used logistic and negative binomial regressions to assess the associations between polygenic risk, SLEs and depressive symptoms in a sample of 8761 participants from the Health and Retirement Study. Polygenic scores were derived from the Psychiatric Genomics Consortium genome-wide association study of MDD. SLEs were operationalized as a dichotomous variable indicating whether participants had experienced at least one stressful event during the previous 2 years. Depressive symptoms were measured using an eight-item Center for Epidemiologic Studies Depression Scale subscale and operationalized as both a dichotomous and a count variable.Results.The odds of reporting four or more depressive symptoms were over twice as high among individuals who experienced at least one SLE (odds ratio 2.19, 95% confidence interval 1.86–2.58). Polygenic scores were significantly associated with depressive symptoms (β= 0.21,p⩽ 0.0001), although the variance explained was modest (pseudor2= 0.0095). None of the interaction terms for polygenic scores and SLEs was statistically significant.Conclusions.Polygenic risk and SLEs are robust, independent predictors of depressive symptoms in older adults. Consistent with an additive model, we found no evidence that SLEs moderated the association between common variant polygenic risk and depressive symptoms.
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Adam, Yagoub, Suraju Sadeeq, Judit Kumuthini, Olabode Ajayi, Gordon Wells, Rotimi Solomon, Olubanke Ogunlana, et al. "Polygenic Risk Score in African populations: progress and challenges." F1000Research 11 (February 14, 2022): 175. http://dx.doi.org/10.12688/f1000research.76218.1.
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Polygenic Risk Score (PRS) analysis is a method that predicts the genetic risk of an individual towards targeted traits. Even when there are no significant markers, it gives evidence of a genetic effect beyond the results of Genome-Wide Association Studies (GWAS). Moreover, it selects single nucleotide polymorphisms (SNPs) that contribute to the disease with low effect size making it more precise at individual level risk prediction. PRS analysis addresses the shortfall of GWAS by taking into account the SNPs/alleles with low effect size but play an indispensable role to the observed phenotypic/trait variance. PRS analysis has applications that investigate the genetic basis of several traits, which includes rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies show that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we review the conventional PRS methods and their application to sub-Saharan African communities. We conclude that lack of sufficient GWAS data and tools is the limiting factor of applying PRS analysis to sub-Saharan populations. We recommend developing Africa-specific PRS methods and tools for estimating and analyzing African population data for clinical evaluation of PRSs of interest and predicting rare diseases.
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Li, Huijuan, Chuyi Zhang, Xin Cai, Lu Wang, Fang Luo, Yina Ma, Ming Li, and Xiao Xiao. "Genome-wide Association Study of Creativity Reveals Genetic Overlap With Psychiatric Disorders, Risk Tolerance, and Risky Behaviors." Schizophrenia Bulletin 46, no. 5 (March 5, 2020): 1317–26. http://dx.doi.org/10.1093/schbul/sbaa025.
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Abstract Creativity represents one of the most important and partially heritable human characteristics, yet little is known about its genetic basis. Epidemiological studies reveal associations between creativity and psychiatric disorders as well as multiple personality and behavioral traits. To test whether creativity and these disorders or traits share genetic basis, we performed genome-wide association studies (GWAS) followed by polygenic risk score (PRS) analyses. Two cohorts of Han Chinese subjects (4,834 individuals in total) aged 18–45 were recruited for creativity measurement using typical performance test. After exclusion of the outliers with significantly deviated creativity scores and low-quality genotyping results, 4,664 participants were proceeded for GWAS. We conducted PRS analyses using both the classical pruning and thresholding (P+T) method and the LDpred method. The extent of polygenic risk was estimated through linear regression adjusting for the top 3 genotyping principal components. R2 was used as a measurement of the explained variance. PRS analyses demonstrated significantly positive genetic overlap, respectively, between creativity with schizophrenia ((P+T) method: R2(max) ~ .196%, P = .00245; LDpred method: R2(max) ~ .226%, P = .00114), depression ((P+T) method: R2(max) ~ .178%, P = .00389; LDpred method: R2(max) ~ .093%, P = .03675), general risk tolerance ((P+T) method: R2(max) ~ .177%, P = .00399; LDpred method: R2(max) ~ .305%, P = .00016), and risky behaviors ((P+T) method: R2(max) ~ .187%, P = .00307; LDpred method: R2(max) ~ .155%, P = .00715). Our results suggest that human creativity is probably a polygenic trait affected by numerous variations with tiny effects. Genetic variations that predispose to psychiatric disorders and risky behaviors may underlie part of the genetic basis of creativity, confirming the epidemiological associations between creativity and these traits.
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Sapkota, Yadav, Qi Liu, Nan Li, Neel S. Bhatt, Matthew J. Ehrhardt, Carmen L. Wilson, Zhaoming Wang, et al. "Contribution of Genome-Wide Polygenic Score to Risk of Coronary Artery Disease in Childhood Cancer Survivors." JACC: CardioOncology 4, no. 2 (June 2022): 258–67. http://dx.doi.org/10.1016/j.jaccao.2022.04.003.
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Opherk, Christian, Mariya Gonik, Marco Duering, Rainer Malik, Eric Jouvent, Dominique Hervé, Poneh Adib-Samii, et al. "Genome-Wide Genotyping Demonstrates a Polygenic Risk Score Associated With White Matter Hyperintensity Volume in CADASIL." Stroke 45, no. 4 (April 2014): 968–72. http://dx.doi.org/10.1161/strokeaha.113.004461.
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Nakahara, Soichiro, Sarah Medland, Jessica A. Turner, Vince D. Calhoun, Kelvin O. Lim, Bryon A. Mueller, Juan R. Bustillo, et al. "Polygenic risk score, genome-wide association, and gene set analyses of cognitive domain deficits in schizophrenia." Schizophrenia Research 201 (November 2018): 393–99. http://dx.doi.org/10.1016/j.schres.2018.05.041.
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Liu, MengZhen, Gianna Rea-Sandin, Johanna Foerster, Lars Fritsche, Katharine Brieger, Christopher Clark, Kevin Li, et al. "Validating Online Measures of Cognitive Ability in Genes for Good, a Genetic Study of Health and Behavior." Assessment 27, no. 1 (November 28, 2017): 136–48. http://dx.doi.org/10.1177/1073191117744048.
Повний текст джерелаАнотація:
Genetic association studies routinely require many thousands of participants to achieve sufficient power, yet accumulation of large well-assessed samples is costly. We describe here an effort to efficiently measure cognitive ability and personality in an online genetic study, Genes for Good. We report on the first 21,550 participants with relevant phenotypic data, 7,458 of whom have been genotyped genome-wide. Measures of crystallized and fluid intelligence reflected a two-dimensional latent ability space, with items demonstrating adequate item-level characteristics. The Big Five Inventory questionnaire revealed the expected five-factor model of personality. Cognitive measures predicted educational attainment over and above personality characteristics, as expected. We found that a genome-wide polygenic score of educational attainment predicted educational level, accounting for 4%, 4%, and 2.7% of the variance in educational attainment, verbal reasoning, and spatial reasoning, respectively. In summary, the online cognitive measures in Genes for Good appear to perform adequately and demonstrate expected associations with personality, education, and an education-based polygenic score. Results indicate that online cognitive assessment is one avenue to accumulate large samples of individuals for genetic research of cognitive ability.
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Chung, Wonil. "Statistical models and computational tools for predicting complex traits and diseases." Genomics & Informatics 19, no. 4 (December 31, 2021): e36. http://dx.doi.org/10.5808/gi.21053.
Повний текст джерелаАнотація:
Predicting individual traits and diseases from genetic variants is critical to fulfilling the promise of personalized medicine. The genetic variants from genome-wide association studies (GWAS), including variants well below GWAS significance, can be aggregated into highly significant predictions across a wide range of complex traits and diseases. The recent arrival of large-sample public biobanks enables highly accurate polygenic predictions based on genetic variants across the whole genome. Various statistical methodologies and diverse computational tools have been introduced and developed to computed the polygenic risk score (PRS) more accurately. However, many researchers utilize PRS tools without a thorough understanding of the underlying model and how to specify the parameters for the best performance. It is advantageous to study the statistical models implemented in computational tools for PRS estimation and the formulas of parameters to be specified. Here, we review a variety of recent statistical methodologies and computational tools for PRS computation.
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Joo, Yoonjung Yoonie, Ky’Era Actkins, Jennifer A. Pacheco, Anna O. Basile, Robert Carroll, David R. Crosslin, Felix Day, et al. "A Polygenic and Phenotypic Risk Prediction for Polycystic Ovary Syndrome Evaluated by Phenome-Wide Association Studies." Journal of Clinical Endocrinology & Metabolism 105, no. 6 (January 9, 2020): 1918–36. http://dx.doi.org/10.1210/clinem/dgz326.
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Abstract Context As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated to be unidentified in clinical practice. Objective Utilizing polygenic risk prediction, we aim to identify the phenome-wide comorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventive treatment. Design, Patients, and Methods Leveraging the electronic health records (EHRs) of 124 852 individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores (PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). We evaluated its predictive capability across different ancestries and perform a PRS-based phenome-wide association study (PheWAS) to assess the phenomic expression of the heightened risk of PCOS. Results The integrated polygenic prediction improved the average performance (pseudo-R2) for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null model across European, African, and multi-ancestry participants respectively. The subsequent PRS-powered PheWAS identified a high level of shared biology between PCOS and a range of metabolic and endocrine outcomes, especially with obesity and diabetes: “morbid obesity”, “type 2 diabetes”, “hypercholesterolemia”, “disorders of lipid metabolism”, “hypertension”, and “sleep apnea” reaching phenome-wide significance. Conclusions Our study has expanded the methodological utility of PRS in patient stratification and risk prediction, especially in a multifactorial condition like PCOS, across different genetic origins. By utilizing the individual genome–phenome data available from the EHR, our approach also demonstrates that polygenic prediction by PRS can provide valuable opportunities to discover the pleiotropic phenomic network associated with PCOS pathogenesis.
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Kothalawala, Dilini M., Latha Kadalayil, John A. Curtin, Clare S. Murray, Angela Simpson, Adnan Custovic, William J. Tapper, S. Hasan Arshad, Faisal I. Rezwan, and John W. Holloway. "Integration of Genomic Risk Scores to Improve the Prediction of Childhood Asthma Diagnosis." Journal of Personalized Medicine 12, no. 1 (January 8, 2022): 75. http://dx.doi.org/10.3390/jpm12010075.
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Genome-wide and epigenome-wide association studies have identified genetic variants and differentially methylated nucleotides associated with childhood asthma. Incorporation of such genomic data may improve performance of childhood asthma prediction models which use phenotypic and environmental data. Using genome-wide genotype and methylation data at birth from the Isle of Wight Birth Cohort (n = 1456), a polygenic risk score (PRS), and newborn (nMRS) and childhood (cMRS) methylation risk scores, were developed to predict childhood asthma diagnosis. Each risk score was integrated with two previously published childhood asthma prediction models (CAPE and CAPP) and were validated in the Manchester Asthma and Allergy Study. Individually, the genomic risk scores demonstrated modest-to-moderate discriminative performance (area under the receiver operating characteristic curve, AUC: PRS = 0.64, nMRS = 0.55, cMRS = 0.54), and their integration only marginally improved the performance of the CAPE (AUC: 0.75 vs. 0.71) and CAPP models (AUC: 0.84 vs. 0.82). The limited predictive performance of each genomic risk score individually and their inability to substantially improve upon the performance of the CAPE and CAPP models suggests that genetic and epigenetic predictors of the broad phenotype of asthma are unlikely to have clinical utility. Hence, further studies predicting specific asthma endotypes are warranted.
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Škorić-Milosavljević, Doris, Rafik Tadros, Fernanda M. Bosada, Federico Tessadori, Jan Hendrik van Weerd, Odilia I. Woudstra, Fleur V. Y. Tjong, et al. "Common Genetic Variants Contribute to Risk of Transposition of the Great Arteries." Circulation Research 130, no. 2 (January 21, 2022): 166–80. http://dx.doi.org/10.1161/circresaha.120.317107.
Повний текст джерелаАнотація:
Rationale: Dextro-transposition of the great arteries (D-TGA) is a severe congenital heart defect which affects approximately 1 in 4,000 live births. While there are several reports of D-TGA patients with rare variants in individual genes, the majority of D-TGA cases remain genetically elusive. Familial recurrence patterns and the observation that most cases with D-TGA are sporadic suggest a polygenic inheritance for the disorder, yet this remains unexplored. Objective: We sought to study the role of common single nucleotide polymorphisms (SNPs) in risk for D-TGA. Methods and Results: We conducted a genome-wide association study in an international set of 1,237 patients with D-TGA and identified a genome-wide significant susceptibility locus on chromosome 3p14.3, which was subsequently replicated in an independent case-control set (rs56219800, meta-analysis P=8.6x10 -10 , OR=0.69 per C allele). SNP-based heritability analysis showed that 25% of variance in susceptibility to D-TGA may be explained by common variants. A genome-wide polygenic risk score derived from the discovery set was significantly associated to D-TGA in the replication set (P=4x10 -5 ). The genome-wide significant locus (3p14.3) co-localizes with a putative regulatory element that interacts with the promoter of WNT5A , which encodes the Wnt Family Member 5A protein known for its role in cardiac development in mice. We show that this element drives reporter gene activity in the developing heart of mice and zebrafish and is bound by the developmental transcription factor TBX20. We further demonstrate that TBX20 attenuates Wnt5a expression levels in the developing mouse heart. Conclusions: This work provides support for a polygenic architecture in D-TGA and identifies a susceptibility locus on chromosome 3p14.3 near WNT5A . Genomic and functional data support a causal role of WNT5A at the locus.
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Fuller, Zachary L., Veronique J. L. Mocellin, Luke A. Morris, Neal Cantin, Jihanne Shepherd, Luke Sarre, Julie Peng, et al. "Population genetics of the coral Acropora millepora: Toward genomic prediction of bleaching." Science 369, no. 6501 (July 16, 2020): eaba4674. http://dx.doi.org/10.1126/science.aba4674.
Повний текст джерелаАнотація:
Although reef-building corals are declining worldwide, responses to bleaching vary within and across species and are partly heritable. Toward predicting bleaching response from genomic data, we generated a chromosome-scale genome assembly for the coral Acropora millepora. We obtained whole-genome sequences for 237 phenotyped samples collected at 12 reefs along the Great Barrier Reef, among which we inferred little population structure. Scanning the genome for evidence of local adaptation, we detected signatures of long-term balancing selection in the heat-shock co-chaperone sacsin. We conducted a genome-wide association study of visual bleaching score for 213 samples, incorporating the polygenic score derived from it into a predictive model for bleaching in the wild. These results set the stage for genomics-based approaches in conservation strategies.
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Hsu, Chih-Chien, Hao-Kai Chuang, Yu-Jer Hsiao, Yuan-Chi Teng, Pin-Hsuan Chiang, Yu-Jun Wang, Ting-Yi Lin, et al. "Polygenic Risk Score Improves Cataract Prediction in East Asian Population." Biomedicines 10, no. 8 (August 8, 2022): 1920. http://dx.doi.org/10.3390/biomedicines10081920.
Повний текст джерелаАнотація:
Cataracts, characterized by crystalline lens opacities in human eyes, is the leading cause of blindness globally. Due to its multifactorial complexity, the molecular mechanisms remain poorly understood. Larger cohorts of genome-wide association studies (GWAS) are needed to investigate cataracts’ genetic basis. In this study, a GWAS was performed on the largest Han population to date, analyzing a total of 7079 patients and 13,256 controls from the Taiwan Biobank (TWB) 2.0 cohort. Two cataract-associated SNPs with an adjustment of p < 1 × 10−7 in the older groups and nine SNPs with an adjustment of p < 1 × 10−6 in the younger group were identified. Except for the reported AGMO in animal models, most variations, including rs74774546 in GJA1 and rs237885 in OXTR, were not identified before this study. Furthermore, a polygenic risk score (PRS) was created for the young and old populations to identify high-risk cataract individuals, with areas under the receiver operating curve (AUROCs) of 0.829 and 0.785, respectively, after covariate adjustments. Younger individuals had 17.45 times the risk while older people had 10.97 times the risk when comparing individuals in the highest and lowest PRS quantiles. Validation analysis on an independent TWB1.0 cohort revealed AUROCs of 0.744 and 0.659.
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Breedon, Joshua R., Charles R. Marshall, Gavin Giovannoni, David A. van Heel, Shaheen Akhtar, Mohammad Anwar, Elena Arciero, et al. "Polygenic risk score prediction of multiple sclerosis in individuals of South Asian ancestry." Brain Communications, February 22, 2023. http://dx.doi.org/10.1093/braincomms/fcad041.
Повний текст джерелаАнотація:
Abstract Polygenic risk scores aggregate an individual’s burden of risk alleles to estimate overall genetic risk for a specific trait or disease. Polygenic risk scores derived from Genome-Wide Association Studies of European populations perform poorly for other ancestral groups. Given the potential for future clinical utility, underperformance of polygenic risk score prediction in South Asian populations has the potential to reinforce health inequalities. To determine whether European-derived polygenic risk scores underperform at Multiple Sclerosis prediction in a South Asian population compared with a European-ancestry cohort, we used data from two longitudinal genetic cohort studies: Genes & Health (2015-), a study of ∼50,000 British-Bangladeshi and British-Pakistani individuals, and UK Biobank (2006-), which is comprised of ∼500,000 predominantly White British individuals. We compared individuals with and without Multiple Sclerosis in both studies (Genes and Health: NCases=42, NControl=40,490; UK Biobank: NCases=2091, NControl=374,866). Polygenic risk scores were calculated using clumping-and-thresholding with risk allele effect sizes from the largest Multiple Sclerosis Genome-Wide Association Study to date. Scores were calculated with and without the Major Histocompatibility Complex region, the most influential locus in determining Multiple Sclerosis risk. Polygenic risk scire prediction was evaluated using Nagelkerke’s pseudo-R2 metric adjusted for case ascertainment, age, sex, and the first four genetic principal components. We found that, as expected, European-derived polygenic risk scores perform poorly in the Genes and Health cohort, explaining 1.1% (including the Major Histocompatibility Complex) and 1.5% (excluding the Major Histocompatibility Complex) of disease risk. In contrast, Multiple Sclerosis polygenic risk scores explained 4.8% (including the Major Histocompatibility Complex) and 2.8% (excluding the Major Histocompatibility Complex) of disease risk in European-ancestry UK Biobank participants. These findings suggest that polygenic risk score prediction of Multiple Sclerosis based on European Genome-Wide Association Study results is less accurate in a South Asian population. Genetic studies of ancestrally-diverse populations are required to ensure that polygenic risk scores can be useful across ancestries.
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Page, Madeline L., Elizabeth L. Vance, Matthew E. Cloward, Ed Ringger, Louisa Dayton, Mark T. W. Ebbert, M. W. Weiner, et al. "The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores." Communications Biology 5, no. 1 (September 2, 2022). http://dx.doi.org/10.1038/s42003-022-03795-x.
Повний текст джерелаАнотація:
AbstractThe process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer’s Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.
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"Cross-ancestry genome-wide polygenic score predicts chronic kidney disease." Nature Medicine, June 23, 2022. http://dx.doi.org/10.1038/s41591-022-01871-7.
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Khan, Atlas, Michael C. Turchin, Amit Patki, Vinodh Srinivasasainagendra, Ning Shang, Rajiv Nadukuru, Alana C. Jones, et al. "Genome-wide polygenic score to predict chronic kidney disease across ancestries." Nature Medicine, June 16, 2022. http://dx.doi.org/10.1038/s41591-022-01869-1.
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Briggs, Sarah E. W., Philip Law, James E. East, Sarah Wordsworth, Malcolm Dunlop, Richard Houlston, Julia Hippisley-Cox, and Ian Tomlinson. "Integrating genome-wide polygenic risk scores and non-genetic risk to predict colorectal cancer diagnosis using UK Biobank data: population based cohort study." BMJ, November 9, 2022, e071707. http://dx.doi.org/10.1136/bmj-2022-071707.
Повний текст джерелаАнотація:
Abstract Objective To evaluate the benefit of combining polygenic risk scores with the QCancer-10 (colorectal cancer) prediction model for non-genetic risk to identify people at highest risk of colorectal cancer. Design Population based cohort study. Setting Data from the UK Biobank study, collected between March 2006 and July 2010. Participants 434 587 individuals with complete data for genetics and QCancer-10 predictions were included in the QCancer-10 plus polygenic risk score modelling and validation cohorts. Main outcome measures Prediction of colorectal cancer diagnosis by genetic, non-genetic, and combined risk models. Using data from UK Biobank, six different polygenic risk scores for colorectal cancer were developed using LDpred2 polygenic risk score software, clumping, and thresholding approaches, and a model based on genome-wide significant polymorphisms. The top performing genome-wide polygenic risk score and the score containing genome-wide significant polymorphisms were combined with QCancer-10 and performance was compared with QCancer-10 alone. Case-control (logistic regression) and time-to-event (Cox proportional hazards) analyses were used to evaluate risk model performance in men and women. Results Polygenic risk scores derived using the LDpred2 program performed best, with an odds ratio per standard deviation of 1.584 (95% confidence interval 1.536 to 1.633), and top age and sex adjusted C statistic of 0.733 (95% confidence interval 0.710 to 0.753) in logistic regression models in the validation cohort. Integrated QCancer-10 plus polygenic risk score models out-performed QCancer-10 alone. In men, the integrated LDpred2 model produced a C statistic of 0.730 (0.720 to 0.741) and explained variation of 28.2% (26.3 to 30.1), compared with 0.693 (0.682 to 0.704) and 21.0% (18.9 to 23.1) for QCancer-10 alone. In women, the C statistic for the integrated LDpred2 model was 0.687 (0.673 to 0.702) and explained variation was 21.0% (18.7 to 23.7), compared with 0.645 (0.631 to 0.659) and 12.4% (10.3 to 14.6) for QCancer-10 alone. In the top 20% of individuals at highest absolute risk, the sensitivity and specificity of the integrated LDpred2 models for predicting colorectal cancer diagnosis was 47.8% and 80.3% respectively in men, and 42.7% and 80.1% respectively in women, with increases in absolute risk in the top 5% of risk in men of 3.47-fold and in women of 2.77-fold compared with the median. Illustrative decision curve analysis indicated a small incremental improvement in net benefit with QCancer-10 plus polygenic risk score models compared with QCancer-10 alone. Conclusions Integrating polygenic risk scores with QCancer-10 modestly improves risk prediction over use of QCancer-10 alone. Given that QCancer-10 data can be obtained relatively easily from health records, use of polygenic risk score in risk stratified population screening for colorectal cancer currently has no clear justification. The added benefit, cost effectiveness, and acceptability of polygenic risk scores should be carefully evaluated in a real life screening setting before implementation in the general population.
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Wagner, Róbert, Benjamin Assad Jaghutriz, Felicia Gerst, Morgana Barroso Oquendo, Jürgen Machann, Fritz Schick, Markus W. Löffler, et al. "Pancreatic Steatosis Associates With Impaired Insulin Secretion in Genetically Predisposed Individuals." Journal of Clinical Endocrinology & Metabolism 105, no. 11 (July 29, 2020). http://dx.doi.org/10.1210/clinem/dgaa435.
Повний текст джерелаАнотація:
Abstract Context Pancreatic steatosis leading to beta-cell failure might be involved in type 2 diabetes (T2D) pathogenesis. Objective We hypothesized that the genetic background modulates the effect of pancreatic fat on beta-cell function and investigated genotype × pancreatic fat interactions on insulin secretion. Design Two observational studies. Setting University hospital. Patients or participants A total of 360 nondiabetic individuals with elevated risk for T2D (Tuebingen Family Study [TUEF]), and 64 patients undergoing pancreatectomy (Pancreas Biobank [PB], HbA1c <9%, no insulin therapy). Main Outcome Measures Insulin secretion calculated from 5-point oral glucose tolerance test (TUEF) and fasting blood collection before surgery (PB). A genome-wide polygenic score for T2D was computed from 484,788 genotyped variants. The interaction of magnetic resonance imaging-measured and histologically quantified pancreatic fat with the polygenic score was investigated. Partitioned risk scores using genome-wide significant variants were also computed to gain insight into potential mechanisms. Results Pancreatic steatosis interacted with genome-wide polygenic score on insulin secretion (P = 0.003), which was similar in the replication cohort with histological measurements (P = 0.03). There was a negative association between pancreatic fat and insulin secretion in participants with high genetic risk, whereas individuals with low genetic risk showed a positive correlation between pancreatic fat and insulin secretion. Consistent interactions were found with insulin resistance-specific and a liver/lipid-specific polygenic scores. Conclusions The associations suggest that pancreatic steatosis only impairs beta-cell function in subjects at high genetic risk for diabetes. Genetically determined insulin resistance specifically renders pancreatic fat deleterious for insulin secretion.