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Journal articles on the topic 'Genetic risk of disease'

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Author: Grafiati

Published: 11 March 2023

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1

McDonald, B. A., and C. Linde. "Disease resistance and pathogen population genetic." Plant Protection Science 38, SI 1 - 6th Conf EFPP 2002 (January 1, 2002): 245–48. http://dx.doi.org/10.17221/10375-pps.

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Plant pathologists have seen many boom-and-bust cycles following the deployment of resistant varieties. These cycles result when pathogen populations adapt to the presence of a major resistance gene by evolving a new population that can overcome this resistance gene. The breakdown of genetic resistance is due to the evolution of the local pathogen population because of selection for mutants, recombinants, or immigrants that are better adapted to the resistant cultivar. To understand the process that leads to breakdown of a resistance gene, we need to understand the processes that govern pathogen evolution. Population geneticists have identified five evolutionary forces that interact to affect the evolution of organisms. We ranked these risks and developed a quantitative framework to predict the risk that a pathogen will evolve to overcome major resistance genes. Our hypothesis is that much of the durability of resistance genes is due to the nature of the pathogen population rather than to the nature of the resistance gene. The framework we developed can be used as a hypothesis to test against a large number of plant pathosystems. The underlying principles of the framework can be tested individually or in combination according to the available knowledge of the population genetics for any pathogen. We propose that this framework can be used to design breeding strategies to break the boom-and-bust cycle and lead to durable resistance.

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2

Roberts, Robert. "Molecular genetics: Cardiac disease and risk-related genes-Genetic risk factors." Clinical Cardiology 18, S4 (September 1995): IV13—IV19. http://dx.doi.org/10.1002/clc.4960181604.

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3

Alonso, Lorena, Ignasi Morán, Cecilia Salvoro, and David Torrents. "In Search of Complex Disease Risk through Genome Wide Association Studies." Mathematics 9, no. 23 (November 30, 2021): 3083. http://dx.doi.org/10.3390/math9233083.

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The identification and characterisation of genomic changes (variants) that can lead to human diseases is one of the central aims of biomedical research. The generation of catalogues of genetic variants that have an impact on specific diseases is the basis of Personalised Medicine, where diagnoses and treatment protocols are selected according to each patient’s profile. In this context, the study of complex diseases, such as Type 2 diabetes or cardiovascular alterations, is fundamental. However, these diseases result from the combination of multiple genetic and environmental factors, which makes the discovery of causal variants particularly challenging at a statistical and computational level. Genome-Wide Association Studies (GWAS), which are based on the statistical analysis of genetic variant frequencies across non-diseased and diseased individuals, have been successful in finding genetic variants that are associated to specific diseases or phenotypic traits. But GWAS methodology is limited when considering important genetic aspects of the disease and has not yet resulted in meaningful translation to clinical practice. This review presents an outlook on the study of the link between genetics and complex phenotypes. We first present an overview of the past and current statistical methods used in the field. Next, we discuss current practices and their main limitations. Finally, we describe the open challenges that remain and that might benefit greatly from further mathematical developments.

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Bloch, Michael J. "Genetic risk scores and coronary heart disease risk." Journal of the American Society of Hypertension 9, no. 8 (August 2015): 580–81. http://dx.doi.org/10.1016/j.jash.2015.06.010.

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5

Skrzypa, Marzena, Natalia Potocka, Halina Bartosik-Psujek, and Izabela Zawlik. "Genetic risk factors of Alzheimer’s disease." European Journal of Clinical and Experimental Medicine 17, no. 1 (2019): 57–66. http://dx.doi.org/10.15584/ejcem.2019.1.10.

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6

Jostins, Luke, and Jeffrey C. Barrett. "Genetic risk prediction in complex disease." Human Molecular Genetics 20, R2 (August 25, 2011): R182—R188. http://dx.doi.org/10.1093/hmg/ddr378.

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7

Secko, D. "Alzheimer's disease: genetic variables and risk." Canadian Medical Association Journal 172, no. 5 (March 1, 2005): 627. http://dx.doi.org/10.1503/cmaj.050111.

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Billingsley, K. J., S. Bandres-Ciga, S. Saez-Atienzar, and A. B. Singleton. "Genetic risk factors in Parkinson’s disease." Cell and Tissue Research 373, no. 1 (March 13, 2018): 9–20. http://dx.doi.org/10.1007/s00441-018-2817-y.

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9

Alliey, Ney. "Genetic Variants And Risk Of Disease." European Neuropsychopharmacology 29 (2019): S715—S716. http://dx.doi.org/10.1016/j.euroneuro.2017.06.025.

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10

Bradshaw, Elizabeth. "CD33 GENETIC RISK IN ALZHEIMER'S DISEASE." Alzheimer's & Dementia 13, no. 7 (July 2017): P1448. http://dx.doi.org/10.1016/j.jalz.2017.07.488.

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11

Tilley, L., K. Morgan, and N. Kalsheker. "Genetic risk factors in Alzheimer's disease." Molecular Pathology 51, no. 6 (December 1, 1998): 293–304. http://dx.doi.org/10.1136/mp.51.6.293.

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12

Sharabitdinova, Gulshad Gafurkhanovna. "GENETIC RISK FACTORS FOR CARDIOVASCULAR DISEASE." Theoretical & Applied Science 59, no. 03 (March 30, 2018): 240–43. http://dx.doi.org/10.15863/tas.2018.03.59.41.

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13

Pimenova, Anna A., Towfique Raj, and Alison M. Goate. "Untangling Genetic Risk for Alzheimer’s Disease." Biological Psychiatry 83, no. 4 (February 2018): 300–310. http://dx.doi.org/10.1016/j.biopsych.2017.05.014.

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14

Wood, Nicholas W. "Genetic risk factors in parkinson's disease." Annals of Neurology 44, S1 (September 1998): S58—S62. http://dx.doi.org/10.1002/ana.410440709.

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15

Liddell, M. B., S. Lovestone, and M. J. Owen. "Genetic risk of Alzheimer's disease: advising relatives." British Journal of Psychiatry 178, no. 1 (January 2001): 7–11. http://dx.doi.org/10.1192/bjp.178.1.7.

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BackgroundClinicians are increasingly asked by relatives of patients with Alzheimer's disease to advise on their genetic risk of developing Alzheimer's disease in later life. Many clinicians find this a difficult question to answer.AimsTo provide information for old age psychiatrists wishing to advise relatives of their risk of developing Alzheimer's disease.MethodA selective review of the key literature on the genetic epidemiology of Alzheimer's disease.ResultsCurrently a DNA diagnosis is attainable in some 70% of families with autosomal dominant Alzheimer's disease. In first-degree relatives of most cases, risk is increased some three- or four-fold relative to controls, but only one-third of this is realised in the average life span. Apolipoprotein E genotyping cannot be used as a predictive test and confers only minimal diagnostic benefit.ConclusionsPedigrees with familial Alzheimer's disease should be referred to a Regional Centre for Medical Genetics. Accurate risk prediction is not possible in the vast majority of pedigrees with Alzheimer's disease, although it is possible for the psychiatrist to give a rough estimate of the risk, which can reasonably the couched in reassuring terms.

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16

Bin, Paola, Emanuele Capasso, Mariano Paternoster, Piergiorgio Fedeli, Fabio Policino, Claudia Casella, and Adelaide Conti. "Genetic risk in insurance field." Open Medicine 13, no. 1 (August 24, 2018): 294–97. http://dx.doi.org/10.1515/med-2018-0045.

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AbstractThe risk-delimiting tools available to insurance companies are therefore substantial and it is also possible to argue that a margin of uncertainty is a natural component of the insurance contract.Despite this, businesses look at the potential of predictive medicine, and in particular the growing understanding of genetic mechanisms that support many common diseases.In particular, the rapid development of genetics has led many insurance companies to glimpse in the predictive diagnosis of disease by genetic testing the possibility of extending the calculation of the individual risk of developing a particular disease to appropriate premiums or even denying insurance coverage.

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Knowles, Joshua W., and Euan A. Ashley. "Cardiovascular disease: The rise of the genetic risk score." PLOS Medicine 15, no. 3 (March 30, 2018): e1002546. http://dx.doi.org/10.1371/journal.pmed.1002546.

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18

Leonenko, Ganna, Maryam Shoai, Eftychia Bellou, Rebecca Sims, Julie Williams, John Hardy, and Valentina Escott‐Price. "Genetic risk for alzheimer disease is distinct from genetic risk for amyloid deposition." Annals of Neurology 86, no. 3 (July 2019): 427–35. http://dx.doi.org/10.1002/ana.25530.

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19

Jabloner, Anna. "Relative Risk." Social Analysis 65, no. 4 (December 1, 2021): 111–30. http://dx.doi.org/10.3167/sa.2021.650406.

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Genetic counselors in the US assess disease risks by drawing on family histories, genetic tests, and patients’ racial, ethnic, national, or religious self-identifications. The bodily risks of kinship articulated by family histories can be defused by genetic tests that highlight the contingency of biological inheritance and decouple kinship from genetics. However, such tests, as well as self-identifying patients, also entwine genetic risk with older indicators of kinship: biologically understood race and ethnicity. Across these scales, counselors calculate relative risks to the future health of individuals, in the process measuring kinship as genealogical closeness, genetic dis/similarity, and biocultural race and ethnicity. As counselors personalize the universal promises of genomics at a biomedical nexus of risk and prophylaxis, they tap into anxieties about the changed natures of American kinship.

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Lombardi, Rosa, Federica Iuculano, Giada Pallini, Silvia Fargion, and Anna Ludovica Fracanzani. "Nutrients, Genetic Factors, and Their Interaction in Non-Alcoholic Fatty Liver Disease and Cardiovascular Disease." International Journal of Molecular Sciences 21, no. 22 (November 19, 2020): 8761. http://dx.doi.org/10.3390/ijms21228761.

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Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in Western countries and expose patients to increased risk of hepatic and cardiovascular (CV) morbidity and mortality. Both environmental factors and genetic predisposition contribute to the risk. An inappropriate diet, rich in refined carbohydrates, especially fructose, and saturated fats, and poor in fibers, polyunsaturated fats, and vitamins is one of the main key factors, as well as the polymorphism of patatin-like phospholipase domain containing 3 (PNPLA3 gene) for NAFLD and the apolipoproteins and the peroxisome proliferator-activated receptor (PPAR) family for the cardiovascular damage. Beyond genetic influence, also epigenetics modifications are responsible for various clinical manifestations of both hepatic and CV disease. Interestingly, data are accumulating on the interplay between diet and genetic and epigenetic modifications, modulating pathogenetic pathways in NAFLD and CV disease. We report the main evidence from literature on the influence of both macro and micronutrients in NAFLD and CV damage and the role of genetics either alone or combined with diet in increasing the risk of developing both diseases. Understanding the interaction between metabolic alterations, genetics and diet are essential to treat the diseases and tailoring nutritional therapy to control NAFLD and CV risk.

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21

McHugh, Jessica. "Shared genetic risk for Behçet disease and Crohn's disease." Nature Reviews Rheumatology 13, no. 4 (March 2, 2017): 197. http://dx.doi.org/10.1038/nrrheum.2017.30.

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22

Pekmezović, Tatjana. "Gene-Environment Interaction: A Genetic-Epidemiological Approach." Journal of Medical Biochemistry 29, no. 3 (July 1, 2010): 131–34. http://dx.doi.org/10.2478/v10011-010-0021-z.

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Gene-Environment Interaction: A Genetic-Epidemiological ApproachClassical epidemiology addresses the distribution and determinants of diseases in populations, and the factors associated with disease causation, with the aim of preventing disease. Both genetic and environmental factors may contribute to susceptibility, and it is still unclear how these factors interact in their influence on risk. Genetic epidemiology is the field which incorporates concepts and methods from different disciplines including epidemiology, genetics, biostatistics, clinical and molecular medicine, and their interaction is crucial to understanding the role of genetic and environmental factors in disease processes. The study of gene-environment interaction is central in the field of genetic epidemiology. Gene-environment interaction is defined as »a different effect of an environmental exposure on disease risk in persons with different genotypes,« or, alternatively, »a different effect of a genotype on disease risk in persons with different environmental exposures.« Five biologically plausible models are described for the relations between genotypes and environmental exposures, in terms of their effects on disease risk. Therefore, the study of gene-environment interaction is important for improving accuracy and precision in the assessment of both genetic and environmental factors, especially in disorders of less defined etiology. Genetic epidemiology is also applied at the various levels of disease prevention.

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23

Treff, Nathan R., Diego Marin, Louis Lello, Stephen Hsu, and Laurent C. A. M. Tellier. "PREIMPLANTATION GENETIC TESTING: Preimplantation genetic testing for polygenic disease risk." Reproduction 160, no. 5 (November 2020): A13—A17. http://dx.doi.org/10.1530/rep-20-0071.

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Since its introduction to clinical practice, preimplantation genetic testing (PGT) has become a standard of care for couples at risk of having children with monogenic disease and for chromosomal aneuploidy to improve outcomes for patients with infertility. The primary objective of PGT is to reduce the risk of miscarriage and genetic disease and to improve the success of infertility treatment with the delivery of a healthy child. Until recently, the application of PGT to more common but complex polygenic disease was not possible, as the genetic contribution to polygenic disease has been difficult to determine, and the concept of embryo selection across multiple genetic loci has been difficult to comprehend. Several achievements, including the ability to obtain accurate, genome-wide genotypes of the human embryo and the development of population-level biobanks, have now made PGT for polygenic disease risk applicable in clinical practice. With the rapid advances in embryonic polygenic risk scoring, diverse considerations beyond technical capability have been introduced.

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24

Asatryan, Babken, and Argelia Medeiros-Domingo. "Molecular and genetic insights into progressive cardiac conduction disease." EP Europace 21, no. 8 (April 26, 2019): 1145–58. http://dx.doi.org/10.1093/europace/euz109.

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Abstract Progressive cardiac conduction disease (PCCD) is often a primarily genetic disorder, with clinical and genetic overlaps with other inherited cardiac and metabolic diseases. A number of genes have been implicated in PCCD pathogenesis with or without structural heart disease or systemic manifestations. Precise genetic diagnosis contributes to risk stratification, better selection of specific therapy and allows familiar cascade screening. Cardiologists should be aware of the different phenotypes emerging from different gene-mutations and the potential risk of sudden cardiac death. Genetic forms of PCCD often overlap or coexist with other inherited heart diseases or manifest in the context of multisystem syndromes. Despite the significant advances in the knowledge of the genetic architecture of PCCD and overlapping diseases, in a measurable fraction of PCCD cases, including in familial clustering of disease, investigations of known cardiac disease-associated genes fail to reveal the underlying substrate, suggesting that new causal genes are yet to be discovered. Here, we provide insight into genetics and molecular mechanisms of PCCD and related diseases. We also highlight the phenotypic overlaps of PCCD with other inherited cardiac and metabolic diseases, present unmet challenges in clinical practice, and summarize the available therapeutic options for affected patients.

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25

Zeng, Lingyao, Ioanna Ntalla, Thorsten Kessler, Adnan Kastrati, Jeanette Erdmann, John Danesh, Hugh Watkins, Nilesh J. Samani, Panos Deloukas, and Heribert Schunkert. "Genetically modulated educational attainment and coronary disease risk." European Heart Journal 40, no. 29 (June 6, 2019): 2413–20. http://dx.doi.org/10.1093/eurheartj/ehz328.

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Abstract Aims Genetic disposition and lifestyle factors are understood as independent components underlying the risk of multiple diseases. In this study, we aim to investigate the interplay between genetics, educational attainment—an important denominator of lifestyle—and coronary artery disease (CAD) risk. Methods and results Based on the effect sizes of 74 genetic variants associated with educational attainment, we calculated a ‘genetic education score’ in 13 080 cases and 14 471 controls and observed an inverse correlation between the score and risk of CAD [P = 1.52 × 10−8; odds ratio (OR) 0.79, 95% confidence interval (CI) 0.73–0.85 for the higher compared with the lowest score quintile]. We replicated in 146 514 individuals from UK Biobank (P = 1.85 × 10−6) and also found strong associations between the ‘genetic education score’ with ‘modifiable’ risk factors including smoking (P = 5.36 × 10−23), body mass index (BMI) (P = 1.66 × 10−30), and hypertension (P = 3.86 × 10−8). Interestingly, these associations were only modestly attenuated by adjustment for years spent in school. In contrast, a model adjusting for BMI and smoking abolished the association signal between the ‘genetic education score’ and CAD risk suggesting an intermediary role of these two risk factors. Mendelian randomization analyses performed with summary statistics from large genome-wide meta-analyses and sensitivity analysis using 1271 variants affecting educational attainment (OR 0.68 for the higher compared with the lowest score quintile; 95% CI 0.63–0.74; P = 3.99 × 10−21) further strengthened these findings. Conclusion Genetic variants known to affect educational attainment may have implications for a health-conscious lifestyle later in life and subsequently affect the risk of CAD.

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Akushevich, Igor, Arseniy Yashkin, Julia Kravchenko, Svetlana Ukraintseva, and Anatoliy Yashin. "EFFECTS OF MEDICARE COMORBIDITIES, SELF-REPORTED FACTORS, AND POLYGENIC RISK SCORES IN RISKS OF AD/ADRD." Innovation in Aging 3, Supplement_1 (November 2019): S484. http://dx.doi.org/10.1093/geroni/igz038.1798.

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Abstract At this time there is no consensus on the origin, development, and progression of Alzheimer’s Disease and related dementias (AD/ADRD) and the extent to which variation in the effects of potential risk factors affects the risk for this disorder is underexplored. In this paper we used HRS-Medicare-genetics data to evaluate the effects of risk factors from three groups: i) Medicare-based indicators of chronic diseases that have shown an association with AD/ADRD in the literature, ii) individual heath state, behavior, functional status, education and socioeconomic status, and iii) polygenic risk scores that incorporate known-to-date genetic risk factors for AD/ADRD. We found that: i) the effects of Medicare disease indicators are higher than the effects of self-reported diseases; ii) heart diseases, cerebrovascular diseases, and depression had a strong impact on AD/ADRD, while the presence of cancers sometimes decreases the risk of AD/ADRD; iii) systemic hypotension, chronic kidney disease, and chronic liver disease showed unexpectedly strong effects; iv) compared to females, males are affected by a lower number of risk factors albeit at higher magnitudes; v) BMI, alcohol, drinking, income, and number of education years are protective, vi) genetic scores associated with neurotransmitters (synapse functioning and loss) and neuroinflammation demonstrated strong significant effects, and vii) Blinder-Oaxaca decomposition demonstrated the important role of genetic factors in racial disparities in AD risk. The analyses show the extent to which links between the distinct differences in comorbidities, behavioral and socioeconomic factors can predict the risk for AD/ADRD.

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MURATA, Mitsuru, Koichi KAWANO, Yumiko MATSUBARA, Takeru ZAMA, Nobuo AOKI, Hideaki YOSHINO, Gentaro WATANABE, Kyozo ISHIKAWA, and Yasuo IKEDA. "Genetic risk factors for coronary artery disease." Journal of Japan Atherosclerosis Society 26, no. 1 (1998): 9–15. http://dx.doi.org/10.5551/jat1973.26.1_9.

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28

Sato, Naoyuki. "Alzheimer disease and non-genetic risk factors." Nippon Ronen Igakkai Zasshi. Japanese Journal of Geriatrics 49, no. 3 (2012): 311–13. http://dx.doi.org/10.3143/geriatrics.49.311.

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29

Lim, Gregory B. "Fitness ameliorates genetic risk of heart disease." Nature Reviews Cardiology 15, no. 7 (April 26, 2018): 380. http://dx.doi.org/10.1038/s41569-018-0019-7.

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30

Araújo, F., A. Santos, V. Araújo, I. Henriques, F. Monteiro, E. Meireles, I. Moreira, D. David, M. J. Maciel, and L. M. Cunha-Ribeiro. "Genetic Risk Factors in Acute Coronary Disease." Pathophysiology of Haemostasis and Thrombosis 29, no. 4 (1999): 212–18. http://dx.doi.org/10.1159/000022504.

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31

Iwaki, Hirotaka, Cornelis Blauwendraat, Hampton L. Leonard, Ganqiang Liu, Jodi Maple-Grødem, Jean-Christophe Corvol, Lasse Pihlstrøm, et al. "Genetic risk of Parkinson disease and progression:." Neurology Genetics 5, no. 4 (July 9, 2019): e348. http://dx.doi.org/10.1212/nxg.0000000000000348.

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ObjectiveTo determine if any association between previously identified alleles that confer risk for Parkinson disease and variables measuring disease progression.MethodsWe evaluated the association between 31 risk variants and variables measuring disease progression. A total of 23,423 visits by 4,307 patients of European ancestry from 13 longitudinal cohorts in Europe, North America, and Australia were analyzed.ResultsWe confirmed the importance of GBA on phenotypes. GBA variants were associated with the development of daytime sleepiness (p.N370S: hazard ratio [HR] 3.28 [1.69–6.34]) and possible REM sleep behavior (p.T408M: odds ratio 6.48 [2.04–20.60]). We also replicated previously reported associations of GBA variants with motor/cognitive declines. The other genotype-phenotype associations include an intergenic variant near LRRK2 and the faster development of motor symptom (Hoehn and Yahr scale 3.0 HR 1.33 [1.16–1.52] for the C allele of rs76904798) and an intronic variant in PMVK and the development of wearing-off effects (HR 1.66 [1.19–2.31] for the C allele of rs114138760). Age at onset was associated with TMEM175 variant p.M393T (−0.72 [−1.21 to −0.23] in years), the C allele of rs199347 (intronic region of GPNMB, 0.70 [0.27–1.14]), and G allele of rs1106180 (intronic region of CCDC62, 0.62 [0.21–1.03]).ConclusionsThis study provides evidence that alleles associated with Parkinson disease risk, in particular GBA variants, also contribute to the heterogeneity of multiple motor and nonmotor aspects. Accounting for genetic variability will be a useful factor in understanding disease course and in minimizing heterogeneity in clinical trials.

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Bourgey, M., G. Calcagno, N. Tinto, D. Gennarelli, P. Margaritte-Jeannin, L. Greco, M. G. Limongelli, et al. "HLA related genetic risk for coeliac disease." Gut 56, no. 8 (August 1, 2007): 1054–59. http://dx.doi.org/10.1136/gut.2006.108530.

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33

Wilson, Carol. "Lipids and cardiovascular disease risk: genetic insights." Nature Reviews Endocrinology 6, no. 11 (October 21, 2010): 598. http://dx.doi.org/10.1038/nrendo.2010.165.

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34

Baum, Andrew, Andrea L. Friedman, and Sandra G. Zakowski. "Stress and genetic testing for disease risk." Health Psychology 16, no. 1 (1997): 8–19. http://dx.doi.org/10.1037/0278-6133.16.1.8.

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Feigin, A. "Redefining the genetic risk for Huntington disease." Neurology 80, no. 22 (April 26, 2013): 2004–5. http://dx.doi.org/10.1212/wnl.0b013e318294b49b.

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Sorbi, Sandro, Paolo Forleo, Andrea Tedde, Elena Cellini, Monica Ciantelli, Silvia Bagnoli, and Benedetta Nacmias. "Genetic risk factors in familial Alzheimer's disease." Mechanisms of Ageing and Development 122, no. 16 (November 2001): 1951–60. http://dx.doi.org/10.1016/s0047-6374(01)00308-6.

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Tsuji, S., K. Okuizumi, O. Onodera, Y. Namba, K. Ikeda, T. Yamamoto, K. Seki, et al. "598 Genetic risk factors for Alzheimer's disease." Neurobiology of Aging 17, no. 4 (January 1996): S149. http://dx.doi.org/10.1016/s0197-4580(96)80600-2.

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Limongelli, M. G., M. Bourgey, O. Esposito, R. Troncone, M. E. Camarca, R. Di Mase, C. Natale, et al. "HLA-related genetic risk for coeliac disease." Digestive and Liver Disease 39, no. 10 (October 2007): A64. http://dx.doi.org/10.1016/j.dld.2007.07.089.

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Montgomery, Grant W. "Discovery of genetic risk factors for disease." Journal of the Royal Society of New Zealand 48, no. 2-3 (November 2, 2017): 191–202. http://dx.doi.org/10.1080/03036758.2017.1392324.

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40

Jones, Emma, and Simon Mead. "Genetic risk factors for Creutzfeldt-Jakob disease." Neurobiology of Disease 142 (August 2020): 104973. http://dx.doi.org/10.1016/j.nbd.2020.104973.

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Ala-Kokko, Leena. "Genetic risk factors for lumbar disc disease." Annals of Medicine 34, no. 1 (January 2002): 42–47. http://dx.doi.org/10.1080/078538902317338634.

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Parnell, Laurence D., Yu-Chi Lee, and Chao-Qiang Lai. "Adaptive genetic variation and heart disease risk." Current Opinion in Lipidology 21, no. 2 (April 2010): 116–22. http://dx.doi.org/10.1097/mol.0b013e3283378e42.

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van Onna, Marieke, Abraham A. Kroon, Alphons J. H. M. Houben, Derk Koster, Maurice P. A. Zeegers, Léon H. G. Henskens, Arian W. Plat, Henri E. J. H. Stoffers, and Peter W. de Leeuw. "Genetic Risk of Atherosclerotic Renal Artery Disease." Hypertension 44, no. 4 (October 2004): 448–53. http://dx.doi.org/10.1161/01.hyp.0000141440.02210.da.

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Marini, Joan C. "Genetic Risk Factors for Lumbar Disk Disease." JAMA 285, no. 14 (April 11, 2001): 1886. http://dx.doi.org/10.1001/jama.285.14.1886.

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Tymchuk, Christopher N., Jaana Hartiala, Pragna I. Patel, Margarete Mehrabian, and Hooman Allayee. "Nonconventional genetic risk factors for cardiovascular disease." Current Atherosclerosis Reports 8, no. 3 (May 2006): 184–92. http://dx.doi.org/10.1007/s11883-006-0072-2.

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46

Doraisamy, Ravichandran, Karthikeyan Ramaswami, Jeevithan Shanmugam, Rashmi Subramanian, and Balasubramanian Sivashankaran. "Genetic risk factors for lumbar disc disease." Clinical Anatomy 34, no. 1 (July 21, 2020): 51–56. http://dx.doi.org/10.1002/ca.23641.

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Semaev, Sergey, and Elena Shakhtshneider. "Genetic Risk Score for Coronary Heart Disease: Review." Journal of Personalized Medicine 10, no. 4 (November 20, 2020): 239. http://dx.doi.org/10.3390/jpm10040239.

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The present review deals with the stages of creation, methods of calculation, and the use of a genetic risk score for coronary heart disease in various populations. The concept of risk factors is generally recognized on the basis of the results of epidemiological studies in the 20th century; according to this concept, the high prevalence of diseases of the circulatory system is due to lifestyle characteristics and associated risk factors. An important and relevant task for the healthcare system is to identify the population segments most susceptible to cardiovascular diseases (CVDs). The level of individual risk of an unfavorable cardiovascular prognosis is determined by genetic factors in addition to lifestyle factors.

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Yun, Jae-Seung. "Polygenic risk score: a useful clinical instrument for disease prediction and risk categorization." Cardiovascular Prevention and Pharmacotherapy 4, no. 1 (January 31, 2022): 13–17. http://dx.doi.org/10.36011/cpp.2022.4.e7.

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Genetic information is one of the essential components of precision medicine. Over the past decade, substantial progress has been made, such as low-cost, high-throughput genotyping arrays, advances in statistical techniques, and progressively larger discovery datasets, enabling the discovery of alleles contributing to common diseases, such as coronary artery disease and type 2 diabetes. The polygenic risk score (PRS) represents the aggregate contribution of numerous common genetic variants, individually conferring small to moderate effects, and can be used as a marker of genetic risk for major chronic diseases. PRSs can be obtained from early childhood, and only one measurement is needed to determine the score. PRSs can potentially be used for triage of further investigations to confirm disease susceptibility and to optimize individualized preventive strategies for high-risk disease groups. We provide an overview and commentary on important advances in deriving and validating PRSs, as well as the implementation of PRSs for clinically useful purposes.

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Jenkins, W. D., A. E. Lipka, A. J. Fogleman, K. R. Delfino, R. S. Malhi, and B. Hendricks. "Variance in disease risk: rural populations and genetic diversity." Genome 59, no. 7 (July 2016): 519–25. http://dx.doi.org/10.1139/gen-2016-0077.

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Over 19% of the US population resides in rural areas, where studies of disease risk and disease outcomes are difficult to assess due to smaller populations and lower incidence. While some studies suggest rural disparities for different chronic diseases, the data are inconsistent across geography and definitions of rurality. We reviewed the literature to examine if local variations in population genomic diversity may plausibly explain inconsistencies in estimating disease risk. Many rural communities were founded over 150 years ago by small groups of ethnically and ancestrally similar families. These have since endured relative geographical isolation, similar to groups in other industrialized nations, perhaps resulting in founder effects impacting local disease susceptibility. Studies in Europe and Asia have found that observably different phenotypes may appear in isolated communities within 100 years, and that genomic variation can significantly vary over small geographical scales. Epidemiological studies utilizing common “rural” definitions may miss significant disease differences due to assumptions of risk homogeneity and misinterpretation of administrative definitions of rurality. Local genomic heterogeneity should be an important aspect of chronic disease epidemiology in rural areas, and it is important to consider for designing studies and interpreting results, enabling a better understanding of the heritable components of complex diseases.

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Abraham, Gad, Loes Rutten-Jacobs, and Michael Inouye. "Risk Prediction Using Polygenic Risk Scores for Prevention of Stroke and Other Cardiovascular Diseases." Stroke 52, no. 9 (September 2021): 2983–91. http://dx.doi.org/10.1161/strokeaha.120.032619.

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Early prediction of risk of cardiovascular disease (CVD), including stroke, is a cornerstone of disease prevention. Clinical risk scores have been widely used for predicting CVD risk from known risk factors. Most CVDs have a substantial genetic component, which also has been confirmed for stroke in recent gene discovery efforts. However, the role of genetics in prediction of risk of CVD, including stroke, has been limited to testing for highly penetrant monogenic disorders. In contrast, the importance of polygenic variation, the aggregated effect of many common genetic variants across the genome with individually small effects, has become more apparent in the last 5 to 10 years, and powerful polygenic risk scores for CVD have been developed. Here we review the current state of the field of polygenic risk scores for CVD including stroke, and their potential to improve CVD risk prediction. We present findings and lessons from diseases such as coronary artery disease as these will likely be useful to inform future research in stroke polygenic risk prediction.

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