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Journal articles on the topic 'Common genetic variants'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

Vandersteen, Joshua G., Pinar Bayrak-Toydemir, Robert A. Palais, and Carl T. Wittwer. "Identifying Common Genetic Variants by High-Resolution Melting." Clinical Chemistry 53, no. 7 (2007): 1191–98. http://dx.doi.org/10.1373/clinchem.2007.085407.

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Abstract Background: Heteroduplex scanning techniques usually detect all heterozygotes, including common variants not of clinical interest. Methods: We conducted high-resolution melting analysis on the 24 exons of the ACVRL1 and ENG genes implicated in hereditary hemorrhagic telangiectasia (HHT). DNA in samples from 13 controls and 19 patients was PCR amplified in the presence of LCGreen® I, and all 768 exons melted in an HR-1® instrument. We used 10 wild-type controls to identify common variants, and the remaining samples were blinded, amplified, and analyzed by melting curve normalization and overlay. Unlabeled probes characterized the sequence of common variants. Results: Eleven common variants were associated with 8 of the 24 HHT exons, and 96% of normal samples contained at least 1 variant. As a result, the positive predictive value (PPV) of a heterozygous exon was low (31%), even in a population of predominantly HHT patients. However, all common variants produced unique amplicon melting curves that, when considered and eliminated, resulted in a PPV of 100%. In our blinded study, 3 of 19 heterozygous disease-causing variants were missed; however, 2 were clerical errors, and the remaining false negative would have been identified by difference analysis. Conclusions: High-resolution melting analysis is a highly accurate heteroduplex scanning technique. With many exons, however, use of single-sample instruments may lead to clerical errors, and routine use of difference analysis is recommended. Common variants can be identified by their melting curve profiles and genotyped with unlabeled probes, greatly reducing the false-positive results common with scanning techniques.

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Navrady, Lauren B., Yanni Zeng, Toni-Kim Clarke, et al. "Genetic and environmental contributions to psychological resilience and coping." Wellcome Open Research 3 (February 15, 2018): 12. http://dx.doi.org/10.12688/wellcomeopenres.13854.1.

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Background: Twin studies indicate that genetic and environmental factors contribute to both psychological resilience and coping style, but estimates of their relative molecular and shared environmental contributions are limited. The degree of overlap in the genetic architectures of these traits is also unclear. Methods: Using data from a large population- and family-based cohort Generation Scotland (N = 8,734), we estimated the genetic and shared environmental variance components for resilience, task-, emotion-, and avoidance-oriented coping style in a linear mixed model (LMM). Bivariate LMM analyses were used to estimate the genetic correlations between these traits. Resilience and coping style were measured using the Brief Resilience Scale and Coping Inventory for Stressful Situations, respectively. Results: The greatest proportion of the phenotypic variance in resilience remained unexplained, although significant contributions from common genetic variants and family-shared environment were found. Both task- and avoidance-oriented coping had significant contributions from common genetic variants, sibling- and couple-shared environments, variance in emotion-oriented coping was attributable to common genetic variants, family- and couple-shared environments. The estimated correlation between resilience and emotion-oriented coping was high for both common-variant-associated genetic effects (rG = -0.79, se = 0.19), and for the additional genetic effects from the pedigree (rK = -0.94, se = 0.30). Genetic correlations between resilience and task- and avoidance-oriented coping did not meet statistical significance. Conclusions: Both genetics and shared environmental effects were major contributing factors to coping style, whilst the variance in resilience remains largely unexplained. Strong genetic overlap between resilience and emotion-oriented coping suggests a relationship whereby genetic factors that increase negative emotionality also lead to decreased resilience. We suggest that genome-wide family-based studies of resilience and coping may help to elucidate tractable methodologies to identify genetic architectures and modifiable environmental risk factors to protect against psychiatric illness, although further work with larger sample sizes is needed.

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MOON, MARY ANN. "Common Genetic Variants Linked to Barrett's Esophagus." Internal Medicine News 45, no. 16 (2012): 35. http://dx.doi.org/10.1016/s1097-8690(12)70718-7.

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Gu, Sue, Rahul Kumar, Michael H. Lee, Claudia Mickael, and Brian B. Graham. "Common genetic variants in pulmonary arterial hypertension." Lancet Respiratory Medicine 7, no. 3 (2019): 190–91. http://dx.doi.org/10.1016/s2213-2600(18)30448-x.

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George, Alfred L. "Common genetic variants in sudden cardiac death." Heart Rhythm 6, no. 11 (2009): S3—S9. http://dx.doi.org/10.1016/j.hrthm.2009.08.024.

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Galton, D. J., J. Thorn, R. Mattu, E. Needham, and J. Stocks. "Common genetic variants relating to familial hypertriglyceridaemia." Fresenius' Journal of Analytical Chemistry 343, no. 1 (1992): 35. http://dx.doi.org/10.1007/bf00331975.

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7

Schmit, Stephanie L., Christopher K. Edlund, Fredrick R. Schumacher, et al. "Novel Common Genetic Susceptibility Loci for Colorectal Cancer." JNCI: Journal of the National Cancer Institute 111, no. 2 (2018): 146–57. http://dx.doi.org/10.1093/jnci/djy099.

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Abstract Background Previous genome-wide association studies (GWAS) have identified 42 loci (P < 5 × 10−8) associated with risk of colorectal cancer (CRC). Expanded consortium efforts facilitating the discovery of additional susceptibility loci may capture unexplained familial risk. Methods We conducted a GWAS in European descent CRC cases and control subjects using a discovery–replication design, followed by examination of novel findings in a multiethnic sample (cumulative n = 163 315). In the discovery stage (36 948 case subjects/30 864 control subjects), we identified genetic variants with a minor allele frequency of 1% or greater associated with risk of CRC using logistic regression followed by a fixed-effects inverse variance weighted meta-analysis. All novel independent variants reaching genome-wide statistical significance (two-sided P < 5 × 10−8) were tested for replication in separate European ancestry samples (12 952 case subjects/48 383 control subjects). Next, we examined the generalizability of discovered variants in East Asians, African Americans, and Hispanics (12 085 case subjects/22 083 control subjects). Finally, we examined the contributions of novel risk variants to familial relative risk and examined the prediction capabilities of a polygenic risk score. All statistical tests were two-sided. Results The discovery GWAS identified 11 variants associated with CRC at P < 5 × 10−8, of which nine (at 4q22.2/5p15.33/5p13.1/6p21.31/6p12.1/10q11.23/12q24.21/16q24.1/20q13.13) independently replicated at a P value of less than .05. Multiethnic follow-up supported the generalizability of discovery findings. These results demonstrated a 14.7% increase in familial relative risk explained by common risk alleles from 10.3% (95% confidence interval [CI] = 7.9% to 13.7%; known variants) to 11.9% (95% CI = 9.2% to 15.5%; known and novel variants). A polygenic risk score identified 4.3% of the population at an odds ratio for developing CRC of at least 2.0. Conclusions This study provides insight into the architecture of common genetic variation contributing to CRC etiology and improves risk prediction for individualized screening.

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Szolnoki, Zoltan. "Evaluation of Common Unfavourable Genetic Variants in Cerebrovascular Diseases: Recommendation for Supportive Genetic Examinations and Methodological Approaches for Common Genetic Variants." Current Medicinal Chemistry 16, no. 24 (2009): 3168–73. http://dx.doi.org/10.2174/092986709788803006.

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Biondi, G., V. Calabró, S. Colonna-Romano, et al. "Common and rare genetic variants of human red blood cell enzymes in ltaly." Anthropologischer Anzeiger 47, no. 2 (1989): 155–74. http://dx.doi.org/10.1127/anthranz/47/1989/155.

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Bobbili, Dheeraj Reddy, Peter Banda, Rejko Krüger, and Patrick May. "Excess of singleton loss-of-function variants in Parkinson’s disease contributes to genetic risk." Journal of Medical Genetics 57, no. 9 (2020): 617–23. http://dx.doi.org/10.1136/jmedgenet-2019-106316.

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BackgroundParkinson’s disease (PD) is a neurodegenerative disorder with complex genetic architecture. Besides rare mutations in high-risk genes related to monogenic familial forms of PD, multiple variants associated with sporadic PD were discovered via association studies.MethodsWe studied the whole-exome sequencing data of 340 PD cases and 146 ethnically matched controls from the Parkinson’s Progression Markers Initiative (PPMI) and performed burden analysis for different rare variant classes. Disease prediction models were built based on clinical, non-clinical and genetic features, including both common and rare variants, and two machine learning methods.ResultsWe observed a significant exome-wide burden of singleton loss-of-function variants (corrected p=0.037). Overall, no exome-wide burden of rare amino acid changing variants was detected. Finally, we built a disease prediction model combining singleton loss-of-function variants, a polygenic risk score based on common variants, and family history of PD as features and reached an area under the curve of 0.703 (95% CI 0.698 to 0.708). By incorporating a rare variant feature, our model increased the performance of the state-of-the-art classification model for the PPMI dataset, which reached an area under the curve of 0.639 based on common variants alone.ConclusionThe main finding of this study is to highlight the contribution of singleton loss-of-function variants to the complex genetics of PD and that disease risk prediction models combining singleton and common variants can improve models built solely on common variants.

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Spurdle, Amanda B., Stephanie Greville-Heygate, Antonis C. Antoniou, et al. "Towards controlled terminology for reporting germline cancer susceptibility variants: an ENIGMA report." Journal of Medical Genetics 56, no. 6 (2019): 347–57. http://dx.doi.org/10.1136/jmedgenet-2018-105872.

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The vocabulary currently used to describe genetic variants and their consequences reflects many years of studying and discovering monogenic disease with high penetrance. With the recent rapid expansion of genetic testing brought about by wide availability of high-throughput massively parallel sequencing platforms, accurate variant interpretation has become a major issue. The vocabulary used to describe single genetic variants in silico, in vitro, in vivo and as a contributor to human disease uses terms in common, but the meaning is not necessarily shared across all these contexts. In the setting of cancer genetic tests, the added dimension of using data from genetic sequencing of tumour DNA to direct treatment is an additional source of confusion to those who are not experienced in cancer genetics. The language used to describe variants identified in cancer susceptibility genetic testing typically still reflects an outdated paradigm of Mendelian inheritance with dichotomous outcomes. Cancer is a common disease with complex genetic architecture; an improved lexicon is required to better communicate among scientists, clinicians and patients, the risks and implications of genetic variants detected. This review arises from a recognition of, and discussion about, inconsistencies in vocabulary usage by members of the ENIGMA international multidisciplinary consortium focused on variant classification in breast-ovarian cancer susceptibility genes. It sets out the vocabulary commonly used in genetic variant interpretation and reporting, and suggests a framework for a common vocabulary that may facilitate understanding and clarity in clinical reporting of germline genetic tests for cancer susceptibility.

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Pfeffer, Tobias J., Stefan Pietzsch, and Denise Hilfiker-Kleiner. "Common genetic predisposition for heart failure and cancer." Herz 45, no. 7 (2020): 632–36. http://dx.doi.org/10.1007/s00059-020-04953-9.

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Abstract Cardiovascular diseases and cancer are major causes of mortality in industrialized societies. They share common risk factors (e.g., genetics, lifestyle, age, infection, toxins, and pollution) and might also mutually promote the onset of the respective other disease. Cancer can affect cardiac function directly while antitumor therapies may have acute- and/or late-onset cardiotoxic effects. Recent studies suggest that heart failure might promote tumorigenesis and tumor progression. In both cancer and cardiovascular diseases, genetic predisposition is implicated in the disease onset and development. In this regard, genetic variants classically associated with cardiomyopathies increase the risk for toxic side effects on the cardiovascular system. Genetic variants associated with increased cancer risk are frequent in patients with peripartum cardiomyopathy complicated by cancer, pointing to a common genetic predisposition for both diseases. Common risk factors, cardiotoxic antitumor treatment, genetic variants (associated with cardiomyopathies and/or cancer), and increased cardiac stress lead us to propose the “multi-hit hypothesis” linking cancer and cardiovascular diseases. In the present review, we summarize the current knowledge on potential connecting factors between cancer and cardiovascular diseases with a major focus on the role of genetic predisposition and its implication for individual therapeutic strategies and risk assessment in the novel field of oncocardiology.

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Kestenbaum, Bryan, Nicole L. Glazer, Anna Köttgen, et al. "Common Genetic Variants Associate with Serum Phosphorus Concentration." Journal of the American Society of Nephrology 21, no. 7 (2010): 1223–32. http://dx.doi.org/10.1681/asn.2009111104.

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Knol, Maria J., Dongwei Lu, Matthew Traylor, et al. "Association of common genetic variants with brain microbleeds." Neurology 95, no. 24 (2020): e3331-e3343. http://dx.doi.org/10.1212/wnl.0000000000010852.

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ObjectiveTo identify common genetic variants associated with the presence of brain microbleeds (BMBs).MethodsWe performed genome-wide association studies in 11 population-based cohort studies and 3 case–control or case-only stroke cohorts. Genotypes were imputed to the Haplotype Reference Consortium or 1000 Genomes reference panel. BMBs were rated on susceptibility-weighted or T2*-weighted gradient echo MRI sequences, and further classified as lobar or mixed (including strictly deep and infratentorial, possibly with lobar BMB). In a subset, we assessed the effects of APOE ε2 and ε4 alleles on BMB counts. We also related previously identified cerebral small vessel disease variants to BMBs.ResultsBMBs were detected in 3,556 of the 25,862 participants, of which 2,179 were strictly lobar and 1,293 mixed. One locus in the APOE region reached genome-wide significance for its association with BMB (lead single nucleotide polymorphism rs769449; odds ratio [OR]any BMB [95% confidence interval (CI)] 1.33 [1.21–1.45]; p = 2.5 × 10−10). APOE ε4 alleles were associated with strictly lobar (OR [95% CI] 1.34 [1.19–1.50]; p = 1.0 × 10−6) but not with mixed BMB counts (OR [95% CI] 1.04 [0.86–1.25]; p = 0.68). APOE ε2 alleles did not show associations with BMB counts. Variants previously related to deep intracerebral hemorrhage and lacunar stroke, and a risk score of cerebral white matter hyperintensity variants, were associated with BMB.ConclusionsGenetic variants in the APOE region are associated with the presence of BMB, most likely due to the APOE ε4 allele count related to a higher number of strictly lobar BMBs. Genetic predisposition to small vessel disease confers risk of BMB, indicating genetic overlap with other cerebral small vessel disease markers.

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Al-Chalabi, Ammar, and Peter M. Visscher. "Common genetic variants and the heritability of ALS." Nature Reviews Neurology 10, no. 10 (2014): 549–50. http://dx.doi.org/10.1038/nrneurol.2014.166.

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Ramdas, Wishal D., Leonieke M. E. van Koolwijk, Hans G. Lemij, et al. "Common genetic variants associated with open-angle glaucoma." Human Molecular Genetics 20, no. 12 (2011): 2464–71. http://dx.doi.org/10.1093/hmg/ddr120.

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Gater, L., and V. Lyssenko. "Three Common Genetic Variants Predict Type 2 Diabetes." MD Conference Express 6, no. 2 (2006): 11. http://dx.doi.org/10.1177/155989770600600203.

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Hibar, Derrek P., Jason L. Stein, Miguel E. Renteria, et al. "Common genetic variants influence human subcortical brain structures." Nature 520, no. 7546 (2015): 224–29. http://dx.doi.org/10.1038/nature14101.

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19

Sedlacek, Kamil, Klaus Stark, Shane R. Cunha, et al. "Common Genetic Variants in ANK2 Modulate QT Interval." Circulation: Cardiovascular Genetics 1, no. 2 (2008): 93–99. http://dx.doi.org/10.1161/circgenetics.108.792192.

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Ho, Yvonne Y. W., David M. Evans, Grant W. Montgomery, et al. "Common Genetic Variants Influence Whorls in Fingerprint Patterns." Journal of Investigative Dermatology 136, no. 4 (2016): 859–62. http://dx.doi.org/10.1016/j.jid.2015.10.062.

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Bearden, Carrie E., David C. Glahn, Agatha D. Lee, et al. "Neural phenotypes of common and rare genetic variants." Biological Psychology 79, no. 1 (2008): 43–57. http://dx.doi.org/10.1016/j.biopsycho.2008.02.005.

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Browning, Jeffrey D. "Common Genetic Variants and Nonalcoholic Fatty Liver Disease." Clinical Gastroenterology and Hepatology 11, no. 9 (2013): 1191–93. http://dx.doi.org/10.1016/j.cgh.2013.05.013.

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Tansey, Katherine E., Michel Guipponi, Xiaolan Hu, et al. "Contribution of Common Genetic Variants to Antidepressant Response." Biological Psychiatry 73, no. 7 (2013): 679–82. http://dx.doi.org/10.1016/j.biopsych.2012.10.030.

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Becker, Kevin G. "The common variants/multiple disease hypothesis of common complex genetic disorders." Medical Hypotheses 62, no. 2 (2004): 309–17. http://dx.doi.org/10.1016/s0306-9877(03)00332-3.

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Thakran, Sarita, Debleena Guin, Pooja Singh, et al. "Genetic Landscape of Common Epilepsies: Advancing towards Precision in Treatment." International Journal of Molecular Sciences 21, no. 20 (2020): 7784. http://dx.doi.org/10.3390/ijms21207784.

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Epilepsy, a neurological disease characterized by recurrent seizures, is highly heterogeneous in nature. Based on the prevalence, epilepsy is classified into two types: common and rare epilepsies. Common epilepsies affecting nearly 95% people with epilepsy, comprise generalized epilepsy which encompass idiopathic generalized epilepsy like childhood absence epilepsy, juvenile myoclonic epilepsy, juvenile absence epilepsy and epilepsy with generalized tonic-clonic seizure on awakening and focal epilepsy like temporal lobe epilepsy and cryptogenic focal epilepsy. In 70% of the epilepsy cases, genetic factors are responsible either as single genetic variant in rare epilepsies or multiple genetic variants acting along with different environmental factors as in common epilepsies. Genetic testing and precision treatment have been developed for a few rare epilepsies and is lacking for common epilepsies due to their complex nature of inheritance. Precision medicine for common epilepsies require a panoramic approach that incorporates polygenic background and other non-genetic factors like microbiome, diet, age at disease onset, optimal time for treatment and other lifestyle factors which influence seizure threshold. This review aims to comprehensively present a state-of-art review of all the genes and their genetic variants that are associated with all common epilepsy subtypes. It also encompasses the basis of these genes in the epileptogenesis. Here, we discussed the current status of the common epilepsy genetics and address the clinical application so far on evidence-based markers in prognosis, diagnosis, and treatment management. In addition, we assessed the diagnostic predictability of a few genetic markers used for disease risk prediction in individuals. A combination of deeper endo-phenotyping including pharmaco-response data, electro-clinical imaging, and other clinical measurements along with genetics may be used to diagnose common epilepsies and this marks a step ahead in precision medicine in common epilepsies management.

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Rainero, Innocenzo, Alessandro Vacca, Flora Govone, Annalisa Gai, Lorenzo Pinessi, and Elisa Rubino. "Migraine: Genetic Variants and Clinical Phenotypes." Current Medicinal Chemistry 26, no. 34 (2019): 6207–21. http://dx.doi.org/10.2174/0929867325666180719120215.

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Migraine is a common, chronic neurovascular disorder caused by a complex interaction between genetic and environmental risk factors. In the last two decades, molecular genetics of migraine have been intensively investigated. In a few cases, migraine is transmitted as a monogenic disorder, and the disease phenotype cosegregates with mutations in different genes like CACNA1A, ATP1A2, SCN1A, KCNK18, and NOTCH3. In the common forms of migraine, candidate genes as well as genome-wide association studies have shown that a large number of genetic variants may increase the risk of developing migraine. At present, few studies investigated the genotype-phenotype correlation in patients with migraine. The purpose of this review was to discuss recent studies investigating the relationship between different genetic variants and the clinical characteristics of migraine. Analysis of genotype-phenotype correlations in migraineurs is complicated by several confounding factors and, to date, only polymorphisms of the MTHFR gene have been shown to have an effect on migraine phenotype. Additional genomic studies and network analyses are needed to clarify the complex pathways underlying migraine and its clinical phenotypes.

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Musfee, Fadi I., A. J. Agopian, Elizabeth Goldmuntz, et al. "Common Variation in Cytoskeletal Genes Is Associated with Conotruncal Heart Defects." Genes 12, no. 5 (2021): 655. http://dx.doi.org/10.3390/genes12050655.

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There is strong evidence for a genetic contribution to non-syndromic congenital heart defects (CHDs). However, exome- and genome-wide studies conducted at the variant and gene-level have identified few genome-wide significant CHD-related genes. Gene-set analyses are a useful complement to such studies and candidate gene-set analyses of rare variants have provided insight into the genetics of CHDs. However, similar analyses have not been conducted using data on common genetic variants. Consequently, we conducted common variant analyses of 15 CHD candidate gene-sets, using data from two common types of CHDs: conotruncal heart defects (1431 cases) and left ventricular outflow tract defects (509 cases). After Bonferroni correction for evaluation of multiple gene-sets, the cytoskeletal gene-set was significantly associated with conotruncal heart defects (βS = 0.09; 95% confidence interval (CI) 0.03–0.15). This association was stronger when analyses were restricted to the sub-set of cytoskeletal genes that have been observed to harbor rare damaging genotypes in at least two CHD cases (βS = 0.32, 95% CI 0.08–0.56). These findings add to the evidence linking cytoskeletal genes to CHDs and suggest that, for cytoskeletal genes, common variation may contribute to the risk of CHDs.

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Liu, Chaochun, William A. Rennie, C. Steven Carmack, et al. "Effects of genetic variations on microRNA: target interactions." Nucleic Acids Research 42, no. 15 (2014): 9543–52. http://dx.doi.org/10.1093/nar/gku675.

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Abstract Genetic variations within microRNA (miRNA) binding sites can affect miRNA-mediated gene regulation, which may lead to phenotypes and diseases. We perform a transcriptome-scale analysis of genetic variants and miRNA:target interactions identified by CLASH. This analysis reveals that rare variants tend to reside in CDSs, whereas common variants tend to reside in the 3′ UTRs. miRNA binding sites are more likely to reside within those targets in the transcriptome with lower variant densities, especially target regions in which nucleotides have low mutation frequencies. Furthermore, an overwhelming majority of genetic variants within or near miRNA binding sites can alter not only the potential of miRNA:target hybridization but also the structural accessibility of the binding sites and flanking regions. These suggest an interpretation for certain associations between genetic variants and diseases, i.e. modulation of miRNA-mediated gene regulation by common or rare variants within or near miRNA binding sites, likely through target structure alterations. Our data will be valuable for discovering new associations among miRNAs, genetic variations and human diseases.

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Umano, Giuseppina, Mariangela Martino, and Nicola Santoro. "The Association between Pediatric NAFLD and Common Genetic Variants." Children 4, no. 6 (2017): 49. http://dx.doi.org/10.3390/children4060049.

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Kohannim, Omid, Neda Jahanshad, Meredith N. Braskie, et al. "Predicting White Matter Integrity from Multiple Common Genetic Variants." Neuropsychopharmacology 37, no. 9 (2012): 2012–19. http://dx.doi.org/10.1038/npp.2012.49.

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Moore, Steven C., Marc J. Gunter, Carrie R. Daniel, et al. "Common Genetic Variants and Central Adiposity Among Asian-Indians." Obesity 20, no. 9 (2012): 1902–8. http://dx.doi.org/10.1038/oby.2011.238.

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Miliku, Kozeta, Suzanne Vogelezang, Oscar H. Franco, Albert Hofman, Vincent W. V. Jaddoe, and Janine F. Felix. "Influence of common genetic variants on childhood kidney outcomes." Pediatric Research 80, no. 1 (2016): 60–66. http://dx.doi.org/10.1038/pr.2016.44.

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Lin, Rong, Ziyu Yuan, Caicai Zhang, et al. "Common genetic variants in ADCY5 and gestational glycemic traits." PLOS ONE 15, no. 3 (2020): e0230032. http://dx.doi.org/10.1371/journal.pone.0230032.

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Marei, Hany E., Asmaa Althani, Jaana Suhonen, et al. "Common and Rare Genetic Variants Associated With Alzheimer's Disease." Journal of Cellular Physiology 231, no. 7 (2015): 1432–37. http://dx.doi.org/10.1002/jcp.25225.

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Shoemaker, M. Benjamin, Andreas Bollmann, Steven A. Lubitz, et al. "Common Genetic Variants and Response to Atrial Fibrillation Ablation." Circulation: Arrhythmia and Electrophysiology 8, no. 2 (2015): 296–302. http://dx.doi.org/10.1161/circep.114.001909.

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Swerdlow, Daniel I., and Steve E. Humphries. "Common and rare genetic variants and risk of CHD." Nature Reviews Cardiology 14, no. 2 (2017): 73–74. http://dx.doi.org/10.1038/nrcardio.2016.209.

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O'Rahilly, Stephen, and Nicholas J. Wareham. "Genetic Variants and Common Diseases — Better Late Than Never." New England Journal of Medicine 355, no. 3 (2006): 306–8. http://dx.doi.org/10.1056/nejme068140.

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Paludan-Müller, Christian, Jesper H. Svendsen, and Morten S. Olesen. "The role of common genetic variants in atrial fibrillation." Journal of Electrocardiology 49, no. 6 (2016): 864–70. http://dx.doi.org/10.1016/j.jelectrocard.2016.08.012.

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Sorosina, Melissa, Paola Brambilla, Ferdinando Clarelli, et al. "Genetic burden of common variants in progressive and bout-onset multiple sclerosis." Multiple Sclerosis Journal 20, no. 7 (2013): 802–11. http://dx.doi.org/10.1177/1352458513512707.

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Background: The contribution of genetic variants underlying the susceptibility to different clinical courses of multiple sclerosis (MS) is still unclear. Objective: The aim of the study is to evaluate and compare the proportion of liability explained by common SNPs and the genetic burden of MS-associated SNPs in progressive onset (PrMS) and bout-onset (BOMS) cases. Methods: We estimated the proportion of variance in disease liability explained by 296,391 autosomal SNPs in cohorts of Italian PrMS and BOMS patients using the genome-wide complex trait analysis (GCTA) tool, and we calculated a weighted genetic risk score (wGRS) based on the known MS-associated loci. Results: Our results identified that common SNPs explain a greater proportion of phenotypic variance in BOMS (36.5%±10.1%) than PrMS (20.8%±6.0%) cases, and a trend of decrease was observed when testing primary progressive (PPMS) without brain MRI inflammatory activity ( p = 7.9 × 10−3). Similarly, the wGRS and the variance explained by MS-associated SNPs were higher in BOMS than PPMS in males (wGRS: 6.63 vs 6.51, p = 0.04; explained variance: 4.8%±1.5% vs 1.7%±0.6%; p = 0.05). Conclusions: Our results suggest that the liability of disease is better captured by common genetic variants in BOMS than PrMS cases. The absence of inflammatory activity and male gender further raise the difference between clinical courses.

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Misawa, Kazuharu, Takanori Hasegawa, Eikan Mishima, et al. "Contribution of Rare Variants of the SLC22A12 Gene to the Missing Heritability of Serum Urate Levels." Genetics 214, no. 4 (2020): 1079–90. http://dx.doi.org/10.1534/genetics.119.303006.

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Gout is a common arthritis caused by monosodium urate crystals. The heritability of serum urate levels is estimated to be 30–70%; however, common genetic variants account for only 7.9% of the variance in serum urate levels. This discrepancy is an example of “missing heritability.” The “missing heritability” suggests that variants associated with uric acid levels are yet to be found. By using genomic sequences of the ToMMo cohort, we identified rare variants of the SLC22A12 gene that affect the urate transport activity of URAT1. URAT1 is a transporter protein encoded by the SLC22A12 gene. We grouped the participants with variants affecting urate uptake by URAT1 and analyzed the variance of serum urate levels. The results showed that the heritability explained by the SLC22A12 variants of men and women exceeds 10%, suggesting that rare variants underlie a substantial portion of the “missing heritability” of serum urate levels.

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Monasky, Michelle M., Emanuele Micaglio, Giuseppe Ciconte, and Carlo Pappone. "Brugada Syndrome: Oligogenic or Mendelian Disease?" International Journal of Molecular Sciences 21, no. 5 (2020): 1687. http://dx.doi.org/10.3390/ijms21051687.

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Brugada syndrome (BrS) is diagnosed by a coved-type ST-segment elevation in the right precordial leads on the electrocardiogram (ECG), and it is associated with an increased risk of sudden cardiac death (SCD) compared to the general population. Although BrS is considered a genetic disease, its molecular mechanism remains elusive in about 70–85% of clinically-confirmed cases. Variants occurring in at least 26 different genes have been previously considered causative, although the causative effect of all but the SCN5A gene has been recently challenged, due to the lack of systematic, evidence-based evaluations, such as a variant’s frequency among the general population, family segregation analyses, and functional studies. Also, variants within a particular gene can be associated with an array of different phenotypes, even within the same family, preventing a clear genotype–phenotype correlation. Moreover, an emerging concept is that a single mutation may not be enough to cause the BrS phenotype, due to the increasing number of common variants now thought to be clinically relevant. Thus, not only the complete list of genes causative of the BrS phenotype remains to be determined, but also the interplay between rare and common multiple variants. This is particularly true for some common polymorphisms whose roles have been recently re-evaluated by outstanding works, including considering for the first time ever a polygenic risk score derived from the heterozygous state for both common and rare variants. The more common a certain variant is, the less impact this variant might have on heart function. We are aware that further studies are warranted to validate a polygenic risk score, because there is no mutated gene that connects all, or even a majority, of BrS cases. For the same reason, it is currently impossible to create animal and cell line genetic models that represent all BrS cases, which would enable the expansion of studies of this syndrome. Thus, the best model at this point is the human patient population. Further studies should first aim to uncover genetic variants within individuals, as well as to collect family segregation data to identify potential genetic causes of BrS.

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Bonaventura, Jiri, Eva Polakova, Veronika Vejtasova, and Josef Veselka. "Genetic Testing in Patients with Hypertrophic Cardiomyopathy." International Journal of Molecular Sciences 22, no. 19 (2021): 10401. http://dx.doi.org/10.3390/ijms221910401.

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Hypertrophic cardiomyopathy (HCM) is a common inherited heart disease with an estimated prevalence of up to 1 in 200 individuals. In the majority of cases, HCM is considered a Mendelian disease, with mainly autosomal dominant inheritance. Most pathogenic variants are usually detected in genes for sarcomeric proteins. Nowadays, the genetic basis of HCM is believed to be rather complex. Thousands of mutations in more than 60 genes have been described in association with HCM. Nevertheless, screening large numbers of genes results in the identification of many genetic variants of uncertain significance and makes the interpretation of the results difficult. Patients lacking a pathogenic variant are now believed to have non-Mendelian HCM and probably have a better prognosis than patients with sarcomeric pathogenic mutations. Identifying the genetic basis of HCM creates remarkable opportunities to understand how the disease develops, and by extension, how to disrupt the disease progression in the future. The aim of this review is to discuss the brief history and recent advances in the genetics of HCM and the application of molecular genetic testing into common clinical practice.

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Jordan, Elizabeth, and Ray E. Hershberger. "Considering complexity in the genetic evaluation of dilated cardiomyopathy." Heart 107, no. 2 (2020): 106–12. http://dx.doi.org/10.1136/heartjnl-2020-316658.

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Dilated cardiomyopathy (DCM) is a cardiovascular disease of genetic aetiology that causes substantial morbidity and mortality, and presents considerable opportunity for disease mitigation and prevention in those at risk. Foundational to the process of caring for patients diagnosed with DCM is a clinical genetic evaluation, which always begins with a comprehensive family history and clinical evaluation. Genetic testing of the proband, the first patient identified in a family with DCM, within the context of genetic counselling is always indicated, regardless of whether the DCM is familial or non-familial. Clinical screening of at-risk family members is also indicated, as is cascade genetic testing for actionable variants found at genetic testing in the proband. Clinicians now have expansive panels with many genes available for DCM genetic testing, and the approaches used to evaluate rare variants to decide which are disease-causing continues to rapidly evolve. Despite these recent advances, only a minority of cases yield actionable variants, even in familial DCM where a genetic aetiology is highly likely. This underscores that our knowledge of DCM clinical genetics remains incomplete, including variant interpretation and DCM genetic architecture. Emerging data suggest that the single-variant Mendelian disease model is insufficient to explain some DCM cases, and rather that multiple variants, both common and rare, and at times key environmental factors, interact to cause DCM. A simple model illustrating the intersection of DCM genetic architecture with environmental impact is provided.

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Power, R. A., T. Wingenbach, S. Cohen-Woods, et al. "Estimating the heritability of reporting stressful life events captured by common genetic variants." Psychological Medicine 43, no. 9 (2012): 1965–71. http://dx.doi.org/10.1017/s0033291712002589.

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BackgroundAlthough usually thought of as external environmental stressors, a significant heritable component has been reported for measures of stressful life events (SLEs) in twin studies.MethodWe examined the variance in SLEs captured by common genetic variants from a genome-wide association study (GWAS) of 2578 individuals. Genome-wide complex trait analysis (GCTA) was used to estimate the phenotypic variance tagged by single nucleotide polymorphisms (SNPs). We also performed a GWAS on the number of SLEs, and looked at correlations between siblings.ResultsA significant proportion of variance in SLEs was captured by SNPs (30%, p = 0.04). When events were divided into those considered to be dependent or independent, an equal amount of variance was explained for both. This ‘heritability’ was in part confounded by personality measures of neuroticism and psychoticism. A GWAS for the total number of SLEs revealed one SNP that reached genome-wide significance (p = 4 × 10−8), although this association was not replicated in separate samples. Using available sibling data for 744 individuals, we also found a significant positive correlation of R2 = 0.08 in SLEs (p = 0.03).ConclusionsThese results provide independent validation from molecular data for the heritability of reporting environmental measures, and show that this heritability is in part due to both common variants and the confounding effect of personality.

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Malik, Manasi, Naiqi Shi, Geraldine Serwald, et al. "3127 The effect of common genetic variants in the oxytocin receptor gene on oxytocin response." Journal of Clinical and Translational Science 3, s1 (2019): 115. http://dx.doi.org/10.1017/cts.2019.263.

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OBJECTIVES/SPECIFIC AIMS: Previous studies suggest that genetic variants in the oxytocin receptor (OXTR) may alter oxytocin dose requirement for labor induction and may increase risk for preterm labor and neurodevelopmental disorders. However, the mechanisms of actions of these variants remain unknown. The goal of this study was to functionally characterize common missense and noncoding variants in OXTR. First, we aimed to determine the effects of missense variants on two major aspects of receptor function: calcium signaling and β-arrestin recruitment. Second, we used allelic expression imbalance assays in an effort to identify regulatory single nucleotide polymorphisms (SNPs) in noncoding regions of OXTR that alter OXTR mRNA expression. METHODS/STUDY POPULATION: We used the Exome Aggregation Consortium database to identify the 12 most prevalent missense single nucleotide variants in OXTR. To determine the functional effects of these variants, we transfected human embryonic kidney cells (a common model system used to study receptor function) with wild type OXTR, variant OXTR, or empty vector control. We used the calcium-sensitive dye Fluo4 to quantify intracellular calcium flux in response to oxytocin treatment, and used bioluminescence resonance energy transfer assays to measure recruitment of the signaling partner β-arrestin to the receptor. To investigate potential effects of noncoding SNPs on OXTR mRNA expression, we quantified allele-specific expression of OXTR in human uterine tissue obtained from participants at the time of Cesarean section. We used next-generation sequencing (Illumina MiSeq) to count alleles of a reporter SNP in OXTR exon 3. RESULTS/ANTICIPATED RESULTS: Of the 12 most prevalent missense single nucleotide variants, four were predicted to be deleterious by PolyPhen variant annotation software. We anticipate that these variants will alter receptor signaling through calcium or β-arrestin pathways. We further observed that a reporter SNP in OXTR exon 3 exhibits significant allelic expression imbalance in a subset of our myometrial tissue samples, indicating that OXTR expression may be regulated by a functional SNP. Our current work focuses on discovering the functional SNPs in OXTR responsible for the pattern of allelic expression imbalance seen in mRNA. In the future, we will seek to explore the effects of these variants on uterine function by using genome editing of uterine smooth muscle cells. DISCUSSION/SIGNIFICANCE OF IMPACT: Our results suggest that both missense and noncoding variants may affect OXTR expression and function. Future studies may suggest that OXTR sequencing, genotyping, or expression analysis would be useful to identify individuals likely to respond or fail to respond to safe doses of oxytocin for labor induction. Personalizing approaches for labor induction in this way would increase the safety of oxytocin and potentially reduce maternal morbidity and mortality.

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Rice, Terri, Daniel H. Lachance, Annette M. Molinaro, et al. "Understanding inherited genetic risk of adult glioma – a review." Neuro-Oncology Practice 3, no. 1 (2015): 10–16. http://dx.doi.org/10.1093/nop/npv026.

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Abstract During the past six years, researchers have made major progress identifying common inherited genetic variation that increases risk for primary adult glioma. This paper summarizes knowledge about rare familial cancer syndromes that include adult glioma and reviews the available literature on the more recently discovered common inherited variation. Ten independent inherited variants in eight chromosomal regions have been convincingly associated with increased risk for adult glioma. Most of these variants increase relative risk of primary adult glioma by 20% to 40%, but the TP53 variant rs78378222 confers a two-fold relative risk (ie, 200%), and rs557505857 on chromosome 8 confers a six-fold relative risk of IDH-mutated astrocytomas and oligodendroglial tumors (ie, 600%). Even with a six-fold relative risk, the overall risk of developing adult glioma is too low for screening for the high-risk variant on chromosome 8. Future studies will help clarify which inherited adult glioma risk variants are associated with subtypes defined by histology and/or acquired tumor mutations. This review also provides an information sheet for primary adult glioma patients and their families.

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Liu, Melissa M., Chi-Chao Chan, and Jingsheng Tuo. "Genetic mechanisms and age-related macular degeneration: common variants, rare variants, copy number variations, epigenetics, and mitochondrial genetics." Human Genomics 6, no. 1 (2012): 13. http://dx.doi.org/10.1186/1479-7364-6-13.

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Zhao, Bingxin, Tengfei Li, Yue Yang, et al. "Common genetic variation influencing human white matter microstructure." Science 372, no. 6548 (2021): eabf3736. http://dx.doi.org/10.1126/science.abf3736.

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Brain regions communicate with each other through tracts of myelinated axons, commonly referred to as white matter. We identified common genetic variants influencing white matter microstructure using diffusion magnetic resonance imaging of 43,802 individuals. Genome-wide association analysis identified 109 associated loci, 30 of which were detected by tract-specific functional principal components analysis. A number of loci colocalized with brain diseases, such as glioma and stroke. Genetic correlations were observed between white matter microstructure and 57 complex traits and diseases. Common variants associated with white matter microstructure altered the function of regulatory elements in glial cells, particularly oligodendrocytes. This large-scale tract-specific study advances the understanding of the genetic architecture of white matter and its genetic links to a wide spectrum of clinical outcomes.

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Park, Jihye, Soo Youn Lee, Su Youn Baik, et al. "Gene-Wise Burden of Coding Variants Correlates to Noncoding Pharmacogenetic Risk Variants." International Journal of Molecular Sciences 21, no. 9 (2020): 3091. http://dx.doi.org/10.3390/ijms21093091.

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Genetic variability can modulate individual drug responses. A significant portion of pharmacogenetic variants reside in the noncoding genome yet it is unclear if the noncoding variants directly influence protein function and expression or are present on a haplotype including a functionally relevant genetic variation (synthetic association). Gene-wise variant burden (GVB) is a gene-level measure of deleteriousness, reflecting the cumulative effects of deleterious coding variants, predicted in silico. To test potential associations between noncoding and coding pharmacogenetic variants, we computed a drug-level GVB for 5099 drugs from DrugBank for 2504 genomes of the 1000 Genomes Project and evaluated the correlation between the long-known noncoding variant-drug associations in PharmGKB, with functionally relevant rare and common coding variants aggregated into GVBs. We obtained the area under the receiver operating characteristics curve (AUC) by comparing the drug-level GVB ranks against the corresponding pharmacogenetic variants-drug associations in PharmGKB. We obtained high overall AUCs (0.710 ± 0.022–0.734 ± 0.018) for six different methods (i.e., SIFT, MutationTaster, Polyphen-2 HVAR, Polyphen-2 HDIV, phyloP, and GERP++), and further improved the ethnicity-specific validations (0.759 ± 0.066–0.791 ± 0.078). These results suggest that a significant portion of the long-known noncoding variant-drug associations can be explained as synthetic associations with rare and common coding variants burden of the corresponding pharmacogenes.

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Ganz, Ariel, Kevin Klatt, and Marie Caudill. "Common Genetic Variants Alter Metabolism and Influence Dietary Choline Requirements." Nutrients 9, no. 8 (2017): 837. http://dx.doi.org/10.3390/nu9080837.

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