Genetic Risk Scores

Igo, Robert P.; Kinzy, Tyler G.; Bailey, Jessica N. Cooke

doi:10.1002/cphg.95

Cited by 91 publications

(103 citation statements)

References 84 publications

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“…For example, according to Igo Jr. et al, the complexities of glaucoma and the definitions of its varying phenotypes present a challenge when determining the outcome of interest in the PRS. For example, in POAG, PRS may be calculated based on endophenotypes, IOP, VF loss, or other parameters alone or together [ 238 ]. From there come challenges of interpreting scores.…”

Section: Discussionmentioning

confidence: 99%

“…From there come challenges of interpreting scores. According to Igo Jr. et al, predictive models utilizing PRS are usually assed with measure of AUC, which generally ranges from 0.5 (even chance) to 1 (perfect model), with an expectation of AUC > 0.75 for informative screening [ 238 ]. For example, the three PRS calculated in the ADAGES III analysis had AUC values of 0.62, 0.74, and 0.94, suggesting varying utility among them [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

“…Other calculations used to demonstrate the strength of PRS are more traditional statistical analyses. Given the complexities of calculating and analyzing these scores, statistical analysis software packages have been written to assist researchers [ 238 ].…”

Section: Discussionmentioning

confidence: 99%

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Molecular Genetics of Glaucoma: Subtype and Ethnicity Considerations

Zukerman

Harris

Vercellin

et al. 2020

Genes

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Glaucoma, the world’s leading cause of irreversible blindness, is a complex disease, with differential presentation as well as ethnic and geographic disparities. The multifactorial nature of glaucoma complicates the study of genetics and genetic involvement in the disease process. This review synthesizes the current literature on glaucoma and genetics, as stratified by glaucoma subtype and ethnicity. Primary open-angle glaucoma (POAG) is the most common cause of glaucoma worldwide, with the only treatable risk factor (RF) being the reduction of intraocular pressure (IOP). Genes associated with elevated IOP or POAG risk include: ABCA1, AFAP1, ARHGEF12, ATXN2, CAV1, CDKN2B-AS1, FOXC1, GAS7, GMDS, SIX1/SIX6, TMCO1, and TXNRD2. However, there are variations in RF and genetic factors based on ethnic and geographic differences; it is clear that unified molecular pathways accounting for POAG pathogenesis remain uncertain, although inflammation and senescence likely play an important role. There are similar ethnic and geographic complexities in primary angle closure glaucoma (PACG), but several genes have been associated with this disorder, including MMP9, HGF, HSP70, MFRP, and eNOS. In exfoliation glaucoma (XFG), genes implicated include LOXL1, CACNA1A, POMP, TMEM136, AGPAT1, RBMS3, and SEMA6A. Despite tremendous progress, major gaps remain in resolving the genetic architecture for the various glaucoma subtypes across ancestries. Large scale carefully designed studies are required to advance understanding of genetic loci as RF in glaucoma pathophysiology and to improve diagnosis and treatment options.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Molecular Genetics of Glaucoma: Subtype and Ethnicity Considerations

Zukerman

Harris

Vercellin

et al. 2020

Genes

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“…However, such analyses, albeit of importance and interest, do not represent unquestionable proof and are not sufficient for identifying the risk factors leading to disease development. To overcome this problem, simultaneous analysis of more SNPs and the calculation of so-called gene risk scores or genetic risk scores (GRSs) or polygenic risk scores (PRSs) seem to be a reasonable approach [ 55 , 56 ]. A variable number of polymorphisms are being used for gene score construction, from a few to thousands.…”

Section: Gene Scorementioning

confidence: 99%

Genetics of Cardiovascular Disease: How Far Are We from Personalized CVD Risk Prediction and Management?

Vráblík

Dlouhá

Todorovová

et al. 2021

IJMS

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Despite the rapid progress in diagnosis and treatment of cardiovascular disease (CVD), this disease remains a major cause of mortality and morbidity. Recent progress over the last two decades in the field of molecular genetics, especially with new tools such as genome-wide association studies, has helped to identify new genes and their variants, which can be used for calculations of risk, prediction of treatment efficacy, or detection of subjects prone to drug side effects. Although the use of genetic risk scores further improves CVD prediction, the significance is not unambiguous, and some subjects at risk remain undetected. Further research directions should focus on the “second level” of genetic information, namely, regulatory molecules (miRNAs) and epigenetic changes, predominantly DNA methylation and gene-environment interactions.

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“…Commonly, calculated PRSs also assume that a trait's genetic architecture is additive and that neither gene-gene nor gene-environment interactions are important factors. This method has seen some success, but often fails to predict an individual's disease status, especially at intermediate values of the PRS (Igo et al 2019), possibly because translating population level data to individual status is problematic and risks falling into the ecological fallacy. PRSs may also not be comparable among populations (Martin et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Estimating Prevalence of Human Traits Among Populations From Polygenic Risk Scores

Graham

Plotkin

Muglia

et al. 2021

Preprint

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The genetic basis of phenotypic variation across populations has not been well explained for most traits. Several factors may cause disparities, from variation in environments to divergent population genetic structure. We hypothesized that a population level polygenic risk score (PRS) can explain phenotypic variation among geographic populations based solely on risk allele frequencies. We applied a population specific PRS (psPRS) to 26 populations from the 1000 Genomes to four phenotypes: lactase persistence (LP), melanoma, multiple sclerosis (MS) and height. Our models assumed additive genetic architecture among the polymorphisms in the psPRS, as is convention. Linear psPRSs explained a significant proportion of trait variance ranging from 0.32 for height in men to 0.88 for melanoma. The best models for LP and height were linear, while those for melanoma and MS were nonlinear. As not all variants in a PRS may confer similar, or even any, risk among diverse populations, we also filtered out SNPs to assess whether variance explained was improved using psPRSs with fewer SNPs. Variance explained usually improved with fewer SNPs in the psPRS and were as high as 0.99 for height in men using only 548 of the initial 4208 SNPs. That reducing SNPs improves psPRS performance may indicate that missing heritability is partially due to complex architecture that does not mandate additivity, undiscovered variants, or spurious associations in the databases. We demonstrated that PRS based-analyses can be used across diverse populations and phenotypes for population prediction and that these comparisons can identify the universal risk variants.

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Genetic Risk Scores

Cited by 91 publications

References 84 publications

Molecular Genetics of Glaucoma: Subtype and Ethnicity Considerations

Molecular Genetics of Glaucoma: Subtype and Ethnicity Considerations

Genetics of Cardiovascular Disease: How Far Are We from Personalized CVD Risk Prediction and Management?

Estimating Prevalence of Human Traits Among Populations From Polygenic Risk Scores

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