Improving polygenic prediction with genetically inferred ancestry

Naret, Olivier; Kutalik, Zoltán; Hodel, Flavia; Xu, Zhi Ming; Marques‐Vidal, Pedro; Fellay, Jacques

doi:10.1016/j.xhgg.2022.100109

Cited by 3 publications

(2 citation statements)

References 49 publications

(47 reference statements)

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“…First, ePRS accounts for population stratification in a similar fashion to genetic PCs, however, because the ePRS is constructed such that it is specific to the PRS of interest, it has more intuitive interpretation. Further, the ePRS represents the same quantity across datasets, in contrast to PCs which are typically constructed independently in each dataset (though some approaches have been proposed for unifying PCs across datasets (e.g., (20)). While these authors also showed that PCs are used for prediction models when aligned across datasets, this alignment procedure requires joint quality control across multiple datasets, which is difficult, and critically, the ePRS framework is not inconsistent with this approach, because PCs can be incorporated to prediction models that include the ePRS.…”

Section: Discussionmentioning

confidence: 99%

The expected polygenic risk score (ePRS) framework: an equitable metric for quantifying polygenetic risk via modeling of ancestral makeup

Huang,

Kurniansyah,

Goodman

et al. 2024

Preprint

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Polygenic risk scores (PRSs) depend on genetic ancestry due to differences in allele frequencies between ancestral populations. This leads to implementation challenges in diverse populations. We propose a framework to calibrate PRS based on ancestral makeup. We define a metric called expected PRS (ePRS), the expected value of a PRS based on the global or local admixture patterns of an individual. We further define the residual PRS (rPRS), measuring the deviation of the PRS from the ePRS. Simulation studies confirm that it suffices to adjust for ePRS to obtain nearly unbiased estimates of the PRS-outcome association without further adjusting for PCs. Using the TOPMed dataset, the estimated effect size of the rPRS adjusting for the ePRS is similar to the estimated effect of the PRS adjusting for genetic PCs. The ePRS framework can protect from population stratification in association analysis and provide an equitable strategy to quantify genetic risk across diverse populations.

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Section: Discussionmentioning

confidence: 99%

The expected polygenic risk score (ePRS) framework: an equitable metric for quantifying polygenetic risk via modeling of ancestral makeup

Huang,

Kurniansyah,

Goodman

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…CoLaus developed and validated clinical tools for screening and diagnosis, e.g., a simple screening tool for obstructive sleep apnea syndrome in depressive disorders as well as risk scores for cardiovascular disease and diabetes [79][80][81]. CoLaus is also contributing to various genome-wide association studies on different behaviors and diseases, most importantly metabolic diseases and anthropometric traits, and is investigating the utility of polygenic risk scores [82,83]. Bus Santé, which evolves as a cohort from annual cross-sectional surveys in Geneva, has produced important trend information on health aspects, including on the small scale geospatial evaluation, of the impact of policies such as mammography screening, tobacco smoking ban, or nutritional guidelines [64,[84][85][86].…”

Section: Population Cohorts In Switzerlandmentioning

confidence: 99%

Swiss Cohort & Biobank – The White Paper

et al. 2022

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A large Swiss Cohort with 100,000+ representative participants of all ages is the backbone of public health related research and an essential point of reference for health systems steering, personalized health, and clinical research. The national multidisciplinary public health sciences community of SSPH+ and the national society of public health professionals-the Swiss Society for Public Health-assembled an ad hoc Steering & Writing Committee led by Prof. Nicole Probst-Hensch, Swiss TPH to publish a White Paper in fall 2022. This is a further complementary step in the national move toward better health data in Switzerland and specifically toward a large population-based cohort and biobank, with its many milestones, including the cohort pilot study of the Federal Office of Public Health (FOPH), largely based on the protocols of the only national population-based cohort and biobank of Switzerland (SAPALDIA) running successfully since >30 years as well as draft questionnaires of HBM4EU, Food frequency questionnaires of MenuCH, worker exposure questionnaires, and others. Some protocols have been developed specifically the cohort pilot, e.g., all blood and urine sampling protocols. Moreover, the parliamentary petitions (Motion 19.4069 1 ; Postulat 21.3220 2 ) call for a Children Cohort as another crucial step in the promotion of more Swiss research for the health of the youngest.

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Adjusting for population stratification in polygenic risk score analyses: a guide for model specifications in the UK Biobank

Lin

Pries

et al. 2023

J Hum Genet

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Improving polygenic prediction with genetically inferred ancestry

Cited by 3 publications

References 49 publications

The expected polygenic risk score (ePRS) framework: an equitable metric for quantifying polygenetic risk via modeling of ancestral makeup

The expected polygenic risk score (ePRS) framework: an equitable metric for quantifying polygenetic risk via modeling of ancestral makeup

Swiss Cohort & Biobank – The White Paper

Adjusting for population stratification in polygenic risk score analyses: a guide for model specifications in the UK Biobank

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