Improving reporting standards for polygenic scores in risk prediction studies

Wand, Hannah; Lambert, Samuel A.; Tamburro, Cecelia P.; Iacocca, Michael A.; O’Sullivan, Jack W; Sillari, Catherine H.; Kullo, Iftikhar J.; Rowley, Robb; Dron, Jacqueline S.; Brockman, Deanna; Venner, Eric; McCarthy, Mark; Antoniou, Antonis C.; Easton, Douglas F.; Hegele, Robert A.; Khera, Amit V.; Chatterjee, Nilanjan; Kooperberg, Charles; Edwards, Karen L.; Vlessis, Katherine; Kinnear, Kim; Danesh, John; Parkinson, Helen; Ramos, Erin M.; Roberts, Megan C.; Ormond, Kelly E.; Khoury, Muin J.; Janssens, A. Cecile J.W.; Goddard, Katrina A.B.; Kraft, Peter; MacArthur, Jaqueline A L; Inouye, Michael; Wojcik, Genevieve L.

doi:10.1038/s41586-021-03243-6

Cited by 322 publications

(228 citation statements)

References 63 publications

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“…In our study, DNA methylation was measured on a reproducible genome-wide array using methods consistent with prior research. Our cumulative DNA methylation scores were calculated using effect estimates from a large consortium [10] and, importantly, our study sample was independent from the sample that generated the weights [43]. We performed multiple essential sensitivity analyses, including using three methods for cumulative DNA methylation score development, adjustment for postnatal exposure, and adjustment for alternate air pollution exposures.…”

Section: Discussionmentioning

confidence: 99%

Prenatal Particulate Matter Exposure Is Associated with Saliva DNA Methylation at Age 15: Applying Cumulative DNA Methylation Scores as an Exposure Biomarker

Bakulski

Fisher

Dou

et al. 2021

Toxics

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Background: Exposure in utero to particulate matter (PM2.5 and PM10) is associated with maladaptive health outcomes. Although exposure to prenatal PM2.5 and PM10 has cord blood DNA methylation signatures at birth, signature persistence into childhood and saliva cross-tissue applicability has not been tested. Methods: In the Fragile Families and Child Wellbeing Study, a United States 20-city birth cohort, average residential PM2.5 and PM10 during the three months prior to birth was estimated using air quality monitors with inverse distance weighting. Saliva DNA methylation at ages 9 (n = 749) and 15 (n = 793) was measured using the Illumina HumanMethylation 450 k BeadArray. Cumulative DNA methylation scores for particulate matter were estimated by weighting participant DNA methylation at each site by independent meta-analysis effect estimates and standardizing the sums. Using a mixed-effects regression analysis, we tested the associations between cumulative DNA methylation scores at ages 9 and 15 and PM exposure during pregnancy, adjusted for child sex, age, race/ethnicity, maternal income-to-needs ratio, nonmartial birth status, and saliva cell-type proportions. Results: Our study sample was 50.5% male, 56.3% non-Hispanic Black, and 19.8% Hispanic, with a median income-to-needs ratio of 1.4. Mean exposure levels for PM2.5 were 27.9 μg/m3/day (standard deviation: 7.0; 23.7% of observations exceeded safety standards) and for PM10 were 15.0 μg/m3/day (standard deviation: 3.1). An interquartile range increase in PM2.5 exposure (10.73 μg/m3/day) was associated with a −0.0287 standard deviation lower cumulative DNA methylation score for PM2.5 (95% CI: −0.0732, 0.0158, p = 0.20) across all participants. An interquartile range increase in PM10 exposure (3.20 μg/m3/day) was associated with a −0.1472 standard deviation lower cumulative DNA methylation score for PM10 (95% CI: −0.3038, 0.0095, p = 0.06) across all participants. The PM10 findings were driven by the age 15 subset where an interquartile range increase in PM10 exposure was associated with a −0.024 standard deviation lower cumulative DNA methylation score for PM10 (95% CI: −0.043, −0.005, p = 0.012). Findings were robust to adjustment for PM exposure at ages 1 and 3. Conclusion: In utero PM10-associated DNA methylation differences were identified at age 15 in saliva. Benchmarking the timing and cell-type generalizability is critical for epigenetic exposure biomarker assessment.

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Section: Discussionmentioning

confidence: 99%

Prenatal Particulate Matter Exposure Is Associated with Saliva DNA Methylation at Age 15: Applying Cumulative DNA Methylation Scores as an Exposure Biomarker

Bakulski

Fisher

Dou

et al. 2021

Toxics

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“…Genomic heterogeneity has been well studied in various cancers (Burrell et al, 2013;Hiley et al, 2016;Maura et al, 2021;Nicholson and Fine, 2021;Wand and Lambert, 2021). However, there are a very few reports about epigenetic heterogeneity in human cancers.…”

Section: Discussionmentioning

confidence: 99%

Intratumor Epigenetic Heterogeneity—A Panel Gene Methylation Study in Thyroid Cancer

et al. 2021

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BackgroundThyroid cancer (TC) is the most common endocrine malignancy, and the incidence is increasing very fast. Surgical resection and radioactive iodine ablation are major therapeutic methods, however, around 10% of differentiated thyroid cancer and all anaplastic thyroid carcinoma (ATC) are failed. Comprehensive understanding the molecular mechanisms may provide new therapeutic strategies for thyroid cancer. Even though genetic heterogeneity is rigorously studied in various cancers, epigenetic heterogeneity in human cancer remains unclear.MethodsA total of 405 surgical resected thyroid cancer samples were employed (three spatially isolated specimens were obtained from different regions of the same tumor). Twenty-four genes were selected for methylation screening, and frequently methylated genes in thyroid cancer were used for further validation. Methylation specific PCR (MSP) approach was employed to detect the gene promoter region methylation.ResultsFive genes (AP2, CDH1, DACT2, HIN1, and RASSF1A) are found frequently methylated (>30%) in thyroid cancer. The five genes panel is used for further epigenetic heterogeneity analysis. AP2 methylation is associated with gender (P < 0.05), DACT2 methylation is associated with age, gender and tumor size (all P < 0.05), HIN1 methylation is associated to tumor size (P < 0.05) and extra-thyroidal extension (P < 0.01). RASSF1A methylation is associated with lymph node metastasis (P < 0.01). For heterogeneity analysis, AP2 methylation heterogeneity is associated with tumor size (P < 0.01), CDH1 methylation heterogeneity is associated with lymph node metastasis (P < 0.05), DACT2 methylation heterogeneity is associated with tumor size (P < 0.01), HIN1 methylation heterogeneity is associated with tumor size and extra-thyroidal extension (all P < 0.01). The multivariable analysis suggested that the risk of lymph node metastasis is 2.5 times in CDH1 heterogeneous methylation group (OR = 2.512, 95% CI 1.135, 5.557, P = 0.023). The risk of extra-thyroidal extension is almost 3 times in HIN1 heterogeneous methylation group (OR = 2.607, 95% CI 1.138, 5.971, P = 0.023).ConclusionFive of twenty-four genes were found frequently methylated in human thyroid cancer. Based on 5 genes panel analysis, epigenetic heterogeneity is an universal event. Epigenetic heterogeneity is associated with cancer development and progression.

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“…5 Distribution (%) of total individuals available in the GWAS catalog as of January 2019 [60] clinical care. To address this, robust Polygenic Risk Score Reporting Standards (PRS-RS) and a publicly accessible Polygenic Score Catalog have been developed that addresses current research environments with advanced methodological developments to inform clinically meaningful reporting on the development and validation of PRSs in the literature, with an emphasis on reproducibility and transparency throughout the development process [40,67].…”

Section: Future Directions Of Prssmentioning

confidence: 99%

Implementation and implications for polygenic risk scores in healthcare

et al. 2021

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Increasing amounts of genetic data have led to the development of polygenic risk scores (PRSs) for a variety of diseases. These scores, built from the summary statistics of genome-wide association studies (GWASs), are able to stratify individuals based on their genetic risk of developing various common diseases and could potentially be used to optimize the use of screening and preventative treatments and improve personalized care for patients. Many challenges are yet to be overcome, including PRS validation, healthcare professional and patient education, and healthcare systems integration. Ethical challenges are also present in how this information is used and the current lack of diverse populations with PRSs available. In this review, we discuss the topics above and cover the nature of PRSs, visualization schemes, and how PRSs can be improved. With these tools on the horizon for multiple diseases, scientists, clinicians, health systems, regulatory bodies, and the public should discuss the uses, benefits, and potential risks of PRSs.

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Improving reporting standards for polygenic scores in risk prediction studies

Cited by 322 publications

References 63 publications

Prenatal Particulate Matter Exposure Is Associated with Saliva DNA Methylation at Age 15: Applying Cumulative DNA Methylation Scores as an Exposure Biomarker

Prenatal Particulate Matter Exposure Is Associated with Saliva DNA Methylation at Age 15: Applying Cumulative DNA Methylation Scores as an Exposure Biomarker

Intratumor Epigenetic Heterogeneity—A Panel Gene Methylation Study in Thyroid Cancer

Implementation and implications for polygenic risk scores in healthcare

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