Relative contributions of family history and a polygenic risk score on COPD and related outcomes: COPDGene and ECLIPSE studies

Moll, Matthew; Lutz, Sharon M.; Ghosh, Auyon; Sakornsakolpat, Phuwanat; Hersh, Craig P.; Beaty, Terri H.; Dudbridge, Frank; Tobin, Martin D.; Mittleman, Murray A.; Silverman, Edwin K.; Hobbs, Brian D.; Cho, Michael H.

doi:10.1136/bmjresp-2020-000755

Cited by 15 publications

(25 citation statements)

References 37 publications

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“…In addition, PRS were still significant after adjusting for family history. All of these indicated that while family history includes at least some genetic components of AUD, PRS provided complementary information, and together they improve the ability to evaluate disease risk as having been demonstrated in previous studies (Abraham et al, 2019;Gronberg et al, 2015;Hujoel et al, 2021;Kachuri et al, 2020;Lu et al, 2018;Moll et al, 2020). As noted previously, early alcohol intervention programs with high-risk individuals were effective in reducing the incidence of AUD (Bersamin et al, 2007;Cronce & Larimer, 2011;Kaner et al, 2007Kaner et al, , 2018Knox et al, 2019;Solberg et al, 2008;Welter et al, 2020;Whitlock et al, 2004); PRS and family history can lead to expedited risk identification, which in turn can guide best practices around these programs, making targeted and personalized prevention strategies possible.…”

Section: Discussionmentioning

confidence: 55%

Evaluating risk for alcohol use disorder: Polygenic risk scores and family history

Lai

Johnson

Colbert

et al. 2022

Alcoholism Clin & Exp Res

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Background Early identification of individuals at high risk for alcohol use disorder (AUD) coupled with prompt interventions could reduce the incidence of AUD. In this study, we investigated whether Polygenic Risk Scores (PRS) can be used to evaluate the risk for AUD and AUD severity (as measured by the number of DSM‐5 AUD diagnostic criteria met) and compared their performance with a measure of family history of AUD. Methods We studied individuals of European ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA). DSM‐5 diagnostic criteria were available for 7203 individuals, of whom 3451 met criteria for DSM‐IV alcohol dependence or DSM‐5 AUD and 1616 were alcohol‐exposed controls aged ≥21 years with no history of AUD or drug dependence. Further, 4842 individuals had a positive first‐degree family history of AUD (FH+), 2722 had an unknown family history (FH?), and 336 had a negative family history (FH−). PRS were derived from a meta‐analysis of a genome‐wide association study of AUD from the Million Veteran Program and scores from the problem subscale of the Alcohol Use Disorders Identification Test in the UK Biobank. We used mixed models to test the association between PRS and risk for AUD and AUD severity. Results AUD cases had higher PRS than controls with PRS increasing as the number of DSM‐5 diagnostic criteria increased (p‐values ≤ 1.85E−05) in the full COGA sample, the FH+ subsample, and the FH? subsample. Individuals in the top decile of PRS had odds ratios (OR) for developing AUD of 1.96 (95% CI: 1.54 to 2.51, p‐value = 7.57E−08) and 1.86 (95% CI: 1.35 to 2.56, p‐value = 1.32E−04) in the full sample and the FH+ subsample, respectively. These values are comparable to previously reported ORs for a first‐degree family history (1.91 to 2.38) estimated from national surveys. PRS were also significantly associated with the DSM‐5 AUD diagnostic criterion count in the full sample, the FH+ subsample, and the FH? subsample (p‐values ≤6.7E−11). PRS remained significantly associated with AUD and AUD severity after accounting for a family history of AUD (p‐values ≤6.8E−10). Conclusions Both PRS and family history were associated with AUD and AUD severity, indicating that these risk measures assess distinct aspects of liability to AUD traits.

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Section: Discussionmentioning

confidence: 55%

Evaluating risk for alcohol use disorder: Polygenic risk scores and family history

Lai

Johnson

Colbert

et al. 2022

Alcoholism Clin & Exp Res

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“…The systematic search resulted in 11 publications; subsequently, two additional studies were included, following perusal of references. Moll et al 20 used data from the COPDGene study and then replicated the study by using data from the ECLIPSE cohort. Results from the two analyses are displayed as two separate studies in the Results section ( Tables 1–3 ).…”

Section: Resultsmentioning

confidence: 99%

“… Adoptees born between 1932 and 2004 To examine the risk of COPD in adoptees with parental COPD in either biological or adoptive parents or in both biological and adoptive parents. Cases: Adoptees with an ICD 7–10 code of COPD in national registries Controls: Adoptees without COPD No information on smoking history COPD based on ICD 7–10 codes from national registries No information on smoking history Moll 20 2020 Case–control, Multicentre, Prognostic study 12 countries ECLIPSE study To assess the predictive performance of a polygenetic risk score and family history. (A replication cohort of the COPDGene cohort.)…”

Section: Resultsmentioning

confidence: 99%

Parental COPD as a Risk Factor for the Development of COPD and Disease Severity in Offspring: A Systematic Scoping Review

Sikjær¹,

Klitgaard²,

Hilberg³

et al. 2022

COPD

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“…Lassosum sets its unique PRS structure through applying L1 penalized regression framework on GWA summary statistics and a LD reference panel [19,20]. Similar to LDpred, we also chose the UK Biobank training set as the LD reference panel in the Lassosum setting.…”

Section: Prs Benchmark Methodsmentioning

confidence: 99%

DL-PRS: a novel deep learning approach to polygenic risk scores

Huang¹,

Ji²,

Cho³

et al. 2021

Preprint

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Background COPD is a complex heterogeneous disease influenced by both environmental and genetic risk factors. Traditional genome wide association studies (GWAS) have been successful in identifying many reproducible risk variants of moderate to small effect. Polygenic risk scores (PRS) were developed as way to aggregate risk alleles weighted by their effect size to produce a score which could be used in clinical practice to identify individuals at high risk of disease. A limitation of both GWAS and PRS is that they make the important assumption that the effect of each allele is independent and not modified by other genetic or environmental factors. Machine learning methods such as deep learning (DL) neural networks complement the GWAS and PRS paradigm by making fewer assumptions about the nature of the genetic effects being modeled. For example, the hidden layers of a DL model have the potential to model gene-gene interactions with non-additive effects on disease risk. The goal of the present study was to develop a DL neural network approach to GWAS and PRS and to compare it to the prevailing paradigm based on modeling independent effects. We applied our DL-PRS method to genetic association data from several GWAS studies of chronic obstructive pulmonary disease (COPD).Results We developed a DL learning algorithm for modeling the relationship between genetic variation from GWAS and risk of COPD in several population-based studies. We then developed a DL-PRS based on nodes and associated weights from the first and second layer of the DL neural network. Our DL-PRS framework has overall satisfactory performance in the prediction of COPD and provides significant contribution to prediction in addition to the current PRS methods. Moreover, regarding the clinical relevance of COPD, our DL-PRS has a consistent and closer relationship regarding individual deciles and lung functions such as FEV1/FVC and predicted FEV1%. Conclusions Not only does DL-PRS show favorable predictive performance with current benchmark PRS methods, but it also extends the ranges of PRS deciles in predicting different stages of COPD. Moreover, our DL-PRS results were replicated in an independent cohort. This study opens the door to the use of machine learning for developing risk scores from models developed using fewer assumptions about the nature of the genetic effects.

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Relative contributions of family history and a polygenic risk score on COPD and related outcomes: COPDGene and ECLIPSE studies

Cited by 15 publications

References 37 publications

Evaluating risk for alcohol use disorder: Polygenic risk scores and family history

Evaluating risk for alcohol use disorder: Polygenic risk scores and family history

Parental COPD as a Risk Factor for the Development of COPD and Disease Severity in Offspring: A Systematic Scoping Review

DL-PRS: a novel deep learning approach to polygenic risk scores

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