Calibrated rare variant genetic risk scores for complex disease prediction using large exome sequence repositories

Lali, Ricky; Chong, Michael; Omidi, Arghavan; Mohammadi-Shemirani, Pedrum; Le, Ann; Cui, Edward; Paré, Guillaume

doi:10.1038/s41467-021-26114-0

Cited by 25 publications

(17 citation statements)

References 34 publications

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“…As an example, a rare variant genetic risk score for CAD was built, using UK Biobank data. Similar to our findings for obesity and extreme obesity, a significant association of this PRS rare with risk of CAD was observed, although the explained variation was only 0.1% of the population variance ( 30 ). We report a similar explained variance of 0.2% for obesity and 0.5% for extreme obesity.…”

Section: Discussionsupporting

confidence: 89%

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The Value of Rare Genetic Variation in the Prediction of Common Obesity in European Ancestry Populations

et al. 2022

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Polygenic risk scores (PRSs) aggregate the effects of genetic variants across the genome and are used to predict risk of complex diseases, such as obesity. Current PRSs only include common variants (minor allele frequency (MAF) ≥1%), whereas the contribution of rare variants in PRSs to predict disease remains unknown. Here, we examine whether augmenting the standard common variant PRS (PRScommon) with a rare variant PRS (PRSrare) improves prediction of obesity. We used genome-wide genotyped and imputed data on 451,145 European-ancestry participants of the UK Biobank, as well as whole exome sequencing (WES) data on 184,385 participants. We performed single variant analyses (for both common and rare variants) and gene-based analyses (for rare variants) for association with BMI (kg/m2), obesity (BMI ≥ 30 kg/m2), and extreme obesity (BMI ≥ 40 kg/m2). We built PRSscommon and PRSsrare using a range of methods (Clumping+Thresholding [C+T], PRS-CS, lassosum, gene-burden test). We selected the best-performing PRSs and assessed their performance in 36,757 European-ancestry unrelated participants with whole genome sequencing (WGS) data from the Trans-Omics for Precision Medicine (TOPMed) program. The best-performing PRScommon explained 10.1% of variation in BMI, and 18.3% and 22.5% of the susceptibility to obesity and extreme obesity, respectively, whereas the best-performing PRSrare explained 1.49%, and 2.97% and 3.68%, respectively. The PRSrare was associated with an increased risk of obesity and extreme obesity (ORobesity = 1.37 per SDPRS, Pobesity = 1.7x10-85; ORextremeobesity = 1.55 per SDPRS, Pextremeobesity = 3.8x10-40), which was attenuated, after adjusting for PRScommon (ORobesity = 1.08 per SDPRS, Pobesity = 9.8x10-6; ORextremeobesity= 1.09 per SDPRS, Pextremeobesity = 0.02). When PRSrare and PRScommon are combined, the increase in explained variance attributed to PRSrare was small (incremental Nagelkerke R2 = 0.24% for obesity and 0.51% for extreme obesity). Consistently, combining PRSrare to PRScommon provided little improvement to the prediction of obesity (PRSrare AUC = 0.591; PRScommon AUC = 0.708; PRScombined AUC = 0.710). In summary, while rare variants show convincing association with BMI, obesity and extreme obesity, the PRSrare provides limited improvement over PRScommon in the prediction of obesity risk, based on these large populations.

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Section: Discussionsupporting

confidence: 89%

“…Recently, a new framework was developed to aggregate rare variant burden into a rare variant PRS ( 30 ). As an example, a rare variant genetic risk score for CAD was built, using UK Biobank data.…”

Section: Discussionmentioning

confidence: 99%

The Value of Rare Genetic Variation in the Prediction of Common Obesity in European Ancestry Populations

et al. 2022

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“…Future studies should evaluate the contribution of rare variants in PRS performances, possibly deploying them based on their deleterious nature. Recent strategies have focused on constructing PRS using rare variants, which could be particularly useful in admixed populations 45 . Alternative approaches have relied on population prediction at the gene level rather than the single polymorphism since the effect of genes on traits is likely to be more highly conserved across ethnicities.…”

Section: Discussionmentioning

confidence: 99%

“…Recent strategies have focused on constructing PRS using rare variants, which could be particularly useful in admixed populations. 45 Alternative approaches have relied on population prediction at the gene level rather than the single polymorphism since the effect of genes on traits is likely to be more highly conserved across ethnicities. This approach converts SNP's effect sizes in predicted transcript abundance 46 resulting in a polygenic transcriptome risk scores.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Polygenic Risk Score for Alzheimer's Disease in Caribbean Hispanics

Sariya

Felsky

Reyes‐Dumeyer

et al. 2021

Annals of Neurology

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Objective Polygenic risk scores (PRSs) assess the individual genetic propensity to a condition by combining sparse information scattered across genetic loci, often displaying small effect sizes. Most PRSs are constructed in European‐ancestry populations, limiting their use in other ethnicities. Here we constructed and validated a PRS for late‐onset Alzheimer's Disease (LOAD) in Caribbean Hispanics (CH). Methods We used a CH discovery (n = 4,312) and independent validation sample (n = 1,850) to construct an ancestry‐specific PRS (“CH‐PRS”) and evaluated its performance alone and with other predictors using the area under curve (AUC) and logistic regression (strength of association with LOAD and statistical significance). We tested if CH‐PRS predicted conversion to LOAD in a subsample with longitudinal data (n = 1,239). We also tested the CH‐PRS in an independent replication CH cohort (n = 200) and brain autopsy cohort (n = 33). Finally, we tested the effect of ancestry on PRS by using European and African American discovery cohorts to construct alternative PRSs (“EUR‐PRS”, “AA‐PRS”). Results The full model (LOAD ~ CH‐PRS + sex + age + APOE‐ɛ4), achieved an AUC = 74% (ORCH‐PRS = 1.51 95%CI = 1.36–1.68), raising to >75% in APOE‐ɛ4 non‐carriers. CH‐PRS alone achieved an AUC = 72% in the autopsy cohort, raising to AUC = 83% in full model. Higher CH‐PRS was significantly associated with clinical LOAD in the replication CH cohort (OR = 1.61, 95%CI = 1.19–2.17) and significantly predicted conversion to LOAD (HR = 1.93, CI = 1.70–2.20) in the longitudinal subsample. EUR‐PRS and AA‐PRS reached lower prediction accuracy (AUC = 58% and 53%, respectively). Interpretation Enriching diversity in genetic studies is critical to provide an effective PRS in profiling LOAD risk across populations. ANN NEUROL 2021;90:366–376

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“…Although there is progress in the field, it is still a long way from variant associations to molecular disease mechanisms as well as treatments (37). Toward the second aim, polygenic scores are still being improved, for example by considering rare variants (40) or inclusion of functional information (41). Optimizing prediction performance is non-trivial, since machine learning models need to be calibrated to generalize to unseen data, i.e.…”

Section: Discussionmentioning

confidence: 99%

A survey of genome-wide association studies, polygenic scores and UK Biobank highlights resources for autoimmune disease genetics

Saurabh

Fouodo²,

König³

et al. 2022

Front. Immunol.

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Autoimmune diseases share a general mechanism of auto-antigens harming tissues. Still. they are phenotypically diverse, with genetic as well as environmental factors contributing to their etiology at varying degrees. Associated genomic loci and variants have been identified in numerous genome-wide association studies (GWAS), whose results are increasingly used for polygenic scores (PGS) that are used to predict disease risk. At the same time, a technological shift from genotyping arrays to next generation sequencing (NGS) is ongoing. NGS allows the identification of virtually all - including rare - genetic variants, which in combination with methodological developments promises to improve the prediction of disease risk and elucidate molecular mechanisms underlying disease. Here we review current, publicly available autoimmune disease GWAS and PGS data based on information from the GWAS and PGS catalog, respectively. We summarize autoimmune diseases investigated, respective studies conducted and their results. Further, we review genetic data and autoimmune disease patients in the UK Biobank (UKB), the largest resource for genetic and phenotypic data available for academic research. We find that only comparably prevalent autoimmune diseases are covered by the UKB and at the same time assessed by both GWAS and PGS catalogs. These are systemic (systemic lupus erythematosus) as well as organ-specific, affecting the gastrointestinal tract (inflammatory bowel disease as well as specifically Crohn’s disease and ulcerative colitis), joints (juvenile ideopathic arthritis, psoriatic arthritis, rheumatoid arthritis, ankylosing spondylitis), glands (Sjögren syndrome), the nervous system (multiple sclerosis), and the skin (vitiligo).

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Calibrated rare variant genetic risk scores for complex disease prediction using large exome sequence repositories

Cited by 25 publications

References 34 publications

The Value of Rare Genetic Variation in the Prediction of Common Obesity in European Ancestry Populations

The Value of Rare Genetic Variation in the Prediction of Common Obesity in European Ancestry Populations

Polygenic Risk Score for Alzheimer's Disease in Caribbean Hispanics

A survey of genome-wide association studies, polygenic scores and UK Biobank highlights resources for autoimmune disease genetics

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