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Lu T, Zhou S, Wu H, et al. Individuals with common diseases but with a low polygenic risk score could be prioritized for rare variant screening. Genet Med. 2020;23(3):508-515doi: 10.1038/s41436-020-01007-7.
Lu, T., Zhou, S., Wu, H., Forgetta, V., Greenwood, C. M. T., & Richards, J. B. (2021). Individuals with common diseases but with a low polygenic risk score could be prioritized for rare variant screening. Genetics in medicine : official journal of the American College of Medical Genetics, 23(3), 508-515. https://doi.org/10.1038/s41436-020-01007-7
Lu, Tianyuan, et al. "Individuals with common diseases but with a low polygenic risk score could be prioritized for rare variant screening." Genetics in medicine : official journal of the American College of Medical Genetics vol. 23,3 (2021): 508-515. doi: https://doi.org/10.1038/s41436-020-01007-7
Lu T, Zhou S, Wu H, Forgetta V, Greenwood CMT, Richards JB. Individuals with common diseases but with a low polygenic risk score could be prioritized for rare variant screening. Genet Med. 2021 Mar;23(3):508-515. doi: 10.1038/s41436-020-01007-7. Epub 2020 Oct 28. PMID: 33110269.
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Genet Med. 2021 Mar;23(3):508-515. doi: 10.1038/s41436-020-01007-7. Epub 2020 Oct 28.

Individuals with common diseases but with a low polygenic risk score could be prioritized for rare variant screening.

Genetics in medicine : official journal of the American College of Medical Genetics

Tianyuan Lu, Sirui Zhou, Haoyu Wu, Vincenzo Forgetta, Celia M T Greenwood, J Brent Richards

Affiliations

  1. Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.
  2. Quantitative Life Sciences Program, McGill University, Montreal, QC, Canada.
  3. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.
  4. Department of Human Genetics, McGill University, Montreal, QC, Canada.
  5. Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada.
  6. Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada. [email protected].
  7. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada. [email protected].
  8. Department of Human Genetics, McGill University, Montreal, QC, Canada. [email protected].
  9. Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom. [email protected].

PMID: 33110269 DOI: 10.1038/s41436-020-01007-7

Abstract

PURPOSE: Identifying rare genetic causes of common diseases can improve diagnostic and treatment strategies, but incurs high costs. We tested whether individuals with common disease and low polygenic risk score (PRS) for that disease generated from less expensive genome-wide genotyping data are more likely to carry rare pathogenic variants.

METHODS: We identified patients with one of five common complex diseases among 44,550 individuals who underwent exome sequencing in the UK Biobank. We derived PRS for these five diseases, and identified pathogenic rare variant heterozygotes. We tested whether individuals with disease and low PRS were more likely to carry rare pathogenic variants.

RESULTS: While rare pathogenic variants conferred, at most, 5.18-fold (95% confidence interval [CI]: 2.32-10.13) increased odds of disease, a standard deviation increase in PRS, at most, increased the odds of disease by 5.25-fold (95% CI: 5.06-5.45). Among diseased patients, a standard deviation decrease in the PRS was associated with, at most, 2.82-fold (95% CI: 1.14-7.46) increased odds of identifying rare variant heterozygotes.

CONCLUSION: Rare pathogenic variants were more prevalent among affected patients with a low PRS. Therefore, prioritizing individuals for sequencing who have disease but low PRS may increase the yield of sequencing studies to identify rare variant heterozygotes.

Keywords: exome sequencing; patient prioritization; polygenic risk scores; rare variants; risk stratification

References

  1. Visscher PM, et al. 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet. 2017;101:5–22. - PubMed
  2. Bomba L, Walter K, Soranzo N. The impact of rare and low-frequency genetic variants in common disease. Genome Biol. 2017;18:77. - PubMed
  3. Esplin ED, Oei L, Snyder MP. Personalized sequencing and the future of medicine: discovery, diagnosis and defeat of disease. Pharmacogenomics. 2014;15:1771–1790. - PubMed
  4. Shepherd M, et al. A genetic diagnosis of HNF1A diabetes alters treatment and improves glycaemic control in the majority of insulin-treated patients. Diabet Med. 2009;26:437–441. - PubMed
  5. Khera AV, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50:1219–1224. - PubMed
  6. Inouye M, et al. Genomic risk prediction of coronary artery disease in 480,000 adults: implications for primary prevention. J Am Coll Cardiol. 2018;72:1883–1893. - PubMed
  7. Mars N, et al. Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers. Nat Med. 2020;26:549–557. - PubMed
  8. Pavan S, et al. Recommendations for choosing the genotyping method and best practices for quality control in crop genome-wide association studies. Front Genet. 2020;11:447. - PubMed
  9. Bycroft C, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562:203–209. - PubMed
  10. Nagai A, et al. Overview of the BioBank Japan Project: study design and profile. J Epidemiol. 2017;27:S2–S8. - PubMed
  11. Chen Z, et al. China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up. Int J Epidemiol. 2011;40:1652–1666. - PubMed
  12. Fry A, et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am J Epidemiol. 2017;186:1026–1034. - PubMed
  13. Yengo L, et al. Meta-analysis of genome-wide association studies for height and body mass index in approximately 700000 individuals of European ancestry. Hum Mol Genet. 2018;27:3641–3649. - PubMed
  14. Ranke MB. Towards a consensus on the definition of idiopathic short stature. Horm Res. 1996;45:64–66. - PubMed
  15. Kalia SS, et al. Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics. Genet Med. 2017;19:249–255. - PubMed
  16. Misra S, Owen KR. Genetics of monogenic diabetes: present clinical challenges. Curr Diab Rep. 2018;18:141. - PubMed
  17. Makitie RE, et al. New insights into monogenic causes of osteoporosis. Front Endocrinol (Lausanne). 2019;10:70. - PubMed
  18. Rappold G, et al. Genotypes and phenotypes in children with short stature: clinical indicators of SHOX haploinsufficiency. J Med Genet. 2007;44:306–313. - PubMed
  19. Grunauer M, Jorge AAL. Genetic short stature. Growth Horm IGF Res. 2018;38:29–33. - PubMed
  20. Cingolani P, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin). 2012;6:80–92. - PubMed
  21. Rentzsch P, et al. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019;47:D886–D894. - PubMed
  22. Mavaddat N, et al. Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am J Hum Genet. 2019;104:21–34. - PubMed
  23. Njeh CF, et al. Assessment of bone status using speed of sound at multiple anatomical sites. Ultrasound Med Biol. 2001;27:1337–1345. - PubMed
  24. Forgetta V, et al. Development of a polygenic risk score to improve screening for fracture risk: a genetic risk prediction study. PLoS Med. 2020;17:e1003152. - PubMed
  25. Wood AR, et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet. 2014;46:1173–1186. - PubMed
  26. Vilhjalmsson BJ, et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am J Hum Genet. 2015;97:576–592. - PubMed
  27. Weigl K, et al. Genetic risk score is associated with prevalence of advanced neoplasms in a colorectal cancer screening population. Gastroenterology. 2018;155:88–98 e10. - PubMed
  28. Dudbridge F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 2013;9:e1003348. - PubMed
  29. Antoniou AC, et al. Breast-cancer risk in families with mutations in PALB2. N Engl J Med. 2014;371:497–506. - PubMed
  30. Southey MC, et al. PALB2, CHEK2 and ATM rare variants and cancer risk: data from COGS. J Med Genet. 2016;53:800–811. - PubMed
  31. Easton DF, et al. Gene-panel sequencing and the prediction of breast-cancer risk. N Engl J Med. 2015;372:2243–2257. - PubMed
  32. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10:37–48. - PubMed
  33. Malkin D, et al. Germ line p53 mutations in a familial syndrome of breast cancer, sarcomas, and other neoplasms. Science. 1990;250:1233–1238. - PubMed
  34. Rivera-Munoz EA, et al. ClinGen Variant Curation Expert Panel experiences and standardized processes for disease and gene-level specification of the ACMG/AMP guidelines for sequence variant interpretation. Hum Mutat. 2018;39:1614–1622. - PubMed
  35. Martin AR, et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51:584–591. - PubMed

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