Derivation and validation of genome-wide polygenic score for urinary tract stone diagnosis

Ishan Paranjpe¹, Noah Tsao², Renae Judy², Manish Paranjpe³, Kumardeep Chaudhary⁴, Derek Klarin⁵, Iain Forrest⁶, Ross O'Hagan⁴, Arjun Kapoor⁷, John Pfail⁷, Suraj Jaladanki⁴, Fayzan Chaudhry⁸, Akhil Vaid⁹, Phan Q Duy¹⁰; Charles Bronfman Institute of Personalized Medicine Genomics Team¹¹; Regeneron Genomics Team¹²; John Cijiang He¹³, Benjamin S Glicksberg¹⁴, Steven G Coca¹⁵, Mantu Gupta⁷, Ron Do¹⁶, Scott M Damrauer², Girish N Nadkarni¹⁷

Affiliations

¹ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
² Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
³ Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, USA.
⁴ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
⁵ Malcolm Randall Veterans Affairs Medical Center, Gainesville, Florida, USA; Division of Vascular Surgery and Endovascular Therapy, University of Florida College of Medicine, Gainesville, Florida, USA; Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
⁶ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
⁷ Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
⁸ The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
⁹ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
¹⁰ Medical Scientist Training Program, Yale University School of Medicine, New Haven, Connecticut, USA.
¹¹ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
¹² Regeneron Genetics Center, Tarrytown, New York, USA.
¹³ Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Renal Program, James J Peters Veterans Affairs Medical Center at Bronx, New York, New York, USA.
¹⁴ The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
¹⁵ Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
¹⁶ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
¹⁷ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Renal Program, James J Peters Veterans Affairs Medical Center at Bronx, New York, New York, USA. Electronic address: girish.nadkarni@mountsinai.org.

PMID: 32540406
PMCID: PMC7606592
DOI: 10.1016/j.kint.2020.04.055

Derivation and validation of genome-wide polygenic score for urinary tract stone diagnosis

Ishan Paranjpe et al. Kidney Int. 2020 Nov.

. 2020 Nov;98(5):1323-1330.

doi: 10.1016/j.kint.2020.04.055. Epub 2020 Jun 12.

Authors

Affiliations

¹ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
² Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
³ Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, USA.
⁴ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
⁵ Malcolm Randall Veterans Affairs Medical Center, Gainesville, Florida, USA; Division of Vascular Surgery and Endovascular Therapy, University of Florida College of Medicine, Gainesville, Florida, USA; Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
⁶ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
⁷ Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
⁸ The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
⁹ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
¹⁰ Medical Scientist Training Program, Yale University School of Medicine, New Haven, Connecticut, USA.
¹¹ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
¹² Regeneron Genetics Center, Tarrytown, New York, USA.
¹³ Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Renal Program, James J Peters Veterans Affairs Medical Center at Bronx, New York, New York, USA.
¹⁴ The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
¹⁵ Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
¹⁶ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
¹⁷ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Renal Program, James J Peters Veterans Affairs Medical Center at Bronx, New York, New York, USA. Electronic address: girish.nadkarni@mountsinai.org.

PMID: 32540406
PMCID: PMC7606592
DOI: 10.1016/j.kint.2020.04.055

Abstract

Urinary tract stones have high heritability indicating a strong genetic component. However, genome-wide association studies (GWAS) have uncovered only a few genome wide significant single nucleotide polymorphisms (SNPs). Polygenic risk scores (PRS) sum cumulative effect of many SNPs and shed light on underlying genetic architecture. Using GWAS summary statistics from 361,141 participants in the United Kingdom Biobank, we generated a PRS and determined association with stone diagnosis in 28,877 participants in the Mount Sinai BioMe Biobank. In BioMe (1,071 cases and 27,806 controls), for every standard deviation increase, we observed a significant increment in adjusted odds ratio of a factor of 1.2 (95% confidence interval 1.13-1.26). In comparison, a risk score comprised of GWAS significant SNPs was not significantly associated with diagnosis. After stratifying individuals into low and high-risk categories on clinical risk factors, there was a significant increment in adjusted odds ratio of 1.3 (1.12-1.6) in the low- and 1.2 (1.1-1.2) in the high-risk group for every standard deviation increment in PRS. In a 14,348-participant validation cohort (Penn Medicine Biobank), every standard deviation increment was associated with a significant adjusted odds ratio of 1.1 (1.03 - 1.2). Thus, a genome-wide PRS is associated with urinary tract stones overall and in the absence of known clinical risk factors and illustrates their complex polygenic architecture.

Keywords: genomics; nephrolithiasis; personalized medicine; polygenic risk score; urinary tract stone.

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Figures

**Figure 1**
A. Generation of polygenic risk score from GWAS summary statistics. B. Overall scheme of polygenic risk score development and application in BioMe and PMBB cohorts

**Figure 2**
A. Association of PRS with urinary tract stone diagnosis stratified by race in BioMe. Odds ratio was computed using a logistic regression adjusted for age, sex, 10 genetic PCs, history of hypertension, gout, and type 2 diabetes. B. Prevalence of urinary tract stone diagnosis for different levels of PRS C. Distribution of normalized PRS in BioMe .

**Figure 3**
Association of PRS with urinary tract stone diagnosis in BioMe stratified by clinical risk adjusted for age, sex, BMI, and 10 genetic principal components. Low clinical risk was defined as BMI <25 and no history of gout, type 2 diabetes and hypertension. High clinical risk included all other patients.

**Figure 4**
A. Association of PRS with urinary tract stone diagnosis in Penn Medicine Bio Bank validation cohort stratified by self-reported race. Prevalence of urinary tract stone diagnosis for different levels of PRS in B. European Americans and C. African Americans. Distribution of PRS in D. European Americans and E. African Americans

See this image and copyright information in PMC

Comment in

Regarding "Derivation and validation of genome-wide polygenic score for urinary tract stone diagnosis".
Geraghty RM, Wilson I, Sayer JA. Geraghty RM, et al. Kidney Int. 2020 Nov;98(5):1347. doi: 10.1016/j.kint.2020.08.016. Kidney Int. 2020. PMID: 33126978 No abstract available.
The authors reply.
Paranjpe I, Nadkarni GN. Paranjpe I, et al. Kidney Int. 2020 Nov;98(5):1347-1348. doi: 10.1016/j.kint.2020.08.017. Kidney Int. 2020. PMID: 33126979 No abstract available.

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Derivation and validation of genome-wide polygenic score for urinary tract stone diagnosis

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Derivation and validation of genome-wide polygenic score for urinary tract stone diagnosis

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