Leveraging TOPMed imputation server and constructing a cohort-specific imputation reference panel to enhance genotype imputation among cystic fibrosis patients

Quan Sun¹, Weifang Liu¹, Jonathan D Rosen¹, Le Huang², Rhonda G Pace³, Hong Dang³, Paul J Gallins⁴, Elizabeth E Blue^{5

6}, Hua Ling^{7

8}, Harriet Corvol⁹, Lisa J Strug^{10

11}, Michael J Bamshad^{12

13

14

6}, Ronald L Gibson¹², Elizabeth W Pugh¹⁵, Scott M Blackman¹⁶, Garry R Cutting^{8

15}, Wanda K O'Neal³, Yi-Hui Zhou¹⁷, Fred A Wright^{4

17}, Michael R Knowles³, Jia Wen¹⁸, Yun Li^{1

18

19}; Cystic Fibrosis Genome Project

Affiliations

¹ Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
² Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
³ Marsico Lung Institute/UNC CF Research Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
⁴ Bioinformatics Research Center and Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.
⁵ Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA.
⁶ Brotman Baty Institute, Seattle, WA 98195, USA.
⁷ Center for Inherited Disease Research (CIDR), Johns Hopkins University, Baltimore, MD 21205, USA.
⁸ McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
⁹ Sorbonne Université, Inserm, Centre de Recherche Saint-Antoine, Assistance Publique-Hôpitaux de Paris (APHP), Hôpital Trousseau, Service de Pneumologie Pédiatrique, Paris, France.
¹⁰ Departments of Statistical Sciences and Computer Science and Division of Biostatistics, University of Toronto, Toronto, ON, Canada.
¹¹ Program in Genetics and Genome Biology and The Centre for Applied Genomics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada.
¹² Department of Pediatrics, University of Washington, Seattle, WA 98105, USA.
¹³ Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
¹⁴ Division of Genetic Medicine, Seattle Children's Hospital, Seattle, WA 98105, USA.
¹⁵ Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
¹⁶ Division of Pediatric Endocrinology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
¹⁷ Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA.
¹⁸ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
¹⁹ Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

PMID: 35128485
PMCID: PMC8804187
DOI: 10.1016/j.xhgg.2022.100090

Leveraging TOPMed imputation server and constructing a cohort-specific imputation reference panel to enhance genotype imputation among cystic fibrosis patients

Quan Sun et al. HGG Adv. 2022.

. 2022 Jan 11;3(2):100090.

doi: 10.1016/j.xhgg.2022.100090. eCollection 2022 Apr 14.

Authors

Affiliations

¹ Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
² Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
³ Marsico Lung Institute/UNC CF Research Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
⁴ Bioinformatics Research Center and Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.
⁵ Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA.
⁶ Brotman Baty Institute, Seattle, WA 98195, USA.
⁷ Center for Inherited Disease Research (CIDR), Johns Hopkins University, Baltimore, MD 21205, USA.
⁸ McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
⁹ Sorbonne Université, Inserm, Centre de Recherche Saint-Antoine, Assistance Publique-Hôpitaux de Paris (APHP), Hôpital Trousseau, Service de Pneumologie Pédiatrique, Paris, France.
¹⁰ Departments of Statistical Sciences and Computer Science and Division of Biostatistics, University of Toronto, Toronto, ON, Canada.
¹¹ Program in Genetics and Genome Biology and The Centre for Applied Genomics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada.
¹² Department of Pediatrics, University of Washington, Seattle, WA 98105, USA.
¹³ Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
¹⁴ Division of Genetic Medicine, Seattle Children's Hospital, Seattle, WA 98105, USA.
¹⁵ Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
¹⁶ Division of Pediatric Endocrinology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
¹⁷ Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA.
¹⁸ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
¹⁹ Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

PMID: 35128485
PMCID: PMC8804187
DOI: 10.1016/j.xhgg.2022.100090

Abstract

Cystic fibrosis (CF) is a severe genetic disorder that can cause multiple comorbidities affecting the lungs, the pancreas, the luminal digestive system and beyond. In our previous genome-wide association studies (GWAS), we genotyped approximately 8,000 CF samples using a mixture of different genotyping platforms. More recently, the Cystic Fibrosis Genome Project (CFGP) performed deep (approximately 30×) whole genome sequencing (WGS) of 5,095 samples to better understand the genetic mechanisms underlying clinical heterogeneity among patients with CF. For mixtures of GWAS array and WGS data, genotype imputation has proven effective in increasing effective sample size. Therefore, we first performed imputation for the approximately 8,000 CF samples with GWAS array genotype using the Trans-Omics for Precision Medicine (TOPMed) freeze 8 reference panel. Our results demonstrate that TOPMed can provide high-quality imputation for patients with CF, boosting genomic coverage from approximately 0.3-4.2 million genotyped markers to approximately 11-43 million well-imputed markers, and significantly improving polygenic risk score (PRS) prediction accuracy. Furthermore, we built a CF-specific CFGP reference panel based on WGS data of patients with CF. We demonstrate that despite having approximately 3% the sample size of TOPMed, our CFGP reference panel can still outperform TOPMed when imputing some CF disease-causing variants, likely owing to allele and haplotype differences between patients with CF and general populations. We anticipate our imputed data for 4,656 samples without WGS data will benefit our subsequent genetic association studies, and the CFGP reference panel built from CF WGS samples will benefit other investigators studying CF.

Keywords: cystic fibrosis; genotype imputation; mendelian disease; polygenic risk score.

PubMed Disclaimer

Conflict of interest statement

M.J.B. is the Editor-in-chief of HGG Advances. All other authors declare no competing interests.

Figures

**Figure 1**
Imputation concordance for F508del using TOPMed and reduced CFGP reference panels. The true R² for TOPMed and reduced CFGP imputed results are 0.835 and 0.926, and the sum of squared error for TOPMed and reduced CFGP are 117.58 and 82.42, respectively. The main reason that TOPMed is slightly worse is that it tends to underestimate the deletion frequency.

**Figure 2**
Histograms of differences between reduced CFGP true R² and TOPMed true R² to compare the imputation quality of the two reference panels. (A) For overall chr7. Almost all variants are located to the left half, which means TOPMed is predominantly better than the reduced CFGP reference panel. (B) For CFTR region only. The advantage of TOPMed reference panel over the reduced CFGP becomes less pronounced.

**Figure 3**
Histograms of mean true R² difference and proportion of variants better imputed by reduced CFGP than TOPMed, across 2,872 1-Mb non-overlapping regions. We calculated the true R² difference of the two reference panels using reduced-CFGP true R² minus TOPMed true R² for each variant, and then summarized variant level true R² difference at the 1-Mb region level using the two statistics: difference of true R2 (A) and proportion of reduced-CFGP better imputed variants (B).

**Figure 4**
Illustration of impact of imputation on PRS construction. (A) Imputation performed in target cohorts. We started with four independent discovery cohorts (I–III are TOPMed imputed data, IV is WGS data), performed association analysis for each subset separately and then meta-analyzed the association results. The meta-GWAS summary statistics was then used to construct PRS using the P+T method. The constructed PRS was applied to the same 1992 target samples but with four different marker densities (in yellow highlight): array genotype, TOPMed imputed, reduced CFGP imputed, or WGS data to compare the benefit of imputation in target cohort. (B) Imputation performed in discovery cohorts. We started with the same first three discovery cohorts as in A, but adopted three different marker sets (again in yellow highlight), as well as a fourth independent WGS cohort. We then performed association analysis and meta-analysis for each marker set, and constructed three different PRSs using the three different meta-GWAS summary statistics. The three PRSs were then applied to the same cohort to compare the performances.

See this image and copyright information in PMC

References

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Leveraging TOPMed imputation server and constructing a cohort-specific imputation reference panel to enhance genotype imputation among cystic fibrosis patients

Affiliations

Leveraging TOPMed imputation server and constructing a cohort-specific imputation reference panel to enhance genotype imputation among cystic fibrosis patients

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Grants and funding

LinkOut - more resources

Full Text Sources

Medical