A Multi-Marker Test for Analyzing Paired Genetic Data in Transplantation
Emerging evidence suggests that donor/recipient matching in non-HLA (human leukocyte antigen) regions of the genome may impact transplant outcomes and recognizing these matching effects may increase the power of transplant genetics studies. Most available matching scores account for either single-nu...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-10-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.745773/full |
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| author | Victoria L. Arthur Zhengbang Li Zhengbang Li Rui Cao William S. Oetting Ajay K. Israni Ajay K. Israni Ajay K. Israni Pamala A. Jacobson Marylyn D. Ritchie Weihua Guan Jinbo Chen |
| author_facet | Victoria L. Arthur Zhengbang Li Zhengbang Li Rui Cao William S. Oetting Ajay K. Israni Ajay K. Israni Ajay K. Israni Pamala A. Jacobson Marylyn D. Ritchie Weihua Guan Jinbo Chen |
| author_sort | Victoria L. Arthur |
| collection | DOAJ |
| description | Emerging evidence suggests that donor/recipient matching in non-HLA (human leukocyte antigen) regions of the genome may impact transplant outcomes and recognizing these matching effects may increase the power of transplant genetics studies. Most available matching scores account for either single-nucleotide polymorphism (SNP) matching only or sum these SNP matching scores across multiple gene-coding regions, which makes it challenging to interpret the association findings. We propose a multi-marker Joint Score Test (JST) to jointly test for association between recipient genotype SNP effects and a gene-based matching score with transplant outcomes. This method utilizes Eigen decomposition as a dimension reduction technique to potentially increase statistical power by decreasing the degrees of freedom for the test. In addition, JST allows for the matching effect and the recipient genotype effect to follow different biological mechanisms, which is not the case for other multi-marker methods. Extensive simulation studies show that JST is competitive when compared with existing methods, such as the sequence kernel association test (SKAT), especially under scenarios where associated SNPs are in low linkage disequilibrium with non-associated SNPs or in gene regions containing a large number of SNPs. Applying the method to paired donor/recipient genetic data from kidney transplant studies yields various gene regions that are potentially associated with incidence of acute rejection after transplant. |
| first_indexed | 2024-12-19T02:19:35Z |
| format | Article |
| id | doaj.art-7656d520342b44fba34ee50ca12f3e19 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-19T02:19:35Z |
| publishDate | 2021-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-7656d520342b44fba34ee50ca12f3e192022-12-21T20:40:17ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-10-011210.3389/fgene.2021.745773745773A Multi-Marker Test for Analyzing Paired Genetic Data in TransplantationVictoria L. Arthur0Zhengbang Li1Zhengbang Li2Rui Cao3William S. Oetting4Ajay K. Israni5Ajay K. Israni6Ajay K. Israni7Pamala A. Jacobson8Marylyn D. Ritchie9Weihua Guan10Jinbo Chen11Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United StatesDepartments of Statistics, Central China Normal University, Wuhan, ChinaDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United StatesDivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United StatesDepartment of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN, United StatesMinneapolis Medical Research Foundation, Minneapolis, MN, United StatesDepartment of Medicine, Hennepin County Medical Center, Minneapolis, MN, United StatesDepartment of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, United StatesDepartment of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN, United StatesDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United StatesDivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United StatesDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United StatesEmerging evidence suggests that donor/recipient matching in non-HLA (human leukocyte antigen) regions of the genome may impact transplant outcomes and recognizing these matching effects may increase the power of transplant genetics studies. Most available matching scores account for either single-nucleotide polymorphism (SNP) matching only or sum these SNP matching scores across multiple gene-coding regions, which makes it challenging to interpret the association findings. We propose a multi-marker Joint Score Test (JST) to jointly test for association between recipient genotype SNP effects and a gene-based matching score with transplant outcomes. This method utilizes Eigen decomposition as a dimension reduction technique to potentially increase statistical power by decreasing the degrees of freedom for the test. In addition, JST allows for the matching effect and the recipient genotype effect to follow different biological mechanisms, which is not the case for other multi-marker methods. Extensive simulation studies show that JST is competitive when compared with existing methods, such as the sequence kernel association test (SKAT), especially under scenarios where associated SNPs are in low linkage disequilibrium with non-associated SNPs or in gene regions containing a large number of SNPs. Applying the method to paired donor/recipient genetic data from kidney transplant studies yields various gene regions that are potentially associated with incidence of acute rejection after transplant.https://www.frontiersin.org/articles/10.3389/fgene.2021.745773/fulltransplant geneticsmulti-marker testingjoint testinggenetic matching scorespaired genetic data |
| spellingShingle | Victoria L. Arthur Zhengbang Li Zhengbang Li Rui Cao William S. Oetting Ajay K. Israni Ajay K. Israni Ajay K. Israni Pamala A. Jacobson Marylyn D. Ritchie Weihua Guan Jinbo Chen A Multi-Marker Test for Analyzing Paired Genetic Data in Transplantation Frontiers in Genetics transplant genetics multi-marker testing joint testing genetic matching scores paired genetic data |
| title | A Multi-Marker Test for Analyzing Paired Genetic Data in Transplantation |
| title_full | A Multi-Marker Test for Analyzing Paired Genetic Data in Transplantation |
| title_fullStr | A Multi-Marker Test for Analyzing Paired Genetic Data in Transplantation |
| title_full_unstemmed | A Multi-Marker Test for Analyzing Paired Genetic Data in Transplantation |
| title_short | A Multi-Marker Test for Analyzing Paired Genetic Data in Transplantation |
| title_sort | multi marker test for analyzing paired genetic data in transplantation |
| topic | transplant genetics multi-marker testing joint testing genetic matching scores paired genetic data |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.745773/full |
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