Genome-wide Association Study of Exercise-induced Skeletal Muscle Hypertrophy and the Construction of Predictive Model
To investigate inter-individual differences in muscle thickness of Rectus Femoris (MTRF) following 12 weeks of Resistance Training (RT) or High-Intensity Interval Training (HIIT) to explore the genetic architecture underlying skeletal muscle hypertrophy and to construct predictive models. We conduct...
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| Veröffentlicht in: | Physiological genomics 2024-08, Vol.56 (8), p.578-589 |
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| creator | Yang, Xiaolin Li, Yanchun Mei, Tao Duan, Jiayan Yan, Xu McNaughton, Lars He, Zihong |
| description | To investigate inter-individual differences in muscle thickness of Rectus Femoris (MTRF) following 12 weeks of Resistance Training (RT) or High-Intensity Interval Training (HIIT) to explore the genetic architecture underlying skeletal muscle hypertrophy and to construct predictive models. We conducted musculoskeletal ultrasound assessments of the MTRF response in 440 physically inactive adults after the 12-week exercise period. A Genome-wide Association study (GWAS) was employed to identify variants associated with MTRF response, separately for RT and HIIT. Utilizing polygenic predictor score (PPS), we estimated the genetic contribution to exercise-induced hypertrophy. Predictive models for MTRF response were constructed using Random Forest (RF), Support Vector Mac (SVM), and Generalized Linear Model (GLM) in 10 cross-validated approach. MTRF increased significantly after both RT (8.8%, P |
| doi_str_mv | 10.1152/physiolgenomics.00019.2024 |
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. The PPS was associated with MTRF response, explaining 47.2% of the variation in response to RT and 38.3% of the variation in response to HIIT. Notably, the GLM and SVM predictive models exhibited superior performance in comparison to RF models (p<0.05), and the GLM demonstrated optimal performance with an AUC of 0.809 (95%CI:0.669-0.949). Factors such as PPS, baseline MTRF, and exercise protocol exerted influence on the MTRF response to exercise, with PPS being the primary contributor. The GLM and SVM predictive model, incorporating both genetic and phenotypic factors, emerged as promising tools for predicting exercise-induced skeletal muscle hypertrophy.</description><identifier>ISSN: 1094-8341</identifier><identifier>ISSN: 1531-2267</identifier><identifier>EISSN: 1531-2267</identifier><identifier>DOI: 10.1152/physiolgenomics.00019.2024</identifier><identifier>PMID: 38881426</identifier><language>eng</language><publisher>United States: American Physiological Society</publisher><subject>Generalized linear models ; Genome-wide association studies ; Genomes ; Hypertrophy ; Musculoskeletal system ; Physical training ; Prediction models ; Single-nucleotide polymorphism ; Skeletal muscle</subject><ispartof>Physiological genomics, 2024-08, Vol.56 (8), p.578-589</ispartof><rights>Copyright American Physiological Society Aug 2024</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c220t-5a80776cd20a10d39be712f60afad5d75abdf515e01bdc9e25e938cc6497f3603</cites><orcidid>0009-0005-0597-1230 ; 0000-0001-8547-4210</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,3025,27903,27904</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38881426$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yang, Xiaolin</creatorcontrib><creatorcontrib>Li, Yanchun</creatorcontrib><creatorcontrib>Mei, Tao</creatorcontrib><creatorcontrib>Duan, Jiayan</creatorcontrib><creatorcontrib>Yan, Xu</creatorcontrib><creatorcontrib>McNaughton, Lars</creatorcontrib><creatorcontrib>He, Zihong</creatorcontrib><title>Genome-wide Association Study of Exercise-induced Skeletal Muscle Hypertrophy and the Construction of Predictive Model</title><title>Physiological genomics</title><addtitle>Physiol Genomics</addtitle><description>To investigate inter-individual differences in muscle thickness of Rectus Femoris (MTRF) following 12 weeks of Resistance Training (RT) or High-Intensity Interval Training (HIIT) to explore the genetic architecture underlying skeletal muscle hypertrophy and to construct predictive models. We conducted musculoskeletal ultrasound assessments of the MTRF response in 440 physically inactive adults after the 12-week exercise period. A Genome-wide Association study (GWAS) was employed to identify variants associated with MTRF response, separately for RT and HIIT. Utilizing polygenic predictor score (PPS), we estimated the genetic contribution to exercise-induced hypertrophy. Predictive models for MTRF response were constructed using Random Forest (RF), Support Vector Mac (SVM), and Generalized Linear Model (GLM) in 10 cross-validated approach. MTRF increased significantly after both RT (8.8%, P<0.05) and HIIT (5.3%, P<0.05), but with considerable inter-individual differences (RT: -13.5~38.4%, HIIT: -14.2%~30.7%). Eleven lead SNPs in RT and eight lead SNPs in HIIT were identified at a significance level of P<1×10
. The PPS was associated with MTRF response, explaining 47.2% of the variation in response to RT and 38.3% of the variation in response to HIIT. Notably, the GLM and SVM predictive models exhibited superior performance in comparison to RF models (p<0.05), and the GLM demonstrated optimal performance with an AUC of 0.809 (95%CI:0.669-0.949). Factors such as PPS, baseline MTRF, and exercise protocol exerted influence on the MTRF response to exercise, with PPS being the primary contributor. The GLM and SVM predictive model, incorporating both genetic and phenotypic factors, emerged as promising tools for predicting exercise-induced skeletal muscle hypertrophy.</description><subject>Generalized linear models</subject><subject>Genome-wide association studies</subject><subject>Genomes</subject><subject>Hypertrophy</subject><subject>Musculoskeletal system</subject><subject>Physical training</subject><subject>Prediction models</subject><subject>Single-nucleotide polymorphism</subject><subject>Skeletal muscle</subject><issn>1094-8341</issn><issn>1531-2267</issn><issn>1531-2267</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNpdkU1LAzEQhoMoflT_ggS9eNmaSTb74U2KX6AoqOeQJrMa3W5qsqv235ta9eBpZuCZdwYeQg6AjQEkP54_L6Lz7RN2fuZMHDPGoB5zxvM1sg1SQMZ5Ua6nntV5VokctshOjC8Jy8tKbpItUVUV5LzYJu8XyxTMPpxFehqjN073znf0vh_sgvqGnn1iMC5i5jo7GLT0_hVb7HVLb4ZoWqSXizmGPvj0FtWdpf0z0onvYh8G8x2VQu4CWpemd6Q33mK7SzYa3Ubc-6kj8nh-9jC5zK5vL64mp9eZ4Zz1mdQVK8vCWM40MCvqKZbAm4LpRltpS6mntpEgkcHUmhq5xFpUxhR5XTaiYGJEjla58-DfBoy9mrlosG11h36ISrCihrIAUSf08B_64ofQpe-UACYlLNlEnawoE3yMARs1D26mw0IBU0s76p8d9W1HLe2k5f2fE8N0hvZv9VeH-AL2CZIu</recordid><startdate>20240801</startdate><enddate>20240801</enddate><creator>Yang, Xiaolin</creator><creator>Li, Yanchun</creator><creator>Mei, Tao</creator><creator>Duan, Jiayan</creator><creator>Yan, Xu</creator><creator>McNaughton, Lars</creator><creator>He, Zihong</creator><general>American Physiological Society</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TM</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><orcidid>https://orcid.org/0009-0005-0597-1230</orcidid><orcidid>https://orcid.org/0000-0001-8547-4210</orcidid></search><sort><creationdate>20240801</creationdate><title>Genome-wide Association Study of Exercise-induced Skeletal Muscle Hypertrophy and the Construction of Predictive Model</title><author>Yang, Xiaolin ; Li, Yanchun ; Mei, Tao ; Duan, Jiayan ; Yan, Xu ; McNaughton, Lars ; He, Zihong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c220t-5a80776cd20a10d39be712f60afad5d75abdf515e01bdc9e25e938cc6497f3603</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Generalized linear models</topic><topic>Genome-wide association studies</topic><topic>Genomes</topic><topic>Hypertrophy</topic><topic>Musculoskeletal system</topic><topic>Physical training</topic><topic>Prediction models</topic><topic>Single-nucleotide polymorphism</topic><topic>Skeletal muscle</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Xiaolin</creatorcontrib><creatorcontrib>Li, Yanchun</creatorcontrib><creatorcontrib>Mei, Tao</creatorcontrib><creatorcontrib>Duan, Jiayan</creatorcontrib><creatorcontrib>Yan, Xu</creatorcontrib><creatorcontrib>McNaughton, Lars</creatorcontrib><creatorcontrib>He, Zihong</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Nucleic Acids Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Physiological genomics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Xiaolin</au><au>Li, Yanchun</au><au>Mei, Tao</au><au>Duan, Jiayan</au><au>Yan, Xu</au><au>McNaughton, Lars</au><au>He, Zihong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Genome-wide Association Study of Exercise-induced Skeletal Muscle Hypertrophy and the Construction of Predictive Model</atitle><jtitle>Physiological genomics</jtitle><addtitle>Physiol Genomics</addtitle><date>2024-08-01</date><risdate>2024</risdate><volume>56</volume><issue>8</issue><spage>578</spage><epage>589</epage><pages>578-589</pages><issn>1094-8341</issn><issn>1531-2267</issn><eissn>1531-2267</eissn><abstract>To investigate inter-individual differences in muscle thickness of Rectus Femoris (MTRF) following 12 weeks of Resistance Training (RT) or High-Intensity Interval Training (HIIT) to explore the genetic architecture underlying skeletal muscle hypertrophy and to construct predictive models. We conducted musculoskeletal ultrasound assessments of the MTRF response in 440 physically inactive adults after the 12-week exercise period. A Genome-wide Association study (GWAS) was employed to identify variants associated with MTRF response, separately for RT and HIIT. Utilizing polygenic predictor score (PPS), we estimated the genetic contribution to exercise-induced hypertrophy. Predictive models for MTRF response were constructed using Random Forest (RF), Support Vector Mac (SVM), and Generalized Linear Model (GLM) in 10 cross-validated approach. MTRF increased significantly after both RT (8.8%, P<0.05) and HIIT (5.3%, P<0.05), but with considerable inter-individual differences (RT: -13.5~38.4%, HIIT: -14.2%~30.7%). Eleven lead SNPs in RT and eight lead SNPs in HIIT were identified at a significance level of P<1×10
. The PPS was associated with MTRF response, explaining 47.2% of the variation in response to RT and 38.3% of the variation in response to HIIT. Notably, the GLM and SVM predictive models exhibited superior performance in comparison to RF models (p<0.05), and the GLM demonstrated optimal performance with an AUC of 0.809 (95%CI:0.669-0.949). Factors such as PPS, baseline MTRF, and exercise protocol exerted influence on the MTRF response to exercise, with PPS being the primary contributor. The GLM and SVM predictive model, incorporating both genetic and phenotypic factors, emerged as promising tools for predicting exercise-induced skeletal muscle hypertrophy.</abstract><cop>United States</cop><pub>American Physiological Society</pub><pmid>38881426</pmid><doi>10.1152/physiolgenomics.00019.2024</doi><tpages>12</tpages><orcidid>https://orcid.org/0009-0005-0597-1230</orcidid><orcidid>https://orcid.org/0000-0001-8547-4210</orcidid></addata></record> |
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| subjects | Generalized linear models Genome-wide association studies Genomes Hypertrophy Musculoskeletal system Physical training Prediction models Single-nucleotide polymorphism Skeletal muscle |
| title | Genome-wide Association Study of Exercise-induced Skeletal Muscle Hypertrophy and the Construction of Predictive Model |
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