Secondary Logo

Journal Logo

Original Research

Three DNA Polymorphisms Previously Identified as Markers for Handgrip Strength Are Associated With Strength in Weightlifters and Muscle Fiber Hypertrophy

Grishina, Elina E.1; Zmijewski, Piotr2,3; Semenova, Ekaterina A.5,6; Cięszczyk, Paweł7; Humińska-Lisowska, Kinga8; Michałowska-Sawczyn, Monika8; Maculewicz, Ewelina9; Crewther, Blair4; Orysiak, Joanna4; Kostryukova, Elena S.5; Kulemin, Nickolay A.5; Borisov, Oleg V.5,10; Khabibova, Sofya A.5; Larin, Andrey K.5; Pavlenko, Alexander V.5; Lyubaeva, Ekaterina V.11; Popov, Daniil V.11; Lysenko, Evgeny A.11; Vepkhvadze, Tatiana F.11; Lednev, Egor M.11; Bondareva, Elvira A.12; Erskine, Robert M.13,14; Generozov, Edward V.5; Ahmetov, Ildus I.1,5,13,15

Author Information
Journal of Strength and Conditioning Research: October 2019 - Volume 33 - Issue 10 - p 2602-2607
doi: 10.1519/JSC.0000000000003304

Abstract

Introduction

Muscle strength and power are highly heritable quantitative traits and critical in athletic events, such as sprinting, jumping, and weightlifting (17). Specifically, 3 studies have estimated 30–82% of heritability for strength-related phenotypes, such as muscle strength and handgrip strength (14,24,29). Considerable variation exists between people in the muscle strength response to resistance training, which may be partly explained by genetic variation (10,11). Overall, these findings provide strong evidence for muscular strength trait to be inheritable.

Several morphological (e.g., composition and cross-sectional area (CSA) of muscle fibers) and neural (e.g., ability to maximally excite the motor neuron pool) adaptations are responsible for the muscle strength (9,13,22). Since the discovery of heritability of muscle strength, there has been a growing interest to find genetic markers that influence muscle phenotypes, such as muscle strength, lean mass, composition and CSA of muscle fibers, testosterone levels, and power athlete status (1,2,4,5,11,12,15,18,31,35,36). The most studied polymorphisms, which are associated with muscle strength and power athlete status are located in ACTN3 and ACE genes (19). Other muscle strength-associated markers identified in candidate gene association studies include, but are not limited to AGT, CCL2, CCR2, CNTF, FST, HIF1A, IGF1, IL6, MCT1, MSTN, NR3C1, PPARA, PPARG, PTK2, and VDR (2,3,11,18).

Using a genome-wide association study (GWAS) approach, 196 new DNA polymorphisms were associated with handgrip strength in 3 large GWASs. Specifically, the study conducted by Willems et al. (32) of 195,180 white Europeans identified 16 single nucleotide polymorphisms (SNPs) near or within the genes involved in muscle structure and function, and associated them with handgrip strength. Matteini et al. (21) examined associations of about 2.7 million SNPs in the GWAS with additional meta-analysis of individuals above the age of 65 and reported 41 variants to be linked with handgrip strength. A more recent meta-analysis study by Tikkanen et al. (30) identified 139 loci associated with handgrip strength in a United Kingdom Biobank cohort. Additional association tests such as expression quantitative trait loci provided insight into a role of the identified markers in biological processes, e.g., some SNPs were seen to play role in brain function and the nervous system. These findings of gene variants involved in neuro-regulation supports the notion that muscle performance requires a well-functioning nervous system. Handgrip strength is a predictor of total muscle strength (33) and is associated with bone fracture risk (7), cardiac function (6), and risk of mortality (20,34). Thus, identification of genetic variants associated with strength is important to create resistance training programs for an aging population to prevent muscle strength decline (dynapenia) and further complications.

The aim of our study was to validate the association of 35 SNPs (of the 196 SNPs previously associated with handgrip strength) in a cohort of elite Russian weightlifters, and to replicate this in Polish weightlifters and other cohorts using functional analyses.

Methods

Experimental Approach to the Problem

To identify genetic markers associated with strength in 53 elite Russian weightlifters, we performed a genotype-phenotype study using 35 SNPs previously discovered in nonathletic cohorts. The results were validated in 76 subelite Polish weightlifters. The functionality of significant SNPs was investigated in the muscle biopsy cohort of 20 male power athletes, in the body composition study of Polish weightlifters, and in the handgrip strength study of 87 physically active men and women.

Subjects

Fifty-three elite weightlifters (22 women and 31 men) from the Russian cohort (all subjects in Olympic Games or World/Europe Championships in 2008–2012, all negatively tested for doping by the WADA-accredited laboratories) and 76 subelite weightlifters (28 women and 48 men) from a Polish cohort (members of the national junior team; no athletes tested were banned for taking any illegal substances) participated in the association study. In addition, 20 male subelite Russian power athletes (9 sprinters, 3 weightlifters, and 8 powerlifters; regional competitors with at least 4 years of experience participating in their sports) and 87 physically active men (n = 54) and women (n = 33) were involved in the functional (muscle biopsy and handgrip strength, respectively) studies. The athletes were all Caucasians. Age, height, and body mass of athletes from different groups are presented in Table 1.

T1
Table 1:
Anthropometric and performance variables in subjects from different groups.

The overall study was approved by the Ethics Committee of the Physiological Section of the Russian National Committee for Biological Ethics, and Ethics Committee of the Regional Medical Chamber in Szczecin (Approval number 09/KB/IV/2011). Written informed consent was obtained from each subject. The study complied with the guidelines set out in the Declaration of Helsinki and ethical standards in sport and exercise science research. The experimental procedures were conducted in accordance with the set of guiding principles for reporting the results of genetic association studies defined by the STrengthening the REporting of Genetic Association studies (STREGA) Statement.

Procedures

Russian Cohorts

Molecular genetic analysis in all Russian athletes was performed with DNA samples obtained from leukocytes (venous blood). Four milliliters of venous blood were collected in tubes containing EDTA (Vacuette EDTA tubes; Greiner Bio-One, Kremsmuenster, Austria). Blood samples were transported to the laboratory at 4° C and DNA was extracted on the same day. DNA extraction and purification were performed using a commercial kit according to the manufacturer's instructions (Technoclon, Moscow, Russia) and included chemical lysis, selective DNA binding on silica spin columns, and ethanol washing. Extracted DNA quality was assessed by agarose gel electrophoresis at this step. HumanOmni1-Quad BeadChips (Illumina Inc., Hayward, CA) were used for genotyping of 1,140,419 SNPs in 53 weightlifters, and HumanOmniExpress BeadChips (Illumina Inc.) were used for genotyping of >700,000 SNPs in subjects of the muscle fiber (20 power athletes) and handgrip strength (87 physically active men and women) studies. The assay required 200 ng of DNA sample as input with a concentration of at least 50 ng·µL−1. Exact concentrations of DNA in each sample were measured using a Qubit Fluorometer (Invitrogen, Waltham, MA). All further procedures were performed according to the instructions of the Infinium high density Assay. Of the 196 SNPs for handgrip strength previously discovered via GWAS, 35 were included in the chips. We therefore performed a validation study to identify whether these 35 DNA polymorphisms are also associated with strength (competition results) in weightlifters.

Polish Cohort

Nylon swabs (Copan, Brescia, Italy) were used to collect buccal cells donated by the subjects. DNA was extracted from the collected material using the GenElute Mammalian Genomic DNA Miniprep Kit (Sigma, Darmstadt, Germany) according to the manufacturer's protocol. The purity and quantity of the DNA samples was determined by measuring their absorbance at 260 and 280 nm on the Eppendorf Biophotometer Plus (Eppendorf, Hamburg, Germany). To prevent multiple freezing and thawing, isolated DNA was aliquoted and stored at −20° C. All samples were genotyped in duplicate (by 2 separate researchers) for the results to be credible (eliminating false positives and false negatives) using an allelic discrimination assay on a CFX Connect Real-Time polymerase chain reaction System (Bio-Rad, München, Germany). Between the 2 researchers, the percentage of agreement in genotyping was 100%. To discriminate ZNF608 rs4626333 alleles, TaqMan Pre-Designed SNP Genotyping Assays were used (Applied Biosystems, Waltham, MA) (assay ID: C__30716090_10) including primers and fluorescently labelled (FAM and VIC) MGB probes together with the Brilliant III Ultra-Fast QPCR MasterMix (Agillent, Santa Clara, CA) to detect alleles. Genotypes were assigned using all of the data from the study simultaneously. For the primary data analysis, CFX Maestro Software (Bio-Rad) was used.

Evaluation of Muscle Fiber Composition and Cross-Sectional Area

Samples of the vastus lateralis muscle of physically active subjects were obtained with the Bergström needle biopsy procedure under local anesthesia with 1% lidocaine solution. After this procedure, serial cross-sections (7 μm) were obtained from frozen samples using an ultratom (Leica Microsystems, Wetzlar, Germany). The sections were thaw-mounted on Polysine glass slides, kept for 15 minutes at room temperature (RT) and incubated in PBS (3 × 5 minutes). Then the sections were incubated at RT in primary antibodies against the slow or fast isoform of the myosin heavy chains (M8421, 1:5,000; M4276; 1:600, respectively; Sigma-Aldrich, St. Louis, MI) for 1 hour and incubated in PBS (3 × 5 minutes). After this, the sections were incubated at RT in secondary antibodies conjugated with FITC (F0257; 1:100; Sigma-Aldrich) for 1 hour. The antibodies were removed and the sections were washed in PBS (3 × 5 minutes), placed in mounting media, and covered with a cover slip. The image was captured using a fluorescent microscope Eclipse Ti-U (Nikon, Tokyo, Japan). All analyzed images contained >100 fibers. The ratio of the number of stained fibers to the total fiber number was calculated. Fibers stained in serial sections with antibodies against slow and fast isoforms were considered as hybrid fibers. The CSA of fast and slow fibers was evaluated using the ImageJ software (NIH, USA).

Measurement of Body Composition

The body composition (relative muscle mass, i.e., muscle mass/body weight, %) in Polish weightlifters was measured in the fasted state (for ≥8 hours) with a stand-on hand-to-foot 8-electrode body composition analyzer Tanita MC-180 MA (Tanita, Tokyo, Japan), according to manufacturer's instructions. Normal, athletic body type was selected for the manufacturer's inbuilt predictive algorithm. Standard positioning was used as described in the instruction manual in all measurements. In brief, subjects were asked to stand with bare feet on the electrode panel and hold electrodes in both hands; arms were extended and hung down in a natural standing position with the electrodes in contact with the thumb and palm during the measurements.

Strength Measurement

Evaluation of strength in elite Russian weightlifters was assessed by their performance in snatch, and clean and jerk (best results in official competitions including Olympic Games, Europe and World Championships). The total weight lifted (in kg) is multiplied by the Wilks Coefficient (Coeff) to find the standard amount lifted normalized across all body weights.where x is the body weight of the weightlifter in kilograms.

Values for males are: a = −216.0475144; b = 16.2606339; c = −0.002388645; d = −0.00113732; e = 7.01863E-06; and f = −1.291E-08. Values for females are: a = 594.31747775582, b = −27.23842536447; c = 0.82112226871; d = −0.00930733913; e = 4.731582E-05; and f = −9.054E-08.

Handgrip Strength

The hand dynamometer (DK-140, Russia) was used for the handgrip strength testing of 87 physically active men and women. The strength of both the left and right hands was measured thrice each in a standing position (with the arm in complete extension without touching any part of the body with the dynamometer), and the best score of the dominant hand (kg) was used in the analysis.

Statistical Analyses

Statistical analyses were conducted using PLINK v1.90, R (version 3.4.3), and GraphPad InStat (GraphPad Software, Inc., USA) software. Differences in phenotype between different genotype groups were analysed using analysis of variance (ANOVA) (for 3 genotypes) or unpaired t tests (for 2 genotypes). Effect estimates (EE) were measured using R. Omega-Squared (ω2) values were calculated as EE for ANOVA using “omega_sq” function from the “sjstats” package. Cohen's D values were calculated as EE for unpaired t tests using “cohens D” function from the “lsr” package. To perform the meta-analysis, the Cochrane Review Manager (RevMan) version 5.3 was used. Random and fixed effect models were applied. The heterogeneity degree between the studies was assessed with the I2 statistics. All data are presented as mean (SD). p values <0.05 were considered statistically significant. Because of the large number of SNPs investigated and, therefore, the large number of multiple comparisons, the Benjamini-Hochberg method was used where appropriate to control the false discovery rate.

Results

Association Studies in Weightlifters

There were no differences in the standard amount lifted between Russian female and male weightlifters (p > 0.05), which allowed us to combine women and men of different body mass into one group. Using a panel of 35 strength-related SNPs (see Table, Supplemental Digital Content 1, https://links.lww.com/JSCR/A150, which demonstrates data of all 35 SNPs), we found that the associations of 3 SNPs were nominally (p < 0.05) replicated in the same direction in Russian weightlifters (Table 2). Specifically, weightlifters with rs12055409 G (p = 0.0016; ω2 = 0.136), rs4626333 G (p = 0.047; ω2 = 0.042), and rs2273555 A (p = 0.0042; ω2 = 0.108) alleles have shown significantly better results (additive model). Two of these markers (rs12055409 and rs2273555) have also passed the Benjamini-Hochberg correction criteria for multiple testing (see Table, Supplemental Digital Content 1, https://links.lww.com/JSCR/A150). However, rs4626333 was not excluded given that it was used in a validation study (Polish cohort).

T2
Table 2:
Summary of the most significant SNPs replicated in Russian weightlifters (n = 53) with their competition scores adjusted by Wilks formula.*†

In weightlifters, 3 SNPs (and rs4626333 in Polish athletes) met Hardy-Weinberg expectations (p > 0.05). There were no differences in allelic frequencies between men and women for these SNPs (rs12055409 G: 62.9 vs. 63.6%; rs4626333 G: 82.3 vs. 86.4%; rs2273555 A: 66.1 vs. 61.4%). In a separate analysis, we found that male weightlifters with rs12055409 G (p = 0.025) and rs4626333 G (p = 0.011) alleles have shown significantly better results (additive model), whereas in female weightlifters, carriers of these alleles only tended to have greater strength (p values from 0.07 to 0.3).

Next, we performed a validation study in the Polish cohort of subelite weightlifters using only one available SNP for analysis (rs4626333). In accordance with Russian results, we found that carriers of the rs4626333 GG genotype had greater personal best results in weightlifting (p = 0.0155 for additive model; Cohen's D = 0.548) adjusted for their sex, weight, and age than carriers of the A allele.

Functional Studies

There were no differences in muscle fiber CSA between Russian sprinters and strength (weightlifters and powerlifters) athletes (p > 0.05), which allowed us to combine them for further analysis. In separate analyses, the positive association between rs12055409 G allele and CSA of fast-twitch muscle fibers was identified (p = 0.0295 for additive model; ω2 = 0.187). We also found that amongst Polish weightlifters, rs4626333 GG homozygotes had greater relative muscle mass (p = 0.046 for additive model; Cohen's D = 0.488) adjusted for their sex, weight, and age than carriers of the A allele. We also have replicated the association between the rs2273555 A-allele in the GBF1 gene and handgrip strength in both 54 physically active men (p = 0.042) and 33 women (p = 0.035) (adjusted for sex p = 0.0026 for additive model; ω2 = 0.092).

Meta-Analysis

Using available data of strength-related phenotypes (i.e., competition results of Russian and Polish weightlifters, and handgrip strength of Russian physically active men and women), we performed a meta-analysis for 3 SNPs. The meta-analysis revealed 2 statistically significant markers, namely rs2273555 (p = 6.34 × 10−5; TE = 0.345; I2 = 0) and rs4626333 (p = 0.0024; TE = 0.211; I2 = 0) after correcting for multiple testing. The rs12055409 remained nominally significant (p = 0.043; TE = 0.174; I2 = 0.77) and was not excluded from further polygenic analysis given its additional association with CSA of fast-twitch muscle fibers.

Polygenic Analyses

We found that CSA of fast-twitch muscle fibers was significantly (p = 0.021) higher in athletes who carried 5–6 strength alleles (i.e., rs12055409 G, rs4626333 G and rs2273555 A) in comparison with carriers of 3 and 4 alleles (Table 3). In addition, physically active individuals (n = 87) with 5–6 strength alleles had significantly (adjusted for sex p = 0.015) higher handgrip strength than individuals with 2–4 alleles.

T3
Table 3:
Cross-sectional area (CSA) of muscle fibers in carriers of different number of strength-related alleles among Russian male power athletes (n = 20).

Discussion

In the current study we used a replication design to avoid finding false-positive results in the identification of strength-related markers in weightlifters. For this, we performed a genotype-phenotype study of 53 elite Russian weightlifters using 35 SNPs previously discovered as handgrip strength variants in nonathletic cohorts. For one SNP, we also validated the obtained results in 76 subelite Polish weightlifters. The functionality of significant SNPs was further investigated in the muscle biopsy cohort of 20 male power athletes and in the handgrip strength study of 87 physically active men and women.

We have thus nominally (p < 0.05) confirmed the associations of 3 SNPs, namely rs12055409, rs4626333, and rs2273555, with strength in Russian weightlifters, and one of the SNPs (rs4626333) was also validated in Polish weightlifters. These findings were further supported by functional studies, where we found that polygenic scores composed of 3 SNPs were significantly associated with fast-twitch muscle fiber CSA and handgrip strength.

The rs12055409 SNP, previously identified as a handgrip strength marker in 334,925 individuals from the United Kingdom Biobank cohort (30), is located in the regulatory region next to the MLN gene. MLN encodes motilin, a polypeptide hormone which is secreted in the small intestine and stimulates gastric motor activity. Sullivan et al. (28) have reported an increase in exercise-induced motilin secretion in endurance athletes. Interestingly, motilin is a peptide that promotes the secretion of growth hormone in a dose-related fashion in rat brain (26). According to the GTEx database, the rs12055409 G allele is associated with a high expression of the IP6K3 gene in thyroid tissue (16). IP6K3 encodes inositol hexakisphosphate kinase 3 which generates inositol pyrophosphates and regulates diverse cellular functions, including metabolism and body weight (23). Given that growth hormone has an anabolic effect on muscle mass, it can be speculated that the rs12055409 SNP in the regulatory region near the MLN gene may change the growth hormone concentration by altering its expression, thus giving athletes an advantage in strength. Indeed, we observed an association between the strength-increasing rs12055409 G allele and CSA of fast-twitch muscle fibers in power athletes.

The rs4626333 SNP, previously described as a handgrip strength marker in 27,581 older individuals of European descent (21), is a variant located in the regulatory region next to the ZNF608 gene, which encodes the transcription factor, paralog of the ZNF609 gene. It has been shown that rs4836133 (which is not in linkage disequilibrium with rs4626333) is associated with body mass index in humans (27). Moreover, a study on pigs mentions ZNF608 as a potential gene that affects absolute fat mass and in this manner may influence other traits as well (25). One may suppose that the association of rs4626333 with muscle strength is mediated via its muscle hypertrophic effect. Indeed, we found that rs4626333 GG homozygotes had greater relative muscle mass among Polish weightlifters.

The rs2273555 SNP, previously discovered as a handgrip strength marker in 195,180 white Europeans (32), is located inside the intron of the GBF1 gene. GBF1 (golgi brefeldin A resistant guanine nucleotide exchange factor 1) encodes a protein that regulates the recruitment of proteins to membranes by mediating the GDP to GTP exchange and plays a role in vesicular trafficking by activating ADP ribosylation factor 1. The protein is expressed in skeletal muscles and is supposed to play a role in exercise-induced lipolysis in skeletal muscle (8).

In conclusion, by replicating previous findings in 4 independent studies, we demonstrate that the rs12055409 G-, rs4626333 G-, and rs2273555 A-alleles are associated with higher levels of strength, lean mass, and muscle fiber size, thus confirming previous associations of these polymorphisms with handgrip strength in different populations. Furthermore, we provide evidence that these genetic associations with strength are underpinned by greater lean mass and muscle fiber size in those with the “favorable” genetic variants, thus improving our understanding of the genetic association with strength. Overall, this suggests that these particular gene variants predispose to greater muscle mass and strength and/or to a more favorable neuromuscular adaptation to chronic resistance training, primarily by influencing muscle fiber size.

Practical Applications

Our results highlight the potential for the rs12055409, rs4626333, and rs2273555 polymorphisms to be used to indicate who is more likely to adapt more favorably to chronic resistance exercise, thus enabling resistance training to be prescribed on a more individual level, both for athletes and the general population.

Acknowledgments

The Russian study was supported in part by grant from the Russian Science Foundation (Grant No. 17-15-01436: “Comprehensive analysis of the contribution of genetic, epigenetic, and environmental factors in the individual variability of the composition of human muscle fibers”; DNA sample collection, genotyping, and determination of muscle fiber composition of Russian subjects).

References

1. Ahmetov II, Donnikov AE, Trofimov DY. Actn3 genotype is associated with testosterone levels of athletes. Biol Sport 31: 105–108, 2014.
2. Ahmetov II, Egorova ES, Gabdrakhmanova LJ, Fedotovskaya ON. Genes and athletic performance: An update. Med Sport Sci 61: 41–54, 2016.
3. Ahmetov II, Gavrilov DN, Astratenkova IV, et al. The association of ACE, ACTN3 and PPARA gene variants with strength phenotypes in middle school-age children. J Physiol Sci 63: 79–85, 2013.
4. Ahmetov II, Mozhayskaya IA, Lyubaeva EV, Vinogradova OL, Rogozkin VA. PPARG Gene polymorphism and locomotor activity in humans. Bull Exp Biol Med 146: 630–632, 2008.
5. Ahmetov II, Vinogradova OL, Williams AG. Gene polymorphisms and fiber-type composition of human skeletal muscle. Int J Sport Nutr Exerc Metab 22: 292–303, 2012.
6. Beyer SE, Sanghvi MM, Aung N, et al. Prospective association between handgrip strength and cardiac structure and function in UK adults. PLoS One 13: e0193124, 2018.
7. Cheung CL, Tan KC, Bow CH, et al. Low handgrip strength is a predictor of osteoporotic fractures: Cross-sectional and prospective evidence from the Hong Kong Osteoporosis study. Age (Dordr) 34: 1239–1248, 2012.
8. Covington JD, Galgani JE, Moro C, et al. Skeletal muscle perilipin 3 and coatomer proteins are increased following exercise and are associated with fat oxidation. PLoS One 9: e91675, 2014.
9. Erskine RM, Fletcher G, Folland JP. The contribution of muscle hypertrophy to strength changes following resistance training. Eur J Appl Physiol 114: 1239–1249, 2014.
10. Erskine RM, Jones DA, Williams AG, Stewart CE, Degens H. Inter-individual variability in the adaptation of human muscle specific tension to progressive resistance training. Eur J Appl Physiol 110: 1117–1125, 2010.
11. Erskine RM, Williams AG, Jones DA, Stewart CE, Degens H. Do PTK2 gene polymorphisms contribute to the interindividual variability in muscle strength and the response to resistance training? A preliminary report. J Appl Physiol 112: 1329–1334, 2012.
12. Erskine RM, Williams AG, Jones DA, Stewart CE, Degens H. The individual and combined influence of ACE and ACTN3 genotypes on muscle phenotypes before and after strength training. Scand J Med Sci Sports 24: 642–648, 2014.
13. Folland JP, Williams AG. The adaptations to strength training: Morphological and neurological contributions to increased strength. Sports Med 37: 145–168, 2007.
14. Frederiksen H, Gaist D, Christian Petersen H, et al. Hand grip strength: A phenotype suitable for identifying genetic variants affecting mid-and late-life physical functioning. Genet Epidemiol 23: 110–122, 2002.
15. Gabbasov RT, Arkhipova AA, Borisova AV, et al. The HIF1A gene Pro582Ser polymorphism in Russian strength athletes. J Strength Cond Res 27: 2055–2058, 2013.
16. GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550: 204–213, 2017.
17. Guth LM, Roth SM. Genetic influence on athletic performance. Curr Opin Pediatr 25: 653, 2013.
18. Hughes DC, Day SH, Ahmetov II, Williams AG. Genetics of muscle strength and power: Polygenic profile similarity limits skeletal muscle performance. J Sports Sci 29: 1425–1434, 2011.
19. Ma F, Yang Y, Li X, et al. The association of sport performance with ACE and ACTN3 genetic polymorphisms: A systematic review and meta-analysis. PLoS One 8: e54685, 2013.
20. Marsh AP, Rejeski WJ, Espeland MA, et al. Muscle strength and BMI as predictors of major mobility disability in the lifestyle Interventions and Independence for Elders pilot (LIFE-P). J Geront A Biol Sci Med Sci 66: 1376–1383, 2011.
21. Matteini AM, Tanaka T, Karasik D, et al. GWAS analysis of handgrip and lower body strength in older adults in the CHARGE consortium. Aging Cell 15: 792–800, 2016.
22. Maughan R, Watson JS, Weir J. Relationships between muscle strength and muscle cross-sectional area in male sprinters and endurance runners. Eur J Appl Physiol Occup Physiol 50: 309–318, 1983.
23. Moritoh Y, Oka M, Yasuhara Y, et al. Inositol hexakisphosphate kinase 3 regulates metabolism and lifespan in mice. Sci Rep 6: 32072, 2016.
24. Reed T, Fabsitz R, Selby J, Carmelli D. Genetic influences and grip strength norms in the NHLBI twin study males aged 59–69. Ann Hum Biol 18: 425–432, 1991.
25. Rothammer S, Kremer PV, Bernau M, et al. Genome-wide QTL mapping of nine body composition and bone mineral density traits in pigs. Genet Select Evol 46: 68, 2014.
26. Samson WK, Lumpkin MD, Nilaver G, McCann SM. Motilin: A novel growth hormone releasing agent. Brain Res Bull 12: 57–62, 1984.
27. Speliotes EK, Willer CJ, Berndt SI, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42: 937, 2010.
28. Sullivan SN, Champion MC, Christofides ND, Adrian TE, Bloom SR. Gastrointestinal regulatory peptide responses in long-distance runners. Physician Sportsmed 12: 77–82, 1984.
29. Thomis M, Beunen G, Leemputte MV, et al. Inheritance of static and dynamic arm strength and some of its determinants. Acta Physiol Scand 163: 59–71, 1998.
30. Tikkanen E, Gustafsson S, Amar D, et al. Biological insights into muscular strength: Genetic findings in the UK Biobank. Sci Rep 8: 6451, 2018.
31. Wang G, Tanaka M, Eynon N, et al. The future of genomic research in athletic performance and adaptation to training. Med Sport Sci 61: 55–67, 2016.
32. Willems SM, Wright DJ, Day FR, et al. Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness. Nat Com 8: 16015, 2017.
33. Wind AE, Takken T, Helders PJ, Engelbert RH. Is grip strength a predictor for total muscle strength in healthy children, adolescents, and young adults? Eur J Pediat 169: 281–287, 2010.
34. Xue QL, Beamer BA, Chaves PH, Guralnik JM, Fried LP. Heterogeneity in rate of decline in grip, hip, and knee strength and the risk of all-cause mortality: The women's health and aging study II. J Am Geriatr Soc 58: 2076–2084, 2010.
35. Zarebska A, Ahmetov II, Sawczyn S, et al. Association of the MTHFR 1298A>C (rs1801131) polymorphism with speed and strength sports in Russian and polish athletes. J Sports Sci 32: 375–382, 2014.
36. Zarębska A, Jastrzębski Z, Moska W, et al. The AGT gene M235T polymorphism and response of power-related variables to aerobic training. J Sports Sci Med 15: 616–624, 2016.
Keywords:

strength performance; muscle hypertrophy; gene; genotype

Supplemental Digital Content

© 2019 National Strength and Conditioning Association