Genotype Score for Iron Status Is Associated with Muscle Fiber Composition in Women

Human muscle fiber composition is heterogeneous and mainly determined by genetic factors. A previous study reported that experimentally induced iron deficiency in rats increases the proportion of fast-twitch muscle fibers. Iron status has been reported to be affected by genetic factors. As the TMPRSS6 rs855791 T/C and HFE rs1799945 C/G polymorphisms are strongly associated with iron status in humans, we hypothesized that the genotype score (GS) based on these polymorphisms could be associated with the muscle fiber composition in humans. Herein, we examined 214 Japanese individuals, comprising of 107 men and 107 women, for possible associations of the GS for iron status with the proportion of myosin heavy chain (MHC) isoforms (I, IIa, and IIx) as markers of muscle fiber composition. No statistically significant correlations were found between the GS for iron status and the proportion of MHC isoforms in all participants. When the participants were stratified based on sex, women showed positive and negative correlations of the GS with MHC-IIa (age-adjusted p = 0.020) and MHC-IIx (age-adjusted p = 0.011), respectively. In contrast, no correlation was found in men. In women, a 1-point increase in the GS was associated with 2.42% higher MHC-IIa level and 2.72% lower MHC-IIx level. Our results suggest that the GS based on the TMPRSS6 rs855791 T/C and HFE rs1799945 C/G polymorphisms for iron status is associated with muscle fiber composition in women.

Genetic Profile in Genes Associated with Cardiorespiratory Fitness in Elite Spanish Male Endurance Athletes

Background: most of the research concerning the influence of genetics on endurance performance has been carried out by investigating target genes separately. However, endurance performance is a complex trait that can stem from the interaction of several genes. The objective of this study was to compare the frequencies of polymorphisms in target genes involving cardiorespiratory functioning in elite endurance athletes vs. non-athlete controls. Methods: genotypic frequencies were determined in 123 elite endurance athletes and in 122 non-athletes. Genotyping of ACE (rs4340), NOS3 (rs2070744 and rs1799983), ADRA2a (rs1800544 and rs553668), ADRB2 (rs1042713 and rs1042714), and BDKRB2 (rs5810761) was performed by polymerase chain reaction. The total genotype score (TGS: from 0 to 100 arbitrary units; a.u.) was calculated from the genotype score in each polymorphism. Results: the mean TGS in non-athletes (47.72 ± 11.29 a.u.) was similar to elite endurance athletes (46.54 ± 11.32 a.u., p = 0.415). The distribution of TGS frequencies were also similar in non-athletes and elite endurance athletes (p = 0.333). There was no TGS cut-off point to discriminate being elite endurance athletes. Conclusions: the genetic profile in the selected genes was similar in elite endurance athletes and in controls, suggesting that the combination of these genes does not determine endurance performance.

Total Genotype Score Modelling of Polygenic Endurance-Power Profiles in Lithuanian Elite Athletes

Total genotype score (TGS) reflects additive effect of genotypes on predicting a complex trait such as athletic performance. Scores assigned to genotypes in the TGS should represent an extent of the genotype’s predisposition to the trait. Then, combination of genotypes highly ranks those individuals, who have a trait expressed. Usually, the genotypes are scored by the evidence of a genotype–phenotype relationship published in scientific studies. The scores can be revised computationally using genotype data of athletes, if available. From the available genotype data of 180 Lithuanian elite athletes we created an endurance-mixed-power performance TGS profile based on known ACE rs1799752, ACTN3 rs1815739, and AMPD1 rs17602729, and an emerging MB rs7293 gene markers. We analysed an ability of this TGS profile to stratify athletes according to the sport category that they practice. Logistic regression classifiers were trained to compute the genotype scores that represented the endurance versus power traits in the group of analysed athletes more accurately. We observed differences in TGS distributions in female and male group of athletes. The genotypes with possibly different effects on the athletic performance traits in females and males were described. Our data-driven analysis and TGS modelling tools are freely available to practitioners.

Can Genetic Testing Predict Talent? A Case Study of 5 Elite Athletes

Purpose: The genetic influence on the attainment of elite athlete status is well established, with a number of polymorphisms found to be more common in elite athletes than in the general population. As such, there is considerable interest in understanding whether this information can be utilized to identify future elite athletes. Accordingly, the aim of this study was to compare the total genotype scores of 5 elite athletes to those of nonathletic controls, to subsequently determine whether genetic information could discriminate between these groups, and, finally, to suggest how these findings may inform debates relating to the potential for genotyping to be used as a talent-identification tool. Methods: The authors compared the total genotype scores for both endurance (68 genetic variants) and speed-power (48 genetic variants) elite athlete status of 5 elite track-and-field athletes, including an Olympic champion, to those of 503 White European nonathletic controls. Results: Using the speed-power total genotype score, the elite speed-power athletes scored higher than the elite endurance athletes; however, using this speed-power score, 68 nonathletic controls registered higher scores than the elite power athletes. Surprisingly, using the endurance total genotype score, the elite speed-power athletes again scored higher than the elite endurance athletes. Conclusions: These results suggest that genetic information is not capable of accurately discriminating between elite athletes and nonathletic controls, illustrating that the use of such information as a talent-identification tool is currently unwarranted and ineffective.

Polymorphisms in the vitamin D system and mortality - The Tromsø study

Background and objectiveVitamin D deficiency is associated with diabetes, cancer, immunological and cardiovascular diseases as well as increased mortality. It has, however, been difficult to show a causal relation in randomized, controlled trials. Mendelian randomization studies provide another option for testing causality, and results indicate relations between the serum 25-hydroxyvitamin D (25(OH)D) level and some diseases, including mortality. We have from the Tromsø Study in 2012 published non-significant relations been vitamin D related single nucleotide polymorphisms (SNPs) and mortality, but have since then genotyped additional subjects, the observation time is longer and new SNPs have been included.MethodsGenotyping was performed for SNPs in the NADSYN1, CYP2R1, VDR, CUBILIN and MEGALIN genes in 11 897 subjects who participated in the fourth survey of the Tromsø Study in 1994-1995. Serum 25(OH)D levels were measured in 6733 of these subjects. A genotype score based on SNPs in the NADSYN1 and CYP2R1 genes (related to the serum 25(OH)D level) and serum 25(OH)D percentile groups were created. Mortality data was updated till end of March 2017 and survival analysed with Cox regression adjusted for sex and age.ResultsDuring the observation period 5491 subjects died. The genotype score and the serum 25(OH)D percentile groups were (without Bonferroni correction) significantly related to mortality in favour of high serum 25(OH)D. None of the SNPs in the VDR or MEGALIN genes were related to mortality. However, for the rs12766939 in the CUBILIN gene with the major homozygote as reference, the hazard ratio for mortality for the minor homozygote genotype was 1.17 (1.06 – 1.29), P < 0.002. This should be viewed with caution, as rs12766939 was not in Hardy-Weinberg equilibrium.ConclusionOur study confirms a probable causal but weak relation between serum 25(OH)D level and mortality. The relation between rs12766939 and mortality needs confirmation in more homogenous cohorts.

New Genetic Model for Predicting Phenotype Traits in Sports

Purpose:The aim of the current study was to construct a genetic model with a new algorithm for predicting athletic-performance variability based on genetic variations.Methods:The influence of 6 polymorphisms (ACE, ACTN-3, BDKRB2, VDR-ApaI, VDR-BsmI, and VDR-FokI) on vertical jump was studied in top-level male Italian soccer players (n = 90). First, the authors calculated the traditional total genotype score and then determined the total weighting genotype score (TWGS), which accounts for the proportion of significant phenotypic variance predicted by the polymorphisms. Genomic DNA was extracted from saliva samples using a standard protocol. Genotyping was performed using polymerase chain reaction (PCR).Results:The results obtained from the new genetic model (TWGS) showed that only 3 polymorphisms entered the regression equation (ACTN-3, ACE, and BDKRB2), and these polymorphisms explained 17.68–24.24% of the verticaljump variance. With the weighting given to each polymorphism, it may be possible to identify a polygenic profile that more accurately explains, at least in part, the individual variance of athletic-performance traits.Conclusions:This model may be used to create individualized training programs based on a player’s genetic predispositions, as well as to identify athletes who need an adapted training routine to account for individual susceptibility to injury.