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Genetics and exercise: an introduction
Published in Adam P. Sharples, James P. Morton, Henning Wackerhage, Molecular Exercise Physiology, 2022
Claude Bouchard, Henning Wackerhage
In contrast, the so-called quantitative traits, such as body height or VO2max trainability, tend to be normally distributed, and their variation in the population is typically influenced by many DNA variants, environmental factors and their interactions. The most thoroughly investigated quantitative trait is human body height. Studies involving hundreds of thousands of subjects suggest that body height is influenced by thousands of DNA variants (3). Each of the body height-influencing DNA sequence variants adds or subtracts a fraction of a millimetre to or from the individual’s height. Studying such traits requires the analysis of DNA variants across the entire genome in many thousands of subjects. The inability to meet these requirements remains a limitation of sport and exercise genetics studies to this day (4).
Glaucoma
Published in Ching-Yu Cheng, Tien Yin Wong, Ophthalmic Epidemiology, 2022
Zhi Da Soh, Victor Koh, Ching-Yu Cheng
In addition, genome-wide association studies (GWAS) have identified several single-nucleotide polymorphisms (SNPs) at different loci that are either associated directly with POAG itself, or through its quantitative traits (e.g., IOP, VCDR, CCT).123,124 These loci include, but are not limited to, CAV1/CAV2,125,126TMC01,127CDKN2BAS,127,128SIX6,128,129AFAP1,130GMDS,130ABCA1,130,131PMM2,131TGFBR3,132FNDC3B,132ARHGEF12,133TXNRD2,134ATXN2,134FOXC1,134 and GAS7.134
Statistical Considerations and Biological Mechanisms Underlying Individual Differences in Adaptations to Exercise Training
Published in Peter M. Tiidus, Rebecca E. K. MacPherson, Paul J. LeBlanc, Andrea R. Josse, The Routledge Handbook on Biochemistry of Exercise, 2020
Jacob T. Bonafiglia, Hashim Islam, Nir Eynon, Brendon J. Gurd
Quantitative trait loci (QTL) are polymorphic regions (loci) associated with variability in a given phenotype. QTL mapping involves correlating tagged genetic variants (i.e., selected variants that are meant to cover a given genomic region) with variation in a given phenotype (87). It is important to note that the goal of QTL mapping is to determine whether phenotype variability is associated with a given locus or loci rather than specific genetic variants per se. Therefore, QTL mapping is often followed with more detailed analysis (e.g., “fine-mapping”) to identify specific genetic variants within a given loci that correlate with a given phenotype.
The diagnostic value of systemic immune-inflammation index in diabetic macular oedema
Published in Clinical and Experimental Optometry, 2022
Ahmet Elbeyli, Bengi Ece Kurtul, Sait Coskun Ozcan, Deniz Ozarslan Ozcan
All statistical analyses were performed by SPSS software (version 21.0, Inc., Armonk, NY, USA). Kolmogorov–Smirnov test was used to evaluate the normality test for the numeric variables. Normally distributed continuous data were presented as means and standard deviations (±SD) and non-normally distributed data were presented as median (inter-quartile range, IQR). Categorical variables were presented as numbers and percentages. Independent Samples t-test was used to compare the means of quantitative traits and the chi-square test was used for qualitative traits. Pearson’s correlation analysis was performed for the correlation of the SII, NLR and PLR with BCVA. The multivariate logistic regression model with a forward procedure was performed to find out independent predictors of DME. Multiple logistic regression analysis was conducted for DME using fasting blood glucose, duration of diabetes, SII, NLR and PLR as explanatory variables. Variables achieving univariate p < 0.10 were included in multivariate logistic regression analysis. The receiver operating characteristic (ROC) curve analysis was carried out to determine the optimal cut-off values of SII, NLR and PLR. The areas under the curve (AUC) values were also calculated. A p value of less than 0.05 was accepted as statistically significant.
Genetics of platelet traits in ischaemic stroke: focus on mean platelet volume and platelet count
Published in International Journal of Neuroscience, 2019
Kanika Vasudeva, Anjana Munshi
Lo and colleagues genotyped 49,094 SNPs using genetic analysis platform (ITMAT-Board-CARe Illumina iSELECT array) in 23,439 Caucasians and 7,112 African-Americans from five different cohorts [Atherosclerosis Risk in Communities study (ARIC),Coronary Artery Risk Development in Young Adults (CARDIA), Cardiovascular Health Study (CHS), Framingham Heart Study (FHS), Jackson Heart Study (JHS)]. This study characterized the genetic composition for the quantitative traits within these two ethnic groups. They identified a novel association between intronic variant rs810928 (TPM4 gene) and PLT in both Caucasians and African Americans. Measurement of PLT was achieved by assessing the in vitro platelet aggregation response from the platelet-rich plasma. An allele at rs810928 was found to be associated with lower PLT and increased MPV. This is in agreement of the fact that there is an inverse relationship between platelet size and count. Previously an intronic SNP (rs11071720) in TPM4 was found to be associated with MPV among Caucasians. Besides, TPM gene has also been reported to play a role in cytoskeleton functions [109].
A new perspective on the genetics of keratoconus: why have we not been more successful?
Published in Ophthalmic Genetics, 2018
Hanne Valgaeren, Carina Koppen, Guy Van Camp
GWAS are large-scale association studies in which a large number of SNP markers (up to several million) across the entire genome are genotyped on microarrays. Most GWAS are designed with a large patient and a large control cohort, but they can also be applied to a general population exhibiting the complete distribution of a quantitative trait. As such, it can be assessed whether a SNP that can be located anywhere in the genome (hypothesis-free) is significantly more frequent in the patient cohort than in the control cohort (or in the group on one extreme of the spectrum of the quantitative trait compared to the other). When a SNP is significantly associated with affection status, this SNP or another variant in linkage disequilibrium with the identified SNP contributes to the disease risk. The power of GWAS depends drastically on the sample size since effect sizes for most complex traits tend to be small, and thus several thousands of samples are needed in order to detect smaller effect variants. Since this approach allows identification of SNPs associated with the disease across the complete genome, multiple disease-associated genetic factors can be identified in one experiment. The GWAS approach has led to the identification of more than 7000 significantly associated variants for multiple diseases and traits (www.ebi.ac.uk/gwas/ (120),), highlighting its success.