Association of whole mtDNA, an NADPH G11914A variant, and haplogroups with high physical performance in an elite military troop

Physical performance is a multifactorial and complex trait influenced by environmental and hereditary factors. Environmental factors alone have been insufficient to characterize all outstanding phenotypes. Recent advances in genomic technologies have enabled the investigation of whole nuclear and mitochondrial genome sequences, increasing our ability to understand interindividual variability in physical performance. Our objective was to evaluate the association of mitochondrial polymorphic loci with physical performance in Brazilian elite military personnel. Eighty-eight male military personnel who participated in the Command Actions Course of the Army were selected. Total DNA was obtained from blood samples and a complete mitochondrial genome (mtDNA) was sequenced using Illumina MiSeq platform. Twenty-nine subjects completed the training program (FINISHED, 'F'), and fifty-nine failed to complete (NOT_FINISHED, 'NF'). The mtDNA from NF was slightly more similar to genomes from African countries frequently related to endurance level. Twenty-two distinct mtDNA haplogroups were identified corroborating the intense genetic admixture of the Brazilian population, but their distribution was similar between the two groups (FST=0.0009). Of 745 polymorphisms detected in the mtDNA, the position G11914A within the NADPH gene component of the electron transport chain, was statistically different between F and NF groups (P=0.011; OR: 4.286; 95%CI: 1.198-16.719), with a higher frequency of the G allele in group F individuals). The high performance of military personnel may be mediated by performance-related genomic traits. Thus, mitochondrial genetic markers such as the ND4 gene may play an important role on physical performance variability.

The association of ACE gene D/I polymorphism with cardiovascular risk factors in a population from Rio de Janeiro

Our aim was to determine the frequencies of the angiotensin-converting enzyme (ACE) gene alleles D and I and any associations to cardiovascular risk factors in a population sample from Rio de Janeiro, Brazil. Eighty-four adults were selected consecutively during a 6-month period from a cohort subgroup of a previous large cross-sectional survey in Rio de Janeiro. Anthropometric data and blood pressure measurements, echocardiogram, albuminuria, glycemia, lipid profile, and ACE genotype and serum enzyme activity were determined. The frequency of the ACE*D and I alleles in the population under study, determined by PCR, was 0.59 and 0.41, respectively, and the frequencies of the DD, DI, and II genotypes were 0.33, 0.51, and 0.16, respectively. No association between hypertension and genotype was detected using the Kruskal-Wallis method. Mean plasma ACE activity (U/mL) in the DD (N = 28), DI (N = 45) and II (N = 13) groups was 43 (in males) and 52 (in females), 37 and 39, and 22 and 27, respectively; mean microalbuminuria (mg/dL) was 1.41 and 1.6, 0.85 and 0.9, and 0.6 and 0.63, respectively; mean HDL cholesterol (mg/dL) was 40 and 43, 37 and 45, and 41 and 49, respectively, and mean glucose (mg/dL) was 93 and 108, 107 and 98, and 85 and 124, respectively. A high level of ACE activity and albuminuria, and a low level of HDL cholesterol and glucose, were found to be associated with the DD genotype. Finally, the II genotype was found to be associated with variables related to glucose intolerance.

Analysis of renin-angiotensin-aldosterone system gene polymorphisms in resistant hypertension

Essential hypertension is a disease multifactorially triggered by genetic and environmental factors. The contribution of genetic polymorphisms of the renin-angiotensin-aldosterone system and clinical risk factors to the development of resistant hypertension was evaluated in 90 hypertensive patients and in 115 normotensive controls living in Southwestern Brazil. Genotyping for insertion/deletion of angiotensin-converting enzyme, angiotensinogen M235T, angiotensin II type 1 receptor A1166C, aldosterone synthase C344T, and mineralocorticoid receptor A4582C polymorphisms was performed by PCR, with further restriction analysis when required. The influence of genetic polymorphisms on blood pressure variation was assessed by analysis of the odds ratio, while clinical risk factors were evaluated by logistic regression. Our analysis indicated that individuals who carry alleles 235-T, 1166-A, 344-T, or 4582-C had a significant risk of developing resistant hypertension (P < 0.05). Surprisingly, when we tested individuals who carried the presumed risk genotypes A1166C, C344T, and A4582C we found that these genotypes were not associated with resistant hypertension. However, a gradual increase in the risk to develop resistant hypertension was detected when the 235-MT and TT genotypes were combined with one, two or three of the supposedly more vulnerable genotypes - A1166C (AC/AA), C344T (TC/TT) and A4582C (AC/CC). Analysis of clinical parameters indicated that age, body mass index and gender contribute to blood pressure increase (P < 0.05). These results suggest that unfavorable genetic renin-angiotensin-aldosterone system patterns and clinical risk variables may contribute to increasing the risk for the development of resistant hypertension in a sample of the Brazilian population.

Personalized diagnosis by cached solutions with hypertension as a study model

Statistical modeling of links between genetic profiles with environmental and clinical data to aid in medical diagnosis is a challenge. Here, we present a computational approach for rapidly selecting important clinical data to assist in medical decisions based on personalized genetic profiles. What could take hours or days of computing is available on-the-fly, making this strategy feasible to implement as a routine without demanding great computing power. The key to rapidly obtaining an optimal/nearly optimal mathematical function that can evaluate the [quot ]disease stage[quot ] by combining information of genetic profiles with personal clinical data is done by querying a precomputed solution database. The database is previously generated by a new hybrid feature selection method that makes use of support vector machines, recursive feature elimination and random sub-space search. Here, to evaluate the method, data from polymorphisms in the renin-angiotensin-aldosterone system genes together with clinical data were obtained from patients with hypertension and control subjects. The disease [quot ]risk[quot ] was determined by classifying the patients' data with a support vector machine model based on the optimized feature; then measuring the Euclidean distance to the hyperplane decision function. Our results showed the association of renin-angiotensin-aldosterone system gene haplotypes with hypertension. The association of polymorphism patterns with different ethnic groups was also tracked by the feature selection process. A demonstration of this method is also available online on the project's web site.