Potassium Iodide

Among the 750 remaining microsatellites that were genotyped in the new samples, 693 had previously been genotyped in the HGDP–CEPH diversity panel [7 (link),11 (link),13 ]. For some of these loci, there was a change in primer length or position between the two studies, or a systematic change occurred in the algorithm by which allele size was determined from raw genotyping products—or both. In cases where the primers changed, allele sizes from the new dataset were adjusted by the appropriate length in order to align its list of allele sizes with the earlier list for the HGDP–CEPH dataset.
To identify systematic changes between datasets, for each locus the allele sizes of one dataset were translated by a constant and the G test statistic of independence between allele frequencies and dataset (older HGDP–CEPH dataset versus newly genotyped dataset) was then computed [23 ]. Considering all possible constants for translation of allele sizes, the one that minimized the G statistic was determined. In implementing the G test, two groups of comparisons were performed. In the first group of comparisons, the constant of translation was determined by comparing 80 Jewish individuals genotyped simultaneously with the Native Americans to all 255 individuals from Europe and the Middle East in the HGDP–CEPH H1048 dataset [109 (link)], excluding Mozabites. The second group of comparisons involved 346 Native American individuals from Central and South America in this newer dataset (all 336 sampled Central and South Americans excluding Ache, and ten additional individuals who were later excluded) and 63 Native American individuals from the Maya, Pima, and Piapoco populations in the older H1048 dataset (the Piapoco population is described as “Colombian” in previous analyses of these data). The constants expected based on the two G tests—labeled S₁ for the comparison of the Jewish populations to European and Middle Eastern populations and S₂ for the Native American comparison—were then compared with the constant of translation expected from consideration of three additional sources of information available for the two datasets: the genotypes of a Mammalian Genotyping Service size standard (S₃), a code letter provided by the Mammalian Genotyping Service indicating the nature of the change in primers (S₄), and the locations of the primers themselves in the human genome sequence (S₅).
Among the 693 markers, 687 had the same optimal constant of translation (that is, the constant that minimizes the G statistic) in the two different sets of population comparisons (S₁ = S₂). The remaining six markers with different optimal constants of translation in the two G tests were compared with the value expected from the locations of the old and new primers in the human genome (S₅). In all six cases, the optimal constant for the comparison of the Jewish and European/Middle Eastern datasets agreed with the value based on the primer locations (S₁ = S₅). As real population differences between datasets are more likely in Native Americans due to the larger overall level of genetic differentiation in the Americas, we used the constant obtained based on the Jewish and European/Middle Eastern comparison (S₁) for allele size calibration.
Of the remaining 687 markers, 638 had an optimal constant of translation that agreed with the value expected based on the code letter provided by the Mammalian Genotyping Service (S₁ = S₂ = S₄). Thus, there were 49 markers for which the code letter was either uninformative or produced a constant of translation that disagreed with S₁ and S₂. For 35 of these markers, the constant of translation based on the size standard (S₃) agreed with S₁ and S₂. For eight of the remaining 14 markers, the constant of translation based on the primer sequences (S₅) agreed with S₁ and S₂. The six markers with disagreements (AAT263P, ATT070, D15S128, D6S1021, D7S817, and TTTAT002Z), having S₁ ≠ S₅, were then discarded. For the remaining 687 markers that were not discarded, 685 had G < 48 in both G tests, while the other two markers (D14S587 and D15S822) had G > 91 in the Jewish versus European/Middle Eastern comparison. These two extreme outliers, which also had the highest G values for the Native American comparison, were then excluded (Figure S6).
To further eliminate loci with extreme genotyping errors, we performed Hardy-Weinberg tests [110 (link)] within individual populations for the 685 remaining markers. This analysis, performed using PowerMarker [111 (link)], used only the 44 populations in which all 685 markers were polymorphic. We calculated the fraction of populations with a significant p-value (<0.05) for the Hardy-Weinberg test (Figure S7). Two markers (GAAA1C11 and GATA88F08P) were extreme outliers, with more than 43% of populations producing p < 0.05. For the remaining markers, the proportion of tests significant at p < 0.05 varied from 0 to 35% without any clear outliers, and with most markers having less than 10% of tests significant at p < 0.05. Excluding the two Hardy-Weinberg outliers, 683 markers remained. Five additional markers (AGAT120, AGAT142P, D14S592, GATA135G01, and TTTA033) were excluded due to missing data: for each of these markers there was at least one population in which all genotypes were missing. Thus, 678 loci remained for the combined analysis with the HGDP–CEPH panel.

The Epigen-Brazil participants were genotyped by the Illumina facility (San Diego, California) using the Omni 2.5M array. We performed the unsupervised tri hybrid (k = 3) ADMIXTURE analyses based on 370,539 SNPs shared by samples from the HapMap Project, the Human Genome Diversity Project (HGDP)23 24 and the Epigen-Brazil study population. As external panels, we used the following HapMap samples: 266 Africans (176 Yoruba in Ibadan, Nigeria [YRI] and 90 Luhya in Webuye, Kenya [LWK]), 262 Europeans (174 Utah residents with Northern and Western European ancestry [CEU] and 88 from Toscans from Italy [TSI]), 170 admixed individuals (77 Mexicans from Los Angeles, California [MEX] and 83 Afro-African from Southwest USA [ASW]), and 93 Native Americans from the HGDP (25 Pima, 22 Karitiana, 25 Maya and 21 Surui). The same set of reference populations was used in analyzing the three cohorts.

Details about HIV including age at HIV diagnosis, ART regimen and duration were collected from hand-held medical records during the interviewer-administered questionnaire for CLWH. CD4⁺ cell count was measured using an Alere PIMA CD4 machine (Waltham, Massachusetts, USA) and HIV viral load using the GeneXpert HIV-1 viral load platform (Cepheid Inc., Sunnyvale, California, USA), with viral suppression defined as <1000 copies/ml as per WHO guidelines [17 ].

The statistical analyses were executed using SPSS Statistics 29 (IBM, Armonk, New York, United States) and Excel (Office 365, Microsoft, Redmond, Washington, United States). Different approaches were used according to the research questions mentioned in the introduction. Basically, all data were normally distributed as confirmed by the Shapiro–Wilk test. Parametric tests were chosen for statistical comparisons (see below). In this case, RM ANOVA was used, and sphericity was checked by Mauchly’s test. In case of significance, the Greenhouse–Geisser correction was applied (F_G). Cohen’s effect size f was given for RM ANOVA, where Cohen’s f was calculated by $\sqrt{\frac{{\eta }^2}{1-{\eta }^2}}$ (Cohen, 1988 ). For t tests (paired or unpaired), Hedges’ effect size g was calculated by SPSS. The effect sizes were interpreted as small (0.2), moderate (0.5), large (0.80), or very large (1.3) (Cohen, 1992 (link); Sullivan and Feinn, 2012 (link)). Significance level was α = 0.05.

1) Behavior of force parameters with respect to repeated AF measurements (n = 12):

The linear mixed model (method: restricted maximum likelihood; REML) was used to investigate the maximal torques regarding time (pre/post or start/end, respectively) and parameter (AF_max, AFiso_max, and MVIC). ‘Time’ and ‘parameters’ were set as fixed factors. Since time*parameter was not found to be significant in terms of fixed factors, it was removed from the mixed model in order to reduce complexity. ‘Subject’ (ID) and ‘parameter’ were defined as random effects for the first estimation. Since ‘parameter’ turned out to be not significant in covariance estimation, consideration of the random factor was not necessary (Baltes-Götz, 2020 ). The Kenward–Roger approximation was used to estimate the degrees of freedom (df) since it provides a better estimation for small sample sizes (Baltes-Götz, 2020 ). This model revealed the best Bayesian information criterion (BIC) and was therefore used.
The ratio of AFiso_max to AF_max was compared between start and end by the paired t-test. Relative declines of parameters from pre to post or start to end (%), respectively, were calculated and compared using the paired t-test for each parameter (one-tailed test for AFiso_max vs. AF_max or MVIC, since AFiso_max was assumed to decrease stronger; two-tailed test for AF_max vs. MVIC).
Slopes of regression lines of the single values of each parameter (AF_max and AFiso_max) regarding the 30 AF measurements (M1–M30) were calculated to describe a possible decline during repetition trials. The paired t-test (one-tailed) was performed to investigate a possible difference between the slopes of AFiso_max and AF_max.
Furthermore, the 30 AF trials were divided into six intervals (I1–I6), which consisted of the arithmetic mean of each five subsequent trials: I1 (M1–M5), I2 (M6–M10), I3 (M11–M15), I4 (M16–M20), I5 (M21–M25), and I6 (M26–M30). RM ANOVAs for every AF parameter considering the six intervals were performed. Pairwise comparisons were executed using the Bonferroni correction (adjusted p-value = p_adj).

2) Comparison of force parameters (n = 12)

The comparison of maxAFiso_max and maxMVICpre is most important regarding the differentiation of HIMA and PIMA. maxAFiso_max and maxAF_max refer to the highest value of all 30 trials regarding AFiso_max and AF_max, respectively. The paired t-test was used to check for differences between the maximal torques.

3) Comparisons between sports groups (endurance vs. strength athletes)

This consideration has to be regarded as preliminary due to the small sample sizes of both groups. Differences regarding the overall maximal torques of MVIC, AF_max, and AFiso_max between endurance and strength athletes were checked by unpaired t-tests (one-tailed) for each parameter separately.
The relative declines (%) of maxMVIC from pre to post and of AF_max and AFiso_max from start to end were calculated and compared between both sports groups using unpaired t-tests (one-tailed).
The slope values of the linear regression line of AF parameters were used to test for differences between endurance and strength athletes by performing unpaired t tests (one-tailed). The comparisons of the slope of regression lines regarding the ratios were considered as well. This should provide information on the assumed different behaviors of athletes with respect to torque relations.
For the six intervals, a REML was executed for AF_max and AFiso_max. Both parameters were considered separately since only the effect of sports types was of interest. ‘Interval’ was regarded as a covariate. Fixed factors were ‘sports’ (endurance and strength), ‘intervals’ (I1–I6), and sports*interval. ‘ID’ and ‘interval’ were set as random factors (unstructured).
Regarding the patterns of decline, the averaged torques of I1 (AF_max and AFiso_max, respectively) were set at 100%, and the values of the subsequent intervals were related to the first value. Differences between strength and endurance athletes were checked by a mixed ANOVA (intervals*sports). In the case of significance, pairwise comparisons were performed.