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Background: Association studies are useful to unravel the genetic basis of common human diseases. However, the presence of undetected population structure can lead to both false positive results and failures to detect genuine associations. Even when most of the approaches to deal with population stratification require genome-wide data, the use of a well-selected panel of ancestry informative markers (AIMs) may appropriately correct for population stratification. Few panels of AIMs have been developed for Latino populations and most contain a high number of markers (> 100 AIMs). For some association studies such as candidate gene approaches, it may be unfeasible to genotype a numerous set of markers to avoid false positive results. In such cases, methods that use fewer AIMs may be appropriate., Results: We validated an accurate and cost-effective panel of AIMs, for use in population stratification correction of association studies and global ancestry estimation in Mexicans, as well as in populations having large proportions of both European and Native American ancestries. Based on genome-wide data from 1953 Mexican individuals, we performed a PCA and SNP weights were calculated to select subsets of unlinked AIMs within percentiles 0.10 and 0.90, ensuring that all chromosomes were represented. Correlations between PC1 calculated using genome-wide data versus each subset of AIMs (16, 32, 48 and 64) were r 2  = 0.923, 0.959, 0.972 and 0.978, respectively. When evaluating PCs performance as population stratification adjustment covariates, no correlation was found between P values obtained from uncorrected and genome-wide corrected association analyses (r 2  = 0.141), highlighting that population stratification correction is compulsory for association analyses in admixed populations. In contrast, high correlations were found when adjusting for both PC1 and PC2 for either subset of AIMs (r 2  > 0.900). After multiple validations, including an independent sample, we selected a minimal panel of 32 AIMs, which are highly informative of the major ancestral components of Mexican mestizos, namely European and Native American ancestries. Finally, the correlation between the global ancestry proportions calculated using genome-wide data and our panel of 32 AIMs was r 2  = 0.972., Conclusions: Our panel of 32 AIMs accurately estimated global ancestry and corrected for population stratification in association studies in Mexican individuals.

Background: Aedes fluviatilis is a neotropical mosquito species thought to be a potential vector of Yellow Fever viruses and can be infected with Plasmodium gallinaceum in laboratory. A better understanding of its genetic structure is very important to understand its epidemiologic potential and how it is responding to urbanization. The objective of this study was to survey the transferability of microsatellites loci developed for other Aedes to Ae. fluviatilis., Findings: We tested in Ae. fluviatilis 40 pairs of primers known to flank microsatellite regions in Aedes aegypti, Aedes albopictus and Aedes caspius, and found eight loci that amplified consistently. The number of alleles per locus ranged from 2 to 15, and the expected heterozygosity ranged from 0.09 to 0.85., Conclusions: We found that several microsatellite primers successfully transferred to Ae. fluviatilis. This finding opens avenues for cost-effective optimization of high-resolution population genetic tools.

Background: Studies of association methods using DNA pooling of single nucleotide polymorphisms (SNPs) have focused primarily on the effects of "machine-error", number of replicates, and the size of the pool. We use the non-centrality parameter (NCP) for the analysis of variance test to compute the approximate power for genetic association tests with DNA pooling data on cases and controls. We incorporate genetic model parameters into the computation of the NCP. Parameters involved in the power calculation are disease allele frequency, frequency of the marker SNP allele in coupling with the disease locus, disease prevalence, genotype relative risk, sample size, genetic model, number of pools, number of replicates of each pool, and the proportion of variance of the pooled frequency estimate due to machine variability. We compute power for different settings of number of replicates and total number of genotypings when the genetic model parameters are fixed. Several significance levels are considered, including stringent significance levels (due to the increasing popularity of 100 K and 500 K SNP "chip" data). We use a factorial design with two to four settings of each parameter and multiple regression analysis to assess which parameters most significantly affect power., Results: The power can increase substantially as the genotyping number increases. For a fixed number of genotypings, the power is a function of the number of replicates of each pool such that there is a setting with maximum power. The four most significant parameters affecting power for association are: (1) genotype relative risk, (2) genetic model, (3) sample size, and (4) the interaction term between disease and SNP marker allele probabilities., Conclusion: For a fixed number of genotypings, there is an optimal number of replicates of each pool that increases as the number of genotypings increases. Power is not substantially reduced when the number of replicates is close to but not equal to the optimal setting.