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Sales Ended. Event description. Enjoy speed dating in your home! Tonight you'll get the chance to meet lots of South Asian and Indian singles! Read more Read less. About this Event Enjoy speed dating in the comfort of your own home! How Does This Work? The Cliff Notes Using Zoom, every man and woman will be in their own speed dating room We will send the event link just before the event Every man will meet every woman Everyone will have their camera and audio on After the event you will gain access to our dating system Our dating system allows you to send messages to anyone you met for 10 days after the event Our mutual match system will notify you of mutual matches Note: The majority of singles attending will be from the Washington, DC Metropolitan Area, which includes the states of Virginia and Maryland.
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HomeLifestyle Party. Save This Event Log in or sign up for Eventbrite to save events you're interested in. Sign Up. Already have an account? Log in. Event Saved. Your message has been sent! Your email will only be seen by the event organizer. Let be the number of populations represented by the individuals. Let be the frequency of allele in population and let be the frequency of allele in population. The informativeness of assignment of a SNP is given by where is defined as 0.
It is similar to a log-likelihood ratio, where the ratio is the likelihood that an allele is assigned to one of the populations , versus the likelihood that the allele is assigned to the average population. The allele frequency of each SNP in the data set was calculated, for each source population, and for the population groups included in a source population for example the East Asian source population comprises the HapMap Japanese and Chinese study groups.
SNPs were discarded if they were heterogeneous in these subgroups, based on a Chi-squared test that has a null hypothesis of equal allele frequencies in the subgroups. SNPs were then selected according to the -statistic calculated across all the source populations, and the -statistic calculated between pairs of populations.
SNPs were selected as follows. The -statistic was calculated for all SNPs, across all the source populations, and used to select SNPs with the highest values. This multiple population -statistic may however be skewed towards populations that are more differentiated i. SNPs from less differentiated populations will contribute less to the statistic and will therefore have a smaller probability of being selected as an informative marker. Additional SNPs were therefore selected by calculating the -statistic of each SNP for each pair of populations, and then selecting SNPs by balancing the total pairwise - statistic.
For example, for five source populations there are pairs of populations. The pair with the smallest total -statistic was identified initially, the total of all pairs are set to zero and are therefore tied and the SNP with the highest -statistic for the identified pair was selected as an AIM. In the case of a tie s , the SNP with the highest -statistic for the tied pair s was selected.
If the SNP was accepted, its -statistic value for the relevant pair was added to the pair's total -statistic. This process was repeated until the required number of AIMs were accepted. We generated panels of AIMs of sizes 25, 50, 75,…, using this approach, and experimented with including versus excluding SNPs that are heterogeneous in the populations that constitute a source population, different minimum distances between SNPs and selecting different proportions of markers 0, 0.
We also experimented with selecting markers using the implementations provided by Lao et al. Let be a matrix of genotypes for each of the individuals in the data set, be a matrix of variant allele frequencies for each of the source populations, and be a matrix of ancestry proportions for each of the individuals.
Ancestry proportions can be estimated by maximizing the likelihood function. A strong correlation between ancestry proportions estimated using AIMs for a particular ancestry and ancestry proportions estimated using genome-wide data for the same ancestry would show that the AIMs are informative for that ancestry, even though the number of markers used in the estimation has been much reduced from genome-wide data. We therefore estimated the ancestry proportions of individuals from a combined genome-wide data set composed of both the source population data sets and the Cape Town admixed study group, and identified ancestries as follows.
The mean ancestry proportion was calculated for each of the possible ancestries, per source population using only individuals from that particular source population. The ancestry of a particular source population was then identified by determining which of the possible ancestries had the largest mean ancestry proportion for that population. The same procedure was used for combined AIM data sets. The correlation between ancestry proportions estimated using the genome-wide data set and proportions estimated using each AIM data set was then calculated per ancestry, using individuals from the admixed study group.
We modified the Python script provided by Galanter et al. Statistical analyses were performed using R. The correlation between ancestry proportions estimated using AIMs and proportions estimated using genome-wide data was calculated for AIM sets of increasing size 25, 50,…, SNPs for different combinations of parameter settings.
For investigating the effect of heterogeneity between subgroups of a source population the subgroups are summarized under the Population Group heading of Table 1 , we used a minimum distance of base pairs between SNPs. We selected different proportions of markers using the multiple population -statistic while the remaining SNPs were selected using the pairwise -statistic.
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The difference between the correlation calculated using a AIM set selected from all markers versus the correlation of a AIM set of the same size selected from a marker set containing no heterogeneous SNPs was measured. A positive difference indicates that the AIM set selected from all markers has a higher correlation. Figure S3 depicts the magnitude and direction of the differences measured for the different AIM set sizes and multiple population -statistic parameter settings. Since of the differences are positive, we ignored heterogeneity in subsequent AIM selections.
Figure S4 shows the differences between correlations estimated using a minimum distance of versus a 1 base pairs between SNPs for different AIM set sizes and multiple population -statistic parameter settings. A positive difference indicates that the base pair distance has a larger correlation. Although the differences are small and the number of positive differences are not much larger than the number of negative differences, the magnitude of the positive differences are greater compared to the negative differences, except for one of the multiple population -statistic parameter settings.
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For this reason, we used a minimum distance of a base pairs between markers in our subsequent AIM selections. A proportion of 0, 0. Selecting all markers using the multiple population statistic i. Figure 1 shows the correlation per source population for AIM sets of increasing size for the first four multiple population -statistic parameter settings.
The figure shows that the optimal estimated proportions in terms of cost vs. Selecting all SNPs by balancing the total pairwise -statistic appears to be slightly better compared to selecting some of the SNPs using the multiple population -statistic and we therefore used this parameter setting for selecting the final panel of AIMs. A proportion of the SNPs in each set of AIMs were selected using the multiple -statistic, indicated in each panel as a percentage, while the remaining SNPs were selected using the pairwise -statistic, as described in the Methods section.
As it is conceivable that future cost reductions may render the cost of genotyping additional SNPs irrelevant, Table S3 presents a panel of ordered AIMs that were selected using the criteria described above. This large panel can potentially also be used for local ancestry inference. It is currently possible to genotype 96 SNPs cost-effectively on a number of platforms, such as the BeadXpress system, and we therefore evaluated the first 96 SNPs roughly the optimal number of markers as our primary panel of AIMs.
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This larger marker set can be genotyped using technologies such as Sequenom plexes and Taqman assays, and the results of its evaluation are detailed in the Supporting Information. As expected, for both the 96 and SNP panels the number of AIMs selected per population pair is inversely proportional to the genetic distance between the two populations Table S4.
Figure S5 shows Bland Altman plots per ancestral population of the difference between the genome-wide and AIMs estimated proportions versus the genome-wide estimated proportions for each individual for the 96 AIMs panel. The figure suggests that there are no systematic differences in the ancestry estimation. As large study groups may require fewer markers to differentiate ancestries [36] , the ability of the AIMs to estimate ancestry proportions of a smaller group of South African Coloured individuals were evaluated using permutation testing.
The correlation with the genome-wide ancestry proportions for those individuals was then calculated. This process was repeated a times. Figure S6 gives boxplots of the correlation coefficients calculated for each permutation. The red diamonds in the figure are the correlation coefficients calculated using all individuals; this shows that the AIMs perform well for a smaller group of individuals.
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Markers used to estimate the ancestry proportions of an admixed population can only perform well if they can also distinguish between the source populations of the admixed population. Figure 2 is a barplot of the estimated ancestry proportions for the combined data set, using AIMs and using genome-wide data for the estimation. It shows that for most of the source population individuals, the largest proportion of ancestry is correctly assigned to the relevant population group using AIMs, albeit less well when compared to using genome-wide data.