Chromosomal abnormalities are often identified in people with neurodevelopmental disorders including intellectual disability, autism, and epilepsy. Ring chromosomes, which usually involve gene copy number loss, are formed by fusion of subtelomeric or telomeric chromosomal regions. Some ring chromosomes, including ring 14, 17, and 20, are strongly associated with seizure disorders. We report an individual with a ring chromosome 17, r(17)(p13.3q25.3), with a terminal 17q25.3 deletion and no short arm copy number loss, and with a phenotype characterized by intellectual disability and drug-resistant epilepsy, including a propensity for nonconvulsive status epilepticus.
Abstract Background We explored the imputation performance of the program IMPUTE in an admixed sample from Mexico City. The following issues were evaluated: (a) the impact of different reference panels (HapMap vs. 1000 Genomes) on imputation; (b) potential differences in imputation performance between single-step vs. two-step (phasing and imputation) approaches; (c) the effect of different posterior genotype probability thresholds on imputation performance and (d) imputation performance in common vs. rare markers. Methods The sample from Mexico City comprised 1,310 individuals genotyped with the Affymetrix 5.0 array. We randomly masked 5% of the markers directly genotyped on chromosome 12 (n = 1,046) and compared the imputed genotypes with the microarray genotype calls. Imputation was carried out with the program IMPUTE. The concordance rates between the imputed and observed genotypes were used as a measure of imputation accuracy and the proportion of non-missing genotypes as a measure of imputation efficacy. Results The single-step imputation approach produced slightly higher concordance rates than the two-step strategy (99.1% vs. 98.4% when using the HapMap phase II combined panel), but at the expense of a lower proportion of non-missing genotypes (85.5% vs. 90.1%). The 1,000 Genomes reference sample produced similar concordance rates to the HapMap phase II panel (98.4% for both datasets, using the two-step strategy). However, the 1000 Genomes reference sample increased substantially the proportion of non-missing genotypes (94.7% vs. 90.1%). Rare variants (<1%) had lower imputation accuracy and efficacy than common markers. Conclusions The program IMPUTE had an excellent imputation performance for common alleles in an admixed sample from Mexico City, which has primarily Native American (62%) and European (33%) contributions. Genotype concordances were higher than 98.4% using all the imputation strategies, in spite of the fact that no Native American samples are present in the HapMap and 1000 Genomes reference panels. The best balance of imputation accuracy and efficiency was obtained with the 1,000 Genomes panel. Rare variants were not captured effectively by any of the available panels, emphasizing the need to be cautious in the interpretation of association results for imputed rare variants.
We studied the distribution of ABO blood group frequencies of the Galo and Mishing subtribes of the Adi tribal cluster in East Siang District, Arunachal Pradesh, India, in order to investigate the intertribal and temporal allelic variation. Blood groups O and AB showed higher frequencies (28.4%, 27.4%) in the Galo, whereas group O (45%) was predominant in the Mishing. Allele r is significantly different in the Galo (44.6%) and Mishing (60.3%). The chi-square test indicated significant deviations from Hardy- Weinberg equilibrium. Adi tribes show high heterogeneity and indicate significant temporal variation in ABO genotype frequencies in the Galo, Mishing, and Padam, whereas the Panggi, a small isolated subtribe of Adi, show similar and stable frequencies.
Isolated tribes in remote areas are important for genetic studies, and one such little known subtribe of the Adi tribe, namely, the Adi Panggi (Pangi) of the Upper Siang District of Arunachal Pradesh, India, was studied for surname distribution to deduce the deviation from random mating and genetic kinship between villages. The estimates of homonymy (homozygosity) vary between villages; husbands show wider variation (0.009 to 0.23) than wives (0.005 to 0.054). The remote villages of Sumsing and Sibum and Geku Town show lower entropy among husbands' surnames than among Panggi wives. The highest equivalent surname number was found among Sibum husbands (9.9), Panggi wives (12.6), and Panggi and non-Panggi wives (13.5). The estimates of unbiased random isonymy among husbands and wives together showthe smallest values in Sibum (0.05) and the highest values in Sumsing and Ramku (0.16). The random and nonrandom components of the inbreeding coefficient show avoidance of inbreeding among the Panggi villages (−0.012 to −0.27) except in Sibum (0.012). Genetic kinship between villages based on theMij distance shows different clusters of villages among husbands and wives. Both the Panggi wives and the Panggi and non-Panggi wives show a similar pattern of clustering between villages. The wide homonymy variation between villages among the patrilocal Adi Panggi indicates differential genetic kinetics among husbands and wives, avoidance of inbreeding, and female-oriented differential gene flow with little effect on the overall intervillage genetic kinship.
The study examines the regional genetic diversity among 23 Arunachal Pradesh tribes based on 2 loci (ABO and PTC). The results show wide variation in allele frequencies. The ‘r’ allele shows higher frequency (than ‘p’ and ‘q’) and show geographical variation. The results of NJ tree and PCA plot show separation of tribal groups that fairly corresponds to their geographical locations and ethno-historical backgrounds. The Harpending and Jenkins regression plot suggests that these tribes are getting differentiated primarily due to genetic drift and genetic isolation, where gene flow plays a significant role in a few tribes. Also, the affinity among the regional groups based on their ethno-historical origin and migration and genetic diversity was considered by a model-based approach especially by Rao’s hierarchical analysis. The results of the study thus support ethno-historical accounts of their antiquity and possible common origin.
POPULATION: Two Tibeto‐Burman‐speaking Adi tribal populations of Arunachal Pradesh, India, Adi Pasi ( n =121) from Upper Siang district, and Adi Minyong ( n =33) from East Siang district were analyzed for polymorphisms at 15 microsatellite loci. The populations belong to Mongoloid ethnicity and are of special significance in genetic studies due to their small population size, relative isolation in remote hilly areas, and traditional subsistence patterns.
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