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Genetic polymorphism and evolutionary differentiation of Eastern Chinese Han: a comprehensive and comparative analysis on KIRs.

November 2, 2018 dna 0
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Genetic polymorphism and evolutionary differentiation of Eastern Chinese Han: a comprehensive and comparative analysis on KIRs.

Sci Rep. 2017 02 16;7:42486

Authors: Yin C, Hu L, Huang H, Yu Y, Li Z, Ji Q, Kong X, Wang Z, Yan J, Yan J, Zhu B, Chen F

Abstract
Killer cell immunoglobulin-like receptor genes, namely KIRs, cluster together within the 160 kb genomic DNA region. In this study, we used PCR-SSP approach and successfully identified the genotype of 17 KIR genes in 123 independent healthy donors residing in the Jiangsu province, China. All individuals were positive at the 7 genes. The observed carrier gene frequencies (OFs) of remaining 10 KIRs ranged from 14.63% (KIR2DS3) to 95.93% (KIR3DL1). We found 27 distinct genotypes excluding KIR1D. The most frequent occurred in 63 individuals (51.22%). The linkage disequilibrium analysis signified 29 positive and 6 negative relations in 45 pairwise comparisons. To study population differentiation, we drew a Heatmap based on the data of KIRs from 59 populations and conducted Hierarchical Clustering by Euclidean distances. We next validated our results by estimating pairwise DA distances and illustrating a Neighbor-Joining tree, as well as a MDS plot covering 3 additional Chinese Han groups. The phylogenetic reconstruction and cluster analysis strongly indicated a genetically close relationship between Eastern and Jilin Hans. In conclusion, the present study provided a meritorious resource of KIR genotyping for population genetics, and could be helpful to uncover the genetic mechanism of KIRs in immune disease in the future.

PMID: 28205529 [PubMed – indexed for MEDLINE]

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iSeg: an efficient algorithm for segmentation of genomic and epigenomic data.

November 2, 2018 dna 0
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iSeg: an efficient algorithm for segmentation of genomic and epigenomic data.

BMC Bioinformatics. 2018 04 11;19(1):131

Authors: Girimurugan SB, Liu Y, Lung PY, Vera DL, Dennis JH, Bass HW, Zhang J

Abstract
BACKGROUND: Identification of functional elements of a genome often requires dividing a sequence of measurements along a genome into segments where adjacent segments have different properties, such as different mean values. Despite dozens of algorithms developed to address this problem in genomics research, methods with improved accuracy and speed are still needed to effectively tackle both existing and emerging genomic and epigenomic segmentation problems.
RESULTS: We designed an efficient algorithm, called iSeg, for segmentation of genomic and epigenomic profiles. iSeg first utilizes dynamic programming to identify candidate segments and test for significance. It then uses a novel data structure based on two coupled balanced binary trees to detect overlapping significant segments and update them simultaneously during searching and refinement stages. Refinement and merging of significant segments are performed at the end to generate the final set of segments. By using an objective function based on the p-values of the segments, the algorithm can serve as a general computational framework to be combined with different assumptions on the distributions of the data. As a general segmentation method, it can segment different types of genomic and epigenomic data, such as DNA copy number variation, nucleosome occupancy, nuclease sensitivity, and differential nuclease sensitivity data. Using simple t-tests to compute p-values across multiple datasets of different types, we evaluate iSeg using both simulated and experimental datasets and show that it performs satisfactorily when compared with some other popular methods, which often employ more sophisticated statistical models. Implemented in C++, iSeg is also very computationally efficient, well suited for large numbers of input profiles and data with very long sequences.
CONCLUSIONS: We have developed an efficient general-purpose segmentation tool and showed that it had comparable or more accurate results than many of the most popular segment-calling algorithms used in contemporary genomic data analysis. iSeg is capable of analyzing datasets that have both positive and negative values. Tunable parameters allow users to readily adjust the statistical stringency to best match the biological nature of individual datasets, including widely or sparsely mapped genomic datasets or those with non-normal distributions.

PMID: 29642840 [PubMed – indexed for MEDLINE]

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Advances in the discovery of genetic risk factors for complex forms of neurodegenerative disorders: contemporary approaches, success, challenges and prospects.

November 2, 2018 dna 0
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Advances in the discovery of genetic risk factors for complex forms of neurodegenerative disorders: contemporary approaches, success, challenges and prospects.

J Genet. 2018 Jul;97(3):625-648

Authors: Kumar S, Yadav N, Pandey S, Thelma BK

Abstract
Neurodegenerative diseases constitute a large proportion of disorders in elderly, majority being sporadic in occurrence with ∼5-10% familial. A strong genetic component underlies the Mendelian forms but nongenetic factors together with genetic vulnerability contributes to the complex sporadic forms. Several gene discoveries in the familial forms have provided novel insights into the pathogenesis of neurodegeneration with implications for treatment. Conversely, findings from genetic dissection of the sporadic forms, despite large genomewide association studies and more recently whole exome and whole genome sequencing, have been limited. This review provides a concise account of the genetics that we know, the pathways that they implicate, the challenges that are faced and the prospects that are envisaged for the sporadic, complex forms of neurodegenerative diseases, taking four most common conditions, namely Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis and Huntington disease as examples. Poor replication across studies, inability to establish genotype-phenotype correlations and the overall failure to predict risk and/or prevent disease in this group poses a continuing challenge. Among others, clinical heterogeneity emerges as the most important impediment warranting newer approaches. Advanced computational and system biology tools to analyse the big data are being generated and the alternate strategy such as subgrouping of case-control cohorts based on deep phenotyping using the principles of Ayurveda to overcome current limitation of phenotype heterogeneity seem to hold promise. However, at this point, with advances in discovery genomics and functional analysis of putative determinants with translation potential for the complex forms being minimal, stem cell therapies are being attempted as potential interventions. In this context, the possibility to generate patient derived induced pluripotent stem cells, mutant/gene/genome correction through CRISPR/Cas9 technology and repopulating the specific brain regions with corrected neurons, which may fulfil the dream of personalized medicine have been mentioned briefly. Understanding disease pathways/biology using this technology, with implications for development of novel therapeutics are optimistic expectations in the near future.

PMID: 30027900 [PubMed – indexed for MEDLINE]