13The water buffalo (Bubalus bubalis) has shown enormous milk production 14 potential in many Asian countries. India is considered as the home tract of some of the best 15 buffalo breeds. However, genetic structure of the Indian river buffalo is poorly understood. 16Hence, for selection and breeding strategies, there is a need to characterize the populations 17 and understand the genetic structure of various buffalo breeds. All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/395681 doi: bioRxiv preprint first posted online Aug. 19, 2018; 2 Pandharpuri and Jaffarabadi but not others. So, there is a need to develop SNP chip based 36 on SNP markers identified by sequence information of local breeds. 37 Author Summary 38Indian buffaloes, through 13 recognised breeds, contribute about 49% in 39 total milk production and play a vital role in enhancing the economic condition of Indian 40 farmers. High density genotyping these breeds will allow us to study differences at the 41 molecular level. Evolutionary relationship and phenotypes relations with genotype could 42 be tested with high density genotyping. Breed structure analysis helps to take effective 43 breeding policy decision. In the present study, we have used the high-throughput 44 microarray based genotyping technology for SNP markers. These markers were used for 45 breed differentiation using various genetic parameters. Population structure reflected the 46 proportion of breed admixture among studied breeds. We have also tried to dig the markers 47 associated with traits based LD calculation. However, these SNPs couldn't explain obvious 48 variation up to the expected level, hence, there is need to develop an indigenous SNP chip 49 based on Indian buffalo populations. 50All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
Background Squamous Cell Carcinoma of horn, also known as horn cancer, is a prevailing type of cancer in cattles especially Bos indicus. It is one of the most prevalent disease in Indian bullocks often resulting in death and huge economic losses to farmers. Here, we have reported the use of targeted exome sequencing to identify variants present in horn cancer affected horn mucosa tissue and blood of the same animal to identify some of the prevalent markers of horn cancer. Results We have observed higher number of variants present in tissue as compared to blood as well as among cancer samples compared to samples from normal animals. Eighty six and 1437 cancer-specific variants were identified among the predicted variants in blood and tissue samples, respectively. Total 25 missense variants were observed distributed over 18 genes. KRT8 gene coding for Keratin8, one of the key constituents of horn, displayed 5 missense variants. Additionally, three other genes involved in apoptosis pathway and two genes involved in antigen presentation and processing also contained variants. Conclusions Several genes involved in various apoptotic pathways were found to contain non-synonymous mutations. Keratin8 coding for Keratin, a chief constituent of horn was observed to have the highest number of mutations. In all, we present a preliminary report of mutations observed in horn cancer.
India is considered as the home tract of some of the best buffalo breeds. However, the genetic structure of the Indian river buffalo is poorly understood. Hence, there is a need to characterize the populations and understand the genetic structure of various buffalo breeds for selection and to design breeding strategies. In this study, we have analyzed genetic variability and population structure of seven buffalo breeds from their respective geographical regions using Axiom Buffalo Genotyping Array. Diversity, as measured by expected heterozygosity, ranged from 0.364 in Surti to 0.384 in Murrah breed, and pair-wise FST values revealed the lowest genetic distance between Murrah and Nili-Ravi (0.0022), while the highest between Surti and Pandharpuri (0.030). Principal component analysis and structure analysis unveiled the differentiation of Surti, Pandharpuri, and Jaffarabadi in first two principal components and at K = 4, respectively, while remaining breeds were grouped together as a separate single cluster and admixed. Murrah and Mehsana showed early linkage disequilibrium (LD) decay, while Surti breed showed late decay. In LD blocks to quantitative trait locis (QTLs) concordance analysis, 4.65% of concordance was observed with 873 LD blocks overlapped with 2,330 QTLs. Overall, total 4,090 markers were identified from all LD blocks for six types of traits. Results of this study indicated that these single-nucleotide polymorphism (SNP) markers could differentiate phenotypically distinct breeds like Surti, Pandharpuri, and Jaffarabadi but not others. So, there is a need to develop SNP chip based on SNP markers identified by sequence information of local breeds.
Hash functions map data of arbitrary length to data of predetermined length. Good hash functions are hard to predict, making them useful in cryptography. We are interested in the elliptic curve CGL hash function, which maps a bitstring to an elliptic curve by traversing an inputdetermined path through an isogeny graph. The nodes of an isogeny graph are elliptic curves, and the edges are special maps betwixt elliptic curves called isogenies. Knowing which hash values are most likely informs us of potential security weaknesses in the hash function. We use stochastic matrices to compute the expected probability distributions of the hash values. We generalize our experimental data into a theorem that completely describes all possible probability distributions of the CGL hash function. We use this theorem to evaluate the collision resistance of the CGL hash function and compare this to the collision resistance of an "ideal" hash function.
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