AI Institute

Jerusalem, Israel

AI Institute

Jerusalem, Israel
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Weller J.I.,Institute of Animal science | Glick G.,Institute of Animal science | Glick G.,Hebrew University of Jerusalem | Ezra E.,Israel Cattle Breeders Association | And 3 more authors.
Animal Genetics | Year: 2010

Incorrect paternity assignment in cattle can have a major effect on rates of genetic gain. Of the 576 Israeli Holstein bulls genotyped by the BovineSNP50 BeadChip, there were 204 bulls for which the father was also genotyped. The results of 38 828 valid single nucleotide polymorphisms (SNPs) were used to validate paternity, determine the genotyping error rates and determine criteria enabling deletion of defective SNPs from further analysis. Based on the criterion of >2% conflicts between the genotype of the putative sire and son, paternity was rejected for seven bulls (3.5%). The remaining bulls had fewer conflicts by one or two orders of magnitude. Excluding these seven bulls, all other discrepancies between sire and son genotypes are assumed to be caused by genotyping mistakes. The frequency of discrepancies was >0.07 for nine SNPs, and >0.025 for 81 SNPs. The overall frequency of discrepancies was reduced from 0.00017 to 0.00010 after deletion of these 81 SNPs, and the total expected fraction of genotyping errors was estimated to be 0.05%. Paternity of bulls that are genotyped for genomic selection may be verified or traced against candidate sires at virtually no additional cost. © 2010 Stichting International Foundation for Animal Genetics.

Seroussi E.,Institute of Animal science | Glick G.,Institute of Animal science | Shirak A.,Institute of Animal science | Yakobson E.,Institute of Animal science | And 3 more authors.
BMC Genomics | Year: 2010

Background: Copy number variation (CNV) has been recently identified in human and other mammalian genomes, and there is a growing awareness of CNV's potential as a major source for heritable variation in complex traits. Genomic selection is a newly developed tool based on the estimation of breeding values for quantitative traits through the use of genome-wide genotyping of SNPs. Over 30,000 Holstein bulls have been genotyped with the Illumina BovineSNP50 BeadChip, which includes 54,001 SNPs (~SNP/50,000 bp), some of which fall within CNV regions.Results: We used the BeadChip data obtained for 912 Israeli bulls to investigate the effects of CNV on SNP calls. For each of the SNPs, we estimated the frequencies of occurrence of loss of heterozygosity (LOH) and of gain, based either on deviation from the expected Hardy-Weinberg equilibrium (HWE) or on signal intensity (SI) using the PennCNV "detect" option. Correlations between LOH/CNV frequencies predicted by the two methods were low (up to r = 0.08). Nevertheless, 418 locations displayed significantly high frequencies by both methods. Efficiency of designating large genomic clusters of olfactory receptors as CNVs was 29%. Frequency values for copy loss were distinguishable in non-autosomal regions, indicating misplacement of a region in the current BTA7 map. Analysis of BTA18 placed major quantitative trait loci affecting net merit in the US Holstein population in regions rich in segmental duplications and CNVs. Enrichment of transporters in CNV loci suggested their potential effect on milk-production traits.Conclusions: Expansion of HWE and PennCNV analyses allowed estimating LOH/CNV frequencies, and combining the two methods yielded more sensitive detection of inherited CNVs and better estimation of their possible effects on cattle genetics. Although this approach was more effective than methodologies previously applied in cattle, it has severe limitations. Thus the number of CNVs reported here for the Holstein breed may represent as little as one-tenth of inherited common structural variation. © 2010 Seroussi et al; licensee BioMed Central Ltd.

Weller J.I.,Institute of Animal science | Glick G.,Institute of Animal science | Glick G.,Hebrew University of Jerusalem | Shirak A.,Institute of Animal science | And 5 more authors.
Animal | Year: 2014

Several studies have shown that computation of genomic estimated breeding values (GEBV) with accuracies significantly greater than parent average (PA) estimated breeding values (EBVs) requires genotyping of at least several thousand progeny-tested bulls. For all published analyses, GEBV computed from the selected samples of markers have lower or equal accuracy than GEBV derived on the basis of all valid single nucleotide polymorphisms (SNPs). In the current study, we report on four new methods for selection of markers. Milk, fat, protein, somatic cell score, fertility, persistency, herd life and the Israeli selection index were analyzed. The 972 Israeli Holstein bulls genotyped with EBV for milk production traits computed from daughter records in 2012 were assigned into a training set of 844 bulls with progeny test EBV in 2008, and a validation set of 128 young bulls. Numbers of bulls in the two sets varied slightly among the nonproduction traits. In EFF12, SNPs were first selected for each trait based on the effects of each marker on the bulls' 2012 EBV corrected for effective relationships, as determined by the SNP matrix. EFF08 was the same as EFF12, except that the SNPs were selected on the basis of the 2008 EBV. In DIFmax, the SNPs with the greatest differences in allelic frequency between the bulls in the training and validation sets were selected, whereas in DIFmin the SNPs with the smallest differences were selected. For all methods, the numbers of SNPs retained varied over the range of 300 to 6000. For each trait, except fertility, an optimum number of markers between 800 and 5000 was obtained for EFF12, based on the correlation between the GEBV and current EBV of the validation bulls. For all traits, the difference between the correlation of GEBV and current EBV and the correlation of the PA and current EBV was >0.25. EFF08 was inferior to EFF12, and was generally no better than PA EBV. DIFmax always outperformed DIFmin and generally outperformed EFF08 and PA. Furthermore, GEBV based on DIFmax were generally less biased than PA. It is likely that other methods of SNP selection could improve upon these results. © 2014 The Animal Consortium.

Golik M.,Israel Agricultural Research Organization | Golik M.,Hebrew University of Jerusalem | Glick G.,Israel Agricultural Research Organization | Glick G.,Hebrew University of Jerusalem | And 9 more authors.
Journal of Dairy Science | Year: 2011

A single nucleotide polymorphism in the intergenic region upstream of the ZNF496 gene on Bos taurus chromosome 7 displayed significant population-wide linkage disequilibrium with milk protein percentage in the Israeli Holstein population. The frequency of the allele associated with increased protein concentration was 10%. This single nucleotide polymorphism was located in the promoter region from which a 10-exon transcript of the bovine and the ovine ZNF496 genes are transcribed. The gene architecture was similar to the mouse ortholog Zkscan17. A 5-exon murine antisense transcript was complementary to the 5′ untranslated Zkscan17 region that included a sequence domain conserved between mouse and ruminants, suggesting a regulatory function. In the bovine ZNF496 chromosomal region, segregation of a quantitative trait locus (QTL) for milk protein percentage was confirmed in a daughter design sire family. Concordance was not obtained between QTL status of bulls and any of the polymorphisms in the functional elements of ZNF496. This excludes these variations as the causative polymorphism under the assumption of no epigenetic effect for this locus. However, ZNF496 variants were differentially expressed in bovine ovaries, and only the paternal variant was expressed in liver and kidney in a sheep family with polymorphic ZNF496 sequence. Thus, the search for the mutation underlying the minor QTL allele, which is a top economically favorable allele in Israeli Holstein cattle, may be complicated by the presence of an imprinting center in this QTL confidence interval. © 2011 American Dairy Science Association.

Glick G.,Hebrew University of Jerusalem | Shirak A.,Institute of Animal science | Seroussi E.,Institute of Animal science | Zeron Y.,AI Institute | And 3 more authors.
G3: Genes, Genomes, Genetics | Year: 2011

A quantitative trait locus (QTL) affecting female fertility, scored as the inverse of the number of inseminations to conception, on Bos taurus chromosome 7 was detected by a daughter design analysis of the Israeli Holstein population (P > 0.0003). Sires of five of the 10 families analyzed were heterozygous for the QTL. The 95% confidence interval of the QTL spans 27 cM from the centromere. Seven hundred and four SNP markers on the Illumina BovineSNP50 BeadChip within the QTL confidence interval were tested for concordance. A single SNP, NGS-58779, was heterozygous for all the five QTL heterozygous patriarchs, and homozygous for the remaining five QTL homozygous sires. A significant effect on fertility was associated with this marker in the sample of 900 sires genotyped (P > 10-6). Haplotype phase was the same for four of the five segregating sires. Thus concordance was obtained in nine of the ten families. We identified a common haplotype region associated with the rare and economically favorable allele of the SNP, spanning 270 kbp on BTA7 upstream to 4.72 Mbp. Eleven genes found in the common haplotype region should be considered as positional candidates for the identification of the causative quantitative trait nucleotide. Copy number variation was found in one of these genes, KIAA1683. Four gene variants were identified, but only the number of copies of a specific variant (V1) was significantly associated with breeding values of sires for fertility. © 2011 by the Giora Glick et al.

News Article | February 15, 2017
Site:, a boutique AI enabling consultancy based in Scottsdale, AZ, gets the bragging rights for introducing the first ever AI event schedule Conversational Chatbot initially built for the upcoming GIGAOM AI summit in San Francisco, Feb. 14-16, 2017.’s natural language chatbot offers summit attendees a hands-on AI experience, and the opportunity to learn about the economics and benefits of a Minimum Viable Product (MVP) AI roll-out strategy in addition to GIGAOM’s AI “blueprints & case studies.” Founder George Polzer, experienced the “irrational exuberance” of the 1.0 AI boom in the late 1980’s working at Bechtel AI Institute. So, to find out “what’s different” he put his IT team on a stopwatch with a shoestring budget to build a working MVP, Intelligent Chatbot using the latest plug-and-play IBM Watson and Google machine learning / natural language processing cloud services, delivered on Facebook Messenger and a Google App. “The challenge turned out to be a typical IT “build vs. buy” scenario. The vendor research and selection process, specifications, scalability, integration issues, feature cost-benefit analysis, were issues any good project manager with a tech-savvy team could undertake. The project came in on time and underbudget. We did not once have to train a neural network or hire a data scientist,” Polzer said. Polzer explained that the 1980’s AI 1.0 boom-bust cycle differs from today’s 2.0 AI cycle in that AI enabled turnkey applications and intelligent plug-and-play services are now becoming pervasive -- so much so that soon AI capabilities will no longer differentiate a company’s product or service. Companies now receiving millions of VC funding branded as AI enabled for “sales call analysis” or “sales acceleration,” for example, may gain little from touting they are AI-based once Machine Intelligence becomes commoditized. At, the vision is to show companies how to quickly AI enable their products and services without needing to hire data scientists or machine learning experts. The next step for’s chatbot, is to continue to build out its intelligent narrative capabilities and to offer it as a conversational interface to the web content of other upcoming 2017 AI conference organizers. Polzer applauded current AI “revolution” marketing efforts because they help accelerate the awareness of new ways to innovate automation - especially now when companies need to learn how to benefit from their big data and exploit the new processing power of distributed, cloud computing. The advances in the optimization math developed over the last 10+ years has allowed computer software to learn. This automated learning capability will turn a business’ digital assets into invaluable digital currency. Machine Learning and AI technology, whether offered as turnkey AI solutions, plug-and-play AI services or next generation learning algorithms, will exponentially transforming businesses. Polzer welcomes interviews with prospective clients and the news media to discuss AI “first-mover advantage” strategies to differentiate and maximize a company's marketing exposure. For more information, email info(at)everymans(dot)ai, or call 1-(480) 382-1331

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