22 research outputs found

    Genomic prediction for growth using a low-density SNP panel in dromedary camels

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    For thousands of years, camels have produced meat, milk, and fiber in harsh desert conditions. For a sustainable development to provide protein resources from desert areas, it is necessary to pay attention to genetic improvement in camel breeding. By using genotyping-by-sequencing (GBS) method we produced over 14,500 genome wide markers to conduct a genome- wide association study (GWAS) for investigating the birth weight, daily gain, and body weight of 96 dromedaries in the Iranian central desert. A total of 99 SNPs were associated with birth weight, daily gain, and body weight (p-value \u3c 0.002). Genomic breeding values (GEBVs) were estimated with the BGLR package using (i) all 14,522 SNPs and (ii) the 99 SNPs by GWAS. Twenty-eight SNPs were associated with birth weight, daily gain, and body weight (p-value \u3c 0.001). Annotation of the genomic region (s) within ± 100 kb of the associated SNPs facilitated prediction of 36 candidate genes. The accuracy of GEBVs was more than 0.65 based on all 14,522 SNPs, but the regression coefficients for birth weight, daily gain, and body weight were 0.39, 0.20, and 0.23, respectively. Because of low sample size, the GEBVs were predicted using the associated SNPs from GWAS. The accuracy of GEBVs based on the 99 associated SNPs was 0.62, 0.82, and 0.57 for birth weight, daily gain, and body weight. This report is the first GWAS using GBS on dromedary camels and identifies markers associated with growth traits that could help to plan breeding program to genetic improvement. Further researches using larger sample size and collaboration of the camel farmers and more profound understanding will permit verification of the associated SNPs identified in this project. The preliminary results of study show that genomic selection could be the appropriate way to genetic improvement of body weight in dromedary camels, which is challenging due to a long generation interval, seasonal reproduction, and lack of records and pedigrees

    Genomic prediction for growth using a low-density SNP panel in dromedary camels

    Get PDF
    For thousands of years, camels have produced meat, milk, and fiber in harsh desert conditions. For a sustainable development to provide protein resources from desert areas, it is necessary to pay attention to genetic improvement in camel breeding. By using genotyping-by-sequencing (GBS) method we produced over 14,500 genome wide markers to conduct a genome- wide association study (GWAS) for investigating the birth weight, daily gain, and body weight of 96 dromedaries in the Iranian central desert. A total of 99 SNPs were associated with birth weight, daily gain, and body weight (p-value \u3c 0.002). Genomic breeding values (GEBVs) were estimated with the BGLR package using (i) all 14,522 SNPs and (ii) the 99 SNPs by GWAS. Twenty-eight SNPs were associated with birth weight, daily gain, and body weight (p-value \u3c 0.001). Annotation of the genomic region (s) within ± 100 kb of the associated SNPs facilitated prediction of 36 candidate genes. The accuracy of GEBVs was more than 0.65 based on all 14,522 SNPs, but the regression coefficients for birth weight, daily gain, and body weight were 0.39, 0.20, and 0.23, respectively. Because of low sample size, the GEBVs were predicted using the associated SNPs from GWAS. The accuracy of GEBVs based on the 99 associated SNPs was 0.62, 0.82, and 0.57 for birth weight, daily gain, and body weight. This report is the first GWAS using GBS on dromedary camels and identifies markers associated with growth traits that could help to plan breeding program to genetic improvement. Further researches using larger sample size and collaboration of the camel farmers and more profound understanding will permit verification of the associated SNPs identified in this project. The preliminary results of study show that genomic selection could be the appropriate way to genetic improvement of body weight in dromedary camels, which is challenging due to a long generation interval, seasonal reproduction, and lack of records and pedigrees

    Splicing defect in FKBP10 gene causes autosomal recessive osteogenesis imperfecta disease: a case report

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    Abstract Background Osteogenesis imperfecta (OI) is a group of connective tissue disorder caused by mutations of genes involved in the production of collagen and its supporting proteins. Although the majority of reported OI variants are in COL1A1 and COL1A2 genes, recent reports have shown problems in other non-collagenous genes involved in the post translational modifications, folding and transport, transcription and proliferation of osteoblasts, bone mineralization, and cell signaling. Up to now, 17 types of OI have been reported in which types I to IV are the most frequent cases with autosomal dominant pattern of inheritance. Case Presentation Here we report an 8- year- old boy with OI who has had multiple fractures since birth and now he is wheelchair-dependent. To identify genetic cause of OI in our patient, whole exome sequencing (WES) was carried out and it revealed a novel deleterious homozygote splice acceptor site mutation (c.1257-2A > G, IVS7-2A > G) in FKBP10 gene in the patient. Then, the identified mutation was confirmed using Sanger sequencing in the proband as homozygous and in his parents as heterozygous, indicating its autosomal recessive pattern of inheritance. In addition, we performed RT-PCR on RNA transcripts originated from skin fibroblast of the proband to analyze the functional effect of the mutation on splicing pattern of FKBP10 gene and it showed skipping of the exon 8 of this gene. Moreover, Real-Time PCR was carried out to quantify the expression level of FKBP10 in the proband and his family members in which it revealed nearly the full decrease in the level of FKBP10 expression in the proband and around 75% decrease in its level in the carriers of the mutation, strongly suggesting the pathogenicity of the mutation. Conclusions Our study identified, for the first time, a private pathogenic splice site mutation in FKBP10 gene and further prove the involvement of this gene in the rare cases of autosomal recessive OI type XI with distinguished clinical manifestations

    A Novel TTC19 Mutation in a Patient With Neurological, Psychological, and Gastrointestinal Impairment

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    Mitochondrial complex III deficiency nuclear type 2 is an autosomal-recessive disorder caused by mutations in TTC19 gene. TTC19 is involved in the preservation of mitochondrial complex III, which is responsible for transfer of electrons from reduced coenzyme Q to cytochrome C and thus, contributes to the formation of electrochemical potential and subsequent ATP generation. Mutations in TTC19 have been found to be associated with a wide range of neurological and psychological manifestations. Herein, we report on a 15-year-old boy born from first-degree cousin parents, who initially presented with psychiatric symptoms. He subsequently developed progressive ataxia, spastic paraparesis with involvement of caudate bodies and lentiform nuclei with cerebellar atrophy. Eventually, the patient developed gastrointestinal involvement. Using whole-exome sequencing (WES), we identified a novel homozygous frameshift mutation in the TTC19 gene in the patient (NM_017775.3, c.581delG: p.Arg194Asnfs(*)16). Advanced genetic sequencing technologies developed in recent years have not only facilitated identification of novel disease genes, but also allowed revelations about novel phenotypes associated with mutations in the genes already linked with other clinical features. Our findings expanded the clinical features of TTC19 mutation to potentially include gastrointestinal involvement. Further functional studies are needed to elucidate the underlying pathophysiological mechanisms

    Additional file 1: of Splicing defect in FKBP10 gene causes autosomal recessive osteogenesis imperfecta disease: a case report

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    Material and Methods, two supplementary tables, Description of data: Detail description of Fibroblast culture, Isolation of PBMCs and Quantitative RT-PCR. Table S1. List of all genes involved in Osteogenesis imperfecta. Table S2. Bioinformatics analysis statistics. (DOCX 25 kb
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