scholarly journals In silico identification of variations in microRNAs with a potential impact on dairy traits using whole ruminant genome SNP datasets

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Céline Bourdon ◽  
Mekki Boussaha ◽  
Philippe Bardou ◽  
Marie-Pierre Sanchez ◽  
Sandrine Le Guillou ◽  
...  
Keyword(s):  
Mammary Gland ◽  
Genetic Variants ◽  
Dairy Production ◽  
Production Traits ◽  
Small Noncoding Rnas ◽  
Genome Data ◽  
Dairy Traits ◽  
Causal Variants ◽  
In Silico Identification ◽  
Potential Impact

AbstractMicroRNAs are small noncoding RNAs that have important roles in the lactation process and milk biosynthesis. Some polymorphisms have been studied in various livestock species from the perspective of pathology or production traits. To target variants that could be the causal variants of dairy traits, genetic variants of microRNAs expressed in the mammary gland or present in milk and localized in dairy quantitative trait loci (QTLs) were investigated in bovine, caprine, and ovine species. In this study, a total of 59,124 (out of 28 millions), 13,427 (out of 87 millions), and 4761 (out of 38 millions) genetic variants in microRNAs expressed in the mammary gland or present in milk were identified in bovine, caprine, and ovine species, respectively. A total of 4679 of these detected bovine genetic variants are located in dairy QTLs. In caprine species, 127 genetic variants are localized in dairy QTLs. In ovine species, no genetic variant was identified in dairy QTLs. This study leads to the detection of microRNA genetic variants of interest in the context of dairy production, taking advantage of whole genome data to identify microRNA genetic variants expressed in the mammary gland and localized in dairy QTLs.

scholarly journals Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shuquan Rao ◽  
Yao Yao ◽  
Daniel E. Bauer
Keyword(s):  
Genome Editing ◽  
Genetic Variants ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Functional Studies ◽  
Functional Genetics ◽  
Genome Wide ◽  
Causal Variants ◽  
Experimental Approaches

AbstractGenome-wide association studies (GWAS) have uncovered thousands of genetic variants that influence risk for human diseases and traits. Yet understanding the mechanisms by which these genetic variants, mainly noncoding, have an impact on associated diseases and traits remains a significant hurdle. In this review, we discuss emerging experimental approaches that are being applied for functional studies of causal variants and translational advances from GWAS findings to disease prevention and treatment. We highlight the use of genome editing technologies in GWAS functional studies to modify genomic sequences, with proof-of-principle examples. We discuss the challenges in interrogating causal variants, points for consideration in experimental design and interpretation of GWAS locus mechanisms, and the potential for novel therapeutic opportunities. With the accumulation of knowledge of functional genetics, therapeutic genome editing based on GWAS discoveries will become increasingly feasible.

scholarly journals CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies

2019 ◽  
Author(s):  
Jianhua Wang ◽  
Dandan Huang ◽  
Yao Zhou ◽  
Hongcheng Yao ◽  
Huanhuan Liu ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Genetic Variants ◽  
Association Studies ◽  
Complex Trait ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Genome Wide ◽  
Credible Sets ◽  
Causal Variants

Abstract Genome-wide association studies (GWASs) have revolutionized the field of complex trait genetics over the past decade, yet for most of the significant genotype-phenotype associations the true causal variants remain unknown. Identifying and interpreting how causal genetic variants confer disease susceptibility is still a big challenge. Herein we introduce a new database, CAUSALdb, to integrate the most comprehensive GWAS summary statistics to date and identify credible sets of potential causal variants using uniformly processed fine-mapping. The database has six major features: it (i) curates 3052 high-quality, fine-mappable GWAS summary statistics across five human super-populations and 2629 unique traits; (ii) estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools; (iii) maps the reported traits to a powerful ontology MeSH, making it simple for users to browse studies on the trait tree; (iv) incorporates highly interactive Manhattan and LocusZoom-like plots to allow visualization of credible sets in a single web page more efficiently; (v) enables online comparison of causal relations on variant-, gene- and trait-levels among studies with different sample sizes or populations and (vi) offers comprehensive variant annotations by integrating massive base-wise and allele-specific functional annotations. CAUSALdb is freely available at http://mulinlab.org/causaldb.

scholarly journals Accuracy and efficiency of germline variant calling pipelines for human genome data

2020 ◽  
Author(s):  
Sen Zhao ◽  
Oleg Agafonov ◽  
Abdulrahman Azab ◽  
Tomasz Stokowy ◽  
Eivind Hovig
Keyword(s):  
Large Scale ◽  
Variant Calling ◽  
Performance Comparison ◽  
Next Generation Sequencing Technology ◽  
Genome Data ◽  
Germline Variant ◽  
Causal Variants ◽  
Variant Detection ◽  
Variant Analysis ◽  
Human Genome Data

AbstractAdvances in next-generation sequencing technology has enabled whole genome sequencing (WGS) to be widely used for identification of causal variants in a spectrum of genetic-related disorders, and provided new insight into how genetic polymorphisms affect disease phenotypes. The development of different bioinformatics pipelines has continuously improved the variant analysis of WGS data, however there is a necessity for a systematic performance comparison of these pipelines to provide guidance on the application of WGS-based scientific and clinical genomics. In this study, we evaluated the performance of three variant calling pipelines (GATK, DRAGEN™ and DeepVariant) using Genome in a Bottle Consortium, “synthetic-diploid” and simulated WGS datasets. DRAGEN™ and DeepVariant show a better accuracy in SNPs and indels calling, with no significant differences in their F1-score. DRAGEN™ platform offers accuracy, flexibility and a highly-efficient running speed, and therefore superior advantage in the analysis of WGS data on a large scale. The combination of DRAGEN™ and DeepVariant also provides a good balance of accuracy and efficiency as an alternative solution for germline variant detection in further applications. Our results facilitate the standardization of benchmarking analysis of bioinformatics pipelines for reliable variant detection, which is critical in genetics-based medical research and clinical application.

PRODUCTION TRAITS OF RED AND WHITE RELATIVE TO BLACK AND WHITE HOLSTEINS

1988 ◽  
Vol 68 (1) ◽  
pp. 299-303
Author(s):  
P. G. SULLIVAN ◽  
J. W. WILTON ◽  
B. J. VAN DOORMAAL
Keyword(s):  
Milk Fat ◽  
Milk Protein ◽  
Dairy Production ◽  
Production Traits ◽  
Black And White ◽  
Genetic Overlap ◽  
Fat Yield ◽  
And Performance ◽  
Genetic Evaluations ◽  
Genetic Trends

Canadian red and white (RW) and black and white (BW) Holsteins were compared for several production traits based on genetic evaluations of 32 RW and 883 BW bulls, and performance data of 4161 RW and 8691 BW cows. Differences (P < 0.05), favoring BW, were observed for milk and milk fat yield of cows, and for milk fat and milk protein yield evaluations of bulls. There was, however, a large genetic overlap between the populations for all traits studied. Pleiotropic effects associated with the color gene were not detected as being important. Phenotypic trends for milk yield, milk fat yield, and milk fat percent were significantly greater for BW than RW cows (P < 0.01). Genetic trends were greater for RW cows, though not significantly (P > 0.05). Key words: Dairy production, genetic trends, Holstein (red and white), Holstein (black and white)

scholarly journals Transcriptome Profile Analysis of Mammary Gland Tissue from Two Breeds of Lactating Sheep

Genes ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 781 ◽  
Author(s):  
Hao ◽  
Zhou ◽  
Hickford ◽  
Gong ◽  
Wang ◽  
...  
Keyword(s):  
Mammary Gland ◽  
Profile Analysis ◽  
Critical Role ◽  
Receptor Interaction ◽  
Production Traits ◽  
Rna Seq ◽  
Transcriptome Profile ◽  
Phenotypic Differences ◽  
Mammary Gland Tissue ◽  
Genetic Mechanisms

The mammary gland is a crucial tissue for milk synthesis and plays a critical role in the feeding and growth of mammalian offspring. The aim of this study was to use RNA-sequencing (RNA-Seq) technology to provide a transcriptome profile of the ovine mammary gland at the peak of lactation. Small-Tailed Han (STH) sheep (n = 9) and Gansu Alpine Merino (GAM) sheep (n = 9), breeds with phenotypic differences in milk production traits, were selected for the RNA-Seq analysis. This revealed 74 genes that were more highly expressed in the STHs than in the GAMs. Similarly, 143 genes that were expressed at lower levels in the STHs than in the GAMs, were identified. Gene ontogeny (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that these differentially expressed genes (DEGs) were associated with binding and catalytic activities, hematopoietic cell lineages, oxytocin signaling pathway and neuroactive ligand–receptor interaction. This is the first study of the transcriptome profile of the ovine mammary gland in these Chinese breeds at peak lactation. The results provide for a better understanding of the genetic mechanisms involved in ovine lactation.

scholarly journals The relevance of milk protein polymorphisms for dairy cattle breeding.

1993 ◽  
Vol 1993 ◽  
pp. 46-46
Author(s):  
Henk Bovenhuis
Keyword(s):  
Genetic Variants ◽  
Milk Protein ◽  
Production Traits ◽  
Milk Production Traits ◽  
Breeding Scheme ◽  
Cattle Breeding ◽  
Dairy Cattle Breeding ◽  
Nucleus Breeding ◽  
Selection For ◽  
Β Lactoglobulin

Several studies have shown milk protein genetic variants to be associated with manufacturing properties of milk. The main findings were that κ-casein genetic variants affect renneting time of milk and βlactoglobulin genetic variants are associated with casein number (reviewed by Grosclaude, 1988). There are reports also of associations between milk protein genetic variants and milk production traits. Results from these studies indicate that κ-casein genotypes are associated with protein content and βlactoglobulin genotypes are related to fat content (reviewed by Bovenhuis et al., 1992). Therefore, κ-casein and βlactoglobulin genotypes might be of value as selection criteria. The aim of the present study was to quantify the potential effects of selection for κ-casein and β-lactoglobulin genotypes by using stochastic simulation of a closed adult MOET nucleus breeding scheme.

scholarly journals Use of Recombinant Bovine Somatotropin for up to Two Consecutive Lactations on Dairy Production Traits

1990 ◽  
Vol 73 (11) ◽  
pp. 3248-3257 ◽  
Author(s):  
B.W. McBride ◽  
J.L. Burton ◽  
J.P. Gibson ◽  
J.H. Burton ◽  
R.G. Eggert
Keyword(s):  
Bovine Somatotropin ◽  
Dairy Production ◽  
Production Traits ◽  
Recombinant Bovine Somatotropin

scholarly journals Exploring the genetic variants of insulin-like growth factor II gene and their associations with two production traits in Langshan chicken

2015 ◽  
Vol 45 (1) ◽  
pp. 60-63 ◽  
Author(s):  
Linjun Yan ◽  
Xingtang Fang ◽  
Yao Liu ◽  
Mauricio A. Elzo ◽  
Chunlei Zhang ◽  
...  
Keyword(s):  
Growth Factor ◽  
Genetic Variants ◽  
Insulin Like Growth Factor ◽  
Production Traits ◽  
Factor Ii

scholarly journals Redefining tissue specificity of genetic regulation of gene expression in the presence of allelic heterogeneity

2021 ◽  
Author(s):  
Marios Arvanitis ◽  
Karl Tayeb ◽  
Benjamin J Strober ◽  
Alexis Battle
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Tissue Specificity ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Genetic Effects ◽  
Allelic Heterogeneity ◽  
Common Genetic Variants ◽  
Causal Variants ◽  
Disease Loci

Understanding the mechanisms that underlie genetic regulation of gene expression is crucial to explaining the diversity that governs complex traits. Large scale expression quantitative trait locus (eQTL) studies have been instrumental in identifying genetic variants that influence the expression of target genes. However, a large fraction of disease-associated genetic variants have not been clearly explained by current eQTL data, frustrating attempts to use these data to comprehensively characterize disease loci. One notable observation from recent studies is that cis-eQTL effects are often shared across different cell types and tissues. This would suggest that common genetic variants impacting steady-state, adult gene expression are largely tolerated, shared across tissues, and less relevant to disease. However, allelic heterogeneity and complex patterns of linkage disequilibrium (LD) within each locus may skew the quantification of sharing of genetic effects between tissues, impede our ability to identify causal variants, and hinder the identification of regulatory effects for disease-associated genetic variants. Indeed, recent research suggests that multiple causal variants are often present in many eQTL and complex trait associated loci. Here, we re-analyze tissue-specificity of genetic effects in the presence of LD and allelic heterogeneity, proposing a novel method, CAFEH, that improves the identification of causal regulatory variants across tissues and their relationship to disease loci.

scholarly journals Ten Genetic Loci Identified for Milk Yield, Fat, and Protein in Holstein Cattle

2020 ◽  
Author(s):  
Liyuan Liu ◽  
Jinghang Zhou ◽  
Chunpeng James Chen ◽  
Juan Zhang ◽  
Wan Wen ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Milk Production ◽  
Milk Yield ◽  
Genetic Variants ◽  
Holstein Cattle ◽  
Genome Wide Association Studies ◽  
Production Traits ◽  
Milk Production Traits ◽  
Breeding Values ◽  
Genome Wide

AbstractHigh-yield and high-quality of milk are the primary goals of dairy production. Understanding the genetic architecture underlying these milk production traits is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we measured five milk production traits in Holstein cattle population from China. These traits included milk yield, protein yield, fat yields; fat percentage and protein percentages. We used the estimated breeding values as dependent variables to conduct the genome-wide association studies (GWAS). Breeding values were estimated through pedigree relationships by using a mixed linear model for individuals with and without phenotypic data. Genotyping was carried out on the individuals with phenotypes by using the Illumina BovineSNP150 BeadChip. The association analyses were conducted by using the Fixed and random model Circulating Probability Unification (FarmCPU) method. A total of ten SNPs was detected above the genome-wide significant threshold, including six located in previously reported QTL regions. We found eight candidate genes within distances of 120 kb upstream or downstream to the associated SNPs. The most significant SNP is on DGAT1 gene affecting milk fat and protein percentage. These genetic variants and candidate genes would be valuable resources to enhance dairy cattle breeding.

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