Identification of Genomic Variants Causing Variation in Quantitative Traits: A Review

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Radio Di Kawasan Perbatasan Indonesia Dalam Centering the Margin

Kawasan perbatasan Indonesia banyak mengalami blank spot layanan informasi sehingga siaran yang menghubungkan warganegara dan pemerintah tidak tersampaikan dengan baik. Padahal, keberadaan media di perbatasan sangat strategis sebagai penyedia informasi yang merefl eksikan dinamika lokal, mengartikulasikan kepentingan daerah sehingga dapat didengar oleh pusat. Harapannya, artikulasi tersebut dapat memberi warna pada dinamika sosial, politik, ekonomi, dan budaya di tanah air. Tulisan ini mengeksplorasi bagaimana radio di wilayah perbatasan memberikan kontribusi dalam peran centering the margin, yakni membawa aspirasi di perbatasan guna “memusatkan yang pinggir”

Using imputed whole-genome sequence data to improve the accuracy of genomic prediction for parasite resistance in Australian sheep

International audienceAbstractBackgroundThis study aimed at (1) comparing the accuracies of genomic prediction for parasite resistance in sheep based on whole-genome sequence (WGS) data to those based on 50k and high-density (HD) single nucleotide polymorphism (SNP) panels; (2) investigating whether the use of variants within quantitative trait loci (QTL) regions that were selected from regional heritability mapping (RHM) in an independent dataset improved the accuracy more than variants selected from genome-wide association studies (GWAS); and (3) comparing the prediction accuracies between variants selected from WGS data to variants selected from the HD SNP panel.ResultsThe accuracy of genomic prediction improved marginally from 0.16 ± 0.02 and 0.18 ± 0.01 when using all the variants from 50k and HD genotypes, respectively, to 0.19 ± 0.01 when using all the variants from WGS data. Fitting a GRM from the selected variants alongside a GRM from the 50k SNP genotypes improved the prediction accuracy substantially compared to fitting the 50k SNP genotypes alone. The gain in prediction accuracy was slightly more pronounced when variants were selected from WGS data compared to when variants were selected from the HD panel. When sequence variants that passed the GWAS -log10(pvalue)\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$- log_{10} (p\,value)$$\end{document} threshold of 3 across the entire genome were selected, the prediction accuracy improved by 5% (up to 0.21 ± 0.01), whereas when selection was limited to sequence variants that passed the same GWAS -log10(pvalue)\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$- log_{10} (p\,value)$$\end{document} threshold of 3 in regions identified by RHM, the accuracy improved by 9% (up to 0.25 ± 0.01).ConclusionsOur results show that through careful selection of sequence variants from the QTL regions, the accuracy of genomic prediction for parasite resistance in sheep can be improved. These findings have important implications for genomic prediction in sheep

The Environmental Risks Associated With the Development of Seaweed Farming in Europe - Prioritizing Key Knowledge Gaps

Cultivation of kelp has been well established throughout Asia, and there is now growing interest in the cultivation of macroalgae in Europe to meet future resource needs. If this industry is to become established throughout Europe, then balancing the associated environmental risks with potential benefits will be necessary to ensure the carrying capacity of the receiving environments are not exceeded and conservation objects are not undermined. This is a systematic review of the ecosystem changes likely to be associated with a developing seaweed aquaculture industry. Monitoring recommendations are made by risk ranking environmental changes, highlighting the current knowledge gaps and providing research priorities to address them. Environmental changes of greatest concern were identified to include: facilitation of disease, alteration of population genetics and wider alterations to the local physiochemical environment. Current high levels of uncertainty surrounding the true extent of some environmental changes mean conservative risk rankings are given. Recommended monitoring options are discussed that aim to address uncertainty and facilitate informed decision-making. Whilst current small-scale cultivation projects are considered ‘low risk,’ an expansion of the industry that includes ‘large-scale’ cultivation will necessitate a more complete understanding of the scale dependent changes in order to balance environmental risks with the benefits that seaweed cultivation projects can offer

Expression of mitochondrial protein genes encoded by nuclear and mitochondrial genomes correlate with energy metabolism in dairy cattle

Background Mutations in the mitochondrial genome have been implicated in mitochondrial disease, often characterized by impaired cellular energy metabolism. Cellular energy metabolism in mitochondria involves mitochondrial proteins (MP) from both the nuclear (NuMP) and mitochondrial (MtMP) genomes. The expression of MP genes in tissues may be tissue specific to meet varying specific energy demands across the tissues. Currently, the characteristics of MP gene expression in tissues of dairy cattle are not well understood. In this study, we profile the expression of MP genes in 29 adult and six foetal tissues in dairy cattle using RNA sequencing and gene expression analyses: particularly differential gene expression and co-expression network analyses. Results MP genes were differentially expressed (DE; over-expressed or under-expressed) across tissues in cattle. All 29 tissues showed DE NuMP genes in varying proportions of over-expression and under-expression. On the other hand, DE of MtMP genes was observed in < 50% of tissues and notably MtMP genes within a tissue was either all over-expressed or all under-expressed. A high proportion of NuMP (up to 60%) and MtMP (up to 100%) genes were over-expressed in tissues with expected high metabolic demand; heart, skeletal muscles and tongue, and under-expressed (up to 45% of NuMP, 77% of MtMP genes) in tissues with expected low metabolic rates; leukocytes, thymus, and lymph nodes. These tissues also invariably had the expression of all MtMP genes in the direction of dominant NuMP genes expression. The NuMP and MtMP genes were highly co-expressed across tissues and co-expression of genes in a cluster were non-random and functionally enriched for energy generation pathway. The differential gene expression and co-expression patterns were validated in independent cow and sheep datasets. Conclusions The results of this study support the concept that there are biological interaction of MP genes from the mitochondrial and nuclear genomes given their over-expression in tissues with high energy demand and co-expression in tissues. This highlights the importance of considering MP genes from both genomes in future studies related to mitochondrial functions and traits related to energy metabolism

Using imputed whole-genome sequence data to improve the accuracy of genomic prediction for parasite resistance in Australian sheep

Background: This study aimed at (1) comparing the accuracies of genomic prediction for parasite resistance in sheep based on whole-genome sequence (WGS) data to those based on 50k and high-density (HD) single nucleotide polymorphism (SNP) panels; (2) investigating whether the use of variants within quantitative trait loci (QTL) regions that were selected from regional heritability mapping (RHM) in an independent dataset improved the accuracy more than variants selected from genome-wide association studies (GWAS); and (3) comparing the prediction accuracies between variants selected from WGS data to variants selected from the HD SNP panel. Results: The accuracy of genomic prediction improved marginally from 0.16 ± 0.02 and 0.18 ± 0.01 when using all the variants from 50k and HD genotypes, respectively, to 0.19 ± 0.01 when using all the variants from WGS data. Fitting a GRM from the selected variants alongside a GRM from the 50k SNP genotypes improved the prediction accuracy substantially compared to fitting the 50k SNP genotypes alone. The gain in prediction accuracy was slightly more pronounced when variants were selected from WGS data compared to when variants were selected from the HD panel. When sequence variants that passed the GWAS -log10(p value) threshold of 3 across the entire genome were selected, the prediction accuracy improved by 5% (up to 0.21 ± 0.01), whereas when selection was limited to sequence variants that passed the same GWAS −log10(p value) threshold of 3 in regions identified by RHM, the accuracy improved by 9% (up to 0.25 ± 0.01). Conclusions: Our results show that through careful selection of sequence variants from the QTL regions, the accuracy of genomic prediction for parasite resistance in sheep can be improved. These findings have important implications for genomic prediction in sheep