Whole-Genome Regression and Prediction Methods Applied to Animal Breeding5 answersWhole-genome regression and prediction methods play a crucial role in animal breeding, enabling the estimation of genetic values for various traits. Traditional approaches like BLUP and GBLUP utilize relationship measures between individuals for prediction. Machine learning methods, such as Kernel Ridge Regression (KRR) optimized with a tree-structured Parzen estimator (TPE), have shown superior predictive abilities in genomic prediction studies. Comparatively evaluating different groups of supervised machine learning methods reveals that the classical linear mixed model and regularized regression methods remain strong contenders due to their competitive predictive performance, computational efficiency, and simplicity. Additionally, non-parameterized methods like Multivariate Adaptive Regression Splines (MARS) have been effective in genomic wide selection (GWS) analysis, particularly for traits with epistatic effects and varying heritability levels. These diverse methods collectively contribute to advancing animal breeding through accurate genomic predictions.
Whole exon sequencing5 answersWhole exome sequencing (WES) is a genetic testing method that analyzes the protein-coding regions of the genome to identify potential disease-causing variants. WES has been used in various contexts, including the diagnosis of prenatal and postnatal neurodevelopmental disorders (NDDs). It has shown a molecular diagnosis rate of 30% in NDD cases, with higher diagnostic yield when combined with copy number variant (CNV) analysis. WES has also been used in the identification of genetic etiologies of ultrasound abnormalities in deceased fetuses, with a diagnostic rate of 45.9% and the detection of previously unreported variants. In the field of foetal structural abnormalities, WES has been employed to improve diagnostic yield by evaluating variants, including introns, and has identified new candidate genes, particularly on chromosome X. Additionally, WES has played a role in the genomic detection and treatment selection for synchronous multiple primary lung cancer (sMPLC). Overall, WES has proven to be a valuable tool in genetic diagnosis and understanding the underlying causes of various conditions.
Whole genome sequencing breast cancer5 answersWhole-genome sequencing of breast cancer has provided valuable insights into the molecular characteristics of the disease and has led to advancements in personalized treatment options. Transcriptomic analysis and molecular subtyping have improved our understanding of breast cancer biology and enabled personalized treatment regimens. Whole-genome sequencing has also revealed the diversity of breast tumors and identified novel subgroups with distinct clinical outcomes. Additionally, whole-genome sequencing has been used to classify triple-negative breast cancers (TNBCs) and predict homologous-recombination-repair deficiency, providing independent prognostic information. Furthermore, deep whole-genome sequencing of breast cancer cell lines and patient-derived xenografts has identified novel genomic alterations and provided a comprehensive resource for studying these models. Overall, whole-genome sequencing has proven to be a valuable tool in understanding the genetic landscape of breast cancer and has the potential to improve clinical decision-making and treatment strategies.
Whole genome sequencing of sars5 answersWhole genome sequencing of SARS-CoV-2 has been a crucial tool in monitoring the pandemic and understanding its dynamics. Different protocols and sequencing technologies have been compared to determine their advantages and limitations. One study compared the outcomes of four different sequencing protocols using Illumina kits and found that miniaturized volumes showed comparable results to full volumes, reducing cost and library preparation time. Another study compared three different protocols using Illumina and Oxford Nanopore Technologies and found that the ARTIC and Midnight protocols were robust, cost-effective, and suitable for clinical environments. A cross-sequence technology benchmarking study compared five protocols and found that the ONT protocol and Illumina AmpliSeq protocol had the highest genome coverage, while the EasySeq protocol had the highest proportion of SARS-CoV-2 reads relative to background sequences. Additionally, a protocol using the Oxford Nanopore Rapid Barcoding library kit was developed for universal SARS-CoV-2 genome sequencing, allowing sequencing of low-titer virus samples and degraded RNA. Finally, a proof-of-concept study demonstrated the feasibility of conducting SARS-CoV-2 whole genome sequencing in a military maritime environment using miniaturized nanopore sequencing technology.
Whole exome sequencing illumina5 answersWhole exome sequencing using Illumina technology is a technique for sequencing all of the genes that code for functioning proteins in the genome. It is being used to identify mutations in families with suspected hereditary cancer syndromes where other testing methods have been unsuccessful. The accuracy of SNP calling in exome sequencing can be evaluated using high-throughput validation with Mendelian inheritance checking and SNP array data. This allows for the assessment of the quality of the derived SNPs and the comparison of different variant discovery tools. The goal is to identify the best variant caller(s) for clinical applications.
Whole genome sequencing in human genetic disease5 answersWhole genome sequencing (WGS) has revolutionized the field of human genetic disease research. WGS utilizes massively-parallel short-read DNA sequencing and genome assembly methods to rapidly search and analyze genomes on a large scale. It has become increasingly prevalent in detecting disease genetics, studying causative relations with cancers, and reconstructing human population history. WGS has applications in both mendelian and complex diseases, providing valuable insights into disease mechanisms and potential therapeutic targets. It has also played a significant role in cancer studies, regulatory variant analysis, predictive medicine, and precision medicine. However, there are challenges associated with WGS, including data analysis and interpretation. Despite these challenges, WGS has the potential to become a common medical practice, aiding in the diagnosis and treatment of genetic diseases.