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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Genetic Screens02:46

Genetic Screens

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
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Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Updated: Oct 1, 2025

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level
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A Fitted Sparse-Group Lasso for Genome-Based Evaluations.

Jan Klosa, Noah Simon, Volkmar Liebscher

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    This study introduces a new statistical method, the fitted sparse-group lasso, for analyzing high-dimensional life science data. It improves prediction accuracy and feature localization in complex datasets like dairy cattle breeding populations.

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    Area of Science:

    • Life Sciences
    • Genomics
    • Statistical Genetics

    Background:

    • High-throughput techniques generate high-dimensional data where covariates exceed observations.
    • Multicollinearity in such data challenges traditional linear regression analysis.
    • Penalization methods like lasso and group lasso are effective for high-dimensional data.

    Purpose of the Study:

    • Introduce a novel penalized regression method combining lasso and standardized group lasso.
    • Develop a method for meaningful weighting of predicted outcomes in high-dimensional settings.
    • Improve prediction abilities and feature localization in complex biological datasets.

    Main Methods:

    • Implemented a novel 'fitted' sparse-group lasso method.
    • Utilized a proximal-averaged gradient descent algorithm for implementation.
    • Developed the method as an R package named 'seagull'.

    Main Results:

    • Conducted an extensive simulation study using dairy cattle genotype and phenotype data.
    • The novel method demonstrated improved prediction abilities compared to existing penalization approaches in most scenarios.
    • Successfully localized simulated genetic features with greater accuracy.

    Conclusions:

    • The fitted sparse-group lasso offers enhanced prediction and localization capabilities for high-dimensional biological data.
    • The method is particularly valuable for applications in breeding populations and quantitative genetics.
    • The 'seagull' R package provides accessible implementation of this advanced statistical technique.