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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.
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Single Nucleotide Polymorphisms-SNPs01:05

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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

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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.
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Genetic Variation01:25

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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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
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Genetic Variants Detection Based on Weighted Sparse Group Lasso.

Kai Che1, Xi Chen1, Maozu Guo1,2,3

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.

Frontiers in Genetics
|March 21, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a weighted sparse group lasso (WSGL) method for identifying genetic variants linked to complex plant traits. The WSGL method effectively detects both common and low-frequency variants, improving plant breeding and resistance strategies.

Keywords:
genetic variantsgenome-wide association studiesminimum allele frequencysingle nucleotide polymorphismssparse group lasso

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Area of Science:

  • Genetics
  • Plant Breeding
  • Bioinformatics

Background:

  • Identifying genetic variants is crucial for enhancing plant resistance and breeding.
  • Existing variant detection methods struggle with variants in small gene groups or low-frequency variants.

Purpose of the Study:

  • To develop a novel method for precise genetic variant detection, focusing on common and low-frequency variants within specific gene groups.
  • To improve the identification of variants associated with complex plant traits.

Main Methods:

  • Proposed a weighted sparse group lasso (WSGL) method.
  • Employed a sparse group lasso approach to select associated gene groups and loci simultaneously.
  • Incorporated biological prior information via re-weighted lasso regularization to enhance low-frequency variant detection.

Main Results:

  • WSGL demonstrated superior performance in detecting genetic variants compared to existing methods.
  • The method successfully identified both common and low-frequency variants in simulated and real SNP data.
  • Experimental results validated the effectiveness of WSGL for variants associated with *Arabidopsis* flowering traits.

Conclusions:

  • The weighted sparse group lasso (WSGL) method offers a significant advancement in genetic variant detection for complex traits.
  • WSGL improves the ability to identify low-frequency variants, which is critical for precise plant breeding.
  • This approach enhances the potential for developing improved plant varieties with increased resistance.