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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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,...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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%...
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...

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Related Experiment Video

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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The LASSO and sparse least square regression methods for SNP selection in predicting quantitative traits.

Zeny Z Feng1, Xiaojian Yang, Sanjeena Subedi

  • 1University of Guelph, Guelph.

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|October 26, 2011
PubMed
Summary

This study compares the Least Absolute Shrinkage and Selection Operator (LASSO) and Sparse Partial Least Squares (SPLS) methods for selecting single nucleotide polymorphisms (SNPs) that predict quantitative traits. Both methods show promise but have limitations depending on specific conditions.

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Last Updated: May 28, 2026

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Published on: June 23, 2012

Area of Science:

  • Genetics
  • Statistical genomics
  • Bioinformatics

Background:

  • Selecting single nucleotide polymorphisms (SNPs) for quantitative traits is challenging due to the large number of variables and their correlations.
  • The Least Absolute Shrinkage and Selection Operator (LASSO) and Sparse Partial Least Squares (SPLS) methods offer potential solutions through sparsity and dimension reduction.

Purpose of the Study:

  • To investigate the application of LASSO and SPLS methods for selecting SNPs that predict quantitative traits.
  • To evaluate the performance of LASSO and SPLS under various simulation scenarios.

Main Methods:

  • Application of LASSO for variable selection.
  • Application of SPLS for subset selection and dimension reduction.
  • Performance evaluation using simulation studies and real-world data (Canadian Holstein cattle).

Main Results:

  • Both LASSO and SPLS can effectively select SNPs for quantitative traits, though performance is conditional.
  • Overall, the methods perform similarly, with each showing advantages in specific situations.
  • The study provides a comparative analysis of these two methods on Canadian Holstein cattle data.

Conclusions:

  • LASSO and SPLS are valuable tools for SNP selection in quantitative trait prediction.
  • Understanding the conditions under which each method excels is crucial for optimal application.
  • Comparative analysis on real data validates simulation findings.