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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...
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%...
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...
Pedigree Analysis01:35

Pedigree Analysis

Overview
Pedigree Analysis01:35

Pedigree Analysis

Overview

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Related Experiment Video

Updated: Jun 19, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

SNP density impact on kinship inference and IBS-machine learning optimization.

Lv Dai1,2, Zi-Chen Tang3, Zhen Jia1,2

  • 1School of Criminal Investigation, People's Public Security University of China, Beijing 100038, China.

Yi Chuan = Hereditas
|June 17, 2026
PubMed
Summary
This summary is machine-generated.

This study assessed how the number of single nucleotide polymorphisms (SNPs) affects kinship inference accuracy in forensic genetics. Increasing SNPs improved accuracy, especially with machine learning, enhancing the identification of distant relatives.

Keywords:
IBS methodkinship inferencelikelihood ratio methodmachine learning

Related Experiment Videos

Last Updated: Jun 19, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Area of Science:

  • Forensic Genetics
  • Computational Biology
  • Human Genetics

Background:

  • Single nucleotide polymorphism (SNP) panels are used for kinship inference in forensic genetics.
  • Systematic evaluation of SNP number impact and machine learning optimization is lacking.

Purpose of the Study:

  • To evaluate the impact of SNP number on kinship inference performance.
  • To assess machine learning optimization of the identity-by-state (IBS) algorithm for kinship analysis.

Main Methods:

  • Constructed SNP panels (15,476–20,838 SNPs).
  • Evaluated likelihood ratio (LR) and IBS algorithms using simulated and real pedigrees.
  • Combined IBS with machine learning to enhance inference.

Main Results:

  • LR method sensitivity for distant kinships (6th-7th degree) positively correlated with SNP number.
  • IBS algorithm showed limited improvement (0.5%–2.2%) with increased SNPs for 4th-7th degree kinships.
  • Machine learning significantly improved IBS sensitivity for 4th-6th degree kinships (e.g., 4th degree from 69.10% to 87.66%).

Conclusions:

  • An optimal 21K SNP panel was determined.
  • The 21K panel with LR achieved 93.65% sensitivity for 6th-degree kinship.
  • The 21K panel with IBS achieved 86.79% sensitivity for 3rd-degree kinship, enhanced by machine learning.