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Related Concept Videos

Multiple Sclerosis l: Introduction01:19

Multiple Sclerosis l: Introduction

Multiple sclerosis is a chronic autoimmune disease of the central nervous system (CNS) that affects the brain, spinal cord, and optic nerves. It is an inflammatory demyelinating disorder and a leading cause of neurological disability in young adults.EpidemiologyMS commonly begins between 20 and 40 years of age and is twice as common in women. Its exact cause remains unclear, but genetic susceptibility contributes, with higher risk in first-degree relatives and identical twins. A greater...
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...
Incomplete Dominance01:43

Incomplete Dominance

Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
Genetic Lingo01:11

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Pedigree Analysis

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Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...

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Related Experiment Video

Updated: May 9, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Genetic burden in multiple sclerosis families.

N Isobe1, V Damotte1,2, V Lo Re3

  • 1Department of Neurology, University of California at San Francisco, San Francisco, CA, USA.

Genes and Immunity
|August 2, 2013
PubMed
Summary

Genetic risk scores help track multiple sclerosis (MS) susceptibility variants in families. While updated markers improve accuracy, predicting MS remains challenging, though genetic architectures for different MS types appear similar.

Related Experiment Videos

Last Updated: May 9, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Genetics
  • Neuroimmunology
  • Epidemiology

Background:

  • Previous studies estimated genetic risk in multiple sclerosis (MS) using limited susceptibility loci (17 loci).
  • The complete set of MS risk genes is not yet fully identified.
  • Genetic burden aggregation in families is a known factor in MS heritability.

Purpose of the Study:

  • To estimate the genetic burden in multiple sclerosis (MS) families using an expanded set of single-nucleotide polymorphism (SNP) markers (64 SNPs).
  • To assess the predictive power of genetic burden for MS development, even within sibling groups.
  • To investigate the shared genetic architecture between primary progressive and relapsing-remitting forms of MS.

Main Methods:

  • Genotyping of 708 controls, 3251 MS patients and relatives, and 117 twin pairs.
  • Calculation of cumulative genetic risk scores using up to 64 SNP markers based on recent literature.
  • Statistical analysis to compare genetic burden aggregation in multi-case versus single-case families and assess predictive accuracy (AUROC).

Main Results:

  • Validated increased aggregation of genetic burden in multi-case compared to single-case families (P=4.14e-03).
  • Limited predictive power for MS, even within sibships (AUROC=0.59).
  • Suggests a common genetic architecture for primary progressive and relapsing-remitting MS (P=0.368).
  • Integration of new GWAS and meta-analysis findings corrects previous MS genetic risk estimations.

Conclusions:

  • Updated genetic risk scores confirm increased genetic burden aggregation in MS families.
  • Current genetic markers offer minimal accuracy for predicting MS onset, even in high-risk individuals.
  • The genetic basis for different clinical forms of MS is largely shared, indicating common underlying susceptibility factors.