Flashcards: Mapping Mendelian Disease (Genomics)

Maintains fluid balance by collecting excess fluid and returning it to the bloodstream.

Plays a key role in immune defense by transporting white blood cells and filtering pathogens.

Absorbs fats and fat-soluble vitamins from the digestive system via the lacteals.

Fluid homeostasis: Maintains balance by returning excess interstitial fluid to the bloodstream.

Immune function: Filters pathogens and supports immune responses.

Fatty acid transport: Absorbs fats and fat-soluble vitamins from the digestive tract.

They form an interweaving network in the capillary bed, working closely with blood vessels.

Chronic oedema caused by a developmental abnormality or dysfunction of the lymphatic system.

Often progressive, with phenotypes varying by age of onset, site, inheritance patterns, associated features, and genetic causes.

Leads to debilitating, embarrassing, and stressful symptoms with recurrent infections.

There is currently no medical cure for lymphoedema.

Research bridges the gap between laboratory findings ("bench") and practical patient treatments ("bedside").

By investigating genetic causes, inheritance patterns, and phenotypes using modern genetic and molecular techniques.

It helps identify loci associated with diseases by tracking how linked alleles segregate together in pedigrees and populations.

If an allele is linked to a disease locus, affected relatives will inherit the same allele more often than expected by chance.

This linkage helps trace the disease locus through families by identifying shared haplotype blocks.

Affected individuals in a family are more likely to inherit the same haplotype block (e.g., M3 – M4) as the disease locus.

This shared inheritance pattern indicates the proximity of the alleles to the disease locus.

Affected individuals in a family are less likely to inherit the same marker alleles (e.g., M5 – M8).

This suggests that the alleles and the disease locus are located far apart or on different chromosomes.

Haplotype blocks mark chromosomal regions and can indicate the genomic location of the disease-causing gene.

Generalized lymphatic dysplasia refers to a condition characterized by abnormal lymphatic development, leading to fluid buildup and edema in various parts of the body.

Antenatal hydrops with ascites and pleural effusions

Oedematous at birth

Intestinal lymphangiectasia

Peripheral lymphoedema affecting the arms, legs, and face

Mild developmental delay

Step 1: Take a pedigree and collect as many DNA samples as possible.

Step 2: Use a tool to observe alleles and generate genotyping data for the pedigree.

Step 3: Generate a file with the pedigree information along with genotyping data from the SNP array.

Step 4: Run the file in a linkage program (e.g., Merlin or PLINK).

NPL stands for Nonparametric Linkage.

NPL provides a plot for each chromosome to assess linkage.

NPL does not require assumptions about the inheritance model (no need to specify a genetic model or pedigree structure), unlike parametric linkage testing.

NPL does not impose any rules on the inheritance pattern, meaning it does not require assumptions about the genetic model.

NPL focuses on detecting linkage by observing allele sharing between affected individuals without needing a predefined inheritance model.

NPL assesses genetic linkage by comparing allele patterns across families and identifying whether certain alleles are shared more frequently in affected individuals.

Parametric analysis imposes rules about inheritance patterns and disease frequency when assessing genetic linkage.

In autosomal dominant inheritance, affected family members are assumed to be heterozygous for the disease-causing allele (e.g., AB).

In autosomal recessive inheritance, affected family members are assumed to be homozygous for the mutant allele (e.g., AA).

Parametric analysis helps refine linkage results by taking into account specific inheritance patterns and disease allele frequencies, improving the accuracy of mapping disease-causing genes.

A LOD score > 3.0 is considered significant evidence for linkage, meaning the genetic locus is likely linked to the disease.

A LOD score > 3 corresponds to a p-value of approximately 0.05, which indicates statistical significance in the association between the genetic locus and the disease.

LOD scores between -2 and 3 are considered inconclusive, as they do not provide strong evidence for or against linkage.

LOD scores below -2 suggest significant non-linkage, meaning the genetic locus is unlikely to be associated with the disease.

Even though a LODmax of 2.1 is not significant, the two regions with LOD scores between -2 and 2.1 are worth exploring further because they were the only two regions above LOD score -2. This suggests they may be linked to the disease, but more investigation is needed.

These regions are considered for further exploration because they may still have a genetic association with the disease, especially as they were the only regions that exceeded the -2 LOD score threshold when applying parametric analysis for autosomal recessive inheritance.

Gene identification by gene mapping: This step involves identifying regions of the genome that are linked to a disease through methods like linkage analysis and GWAS.

Sequencing: This technique is used to analyze the DNA of candidate genes to find mutations that could potentially cause the disease.

In silico tools: These involve computer-based predictions, such as computational modeling, to assess the potential impact of mutations.

In vitro tools: These are laboratory-based techniques, such as cell culture studies, to observe the effects of mutations in a controlled environment.

In vivo tools: These refer to animal models or clinical studies where mutations are studied in living organisms to observe their effects on disease development.

Sanger sequencing: This method is used to sequence the DNA of the candidate genes to identify specific mutations that may be causing the disease.

Sanger sequencing: A method of DNA sequencing that uses selective incorporation of chain-terminating dideoxynucleotides.

Process: The gene of interest is amplified by PCR, and the sequence is read using fluorescent markers that indicate which base (A, T, C, G) is incorporated.

Identification of mutations: Any variations in the normal DNA sequence (mutations) can be detected by comparing the obtained sequence to a reference genome.

High accuracy: Sanger sequencing provides high-quality, accurate sequences for small regions of DNA, allowing precise identification of mutations.

Targeted sequencing: It allows for focused analysis of specific genes or regions within the genome, such as the 130 candidate genes, making it ideal for finding rare or known mutations associated with diseases.

Using in silico, in vitro, and in vivo tools: These methods are used to validate that a mutation in a gene is actually responsible for the disease.

In silico tools: These are computational methods used to predict the effect of genetic mutations on protein function and structure.

Role in proving causality: In silico tools can analyze the mutation's potential to alter the gene’s function or lead to disease by comparing it to known mutation databases and using predictive models.

In vitro tools: Laboratory-based experiments (such as cell cultures or protein assays) used to test the effects of the mutation on gene expression or protein function.

Validation: For example, scientists might introduce the mutation into a cell culture model to observe if the mutation disrupts the normal cellular function, suggesting a causal relationship to disease.

In vivo tools: Experiments conducted in living organisms, such as animal models (e.g., mice), that carry the mutation of interest.

Validation: By observing how the mutation affects the organism's phenotype or health, researchers can determine if the mutation leads to the disease, confirming its causality.

Collect DNA samples: Obtain DNA samples from family members, especially affected individuals, and unaffected controls.

Genotyping: Use a genotyping tool, like a SNP array or microarray, to analyze the DNA and generate data on genetic variants (SNPs) across the genome. The data will include information about the alleles present at specific loci for each family member.

Create a file with pedigree and genotyping data: Combine the pedigree information (family tree showing relationships and affected status) with the genotyping data to create a file for analysis.

Choose a linkage program: Use software such as Merlin or PLINK to run the linkage analysis.

Set inheritance model: In the software, set the inheritance pattern to autosomal dominant. This will assume that one copy of the mutant allele is sufficient to cause the disease. The program will then compare the genotypes between affected and unaffected family members to identify linked genomic regions.

Identifying linked regions: The analysis will identify regions of the genome that show consistent inheritance of certain alleles (haplotypes) in affected family members. If a region is linked to the disease, affected individuals will more frequently inherit the same alleles at specific loci.