Poulation genetics Flashcards
(148 cards)
What is Linkage Analysis?
Linkage analysis is a method for linking heritable traits to their chromosomal location. It relies on the tendency for genes and genetic markers to be inherited together at meiosis because they are located nearby on the same chromosome
What types of disorders is Linkage Analysis best suited for?
Linkage analysis is best suited for highly penetrant monogenic disorders with Mendelian inheritance. It is also suitable for single gene defects in families. While historically used mostly for major effect genes, linkage analysis methods can also be applied to complex diseases using model-free or non-parametric approaches.
What are the typical requirements for conducting a Linkage Analysis study? A: Linkage analysis typically requires large, multi-generational families
Detailed pedigree information is valuable. Researchers need to identify many families with several affected generations. It also requires collecting families where the phenotype of interest segregates and the scoring of meioses as recombinant or non-recombinant for the locus in question.
What is a LOD score and what does it measure in Linkage Analysis
A LOD score (Logarithm of the Odds) is a measure for the likelihood of linkage. It is the logarithm of the ratio of the odds that two loci are linked with a specified recombination frequency (θ) to the odds that they are unlinked. Positive LOD scores favour the presence of linkage; negative scores indicate linkage is less likely. LOD scores can be added across families
What are the significance thresholds for LOD scores?
A LOD score higher than 3.0 is generally accepted as evidence for linkage. This corresponds to odds of 1000:1 favouring linkage. A LOD score lower than -2 is accepted as evidence against linkage (exclusion). Values between -2 and +3 are inconclusive
: How does recombination affect Linkage Analysis?
Recombination can take place during meiosis, separating a marker and a disease gene. The further apart two loci are, the more likely recombination is. Linkage analysis requires scoring meioses to see if recombination occurred. The recombination fraction (θ) in a pedigree is the frequency with which a crossover occurs between two loci. If θ = 0.5, the loci are not linked. If there are no recombinants (θ=0), the highest LOD score is observed.
What are the main types of Linkage Analysis methods?
The main types include Parametric linkage analysis (standard LOD score analysis) and Non-parametric linkage analysis (model-free). Affected Sib Pair (ASP) analysis is a common non-parametric method. Autozygosity mapping is a form of linkage analysis used in consanguineous families.
What is the difference between Parametric and Non-parametric Linkage Analysis?
Parametric linkage analysis requires specifying parameters like the mode of inheritance, gene frequencies, and penetrance. It is used for simple Mendelian disorders but is prone to errors and problems with locus heterogeneity and specifying the genetic model. Non-parametric linkage analysis does not require an inheritance model and makes no assumptions about other genes involved in disease risk. Its principle is that affected family members will co-inherit the disease region from a common ancestor more often than by chance. Non-parametric methods are generally considered more robust
What are the advantages of Linkage Analysis?
Linkage studies are good for localising areas of disease risk across the genome. They can be used to study multiple genetic markers simultaneously. They are suitable for single gene defects in families. They can help determine if a phenotype is caused by a single gene or mutations in other genes. Linkage analysis can also be useful in diagnosis using flanking markers. Linkage is not affected by population structur
What are the disadvantages of Linkage Analysis?
Disadvantages include the need to identify many families with several affected generations, which is difficult for late-onset diseases. Linkage studies are less helpful for complex traits (using parametric methods). LOD score analysis requires precise genetic models and is vulnerable to errors like misdiagnosis, reduced penetrance, switched samples, and non-paternity. They have limits of resolution dependent on the number of meioses. Detecting loci with modest effects requires large numbers of ASPs
What are some applications of Linkage Analysis in diagnostic laboratories?
Linkage analysis can be applied in diagnostic labs when the causative pathogenic variant hasn’t been identified or quantitative deletion analysis isn’t feasible. It requires the locus to be known and the clinical diagnosis clearly defined. It uses flanking markers to identify the high-risk haplotype. Examples include identifying high-risk haplotypes in DMD where the mutation isn’t found, clarifying SMA carrier risk when a parent has two SMN1 copies on one allele, and Huntington’s disease (HD) using the linkage exclusion method for prenatal testing
How is Next-Generation Sequencing (NGS) impacting Linkage Analysis?
Whole exome (WES) and whole genome sequencing (WGS) are powerful tools to identify candidate disease variants. Combining WES/WGS with linkage analysis provides statistical support for identified variants being associated with the disease
What is Linkage Disequilibrium (LD)?
Linkage disequilibrium (LD) is the non-random association of alleles at two or more loci with a frequency greater than expected by chance. It means that combinations of alleles or genetic markers occur in a population more often or less often than expected from random formation based on allele frequencies. If loci are in linkage equilibrium, their genotypes appear independently
What are the main causes of Linkage Disequilibrium?
LD can be due to natural selection or chance. It can also result from a new mutation arising on a founder chromosome, population structure (like subdivision, inbreeding, non-random mating), genetic drift, gene flow between populations with different allele frequencies, and population history (older populations tend to have shorter segments of LD
How does recombination affect Linkage Disequilibrium over time
Over time, recombination between loci gradually reduces LD as alleles that were shared on an ancestral chromosome are separated. It can be harder to find LD in older populations. Areas of the genome with a lower recombination rate can maintain LD for longer
What is an Association Study?
Association studies compare cases (people with a genetic trait/disease) to controls (people without) to identify a statistical relationship (association) between a particular allele, genotype, haplotype, or polymorphism(s) and the trait. The aim is to identify disease susceptibility gene variants
What types of diseases are Association Studies suitable for?
Association studies can identify association in complex diseases like diabetes and hypertension. They can also be used for studying rare diseases and are particularly powerful for detecting genes associated with multifactorial disease like polygenic disorders at a population level
What is GWAS and how does it work?
GWAS (Genome-Wide Association Study) is an approach that rapidly scans genetic markers (typically SNPs) across the genomes of many people to find variations associated with a trait or disease. It is generally based on a case-control design, comparing genetic variants in people with the disease (cases) to similar people without (controls). It is a ‘hypothesis free’ approach that investigates the entire genome. GWAS usually uses SNP arrays to read millions of genetic variants. If a variant is more frequent in cases, it’s associated with the disease. Associated SNPs mark a region influencing risk
What are the potential causes for finding a positive association in an Association Study?
There are four main causes for a positive association: 1. Chance. 2. False association due to Linkage Disequilibrium between the studied marker and the true disease-causing variant. 3. Bias resulting from population stratification. 4. True association, where the genetic variant is important in disease causation.
What are the advantages of Association Studies?
Association studies can be used for studying rare diseases, testing specific markers, and investigating gene-gene or gene-environment interactions. GWAS is likely always a more powerful method for detecting genes associated with multifactorial disease compared to linkage analysis in human populations. They have fine genetic resolution
What are the disadvantages of Association Studies (including GWAS)?
Association studies cannot test causality; they only measure statistical associations. They are prone to confounding variables, particularly population stratification. They can be expensive. GWAS requires necessity for multiple testing correction, making the statistical threshold for significance very low and hard to reach. They need large studies to have sufficient power. Most GWAS identify SNPs conferring only small effects, contributing to missing heritability. GWAS limitations include potential for false positives, lack of information on gene function, insensitivity to rare variants, need for large sample sizes, and potential biases from selection/genotyping error
What is the primary difference between Linkage Analysis and Association Analysis?
The primary difference is that linkage analysis looks at the relation between the transmission of a locus and the disease/trait within families, whereas association analysis focuses on the relation between a specific allele/variant and the disease/trait within a populatio
What are some benefits to human health from GWAS discoveries?
GWAS are contributing to personalized medicine. They have identified genetic variations contributing to risk for conditions like type 2 diabetes, Parkinson’s disease, heart disorders, obesity, Crohn’s disease, and prostate cancer. They have also identified variants influencing response to anti-depressant medications.
How does genetic screening differ from genetic testing?
Genetic screening targets populations/sub-populations rather than at-risk individuals to detect future disease risks in individuals/progeny for which established preventive interventions exist