15- Using the Results of Genetic Studies Flashcards
discuss the difficulties in interpreting and analysing data from complex genetic experiments such as GWAS
- many complex traits are influenced by multiple genes/ polygenic = hard to pinpoint specific genetic contributors
- genetic variations may differ among diverse populations - some variants may have different effects in different ethnic groups
- linkage disequilibrium = strong linkage disequilibrium may exist between multiple genetic variant, hard to identify a causal variant
- conducting numerous tests across the genome increases chances of a false positive
- GWAS may not capture all genetic variants, especially rare variants that might have significant effects
what is linkage disequilibrium?
when the inheritance of alleles at one locus does not affect the inheritance of alleles at another locus
observed at genes located on different chromosomes, or located far apart on the same chromosome
what is fine mapping?
narrowing down the region of the genome associated with a particular trait or disease
aims to identify the specific genetic variants responsible for the observed associations in a GWAS
requires more detailed genotyping, larger sample sizes, and advanced statistical methods to pinpoint the causal variants
what is SNP location analysis?
studying the positions of SNPs within the genome
with GWAS - locations of SNPs associated with a particular trait or disease are analysed
- can see whether they’re located in coding or non-coding regions, or regulatory elements
what is GWAS?
genome wide association studies - identifies genetic variants associated with specific traits or diseases
analyses the entire genome for common genetic variations, typically using high-throughput genotyping technology
- focuses on the identification of common variants/SNPs
- involves studying large populations to detect statistically significant associations
- traits are often polygenic, need to determine linkage disequilibrium for the presence of associations = are the genes just inherited together because they’re close together?
what is a small effect size in the context of GWAS?
genetic variant associated with a disease has a small effect on disease traits
how are GWAS results linked to causal genes through fine mapping and analysis of SNP locations
fine mapping - high-resultion mapping of genomic regions to narrow down the potential causal variants
- requires large sample sizes and complex genotyping
SNP location analysis - analysing the locations of a SNP associated with a trait
- requires identifying functional SNPs among associated variants
what is eQTL mapping?
expression quantitative trait loci mapping
- identifies genetic variants that influence gene expression levels
- correlates genetic variants/SNPs with the expression levels of nearby or distant genes
- allows us to understand the molecular mechanisms underlying complex disease traits
how can RNAseq data be used to annotate GWAS data via eQTL mapping
eQTL (expression quantitative trait loci) mapping = correlating genetic variants with gene expression levels
benefit - helps identify potential causal genes by linking genetic variants with changes in gene expression
risk - complexity of gene regulation and tissue specific effects can affect interpretation
how to use in vitro and in vivo experiments to validate results from GWAS studies
in vitro experiments:
- cell cultures, conducted outside living organisms
- provides insights into functional effects of genetic variants on any cell type/ lineage
- however simplified systems can’t fully represent complex in vivo conditions, cell and tissue interactions
in vivo experiments:
- transgenic mice models, genetic variants introduced to zebrafish embryos, humanised mouse models by introducing human GOIs into mice, cross-breeding specific genetic variants, tissue-specific knockout studies
- reflect complexity of biological systems
- but ethical considerations, resource-intensive, not always feasible for all identified variants
