Using the Results of Genetics Studies

Question 1

What is the purpose of GWAS?

Answer 1

Identify loci in the genome associated with a phenotype/disease

Question 2

What is the purpose of RNA-Seq?

Answer 2

Isolating RNA from a tissue or cell type and use the method to generate a snapshot of the transcriptome

Question 3

What are some difficulties using GWAS?

Answer 3

→ Large GWAS for complex diseases detect many loci
Prioritisation- which ones do you look at?

→ 90% of GWAS SNPs are in non-coding regions of the genome
Causal variant? Causal genes?

→ What is the mechanism of action explaining the association?
Tissue/cell type?
Molecular mechanism?

Question 4

What is required for RNA-Seq data?

Answer 4

→ Need to set significance threshold
→ P-value- low p-value so its not due to chance
→ Fold change
Normally 1.5 to 2 fold in expression

Question 5

What is fold change in RNA-Seq?

Answer 5

the degree by which the expression of a gene has changed.

Question 6

What are other applications of RNAseq?

Answer 6

→ Cell populations response to treatments

→ How gene expression changes through development or under disease conditions

→ Single cell transcriptome analysis

Question 7

What are the difficulties using RNAseq?

Answer 7

→ Many expression changes likely to be found
→ Identification of differential expression does not provide biological reasoning

Question 8

Compare GWAS and RNAseq

Answer 8

GWAS
→ Identifies associations across whole genome
→ Large number of loci
→ Doesn’t identify causal variants or genes
→ Doesn’t identify cell type/tissue/developmental stage

RNA Sequencing
→ Transcriptome of single cell/tissue type
→ Large number of differentially expressed genes
→ Misses changes in other cell types or stages of development
→ Doesn’t identify reason for differential gene expression

Question 9

How are osetocytes found in mature bone?

Answer 9

→ Embedded in lacunae in mature bone

→ Connected via processes through canalicular channels

→ Form a mechanosensory network throughout bone

Question 10

What is involved in pathway analysis?

Answer 10

→Generate a gene set, and compare to database
→Gene ontology (GO) and Kyoto Encyclopedia of Genes
→ Allows you to identify new biology by determining the type of genes with association/differential expression

Question 11

What is required of using gene ontology for pathway analysis?

Answer 11

Must have been previously annotated

Question 12

What are the difficulties linking loci to gene?

Answer 12

→Linkage Disequilibrium makes it difficult to distinguish causal variant
→90% of GWAS SNPs are in non-coding regions- so causal variants is unlikely to effect any protein sequence
→May act at a distance from effected gene(s)
→Need to determine relevant cells/tissues

Question 13

What is linkage disequilibrium?

Answer 13

The nonrandom association of alleles of different loci

Question 14

What is fine mapping?

Answer 14

→ High resolution study of loci attempting to pinpoint individual variants directly effecting trait
→ Statistical and probabilistic methods or comparison to a SNP correlation reference panel

Question 15

How can you assign causal genes?

Answer 15

→ Closest gene to any fine mapping causal SNP
→ If the gene body overlapped with any of the causal SNP
→ If SNP directly caused coding change in the gene

Question 16

What is ATACseq?

Answer 16

Method for determining chromatin accessibility across the genome

Question 17

What is Hi-C?

Answer 17

Chromosome conformation capture technique to analyze spatial genome organization

Question 18

How to progress from GWAS to biological reasoning?

Answer 18

→ Fine mapping to attempt to define causal variants at loci
→ Analysis of causal SNP location to predict causal gene
→ Cell type SNP enrichment analysis to determine relevant cell types

Question 19

What are the three ways to combine genotype and expression data?

Answer 19

→ eQTL
→ Colocalisation analysis
→ TWAS

Question 20

What is eQTL?

Answer 20

A locus that explains a fraction of the genetic variance of a gene expression phenotype

Question 21

How do you generate eQTL?

Answer 21

→ Combine gene expression data from RNA-Seq and SNP genotyping data of the same individuals
→ Test SNPs local to each gene for association between SNP genotype and gene expression

Question 22

What is colocalisation analysis?

Answer 22

Used to test whether two independent association signals at a locus are consistent with having a shared casual variant

Question 23

How is colocalisation analysis carried out?

Answer 23

→ Identify the eQTL and GWAS loci that has an overlapping position
→ Compare the results of GWAS fine mapping and eQTL

Question 24

How do you interpret colocalization graphs?

Answer 24

→ If signals from eQTL and GWAS colocalise then association peaks appear similar indicating their due to single causal variant
→ If not then two peaks, and signals are different indicating linkage disequilibrium

Question 25

What are the explanations for locus overlap?

Answer 25

→ Independent causal variants in LD
→ A single causal SNP
→ Pleiotropy

Question 26

What is the purpose of TWAS?

Answer 26

→ Directly test for associations between gene expression levels and phenotypes
→ Overcomes most issues with GWAS and RNAseq

Question 27

How can we use our knowledge of the genome and gene expression to prioritise loci/genes for further investigation?

Answer 27

→ eQTLs – expression quantative trait loci
→ Colocalisation analysis to annotate causal genes
→ TWAS to directly associate gene expression to trait phenotype

Question 28

What is the first step in validating results?

Answer 28

In vitro studies to see if prioritised genes have effects

Question 29

What is involved in using knockout animals for validation?

Answer 29

→ Total knockout
→ Cell specific
→ Inducible
→ Gene editing

Question 30

Compare forward and reverse screens

Answer 30

→ Forward genetics- looking at phenotypes and uncovering genes responsible
→ Reverse – introducing mutations into known genes and identify what phenotypes result

Question 31

What is the IMPC?

Answer 31

→ Generating knockout mice strains for each mouse gene
→ Results freely available