Functional Neurogenomics - 2

Question 1

ChromHMM

Answer 1

Segments the genome & assigns chromatin states to whole-genome regions –> Hidden Markov Model

Question 2

Methods to asses Histone modifications/DNA accessibility

Answer 2

ChIP-seq: Method to map histone modifications
Bisulfate sequencig: Method to map DNA modifications
ATAC-seq: Method to map DNA accessibility

Question 3

ATAC-seq

Answer 3

Assay for Transponase-Accessible Chromatin
Probing open chromatin regions with hyperactive mutant TN5 Transponase that inserts sequencing adaptors into open regions of the genome –> Tn5 cleaves & tags dsDNA with sequencing adatpros –> Tagged DNA fragments are purified, PCR-amplieifed & sequenced by NGS

Question 4

Methods to study chromatin organization

Answer 4

Ligation-based HiC
Non-ligation based GAM –> Genome wide identification of chromatin
Ultrathin sectioning of nuclei –> DNA-sequencing –> Measures contacts, compaction & radial positioning

Question 5

Linkage mapping

Answer 5

For monogenic disorders - in affected families
Exploits human polymorphic markers to search for co-inheritance
Test for co-segregation in many independent families

Polymorphic markers: Heterozygous in population –> Mark specific points in the genome (e.g. Can microsatellites)
Positional cloning necessary to discover disease-causing mutations

Question 6

SNP array

Answer 6

Small fragments of DNA where multiple alleles exist
Arrays contain immobilized allele-specific oligonucleotide (ASO) probes –> Patient DNA hybridizes onto it -> Detection
Copy numbers determined by the relative intensity of bound DNA at each allele

Question 7

GWAS (Genome-Wide Association studies)

Answer 7

Compare frequency of genetic markers (SNPs) between patients & controls –> Find genetic variants that are siginifcantly more frequent in cases than controls
Significant: If 5x10^8 (P = 0.05)
Results shown in LD or Manhattan plot

Question 8

Terminology of GWAS

Answer 8

Tag SNP: SNP on microarray in LD with neighbouring SNPs, occur one every 1000 nucleotides, found in databases; surrogates for large regions
Risk-associated SNP: Statistically significantly over-represented in disease populations –> Might be an artifact of LD!
Index/Lead SNP: SNP with smallest p-values in distinct regions –> Not necessarily a causal variant

Question 9

eQTL (GWAS integration)

Answer 9

= Region of DNA associated with a trait with continous variance (e.g. height) –> Altered TF binding, altered histone modification, altered splicing, altered mRNA silencing
Cis-eQTLs: Variant resides in close proximity to target gene location
Trans-eQTLs: Variant resides very distant to the target –> Mechanisms less clear

Hypothesis: If a risk variant also causes gene expression changes – Might identified a potentially causal biological mechanism –> Co-Localization of GWAS results & eQTL

Question 10

Epigenimic GWAS integration

Answer 10

1. Expand each GWAS locus using SNP linkage disequilibrium
2. Overlap SNPs with tissue-specific enhancers to find relevant cell types
3. Search for enriched TF motifs in multiple GWAS loci to find driver TF
4. Recognize target genes

Question 11

GWAS summary

Answer 11

1. Sequence tagSNPs –> Identify risk-associated SNP
2. molQTL mapping & Chromatin interaction mapping –> Fine mapping
3. Positional mapping –> Map SNPs to genes based on physical location (For variants in coding region)
4. Gene-set analysis –> Test if trait associated variants converge on specific gene sets

Question 12

Genome sequencing

Answer 12

Whole-genome sequencing: Captures all types of genetic variation –> Coding & non-coding SNVs, indels & CNVs (Complicated analysis)
Whole-exome sequencing: Focused on protein coding parts (SNVs & Indels) –> Less expensive

Question 13

Droplet-based single-RNA sequencing

Answer 13

RNA expression profile of a single cell
1. Single-cell (nuclei) suspension from a tissue
2. Microfluidic device –> Encapsulate cell in nanoliter droplets with barcoded beads –> Cell lysis –> Captur of poly-A- RNA, RT with molecular identifiers + DNA amplification
3. NGS of cDNA library –> Solid-phase ampliification

Analysis: Clustering of cells
Clusters assigned to specific cell types on basis of marker genes
Pseudo-time plot: Order cells along one/more trajectories –> Represent developmental ones

Question 14

MPRA (Massively parallel reporter Assays)

Answer 14

Gene regulatory effects of thousand of sequences assayed in parallel
Regulatory element drives RNA expression of unique barcodes –> Transcriptional activity is quantified as barcode transcription (RNA-seq of barcodes) normalized to initial input of barcodes

Question 15

Limitations of MPRA

Answer 15

Limitations: Cannot identifiy target genes (Identifiy variant &element with regulatory activity)
Use of exogenous DNA (Do not account for effect in genomic context)

Question 16

CITE-seq

Answer 16

Cellular indexing of Transcriptomes & Epitopes by Sequencing
DNA-barcoded ABs to convert detection of protein into a quantitative sequencable readout

Question 17

CRISPR editing

Answer 17

DNA + sg RNA + Cas9 + Repair template = HR = Edited DNA
DNA + sgRNA + Cas9 = NHEJ = Repair errors = Frameshift & LoF –> If at two sites in parallel –> Loss of big pieces

Question 18

CRISPR interference & activation

Answer 18

dCas): Cas9 with removed endonuclease activity –> Binding only
Coupled to Base-modifying enzyme = Base editing = Single variant editing
Coupled to Repressor = CRISPR interference
Coupled to Activator = CRISPR activation

Question 19

CRISPR screen

Answer 19

Helps validate subsets of interactions identified by MPRA in genome-specific context

sgRNA pool introduced in bulk of cells (induce pertubations)
Integration os sgRNA in DNA upon pertubation –> Cell proliferates under biological challange –> Pertudbation induced effects analyzed by sequencing-based counting og the guide RNAs that specify each pertubation
Result: Ranked list of genes that confer sensitivity or resistance to challange

Question 20

Types of CRIPSR screens

Answer 20

Arrayed: Each well with specific & known sgRNA targeting specific gene
Pooled: Cells transduced with pooled sgRNA library –> Single RNA-seq –> High dimensional transcriptomic readout