Role of genomics in target discovery MCQs With Answer

Role of genomics in target discovery MCQs With Answer

This quiz set explores how genomics accelerates identification and validation of therapeutic targets, tailored for M.Pharm students studying Principles of Drug Discovery. It covers concepts from genome-wide association studies (GWAS), whole-genome and exome sequencing, transcriptomics, eQTL and epigenomics, to functional genomics tools such as CRISPR screens and RNAi. Questions emphasize integration of multi-omics, bioinformatics prioritization, pharmacogenomics implications, and challenges like variant interpretation and population diversity. Designed to deepen understanding of how genomic evidence supports target selection, mechanism elucidation, and translational decisions, these MCQs will help students critically evaluate genomic data applications in modern drug discovery workflows.

Q1. What best defines the role of genomics in target discovery?

• Studying single proteins in isolation to find drug binding sites
• Systematic use of genome-scale data to identify and prioritize drug targets
• Replacing all biochemical assays with computational predictions
• Focusing only on non-coding DNA because coding regions are well understood

Correct Answer: Systematic use of genome-scale data to identify and prioritize drug targets

Q2. Which genomic approach is most appropriate for identifying common genetic variants associated with a complex disease?

• Whole-genome sequencing (at very low coverage)
• Genome-wide association study (GWAS)
• Targeted Sanger sequencing of a candidate gene
• Protein mass spectrometry profiling

Correct Answer: Genome-wide association study (GWAS)

Q3. What is an expression quantitative trait locus (eQTL)?

• A genomic region where variants influence gene expression levels
• A protein domain that determines ligand binding
• A specific epigenetic modification like DNA methylation
• A pharmacokinetic parameter affecting drug absorption

Correct Answer: A genomic region where variants influence gene expression levels

Q4. Which statement best describes how integrative multi-omics improves target discovery?

• Combining genomics with proteomics and metabolomics reduces experimental cost but adds little information
• Multi-omics integrates complementary layers (DNA, RNA, protein, epigenetics) to better infer disease mechanisms and prioritize targets
• Multi-omics only refers to combining different sequencing platforms for the same omic
• It replaces the need for any functional validation experiments

Correct Answer: Multi-omics integrates complementary layers (DNA, RNA, protein, epigenetics) to better infer disease mechanisms and prioritize targets

Q5. What is the primary advantage of using rare variant (exome) sequencing in target discovery?

• It identifies common loci with small effect sizes across populations
• It reveals protein-coding variants with potentially high impact on disease biology
• It is cheaper and less informative than GWAS
• It focuses exclusively on regulatory non-coding variants

Correct Answer: It reveals protein-coding variants with potentially high impact on disease biology

Q6. How do CRISPR-based functional genomic screens contribute to target validation?

• By predicting protein structure from sequence alone
• By enabling systematic loss- or gain-of-function perturbations to identify genes affecting a phenotype
• By sequencing the transcriptome without perturbation
• By measuring small molecule binding kinetics in vitro

Correct Answer: By enabling systematic loss- or gain-of-function perturbations to identify genes affecting a phenotype

Q7. Which genomic evidence strengthens causal inference that a gene is a therapeutic target rather than a passive biomarker?

• Consistent GWAS association with nearby non-coding variants only
• Mendelian randomization or human genetics showing variants in the gene alter disease risk and intermediate traits
• High expression of the gene in a cell line with no disease relevance
• Presence of the gene in an unrelated species

Correct Answer: Mendelian randomization or human genetics showing variants in the gene alter disease risk and intermediate traits

Q8. What is a major limit when translating GWAS loci into drug targets?

• GWAS loci always point only to protein-coding exons
• Most GWAS signals map to non-coding regions, making it hard to identify the causal gene or mechanism
• GWAS is unaffected by population structure or linkage disequilibrium
• GWAS provides direct pharmacological information such as druggability

Correct Answer: Most GWAS signals map to non-coding regions, making it hard to identify the causal gene or mechanism

Q9. What role does eQTL colocalization analysis play in prioritizing targets at a GWAS locus?

• It determines the chemical properties of candidate drugs
• It assesses whether the same genetic variant affects both gene expression and disease risk, implicating the gene as causal
• It sequences the promoter region of the gene for mutations
• It measures drug concentration in plasma samples

Correct Answer: It assesses whether the same genetic variant affects both gene expression and disease risk, implicating the gene as causal

Q10. Which genomic resource is particularly useful for assessing target safety by studying human loss-of-function variants?

• Pharmacovigilance spontaneous reporting systems
• Human knockout/LOF cohorts and databases like gnomAD
• Mouse knockout databases only
• Mass spectrometry peptide libraries

Correct Answer: Human knockout/LOF cohorts and databases like gnomAD

Q11. How does Mendelian randomization (MR) inform target validity?

• By observing drug side effects in clinical trials only
• By using genetic variants as instruments to test whether modulating a biomarker causally affects disease risk
• By measuring in vitro binding affinity between target and ligand
• By sequencing microbial genomes in the gut

Correct Answer: By using genetic variants as instruments to test whether modulating a biomarker causally affects disease risk

Q12. Which approach helps predict whether a genomic-identified target is druggable by small molecules?

• Evaluating protein structure, active sites, domain architecture and known ligandability databases
• Counting the number of exons in the gene
• Checking only the gene expression in a single tissue
• Assessing regulatory SNP density without protein information

Correct Answer: Evaluating protein structure, active sites, domain architecture and known ligandability databases

Q13. What is proteogenomics and why is it useful in target discovery?

• Combining proteomics and genomics to confirm variant-driven changes at the protein level and identify actionable targets
• Using protein databases to annotate non-coding RNAs exclusively
• Sequencing proteins like DNA to infer gene sequences
• It is an old term for population genomics

Correct Answer: Combining proteomics and genomics to confirm variant-driven changes at the protein level and identify actionable targets

Q14. How does population diversity in genomic studies impact target discovery?

• It reduces statistical power universally and should be avoided
• It reveals population-specific variants, improves fine-mapping, and reduces bias in translating targets across ancestries
• It only matters for infectious disease studies
• It is irrelevant because functional mechanisms are identical across populations

Correct Answer: It reveals population-specific variants, improves fine-mapping, and reduces bias in translating targets across ancestries

Q15. What is synthetic lethality and how can genomics guide its exploitation as a therapeutic strategy?

• It is when two drugs neutralize each other; genomics is not involved
• It refers to lethal drug interactions in animals; genomics tests tissue specificity
• When loss of two genes together causes cell death; genomics identifies gene pairs where tumor-specific mutations create therapeutic vulnerabilities
• It describes the lethal effects of antibiotics on bacterial genomes

Correct Answer: When loss of two genes together causes cell death; genomics identifies gene pairs where tumor-specific mutations create therapeutic vulnerabilities

Q16. Which epigenomic assay is most informative for identifying regulatory elements that might mediate GWAS associations?

• ChIP-seq for histone marks and transcription factors
• Whole-exome sequencing
• ELISA for cytokine levels
• Northern blot for small RNAs

Correct Answer: ChIP-seq for histone marks and transcription factors

Q17. In the context of target discovery, what is the significance of a gene with strong coding variant associations and supportive functional data?

• It is likely an artifact and should be ignored
• It represents a high-confidence target candidate because genetic association, coding impact, and functional evidence converge
• Coding variant associations are less informative than non-coding signals and are therefore deprioritized
• Functional data is unnecessary if the genetic association exists

Correct Answer: It represents a high-confidence target candidate because genetic association, coding impact, and functional evidence converge

Q18. What bioinformatic method helps prioritize causal genes at loci with multiple genes in linkage disequilibrium?

• Pathway enrichment only
• Fine-mapping combined with colocalization and integration of functional annotations
• Randomly selecting the nearest gene to the lead SNP
• Relying solely on gene expression level in an unrelated tissue

Correct Answer: Fine-mapping combined with colocalization and integration of functional annotations

Q19. Which ethical consideration is particularly important when using human genomic data for target discovery?

• That genomic data are always publicly anonymous and require no consent
• Ensuring informed consent, data privacy, equitable benefit sharing, and consideration of population-specific implications
• That commercial interests should override participant autonomy
• That genomic results have no potential for psychosocial impact

Correct Answer: Ensuring informed consent, data privacy, equitable benefit sharing, and consideration of population-specific implications

Q20. Which combination of evidence would most strengthen a candidate target for small-molecule drug development?

• GWAS signal in a non-coding region with no downstream functional data
• Multiple independent lines: human genetic association, coding variant effect, expression change by eQTL, functional perturbation causing disease-relevant phenotype, and structural druggability
• Only high expression in a cancer cell line with unknown consequence on tumor biology
• Presence of the gene only in non-human organisms

Correct Answer: Multiple independent lines: human genetic association, coding variant effect, expression change by eQTL, functional perturbation causing disease-relevant phenotype, and structural druggability

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