π Genomic comparisons in medicine (8 MCQs)
π From Principles of Biochemistry β’ 1. The Foundations of Biochemistry β’ 8 questions available
What is Genomic comparisons in medicine?
Definition:
Genomic comparisons in medicine involve comparing the genomes of individuals, populations, or species to identify variations associated with diseases, drug responses, and evolutionary factors, and these comparisons are used to discover genetic markers for disease susceptibility, develop diagnostic tools, and guide personalized medicine, and it uses techniques like genome-wide association studies (GWAS) and whole-genome sequencing.
Working:
Genomic comparisons work by aligning DNA sequences from different individuals and identifying differences (single nucleotide polymorphisms, SNPs, or structural variants), and statistical tests determine if certain variants are significantly associated with a disease, often using a p-value threshold; for example, a GWAS might compare the genomes of thousands of cancer patients and healthy controls to find SNPs that increase cancer risk, with the odds ratio calculated as ; these comparisons also help in understanding drug metabolism and predicting drug efficacy, leading to more effective and safer treatments.
Example:
A simple example is the comparison of breast cancer patients' genomes to identify mutations in the BRCA1 and BRCA2 genes, which increase the risk of breast and ovarian cancer, and genetic testing for these mutations allows for early screening and preventive measures, illustrating how genomic comparisons are directly applied in clinical medicine.
Reason:
Genomic comparisons are revolutionizing medicine by enabling personalized treatments, early disease detection, and drug development, and they are essential for advancing precision medicine, understanding genetic diseases, and improving public health outcomes.
π All Genomic comparisons in medicine MCQs
Q1. A researcher compares human genomic sequences with those of several other mammals and finds that a DNA region is highly conserved. Which conclusion is most scientifically justified?
π Explanation: Strong conservation across species suggests that changes in the region may have been selected against, indicating functional importance. However, conservation alone does not establish whether the region is coding, regulatory, or directly associated with a particular disease.
Q2. Two patients have the same clinical disorder. Genomic comparison shows that patient A has a rare variant also found in unaffected individuals, whereas patient B has a rare variant absent from healthy controls and located in a highly conserved region. Which interpretation is strongest?
π Explanation: The evidence for patient B is stronger because the variant is rare, absent from healthy controls, and occurs in a conserved region. These features increase suspicion of functional relevance, although functional experiments and clinical evidence are still required.
Q3. A hospital wants to identify genomic variants that may explain an inherited metabolic disorder. Researchers compare affected individuals with unaffected relatives and then compare candidate regions across multiple species. Why does combining these comparisons improve the analysis?
π Explanation: Comparing affected and unaffected relatives helps identify variants associated with the phenotype, while cross-species comparison can reveal whether candidate positions are evolutionarily constrained. Combining independent evidence therefore strengthens prioritization without proving causation.
Q4. A student claims, 'If a DNA sequence differs between humans and chimpanzees, the difference must explain a human-specific trait.' What is the main flaw in this reasoning?
π Explanation: A genomic difference is not automatically causal. It may be neutral, occur in a region unrelated to the phenotype, or interact with many other genetic and environmental factors. Establishing causation requires additional genetic, functional, and biological evidence.
Q5. A graph shows that the fraction of candidate human genomic variants supported by evolutionary conservation increases from 20% in low-priority variants to 65% in high-priority variants. Which inference best matches the graph?
π Explanation: The increasing proportion of conserved sequences among higher-priority candidates suggests that conservation can help rank variants for investigation. However, the graph does not establish disease causation or prove a particular evolutionary relationship.
Q6. Researchers identify a human variant associated with a disease. The same genomic position is conserved in several mammals, and laboratory testing shows that changing the human sequence alters gene expression. What is the best next interpretation?
π Explanation: Conservation suggests that the genomic position has functional importance, while the laboratory result directly demonstrates an effect on gene expression. Together these findings provide stronger evidence for biological relevance, although clinical causation may still require additional evidence.
Q7. A physician uses genomic comparison to prioritize variants in a patient with an unexplained disorder. Variant X is common in humans and occurs in rapidly changing genomic regions, while variant Y is rare and lies in a strongly conserved region. Which should generally receive higher initial priority for functional testing?
π Explanation: Variant Y has characteristics that make functional significance more plausible: it is rare and located in a conserved region. These clues do not prove pathogenicity, but they provide a rational basis for prioritizing experimental investigation.
Q8. A research team compares genomes from many species and notices that a particular regulatory sequence is conserved in closely and distantly related species. A disease-associated human mutation occurs within that sequence. Which reasoning chain is most defensible?
π Explanation: The strongest reasoning proceeds cautiously from conservation to probable functional constraint, then considers whether the human mutation could disrupt regulation and contribute to disease. Each step generates a testable hypothesis rather than assuming that association proves causation.