Download appendix 1: answers to selected discussion questions

ANSWERS TO SELECTED DISCUSSION QUESTIONS
CHAPTER 3: BASIC MOLECULAR GENETICS
1) Given the following DNA template:
TACGGAGCT

the resulting mRNA base sequence will be:
AUGCCUCGA

the base sequence of the corresponding tRNA anticodon will be:
UACGGAGCU

the amino acid sequence that would result would correlate with the mRNA codons
AUG, CCU and CGA, which respectively signify:
“START”—PROLINE—ARGININE
2) If the 6th base of the given DNA template were changed to a different
base, what affect would this have on the resulting protein?
Presumably it would not have an effect because whatever the resulting
mRNA codon sequence becomes (CCU, CCC, CCA, or CCG), it will
signify the same amino acid—glycine.
3) If the 7th base of the given DNA template were changed to adenine,
what affect would this have on the protein?
The amino acid chain would be truncated because the resulting mRNA
codon will become a “STOP” codon.
CHAPTER 10: METABOLIC GENETICS, NEWBORN SCREENING, &
HEALTH CARE COSTS
4) Given a metabolic disease with a prevalence of 1/100, calculate the
positive predictive value of newborn screening tests for each, assuming
95% sensitivity and 99.9% specificity. Assume you are analyzing a
population of 1 million people. Compare your results to Table 10.2.
What affect does prevalence have on the positive predictive value of a
test, given identical sensitivity and specificity parameters?
The way to approach this problem is to set up a table similar to Table
10.2, noting that this time the disease prevalence is significantly greater
than in Table 10.2.
Screening test
DO NOT HAVE
results for disorder
HAVE DISEASE
TOTAL
DISEASE
with HIGH
prevalence
TEST POSITIVE
A
9500
B
990
A+B
10,490
TEST NEGATIVE C
500
D
989,010
C+D
989,510
B+D
990,000
TOTAL
A+C
10,000
A+B+C+D =1,000,000
Remember that A= True positive, B=False positive, C=False negative, D=True
negative, A/A+C=Sensitivity, D/B+D=Specificity, A/A+B=Positive predictive
value, D/C+D=Negative predictive value, and A+B+C+D=Total sample size. In
a sample of 1 million people, if disease prevalence is 1/100, 10,000 people (which
is 1 million divided by 100) will be affected with the disease and 990,000 (which
is 1 million minus 10,000) will be unaffected. If the analyte cutoff is set to achieve
95% sensitivity and 99.9% specificity for the disorder, 95% percent of the 10,000
affected people, which equals 9500, will test positive (true positive) and the
remaining 500 will test negative (false negative). 99.9% of the 990,000 unaffected
people, which equals 989,010, will test negative (true negative) and the remaining
990 unaffected people will test positive (false positive). The positive predictive
value equals true positives/(true positives + false positives), which in this case=
9500/(9500+990)=91%, much greater than the 2% found when there was a
disease prevalence of 1/50,000 in Table 10.2.