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ICAR-IASRI MSc and PhD Statistical Genomics: QTL Mapping, GWAS & Genomic Selection: practice questions

ICAR-IASRI MSc and PhD (Agricultural Statistics, Computer Application, Bioinformatics) · India · ICAR-Indian Agricultural Statistics Research Institute with the IARI Graduate School

Original practice questions written to the published syllabus · not past papers
10worked here, free
19written to this exam in this subject
10syllabus units

What this subject covers

Statistics for molecular-breeding data: genetic map construction, interval mapping of QTL, genome-wide association with mixed models, multiple-testing control, genomic prediction (GBLUP, ridge and Bayesian alphabet), and the analysis of high-throughput expression data.

In the exams: AICE-JRF Bioinformatics Unit 5 'Statistical Genomics' (heavy); AICE-JRF Ag Stat Unit 3 (present); AICE-JRF Genetics & Plant Breeding molecular units (present)

Where the 19 questions fall

How our questions for ICAR-IASRI MSc and PhD spread across the units of Statistical Genomics: QTL Mapping, GWAS & Genomic Selection. Difficulty: 19 easy, 0 medium, 0 hard.

Multiple testing in genomics: Bonferroni and FDR3
Gene expression and RNA-seq differential analysis3
Molecular markers and genotype data quality2
Linkage map construction and map functions2
QTL mapping: single-marker, interval and composite interval mapping2
Genomic relationship matrices and GBLUP2
Statistical genomics descriptive and inferential basics2
Linkage disequilibrium and population structure1
Genome-wide association studies with mixed models1
Genomic selection: ridge regression BLUP and Bayesian methods (BayesA, BayesB)1

10 practice questions, worked

Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.

1 · Molecular markers and genotype data quality · easy

How are SNP genotypes of a diploid most commonly coded for statistical analysis?

  1. A, C, G or T as text
  2. 0, 1 or 2 copies of a chosen allele
  3. 1 for present and 0 for absent
  4. The fragment length in base pairs
Answer and working

B. 0, 1 or 2 copies of a chosen allele

Allele-count coding makes the genotype a numeric covariate for regression and relationship matrices. Presence-absence coding suits dominant markers; length suits SSRs.

Further reading: Lynch and Walsh, Genetics and Analysis of Quantitative Traits; Isik, Holland and Maltecca, Genetic Data Analysis for Plant and Animal Breeding; original papers cited in the manual

2 · Linkage map construction and map functions · easy

What LOD score is the traditional threshold for declaring linkage between two markers in an experimental population?

  1. 3
  2. 1.96
  3. 0.05
  4. 10
Answer and working

A. 3

LOD 3 means the data are 1,000 times more likely under linkage than under independence.

Further reading: Lynch and Walsh, Genetics and Analysis of Quantitative Traits; Isik, Holland and Maltecca, Genetic Data Analysis for Plant and Animal Breeding; original papers cited in the manual

3 · QTL mapping: single-marker, interval and composite interval mapping · easy

In an F₂, mean trait values of QTL genotypes QQ, Qq and qq are 12, 11 and 6. What are the additive and dominance effects?

  1. a = 6, d = 2
  2. a = 3, d = −1
  3. a = 3, d = 2
  4. a = 3, d = 5
Answer and working

C. a = 3, d = 2

a = (QQ − qq)/2 = (12 − 6)/2 = 3; d = Qq − (QQ + qq)/2 = 11 − 9 = 2. 6 is the full homozygote difference, 2a.

Further reading: Lynch and Walsh, Genetics and Analysis of Quantitative Traits; Isik, Holland and Maltecca, Genetic Data Analysis for Plant and Animal Breeding; original papers cited in the manual

4 · Multiple testing in genomics: Bonferroni and FDR · easy

A GWAS tests 500,000 SNPs. What is the Bonferroni per-test threshold for a family-wise error rate of 0.05?

  1. 5 × 10⁻⁸
  2. 0.05
  3. 1 × 10⁻⁷
  4. 1 × 10⁻⁵
Answer and working

C. 1 × 10⁻⁷

α/m = 0.05/500,000 = 1 × 10⁻⁷, i.e. −log₁₀ p = 7. 5 × 10⁻⁸ is the conventional threshold in human GWAS for about a million independent tests.

Further reading: Lynch and Walsh, Genetics and Analysis of Quantitative Traits; Isik, Holland and Maltecca, Genetic Data Analysis for Plant and Animal Breeding; original papers cited in the manual

5 · Multiple testing in genomics: Bonferroni and FDR · easy

If 500,000 SNPs with no true effects are each tested at p < 0.05, about how many false positives are expected?

  1. 5
  2. 500
  3. 25,000
  4. 0
Answer and working

C. 25,000

0.05 × 500,000 = 25,000, which is why multiple-testing correction is needed.

Further reading: Lynch and Walsh, Genetics and Analysis of Quantitative Traits; Isik, Holland and Maltecca, Genetic Data Analysis for Plant and Animal Breeding; original papers cited in the manual

6 · Multiple testing in genomics: Bonferroni and FDR · easy

When is FDR control generally preferred to FWER control?

  1. When any single false positive would be disastrous
  2. When many true effects are expected
  3. When only one hypothesis is tested
  4. When the tests are perfectly correlated
Answer and working

B. When many true effects are expected

FWER control is very strict with thousands of tests; FDR keeps the proportion of false discoveries low while retaining power to find many true effects.

Further reading: Lynch and Walsh, Genetics and Analysis of Quantitative Traits; Isik, Holland and Maltecca, Genetic Data Analysis for Plant and Animal Breeding; original papers cited in the manual

7 · Genomic relationship matrices and GBLUP · easy

In GBLUP mixed model equations, what is λ = σ²_e/σ²_g when heritability is 0.25?

  1. 3
  2. 0.33
  3. 0.25
  4. 4
Answer and working

A. 3

λ = (1 − h²)/h² = 0.75/0.25 = 3. 0.33 inverts it.

Further reading: Lynch and Walsh, Genetics and Analysis of Quantitative Traits; Isik, Holland and Maltecca, Genetic Data Analysis for Plant and Animal Breeding; original papers cited in the manual

8 · Genomic relationship matrices and GBLUP · easy

How do young candidates with genotypes but no phenotypes receive GEBVs in GBLUP?

  1. They receive the population mean
  2. From their own future progeny
  3. Through genomic relationships
  4. From pedigree information only
Answer and working

C. Through genomic relationships

Prediction flows through the G matrix: a candidate closely related genomically to good-performing reference animals receives a higher GEBV.

Further reading: Lynch and Walsh, Genetics and Analysis of Quantitative Traits; Isik, Holland and Maltecca, Genetic Data Analysis for Plant and Animal Breeding; original papers cited in the manual

9 · Gene expression and RNA-seq differential analysis · easy

A gene has mean normalised count 400 under drought and 100 under control. What is the log₂ fold change?

  1. 4
  2. 300
  3. 0.25
  4. 2
Answer and working

D. 2

log₂(400/100) = log₂ 4 = 2; 4 is the fold change itself.

Further reading: Lynch and Walsh, Genetics and Analysis of Quantitative Traits; Isik, Holland and Maltecca, Genetic Data Analysis for Plant and Animal Breeding; original papers cited in the manual

10 · Statistical genomics descriptive and inferential basics · easy

A sample has 360 AA, 480 Aa and 160 aa individuals. What is the frequency of allele A?

  1. 0.36
  2. 0.60
  3. 0.48
  4. 0.40
Answer and working

B. 0.60

p = (2 × 360 + 480)/2,000 = 0.60. 0.36 is the AA genotype frequency.

Further reading: Lynch and Walsh, Genetics and Analysis of Quantitative Traits; Isik, Holland and Maltecca, Genetic Data Analysis for Plant and Animal Breeding; original papers cited in the manual

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Statistical Genomics: QTL Mapping, GWAS & Genomic SelectionThe full study manual, free ICAR-IASRI MSc and PhDFormat, sections and how it has changed

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