Technical interview guide

Statistical Genomics

The statistical machinery behind genome-wide association studies — testing millions of variants for association with a trait without drowning in false positives.

Read: 50 min
Practice MCQs: 25
Interview QA: 25
Edition: v4
Editorial status: Reviewed
Relevant for: Computational Biologist

Scope: PLINK 2 alpha 7.x; GWAS Catalog and summary-statistics standards; EIGENSTRAT, BOLT-LMM, LD Score, KING, imputation, TOPMed and PRS source publications reviewed 2026-09-04.

Interview QA

Treat each question like a live interview question: answer out loud first (structure, assumptions, tradeoffs), then open the model answer to spot gaps and rehearse a tighter follow-up.

Curated: 2026-10-05 · Written: 2026-10-05 · Reviewed: 2026-10-04

QA-1

Design and defend an analysis for sample identity QC.

QA-2

Design and defend an analysis for variant QC.

QA-3

Design and defend an analysis for study estimands.

QA-4

Design and defend an analysis for Hardy-Weinberg equilibrium.

QA-5

Design and defend an analysis for allele orientation.

QA-6

Design and defend an analysis for population stratification.

QA-7

How do modern linear mixed models such as BOLT-LMM correct for both cryptic relatedness and fine-scale population stratification without losing power compared to fixed-effect PC adjustment?

QA-8

Design and defend an analysis for relatedness.

QA-9

How do you statistically fine-map a GWAS locus and evaluate whether it shares a single causal variant with an eQTL signal?

QA-10

Design and defend an analysis for binary-trait association.

QA-11

Design and defend an analysis for quantitative traits.

QA-12

Design and defend an analysis for multiple testing.

QA-13

Design and defend an analysis for genomic inflation.

QA-14

Design and defend an analysis for linkage disequilibrium.

QA-15

Design and defend an analysis for genotype imputation.

QA-16

Design and defend an analysis for imputation quality.

QA-17

Design and defend an analysis for meta-analysis harmonization.

QA-18

Design and defend an analysis for heterogeneity.

QA-19

Design and defend an analysis for rare-variant aggregation.

QA-20

Design and defend an analysis for LD score regression.

QA-21

Design and defend an analysis for Mendelian randomization.

QA-22

Design and defend an analysis for polygenic scores.

QA-23

Design and defend an analysis for cross-population portability.

QA-24

Design and defend an analysis for summary-statistic provenance.

QA-25