Overview

Master the forces that shape genetic variation-and turn them into confident predictions about complex traits. This comprehensive, classroom-tested guide walks you from Hardy-Weinberg intuition and Wright-Fisher dynamics to modern GWAS, QTL mapping, and causal inference. With 33 tightly organized chapters, crystal-clear explanations, and 500+ exam-style multiple choice questions with fully explained answers, it's the fastest path from theory to mastery. What you'll learn How selection, drift, mutation, and migration jointly drive allele-frequency change-and when each dominates Why effective population size and inbreeding transform variation, fitness, and inference The logic of linkage disequilibrium, recombination, F-statistics, and demographic footprints The full quantitative toolkit: variance components, heritability, GxE, epistasis, breeder's equation, and G-matrices How to design, power, and interpret QTL studies and GWAS with mixed models, fine-mapping, and polygenic scores How to separate signal from artifact: stratification, relatedness, assortative mating, pleiotropy, and measurement error From association to causation: robust thinking with within-family designs and Mendelian randomization How to read complex-trait claims critically, reproduce results, and communicate them responsibly Practice and mastery built in 500+ carefully crafted multiple choice questions with step-by-step answers that teach, not just test End-of-chapter summaries, key formulas, and common-pitfall checklists to lock in learning quickly Real-world case studies spanning human disease, agriculture, and conservation genetics Why this book stands out Assumptions-first approach: know when methods work-and when they don't Balanced intuition and math: derivations you can follow, insights you can use Designed for self-study, exam prep, and course adoption across genetics, evolution, biostatistics, and bioinformatics Who it's for Undergraduates and graduate students aiming for top exam scores and deep understanding Instructors seeking a rigorous, modular text with built-in assessment Researchers and data scientists entering genomics who need a practical, assumption-aware roadmap

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