THE HANDBOOK OF STATISTICAL GENOMICS: Quantitative Methods, Computational Frameworks, and Applications in Modern Genetic Research.

Genomic data doesn't interpret itself — and the researchers who master the statistics behind it are the ones driving discovery. The Handbook of Statistical Genomics is the comprehensive quantitative reference for biostatisticians, computational biologists, geneticists, and data scientists operating in an era defined by whole-genome sequencing, population-scale biobanks, and multi-omics integration. This is the methodological backbone your research has been missing. What You'll Master: Probability frameworks, linkage analysis, and population stratification correction for genetic association studies GWAS design, quality control pipelines, and polygenic risk score construction Bayesian approaches to variant interpretation and rare variant analysis Epistasis modeling, haplotype reconstruction, and admixed population genomics Machine learning and deep learning architectures applied to sequence and omics data Dimensionality reduction strategies for high-dimensional genomic datasets Reproducible bioinformatics pipelines using PLINK, GATK, BOLT-LMM, and R/Bioconductor Cutting-edge chapters on single-cell genomics statistics, spatial transcriptomics, and EHR-genomic integration Every method is presented with an emphasis on reproducibility, transparency, and real-world application — so what you learn here translates directly into publication-quality research. The most rigorous genomic research starts with the most rigorous statistical foundation. Secure your copy today and bring the quantitative precision your genomics work deserves.