Appendix C — Reading path

Read in the order of the book: each topic has something to apply now and something for later. The classic papers in the second half give the history behind today’s methods.

C.1 By topic

#	Topic	To apply now	To go deeper
1	GWAS overview	Bush & Moore (2012), Chapter 11: Genome-wide association studies, PLoS Comput Biol	Uffelmann et al. (2021), “Genome-wide association studies”, Nat Rev Methods Primers
2	Genotype QC	Marees et al. (2018), A tutorial on conducting GWAS, Int J Methods Psychiatr Res	Anderson et al. (2010), Data quality control in genetic case-control association studies, Nat Protoc
3	QC in biobanks	Turner et al. (2011), Quality control procedures for GWAS, Curr Protoc Hum Genet	—
4	Population structure	Novembre & Stephens (2008), Interpreting PCA of spatial population genetic variation, Nat Genet	—
5	PLINK	PLINK 2.0 documentation	Chang et al. (2015), “Second-generation PLINK”, GigaScience
6	Mixed models	SAIGE documentation	Zhou et al. (2018), “Efficiently controlling for case-control imbalance and sample relatedness”, Nat Genet
7	Meta-analysis	METAL documentation	Willer, Li & Abecasis (2010), “METAL: fast and efficient meta-analysis”, Bioinformatics
8	PheWAS	Bush, Oetjens & Crawford (2016), Unravelling the human genome–phenome relationship, Nat Rev Genet	—
9	Pharmacogenomics	CPIC guidelines and PharmVar	PharmCAT documentation
10	Multi-omics	Ritchie et al. (2015), Methods of integrating data to uncover genotype–phenotype interactions, Nat Rev Genet	—
11	Knowledge graphs	Barrasa & Webber (2023), Building Knowledge Graphs, O’Reilly	Bechberger & Perryman (2020), Graph Databases in Action, Manning

C.2 Classic papers

These explain why the field looks the way it does.

Genetic association

• Risch & Merikangas (1996). The future of genetic studies of complex human diseases. Science. The argument for association over linkage.
• Cordell & Clayton (2005). Genetic association studies. Lancet.
• Hirschhorn & Daly (2005). Genome-wide association studies for common diseases and complex traits. Nat Rev Genet.
• Wellcome Trust Case Control Consortium (2007). GWAS of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. The study that set the template.
• Maher (2008). Personal genomes: the case of the missing heritability. Nature.
• Hindorff et al. (2009). Potential etiologic and functional implications of GWAS loci. PNAS. The origin of the GWAS Catalog.

Epistasis and biological knowledge

• Cordell (2002). Epistasis: what it means, what it doesn’t mean, and statistical methods to detect it in humans. Hum Mol Genet.
• Ritchie et al. (2001). Multifactor-dimensionality reduction reveals high-order interactions. Am J Hum Genet.
• Moore (2003). The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered.
• Ritchie (2011). Using biological knowledge to uncover the mystery in the search for epistasis in GWAS. Ann Hum Genet.
• Thomas et al. (2009). Use of pathway information in molecular epidemiology. Hum Genomics.

Phenotypes, biobanks, and study design

• Thornton-Wells, Moore & Haines (2004). Genetics, statistics and human disease: analytical retooling for complexity. Trends Genet.
• McCarty et al. (2011). The eMERGE Network. BMC Med Genomics. Biobanks linked to electronic health records.
• Peduzzi et al. (1996). A simulation study of the number of events per variable in logistic regression. J Clin Epidemiol. Why too few cases break a logistic model.

C.3 Skills outside genetics

Skill	Start with
Unix shell	The Unix Shell, Software Carpentry
R	R for Data Science
Python data analysis	pandas documentation, getting started
SQL	SQLBolt
Git	Pro Git and Conventional Commits
Notebooks	Jupyter

C.4 How to read without stalling your project

One paper per week, tied to the step you’re on. After reading, write one line in your analysis plan about what you’ll do differently. A read that changes nothing fades within weeks.