Rigor and Reproducibility in Genetics and Genomics: Peer-Reviewed, Published, Cited (Paperback)

Section 1: Introduction 1. Review of reproducibility in genetic studies (John Ioannidis, PhD) 2. Review of common statistical practices and assumptions (Daniel Benjamin, PhD) 3. Rigor in the classroom and in the mentor/mentee relationship (Douglas F. Dluzen, PhD)

Section 2: Genotyping 4. Review of GWAS studies (Naomi Wray, PhD) 5. Best practices in GWAS and pitfalls to avoid (Daniel Benjamin, PhD) 6. GWAS learning and training activities (Bethany Bowling, PhD, Northern Kentucky University) 7. DNA sequencing for genotyping and best practices for comparative genomics (Heidi Rehm, PhD) 8. Statistical approaches for rigorous genome sequence analyses and genotype imputations (Nianjun Liu, PhD, Indiana University) 9. DNA sequencing activities; classroom case studies (Rivka Glaser, PhD, Stevenson University)

Section 3: Gene Expression 10. Review of gene expression using microarray and RNA-seq (Alexandra Soboleva) 11. Best statistical approaches for analysis of gene expression data (Purvesh Katri, PhD) 12. Validating approaches to gene expression studies (Yaov Benjamin, PhD) 13. Misconceptions in the classroom and case studies (Dina Newman, PhD)

Section 4: Epigenetic Analyses 14. Review of DNA methylation and other omits data resources (Thomas Jenuwein) 15. Statistical approaches to improve rigor in Chip-Seq, methyl-seq, and epi datasets (Olga Troyanskaya, PhD) 16. Best methods for combining DNA, RNA, and methylation data (Jennie Williams, PhD) 17. Teaching epigenetics in the classroom (Louisa Stark, PhD)

Section 5: Gene Editing Technologies 18. Review of current gene editing technologies, including CRISPR (Lei Stanley Qi, PhD) 19. Best strategies to design and implement CRISPR-based genetic analysis (Kathy Niakan, Phd) 20. CRISPR classroom activities and/or case studies (Sylvain Moineau, PhD)

Section 6: Conclusion

Appendix: Links to supplementary resources