This is a reference manual for the elegant, yet hideously complex concept of effective population size (Ne), inspired by a classic, self-published manual of automotive repair ‘for the compleat idiot’. The Guide is timely, given the recent Kunming-Montreal Global Biodiversity Framework, where 196 Parties committed to tracking genetic diversity—and estimating Ne—for all species. Ne is a human construct, but a useful one that allows us to capture diverse aspects of an organism's biology in a single number. The Guide collates in one location factual information about effective population size, with a focus on topics of practical relevance to scientists and managers studying real populations; it covers definition, computation and estimation of effective size, both demographically and genetically. As appropriate, the reader is directed to other primary sources for more details. A ‘Don't Do These Things’ section lists several ill-advised approaches to dealing with Ne, and an Appendix provides useful tools and practical suggestions for interested users. A special section considers both possibilities and challenges presented by the genomics revolution. Availability of vast numbers of genetic markers increases precision, but less than some might think, and simultaneously introduces new challenges involving filtering and bioinformatics processing. As annotated genomes become more common for non-model species, opportunities are opened to address qualitatively different questions, including reconstructing historical changes in Ne through time.

Summary: Familial aggregation analysis is the first fundamental step to perform when assessing the extent of genetic background of a disease. However, there is a lack of software to analyze the familial clustering of complex phenotypes in very large pedigrees. Such pedigrees can be utilized to calculate measures that express trait aggregation on both the family and individual level, providing valuable directions in choosing families for detailed follow-up studies. We developed FamAgg, an open source R package that contains both established and novel methods to investigate familial aggregation of traits in large pedigrees. We demonstrate its use and interpretation by analyzing a publicly available cancer dataset with more than 20 000 participants distributed across approximately 400 families.