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The increasing availability and complexity of next-generation sequencing (NGS) data sets make ongoing training an essential component of conservation and population genetics research. A workshop entitled "ConGen 2018" was recently held to train researchers in conceptual and practical aspects of NGS data production and analysis for conservation and ecological applications. Sixteen instructors provided helpful lectures, discussions, and hands-on exercises regarding how to plan, produce, and analyze data for many important research questions. Lecture topics ranged from understanding probabilistic (e.g., Bayesian) genotype calling to the detection of local adaptation signatures from genomic, transcriptomic, and epigenomic data. We report on progress in addressing central questions of conservation genomics, advances in NGS data analysis, the potential for genomic tools to assess adaptive capacity, and strategies for training the next generation of conservation genomicists., (© The American Genetic Association 2020.)

The r computing and statistical language community has developed a myriad of resources for conducting population genetic analyses. However, resources for learning how to carry out population genetic analyses in r are scattered and often incomplete, which can make acquiring this skill unnecessarily difficult and time consuming. To address this gap, we developed an online community resource with guidance and working demonstrations for conducting population genetic analyses in r. The resource is freely available at http://popgen.nescent.org and includes material for both novices and advanced users of r for population genetics. To facilitate continued maintenance and growth of this resource, we developed a toolchain, process and conventions designed to (i) minimize financial and labour costs of upkeep; (ii) to provide a low barrier to contribution; and (iii) to ensure strong quality assurance. The toolchain includes automatic integration testing of every change and rebuilding of the website when new vignettes or edits are accepted. The process and conventions largely follow a common, distributed version control-based contribution workflow, which is used to provide and manage open peer review by designated website editors. The online resources include detailed documentation of this process, including video tutorials. We invite the community of population geneticists working in r to contribute to this resource, whether for a new use case of their own, or as one of the vignettes from the 'wish list' we maintain, or by improving existing vignettes., (© 2016 John Wiley & Sons Ltd.)

Summary: GenAlEx: Genetic Analysis in Excel is a cross-platform package for population genetic analyses that runs within Microsoft Excel. GenAlEx offers analysis of diploid codominant, haploid and binary genetic loci and DNA sequences. Both frequency-based (F-statistics, heterozygosity, HWE, population assignment, relatedness) and distance-based (AMOVA, PCoA, Mantel tests, multivariate spatial autocorrelation) analyses are provided. New features include calculation of new estimators of population structure: G'(ST), G''(ST), Jost's D(est) and F'(ST) through AMOVA, Shannon Information analysis, linkage disequilibrium analysis for biallelic data and novel heterogeneity tests for spatial autocorrelation analysis. Export to more than 30 other data formats is provided. Teaching tutorials and expanded step-by-step output options are included. The comprehensive guide has been fully revised., Availability and Implementation: GenAlEx is written in VBA and provided as a Microsoft Excel Add-in (compatible with Excel 2003, 2007, 2010 on PC; Excel 2004, 2011 on Macintosh). GenAlEx, and supporting documentation and tutorials are freely available at: http://biology.anu.edu.au/GenAlEx., Contact: rod.peakall@anu.edu.au.

Population genetics is often taught in introductory biology classes, starting with the Hardy-Weinberg principle (HWP) and genetic drift. Here I argue that teaching these two topics first aligns neither with current expert knowledge, nor with good pedagogy. Student difficulties with mathematics in general, and probability in particular, make population genetics difficult to teach and learn. I recommend an alternative, historically inspired ordering of population genetics topics, based on progressively increasing mathematical difficulty. This progression can facilitate just-in-time math instruction. This alternative ordering includes, but does not privilege, the HWP and genetic drift. Stochastic events whose consequences are felt within a single generation, and the deterministic accumulation of the effects of selection across multiple generations, are both taught before tackling the stochastic accumulation of the effects of accidents of sampling., (Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Competing Interests: The author has declared that no competing interests exist.

This essay suggests a colonial and Enlightenment genealogy for racial ideas more commonly associated with the nineteenth and twentieth centuries. Nelson exposes unfulfilled pseudo-eugenic plans, focused on the French Caribbean colony of Saint-Domingue, in which racial engineering through controlled "breeding" was seen as a solution to challenges to stability after the Seven Years' War.

The computer-assisted software for population genetics' teaching and research is designed with Microsoft Visual Basic 6.0. It takes all factors affecting population genetic structure into account. By way of setting various conditional parameters, calculating theoretically, simulating with computer and choosing the states of show, the system displays the results in charts and tables accurately. It also presents distribution pattern, statistical characteristics and the change of population gene frequencies and genotype frequencies in the inter-generation visually. The software is a mature system for teaching and researches on the aspects of its functions, operations and interface.

With the baccalaureate as the desired entry level to practice for community health nurses, schools of nursing must ensure their content and methods are robust and current to the fields of both nursing and public health. A foundation in such diverse areas as population biology and population genetics is now desirable for practice. Combining principles of adult learning and case study methodology, this article describes the development of a central case study with extensive related components. Students explored the principles of population biology, epidemiology, community health nursing, and public health sciences and policy in multiple media using several learning strategies.

How has growing knowledge about human genetics affected how American textbooks present race? This article analyzes 80 biology textbooks published from 1952 to 2002 to reveal that U.S. biology texts have pursued the topic of race with renewed vigor in recent years. Moreover, textbooks have redefined race as genetic without furnishing empirical evidence for this framing. The textbooks' transformation sheds light on the broader relationship between race and science in the United States, where claims about racial difference have not only drawn instrumentally and selectively from empirical research, but at times forgo scientific grounding altogether. As the textbooks show, both the tight and the loose linkage of race to science can preserve the cultural authority of the race concept. The texts also make clear that race is not a one-time construct or a relic of centuries past. Instead, it is continually remade -- and is being reworked today -- suggesting its dynamic adaptation for ongoing use as a fundamental tool of social stratification.

The four comprehensive experiments were concreted from a series of verifying genetics experiments, which are relatively specific. They were divided into four pieces of experiment modules: Drosophila melanogaster's classical comprehensive genetics experiment, Bio-Cytogenetics comprehensive experiment, Molecular Genetics comprehensive experiment and Population genetics experiment. The first three are major courses and the last one is a selective course. All of these four require experiment modules. These experiments should be comprehensive, designable and innovative. The lab should open to students the whole day in order to improve students' interest and skill in experiment. The ability to design and innovate experiment could be developed through the training.