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We draw on our experiences with mentoring two students to develop a framework for undergraduate research projects with Twitter data. Leveraging backward design principles, we share our learning objectives and rubric for summative assessments. To illustrate the value of Twitter as a data source, we detail methods for collecting and analyzing tweets. We conclude by emphasizing how Twitter text analysis projects enable students to formulate original research questions, collect and analyze data, and communicate findings and their implications. Supplementary materials for this article are available online.

The high mapping resolution of multiparental populations, combined with technology to measure tens of thousands of phenotypes, presents a need for quantitative methods to enhance understanding of the genetic architecture of complex traits. When multiple traits map to a common genomic region, knowledge of the number of distinct loci provides important insight into the underlying mechanism and can assist planning for subsequent experiments. We extend the method of Jiang and Zeng (1995), for testing pleiotropy with a pair of traits, to the case of more than two alleles. We also incorporate polygenic random effects to account for population structure. We use a parametric bootstrap to determine statistical significance. We apply our methods to a behavioral genetics data set from Diversity Outbred mice. Our methods have been incorporated into the R package qtl2pleio., (Copyright © 2019 Boehm et al.)

A central goal in the analysis of complex traits is to identify genes that modify a phenotype. Modifiers of a cancer phenotype may act either intrinsically or extrinsically on the salient cell lineage. Germline point mutagenesis by ethylnitrosourea can provide alleles for a gene of interest that include loss-, gain-, or alteration-of-function. Unlike strain polymorphisms, point mutations with heterozygous quantitative phenotypes are detectable in both essential and nonessential genes and are unlinked from other variants that might confound their identification and analysis. This report analyzes strategies seeking quantitative mutational modifiers of Apc(Min) in the mouse. To identify a quantitative modifier of a phenotype of interest, a cluster of test progeny is needed. The cluster size can be increased as necessary for statistical significance if the founder is a male whose sperm is cryopreserved. A second critical element in this identification is a mapping panel free of polymorphic modifiers of the phenotype, to enable low-resolution mapping followed by targeted resequencing to identify the causative mutation. Here, we describe the development of a panel of six "isogenic mapping partner lines" for C57BL/6J, carrying single-nucleotide markers introduced by mutagenesis. One such derivative, B6.SNVg, shown to be phenotypically neutral in combination with Apc(Min), is an appropriate mapping partner to locate induced mutant modifiers of the Apc(Min) phenotype. The evolved strategy can complement four current major initiatives in the genetic analysis of complex systems: the Genome-wide Association Study; the Collaborative Cross; the Knockout Mouse Project; and The Cancer Genome Atlas., (Copyright © 2014 Dove et al.)