Complexities of recapitulating polygenic effects in natural populations: replication of genetic effects on wing shape in artificially selected and wild-caught populations of Drosophila melanogaster.

Identifying the genetic architecture of complex traits is important to many geneticists, including those interested in human disease, plant and animal breeding, and evolutionary genetics. Advances in sequencing technology and statistical methods for genome-wide association studies have allowed for the identification of more variants with smaller effect sizes, however, many of these identified polymorphisms fail to be replicated in subsequent studies. In addition to sampling variation, this failure to replicate reflects the complexities introduced by factors including environmental variation, genetic background, and differences in allele frequencies among populations. Using Drosophila melanogaster wing shape, we ask if we can replicate allelic effects of polymorphisms first identified in a genome-wide association studies in three genes: dachsous, extra-macrochaete, and neuralized, using artificial selection in the lab, and bulk segregant mapping in natural populations. We demonstrate that multivariate wing shape changes associated with these genes are aligned with major axes of phenotypic and genetic variation in natural populations. Following seven generations of artificial selection along the dachsous shape change vector, we observe genetic differentiation of variants in dachsous and genomic regions containing other genes in the hippo signaling pathway. This suggests a shared direction of effects within a developmental network. We also performed artificial selection with the extra-macrochaete shape change vector, which is not a part of the hippo signaling network, but showed a largely shared direction of effects. The response to selection along the emc vector was similar to that of dachsous, suggesting that the available genetic diversity of a population, summarized by the genetic (co)variance matrix (G), influenced alleles captured by selection. Despite the success with artificial selection, bulk segregant analysis using natural populations did not detect these same variants, likely due to the contribution of environmental variation and low minor allele frequencies, coupled with small effect sizes of the contributing variants.

Introductory biology undergraduate students' mixed ideas about genetic information flow.

The core concept of genetic information flow was identified in recent calls to improve undergraduate biology education. Previous work shows that students have difficulty differentiating between the three processes of the Central Dogma (CD; replication, transcription, and translation). We built upon this work by developing and applying an analytic coding rubric to 1050 student written responses to a three-question item about the CD. Each response was previously coded only for correctness using a holistic rubric. Our rubric captures subtleties of student conceptual understanding of each process that previous work has not yet captured at a large scale. Regardless of holistic correctness scores, student responses included five or six distinct ideas. By analyzing common co-occurring rubric categories in student responses, we found a common pair representing two normative ideas about the molecules produced by each CD process. By applying analytic coding to student responses preinstruction and postinstruction, we found student thinking about the processes involved was most prone to change. The combined strengths of analytic and holistic rubrics allow us to reveal mixed ideas about the CD processes and provide a detailed picture of which conceptual ideas students draw upon when explaining each CD process.

Mixed Student Ideas about Mechanisms of Human Weight Loss.

Recent calls for college biology education reform have identified "pathways and transformations of matter and energy" as a big idea in biology crucial for students to learn. Previous work has been conducted on how college students think about such matter-transforming processes; however, little research has investigated how students connect these ideas. Here, we probe student thinking about matter transformations in the familiar context of human weight loss. Our analysis of 1192 student constructed responses revealed three scientific (which we label "Normative") and five less scientific (which we label "Developing") ideas that students use to explain weight loss. Additionally, students combine these ideas in their responses, with an average number of 2.19 ± 1.07 ideas per response, and 74.4% of responses containing two or more ideas. These results highlight the extent to which students hold multiple (both correct and incorrect) ideas about complex biological processes. We described student responses as conforming to either Scientific, Mixed, or Developing descriptive models, which had an average of 1.9 ± 0.6, 3.1 ± 0.9, and 1.7 ± 0.8 ideas per response, respectively. Such heterogeneous student thinking is characteristic of difficulties in both conceptual change and early expertise development and will require careful instructional intervention for lasting learning gains.

What are they thinking? Automated analysis of student writing about acid-base chemistry in introductory biology.

Students' writing can provide better insight into their thinking than can multiple-choice questions. However, resource constraints often prevent faculty from using writing assessments in large undergraduate science courses. We investigated the use of computer software to analyze student writing and to uncover student ideas about chemistry in an introductory biology course. Students were asked to predict acid-base behavior of biological functional groups and to explain their answers. Student explanations were rated by two independent raters. Responses were also analyzed using SPSS Text Analysis for Surveys and a custom library of science-related terms and lexical categories relevant to the assessment item. These analyses revealed conceptual connections made by students, student difficulties explaining these topics, and the heterogeneity of student ideas. We validated the lexical analysis by correlating student interviews with the lexical analysis. We used discriminant analysis to create classification functions that identified seven key lexical categories that predict expert scoring (interrater reliability with experts = 0.899). This study suggests that computerized lexical analysis may be useful for automatically categorizing large numbers of student open-ended responses. Lexical analysis provides instructors unique insights into student thinking and a whole-class perspective that are difficult to obtain from multiple-choice questions or reading individual responses.