Fly-CURE, a multi-institutional CURE using Drosophila, increases students' confidence, sense of belonging, and persistence in research

The Fly-CURE is a genetics-focused multi-institutional Course-Based Undergraduate Research Experience (CURE) that provides undergraduate students with hands-on research experiences within a course. Through the Fly-CURE, undergraduate students at diverse types of higher education institutions across the United States map and characterize novel mutants isolated from a genetic screen in Drosophila melanogaster. To date, more than 20 mutants have been studied across 20 institutions, and our scientific data have led to eleven publications with more than 500 students as authors. To evaluate the impact of the Fly-CURE experience on students, we developed and validated assessment tools to identify students' perceived research self-efficacy, sense of belonging in science, and intent to pursue additional research opportunities. Our data, collected over three academic years and involving 14 institutions and 480 students, show gains in these metrics after completion of the Fly-CURE across all student subgroups analyzed, including comparisons of gender, academic status, racial and ethnic groups, and parents' educational background. Importantly, our data also show differential gains in the areas of self-efficacy and interest in seeking additional research opportunities between Fly-CURE students with and without prior research experience, illustrating the positive impact of research exposure (dosage) on student outcomes. Altogether, our data indicate that the Fly-CURE experience has a significant impact on students' efficacy with research methods, sense of belonging to the scientific research community, and interest in pursuing additional research experiences.

Clock genes may influence bipolar disorder susceptibility and dysfunctional circadian rhythm

Several previous studies suggest that dysfunction of circadian rhythms may increase susceptibility to bipolar disorder (BP). We conducted an association study of five circadian genes (CRY2, PER1-3, and TIMELESS) in a family collection of 36 trios and 79 quads (Sample I), and 10 circadian genes (ARNTL, ARNTL2, BHLHB2, BHLHB3, CLOCK, CRY1, CSNK1D, CSNK1E, DBP, and NR1D1) in an extended family collection of 70 trios and 237 quads (Sample II), which includes the same 114 families but not necessarily the same individuals as Sample I. In Sample II, the Sibling-Transmission Disequilibrium Test (sib-tdt) analysis showed nominally significant association of BP with three SNPs within or near the CLOCK gene (rs534654, P = 0.0097; rs6850524, P = 0.012; rs4340844, P = 0.015). In addition, SNPs in the ARNTL2, CLOCK, DBP, and TIMELESS genes and haplotypes in the ARNTL, CLOCK, CSNK1E, and TIMELESS genes showed suggestive evidence of association with several circadian phenotypes identified in BP patients. However, none of these associations reached gene-wide or experiment-wide significance after correction for multiple-testing. A multi-locus interaction between rs6442925 in the 5' upstream of BHLHB2, rs1534891 in CSNK1E, and rs534654 near the 3' end of the CLOCK gene, however, is significantly associated with BP (P = 0.00000172). It remains significant after correcting for multiple testing using the False Discovery Rate method. Our results indicate an interaction between three circadian genes in susceptibility to bipolar disorder.

Genetic studies of stuttering in a founder population

Genome-wide linkage and association analyses were conducted to identify genetic determinants of stuttering in a founder population in which 48 individuals affected with stuttering are connected in a single 232-person genealogy. A novel approach was devised to account for all necessary relationships to enable multipoint linkage analysis. Regions with nominal evidence for linkage were found on chromosomes 3 (P=0.013, 208.8 centiMorgans (cM)), 13 (P=0.012, 52.6 cM), and 15 (P=0.02, 100 cM). Regions with nominal evidence for association with stuttering that overlapped with a linkage signal are located on chromosomes 3 (P=0.0047, 195 cM), 9 (P=0.0067, 46.5 cM), and 13 (P=0.0055, 52.6 cM). We also conducted the first meta-analysis for stuttering using results from linkage studies in the Hutterites and The Illinois International Genetics of Stuttering Project and identified regions with nominal evidence for linkage on chromosomes 2 (P=0.013, 180-195 cM) and 5 (P=0.0051, 105-120 cM; P=0.015, 120-135 cM). None of the linkage signals detected in the Hutterite sample alone, or in the meta-analysis, meet genome-wide criteria for significance, although some of the stronger signals overlap linkage mapping signals previously reported for other speech and language disorders.

EDUCATIONAL OBJECTIVES

After reading this article, the reader will be able to: (1) summarize information about the background of common disorders and methodology of genetic studies; (2) evaluate the role of genetics in stuttering; (3) discuss the value of using founder populations in genetic studies; (4) articulate the importance of combining several studies in a meta-analysis; (5) discuss the overlap of genetic signals identified in stuttering with other speech and language disorders.

New complexities in the genetics of stuttering: significant sex-specific linkage signals

Stuttering is a speech disorder long recognized to have a genetic component. Recent linkage studies mapped a susceptibility locus for stuttering to chromosome 12 in 46 highly inbred families ascertained in Pakistan. We report here on linkage studies in 100 families of European descent ascertained in the United States, Sweden, and Israel. These families included 252 individuals exhibiting persistent stuttering, 45 individuals classified as recovered from stuttering, and 19 individuals too young to classify. Primary analyses identified moderate evidence for linkage of the broader diagnosis of "ever stuttered" (including both persistent and recovered stuttering) on chromosome 9 (LOD = 2.3 at 60 cM) and of the narrower diagnosis of persistent stuttering on chromosome 15 (LOD = 1.95 at 23 cM). In contrast, sex-specific evidence for linkage on chromosome 7 at 153 cM in the male-only data subset (LOD = 2.99) and on chromosome 21 at 34 cM in the female-only data subset (LOD = 4.5) met genomewide criteria for significance. Secondary analyses revealed a significant increase in the evidence for linkage on chromosome 12, conditional on the evidence for linkage at chromosome 7, with the location of the increased signal congruent with the previously reported signal in families ascertained in Pakistan. In addition, a region on chromosome 2 (193 cM) showed a significant increase in the evidence for linkage conditional on either chromosome 9 (positive) or chromosome 7 (negative); this chromosome 2 region has been implicated elsewhere in studies on autism, with increased evidence for linkage observed when the sample is restricted to those with delayed onset of phrase speech. Our results support the hypothesis that the genetic component to stuttering has significant sex effects.

Rational inferences about departures from Hardy-Weinberg equilibrium

Previous studies have explored the use of departure from Hardy-Weinberg equilibrium (DHW) for fine mapping Mendelian disorders and for general fine mapping. Other studies have used Hardy-Weinberg tests for genotyping quality control. To enable investigators to make rational decisions about whether DHW is due to genotyping error or to underlying biology, we developed an analytic framework and software to determine the parameter values for which DHW might be expected for common diseases. We show analytically that, for a general disease model, the difference between population and Hardy-Weinberg expected genotypic frequencies (delta) at the susceptibility locus is a function of the susceptibility-allele frequency (q), heterozygote relative risk (beta), and homozygote relative risk (gamma). For unaffected control samples, is a function of risk in nonsusceptible homozygotes (alpha), the population prevalence of disease (KP), q, beta, and gamma. We used these analytic functions to calculate and the number of cases or controls needed to detect DHW for a range of genetic models consistent with common diseases (1.1 < or = gamma < or = 10 and 0.005 < or = KP < or = 0.2). Results suggest that significant DHW can be expected in relatively small samples of patients over a range of genetic models. We also propose a goodness-of-fit test to aid investigators in determining whether a DHW observed in the context of a case-control study is consistent with a genetic disease model. We illustrate how the analytic framework and software can be used to help investigators interpret DHW in the context of association studies of common diseases.