Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model

This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the corrected estimators. The numerical results show that the bias correction scheme yields nearly unbiased estimates. We also give an application to a real data set.Comment: 12 pages. Statistical Paper

Using comparative genomic hybridization to survey genomic sequence divergence across species: a proof-of-concept from Drosophila

<p>Abstract</p> <p>Background</p> <p>Genome-wide analysis of sequence divergence among species offers profound insights into the evolutionary processes that shape lineages. When full-genome sequencing is not feasible for a broad comparative study, we propose the use of array-based comparative genomic hybridization (aCGH) in order to identify orthologous genes with high sequence divergence. Here we discuss experimental design, statistical power, success rate, sources of variation and potential confounding factors. We used a spotted PCR product microarray platform from <it>Drosophila melanogaster </it>to assess sequence divergence on a gene-by-gene basis in three fully sequenced heterologous species (<it>D. sechellia</it>, <it>D. simulans</it>, and <it>D. yakuba</it>). Because complete genome assemblies are available for these species this study presents a powerful test for the use of aCGH as a tool to measure sequence divergence.</p> <p>Results</p> <p>We found a consistent and linear relationship between hybridization ratio and sequence divergence of the sample to the platform species. At higher levels of sequence divergence (< 92% sequence identity to <it>D. melanogaster</it>) ~84% of features had significantly less hybridization to the array in the heterologous species than the platform species, and thus could be identified as "diverged". At lower levels of divergence (≥ 97% identity), only 13% of genes were identified as diverged. While ~40% of the variation in hybridization ratio can be accounted for by variation in sequence identity of the heterologous sample relative to <it>D. melanogaster</it>, other individual characteristics of the DNA sequences, such as GC content, also contribute to variation in hybridization ratio, as does technical variation.</p> <p>Conclusions</p> <p>Here we demonstrate that aCGH can accurately be used as a proxy to estimate genome-wide divergence, thus providing an efficient way to evaluate how evolutionary processes and genomic architecture can shape species diversity in non-model systems. Given the increased number of species for which microarray platforms are available, comparative studies can be conducted for many interesting lineages in order to identify highly diverged genes that may be the target of natural selection.</p

Neurogenomics and the role of a large mutational target on rapid behavioral change

© 2016 The Author(s). Background: Behavior, while complex and dynamic, is among the most diverse, derived, and rapidly evolving traits in animals. The highly labile nature of heritable behavioral change is observed in such evolutionary phenomena as the emergence of converged behaviors in domesticated animals, the rapid evolution of preferences, and the routine development of ethological isolation between diverging populations and species. In fact, it is believed that nervous system development and its potential to evolve a seemingly infinite array of behavioral innovations played a major role in the successful diversification of metazoans, including our own human lineage. However, unlike other rapidly evolving functional systems such as sperm-egg interactions and immune defense, the genetic basis of rapid behavioral change remains elusive. Presentation of the hypothesis: Here we propose that the rapid divergence and widespread novelty of innate and adaptive behavior is primarily a function of its genomic architecture. Specifically, we hypothesize that the broad diversity of behavioral phenotypes present at micro- and macroevolutionary scales is promoted by a disproportionately large mutational target of neurogenic genes. We present evidence that these large neuro-behavioral targets are significant and ubiquitous in animal genomes and suggest that behavior's novelty and rapid emergence are driven by a number of factors including more selection on a larger pool of variants, a greater role of phenotypic plasticity, and/or unique molecular features present in large genes. We briefly discuss the origins of these large neurogenic genes, as they relate to the remarkable diversity of metazoan behaviors, and highlight key consequences on both behavioral traits and neurogenic disease across, respectively, evolutionary and ontogenetic time scales. Testing the hypothesis: Current approaches to studying the genetic mechanisms underlying rapid phenotypic change primarily focus on identifying signatures of Darwinian selection in protein-coding regions. In contrast, the large mutational target hypothesis places genomic architecture and a larger allelic pool at the forefront of rapid evolutionary change, particularly in genetic systems that are polygenic and regulatory in nature. Genomic data from brain and neural tissues in mammals as well as a preliminary survey of neurogenic genes from comparative genomic data support this hypothesis while rejecting both positive and relaxed selection on proteins or higher mutation rates. In mammals and invertebrates, neurogenic genes harbor larger protein-coding regions and possess a richer regulatory repertoire of miRNA targets and transcription factor binding sites. Overall, neurogenic genes cover a disproportionately large genomic fraction, providing a sizeable substrate for evolutionary, genetic, and molecular mechanisms to act upon. Readily available comparative and functional genomic data provide unexplored opportunities to test whether a distinct neurogenomic architecture can promote rapid behavioral change via several mechanisms unique to large genes, and which components of this large footprint are uniquely metazoan. Implications of the hypothesis: The large mutational target hypothesis highlights the eminent roles of mutation and functional genomic architecture in generating rapid developmental and evolutionary change. It has broad implications on our understanding of the genetics of complex adaptive traits such as behavior by focusing on the importance of mutational input, from SNPs to alternative transcripts to transposable elements, on driving evolutionary rates of functional systems. Such functional divergence has important implications in promoting behavioral isolation across short- and long-term timescales. Due to genome-scaled polygenic adaptation, the large target effect also contributes to our inability to identify adapted behavioral candidate genes. The presence of large neurogenic genes, particularly in the mammalian brain and other neural tissues, further offers emerging insight into the etiology of neurodevelopmental and neurodegenerative diseases. The well-known correlation between neurological spectrum disorders in children and paternal age may simply be a direct result of aging fathers accumulating mutations across these large neurodevelopmental genes. The large mutational target hypothesis can also explain the rapid evolution of other functional systems covering a large genomic fraction such as male fertility and its preferential association with hybrid male sterility among closely related taxa. Overall, a focus on mutational potential may increase our power in understanding the genetic basis of complex phenotypes such as behavior while filling a general gap in understanding their evolution

The biological, biographical, and biospheric dimensions of puberty onset: Using Bio3Science to frame transdisciplinary health research on puberty

This paper uses the case of puberty to characterize a new health science framework called Bio3Science and to provide an example of how trending research on biosocial mechanisms can be put to use to bridge siloed disciplines as well as the translational gap. Examined as an intricate, open-ended problem of scientific understanding, puberty offers a window to examine how three dimensions of human life – biology, biography, and biosphere – can be understood to shape human health and disease. Methods: Using the Bio3Science framework, a biosocial model of puberty was developed and critiqued by an interdisciplinary group of health science and social science researchers in a design studio setting. Results: The design and critique process resulted in a model and new conceptual framework that depicts puberty as a highly variable life experience that integrates multiple dense interactions and context-specific responses; within this model, the gene regulatory network (GRN) transformed from a biological to a biosocial mechanism, with conceptual and concrete applications. Conclusions: By providing a new, generalizable framework for understanding the integration of biology, biography, and biosphere in health research, opportunities emerge for more interdisciplinary work puberty, but also and more broadly, for more collaborative, inter-epistemological health research through the Bio3Science framework.Temple University. College of Liberal ArtsTemple University. College of Science and TechnologyGeography and Urban StudiesBiolog

Exposure misclassification bias in the estimation of vaccine effectiveness

In epidemiology, a typical measure of interest is the risk of disease conditional upon exposure. A common source of bias in the estimation of risks and risk ratios is misclassification. Exposure misclassification affects the measurement of exposure, i.e. the variable one conditions on. This article explains how to assess biases under non-differential exposure misclassification when estimating vaccine effectiveness, i.e. the vaccine-induced relative reduction in the risk of disease. The problem can be described in terms of three binary variables: the unobserved true exposure status, the observed but potentially misclassified exposure status, and the observed true disease status. The bias due to exposure misclassification is quantified by the difference between the naive estimand defined as one minus the risk ratio comparing individuals observed as vaccinated with individuals observed as unvaccinated, and the vaccine effectiveness defined as one minus the risk ratio comparing truly vaccinated with truly unvaccinated. The magnitude of the bias depends on five factors: the risks of disease in the truly vaccinated and the truly unvaccinated, the sensitivity and specificity of exposure measurement, and vaccination coverage. Non-differential exposure misclassification bias is always negative. In practice, if the sensitivity and specificity are known or estimable from external sources, the true risks and the vaccination coverage can be estimated from the observed data and, thus, the estimation of vaccine effectiveness based on the observed risks can be corrected for exposure misclassification. When analysing risks under misclassification, careful consideration of conditional probabilities is crucial.Peer reviewe

flyDIVaS: A comparative genomics resource for Drosophila divergence and selection

© 2016 Stanley and Kulathinal. With arguably the best finished and expertly annotated genome assembly, Drosophila melanogaster is a formidable genetics model to study all aspects of biology. Nearly a decade ago, the 12 Drosophila genomes project expanded D. melanogaster's breadth as a comparative model through the community-development of an unprecedented genus- and genome-wide comparative resource. However, since its inception, these datasets for evolutionary inference and biological discovery have become increasingly outdated, outmoded, and inaccessible. Here, we provide an updated and upgradable comparative genomics resource of Drosophila divergence and selection, flyDIVaS, based on the latest genomic assemblies, curated FlyBase annotations, and recent OrthoDB orthology calls. flyDIVaS is an online database containing D. melanogaster-centric orthologous gene sets, CDS and protein alignments, divergence statistics (% gaps, dN, dS, dN/dS), and codon-based tests of positive Darwinian selection. Out of 13,920 protein-coding D. melanogaster genes, ~80% have one aligned ortholog in the closely related species, D. simulans, and ~50% have 1-1 12-way alignments in the original 12 sequenced species that span over 80 million yr of divergence. Genes and their orthologs can be chosen from four different taxonomic datasets differing in phylogenetic depth and coverage density, and visualized via interactive alignments and phylogenetic trees. Users can also batch download entire comparative datasets. A functional survey finds conserved mitotic and neural genes, highly diverged immune and reproduction-related genes, more conspicuous signals of divergence across tissue-specific genes, and an enrichment of positive selection among highly diverged genes. flyDIVaS will be regularly updated and can be freely accessed at www. flydivas.info. We encourage researchers to regularly use this resource as a tool for biological inference and discovery, and in their classrooms to help train the next generation of biologists to creatively use such genomic big data resources in an integrative manner

Genomic signatures of domestication on neurogenetic genes in Drosophila melanogaster

© 2016 Stanley and Kulathinal. Background: Domesticated animals quickly evolve docile and submissive behaviors after isolation from their wild conspecifics. Model organisms reared for prolonged periods in the laboratory also exhibit similar shifts towards these domesticated behaviors. Yet whether this divergence is due to inadvertent selection in the lab or the fixation of deleterious mutations remains unknown. Results: Here, we compare the genomes of lab-reared and wild-caught Drosophila melanogaster to understand the genetic basis of these recently endowed behaviors common to laboratory models. From reassembled genomes of common lab strains, we identify unique, derived variants not present in global populations (lab-specific SNPs). Decreased selective constraints across low frequency SNPs (unique to one or two lab strains) are different from patterns found in the wild and more similar to neutral expectations, suggesting an overall accumulation of deleterious mutations. However, high-frequency lab SNPs found in most or all lab strains reveal an enrichment of X-linked loci and neuro-sensory genes across large extended haplotypes. Among shared polymorphisms, we also find highly differentiated SNPs, in which the derived allele is higher in frequency in the wild (Fstwild>lab), enriched for similar neurogenetic ontologies, indicative of relaxed selection on more active wild alleles in the lab. Conclusions: Among random mutations that continuously accumulate in the laboratory, we detect common adaptive signatures in domesticated lab strains of fruit flies. Our results demonstrate that lab animals can quickly evolve domesticated behaviors via unconscious selection by humans early on a broad pool of disproportionately large neurogenetic targets followed by the fixation of accumulated deleterious mutations on functionally similar targets