A major challenge of biology is understanding the relationship between molecular genetic variation and variation in quantitative traits, including fitness. This relationship determines our ability to predict phenotypes from genotypes and to understand how evolutionary forces shape variation within and between species. Previous efforts to dissect the genotype–phenotype map were based on incomplete genotypic information. Here, we describe the Drosophila melanogaster Genetic Reference Panel (DGRP), a community resource for analysis of population genomics and quantitative traits. The DGRP consists of fully sequenced inbred lines derived from a natural population. Population genomic analyses reveal reduced polymorphism in centromeric autosomal regions and the X chromosome, evidence for positive and negative selection, and rapid evolution of the X chromosome. Many variants in novel genes, most at low frequency, are associated with quantitative traits and explain a large fraction of the phenotypic variance. The DGRP facilitates genotype–phenotype mapping using the power of Drosophila genetics.

In the life sciences, many assays measure only the relative abundances of components in each sample. Such data, called compositional data, require special treatment to avoid misleading conclusions. Awareness of the need for caution in analyzing compositional data is growing, including the understanding that correlation is not appropriate for relative data. Recently, researchers have proposed proportionality as a valid alternative to correlation for calculating pairwise association in relative data. Although the question of how to best measure proportionality remains open, we present here a computationally efficient R package that implements three measures of proportionality. In an effort to advance the understanding and application of proportionality analysis, we review the mathematics behind proportionality, demonstrate its application to genomic data, and discuss some ongoing challenges in the analysis of relative abundance data.

Wolbachia are maternally inherited symbiotic bacteria, commonly found in arthropods, which are able to manipulate the reproduction of their host in order to maximise their transmission. The evolutionary history of endosymbionts like Wolbachia can be revealed by integrating information on infection status in natural populations with patterns of sequence variation in Wolbachia and host mitochondrial genomes. Here we use whole-genome resequencing data from 290 lines of Drosophila melanogaster from North America, Europe, and Africa to predict Wolbachia infection status, estimate relative cytoplasmic genome copy number, and reconstruct Wolbachia and mitochondrial genome sequences. Overall, 63% of Drosophila strains were predicted to be infected with Wolbachia by our in silico analysis pipeline, which shows 99% concordance with infection status determined by diagnostic PCR. Complete Wolbachia and mitochondrial genomes show congruent phylogenies, consistent with strict vertical transmission through the maternal cytoplasm and imperfect transmission of Wolbachia. Bayesian phylogenetic analysis reveals that the most recent common ancestor of all Wolbachia and mitochondrial genomes in D. melanogaster dates to around 8,000 years ago. We find evidence for a recent global replacement of ancestral Wolbachia and mtDNA lineages, but our data suggest that the derived wMel lineage arose several thousand years ago, not in the 20th century as previously proposed. Our data also provide evidence that this global replacement event is incomplete and is likely to be one of several similar incomplete replacement events that have occurred since the out-of-Africa migration that allowed D. melanogaster to colonize worldwide habitats. This study provides a complete genomic analysis of the evolutionary mode and temporal dynamics of the D. melanogaster–Wolbachia symbiosis, as well as important resources for further analyses of the impact of Wolbachia on host biology. Host–microbe interactions play important roles in the physiology, development, and ecology of many organisms. Studying how hosts and their microbial symbionts evolve together over time is crucial for understanding the impact that microbes have on host biology. With the advent of high-throughput sequencing technologies, it is now possible to obtain complete genomic information for hosts and their associated microbes. Here we use whole-genome sequences from ∼300 strains of the fruitfly Drosophila melanogaster to reveal the evolutionary history of this model species and its intracellular bacterial symbiont Wolbachia. The major findings of this study are that Wolbachia in D. melanogaster is inherited strictly through the egg with no evidence of horizontal transfer from other species, that the genealogies of Wolbachia and mitochondrial genomes are virtually the same, and that both Wolbachia and mitochondrial genomes show evidence for a recent incomplete global replacement event, which has left remnant lineages in North America, Europe, and Africa. We also use the fact that Wolbachia and mitochondrial genomes have the same genealogy to estimate the rate of molecular evolution for Wolbachia, which allows us to put dates on key events in the history of this important host–microbe model system.

Count data generated by next-generation sequencing assays do not measure absolute transcript abundances. Instead, the data are constrained to an arbitrary “library size” by the sequencing depth of the assay, and typically must be normalized prior to statistical analysis. The constrained nature of these data means one could alternatively use a log-ratio transformation in lieu of normalization, as often done when testing for differential abundance (DA) of operational taxonomic units (OTUs) in 16S rRNA data. Therefore, we benchmark how well the ALDEx2 package, a transformation-based DA tool, detects differential expression in high-throughput RNA-sequencing data (RNA-Seq), compared to conventional RNA-Seq methods such as edgeR and DESeq2. To evaluate the performance of log-ratio transformation-based tools, we apply the ALDEx2 package to two simulated, and two real, RNA-Seq data sets. One of the latter was previously used to benchmark dozens of conventional RNA-Seq differential expression methods, enabling us to directly compare transformation-based approaches. We show that ALDEx2, widely used in meta-genomics research, identifies differentially expressed genes (and transcripts) from RNA-Seq data with high precision and, given sufficient sample sizes, high recall too (regardless of the alignment and quantification procedure used). Although we show that the choice in log-ratio transformation can affect performance, ALDEx2 has high precision (i.e., few false positives) across all transformations. Finally, we present a novel, iterative log-ratio transformation (now implemented in ALDEx2) that further improves performance in simulations. Our results suggest that log-ratio transformation-based methods can work to measure differential expression from RNA-Seq data, provided that certain assumptions are met. Moreover, these methods have very high precision (i.e., few false positives) in simulations and perform well on real data too. With previously demonstrated applicability to 16S rRNA data, ALDEx2 can thus serve as a single tool for data from multiple sequencing modalities.

The relationship between PhD candidate and supervisor influences successful candidate completion and helps maintain candidate satisfaction and mental health. We quantified potential mismatches between the PhD candidates’ and supervisors’ expectations as a potential mechanism that facilitates poor candidate experiences and research training outcomes based on a quantitative survey which including rank order and Likert responses. 114 PhD candidates and 52 supervisors in Australia ranked the importance of student attributes and outcomes at the beginning and end of candidature. In relation to specific attributes, supervisors indicated the level of guidance they expected to give the candidate, and candidates indicated the level of guidance they expected to receive. Candidates also report on whether different aspects of candidature influenced their mental well-being. Using rank correlation and multivariate regression statistics, we identified differences between candidates’ and supervisors’ perceived supervisor teaching responsibility and candidatures influence on mental well-being. Results indicate the majority of candidates were satisfied overall with their supervision, and find alignment of many expectations between both parties. Yet, candidates tended to have much higher expectations about achieving quantitative outcomes than supervisors. Supervisors believed they give more guidance to candidates than candidates perceive they received, and supervisors often only provided guidance when the candidate explicitly asked. Personal expectations and research progress significantly and negatively influenced over 50% of candidates’ mental well-being. Our results highlight the importance of candidates and supervisors explicitly communicating the responsibilities and expectations of the roles they play in helping candidates develop research skills. We provide four suggestions to supervisors that may be particularly effective at increasing communication, avoiding potential conflict and promoting candidate success and well-being.