Despite the increasing feasibility of sequencing whole genomes from diverse taxa, a persistent problem in phylogenomics is the selection of appropriate genetic markers or loci for a given taxonomic group or research question. In this review, we aim to streamline the decision-making process when selecting specific markers to use in phylogenomic studies by introducing commonly used types of genomic markers, their evolutionary characteristics, and their associated uses in phylogenomics. Specifically, we review the utilities of ultraconserved elements (including flanking regions), anchored hybrid enrichment loci, conserved nonexonic elements, untranslated regions, introns, exons, mitochondrial DNA, single nucleotide polymorphisms, and anonymous regions (nonspecific regions that are evenly or randomly distributed across the genome). These various genomic elements and regions differ in their substitution rates, likelihood of neutrality or of being strongly linked to loci under selection, and mode of inheritance, each of which are important considerations in phylogenomic reconstruction. These features may give each type of marker important advantages and disadvantages depending on the biological question, number of taxa sampled, evolutionary timescale, cost effectiveness, and analytical methods used. We provide a concise outline as a resource to efficiently consider key aspects of each type of genetic marker. There are many factors to consider when designing phylogenomic studies, and this review may serve as a primer when weighing options between multiple potential phylogenomic markers.

Disentangling the drivers of genomic divergence during speciation is essential to our broader understanding of the generation of biological diversity. Genetic changes accumulate at variable rates across the genome as populations diverge, leading to heterogenous landscapes of genetic differentiation. The ‘islands of differentiation’ that characterise these landscapes harbour genetic signatures of the evolutionary processes that led to their formation, providing insight into the roles of these processes in adaptation and speciation. Here, we study swallows in the genus Hirundo to investigate genomic landscapes of differentiation between species spanning a continuum of evolutionary divergence. Genomic differentiation spans a wide range of values ( F _ST = 0.01–0.8) between species, with substantial heterogeneity in genome‐wide patterns. Genomic landscapes are strongly correlated among species ( ρ = 0.46–0.99), both at shallow and deep evolutionary timescales, with broad evidence for the role of linked selection together with recombination rate in shaping genomic differentiation. Further dissection of genomic islands reveals patterns consistent with a model of ‘recurrent selection’, wherein differentiation increases due to selection in the same genomic regions in ancestral and descendant populations. Finally, we use measures of the site frequency spectrum to differentiate between alternative forms of selection, providing evidence that genetic hitchhiking due to positive selection has contributed substantially to genomic divergence. Our results demonstrate the pervasive role of recurrent linked selection in shaping genomic divergence despite a history of gene flow and underscore the importance of non‐neutral evolutionary processes in predictive frameworks for genomic divergence in speciation genomics studies.

Hirundo is the most species-rich genus of the passerine swallow family (Hirundinidae) and has a cosmopolitan distribution. Here we report the complete, annotated mitochondrial genomes for 25 individuals from 10 of the 14 extant Hirundo species; these include representatives from four subspecies of the barn swallow, H. rustica. Mitogenomes were conserved in size, ranging from 18,500 to 18,700 base pairs. They all contained 13 protein-coding regions, 22 tRNAs, a control region, and large and small ribosomal subunits. Phylogenetic analysis resolved most of the relationships between the studied species and subspecies which were largely consistent with previously published trees. Several new relationships were observed within the phylogeny that could have only been discovered with the increased amount of genetic material. This study represents the largest Hirundo mitochondrial phylogeny to date, and could serve as a vital tool for other studies focusing on the evolution of the Hirundo genus.

Duchenne muscular dystrophy (DMD) is an X-linked progressive disorder and the most common type of muscular dystrophy in children. As newborn screening (NBS) for DMD undergoes evaluation for the Recommended Uniform Screening Panel and is already mandated in multiple states, refining NBS algorithms is of utmost importance. NBS for DMD involves measuring creatine kinase-MM (CK-MM) concentration—a biomarker of muscle damage—in dried blood spots. The current test is FDA-approved for samples obtained less than 72 h after birth. Separate reference ranges are needed for samples collected later than 72 h after birth. In this study, we investigated the relationship between age and CK-MM in presumed healthy newborns to inform NBS algorithm designs. In patients with DMD, CK-MM is persistently elevated in childhood and adolescence, while it may be transiently elevated for other reasons in healthy newborns. CK-MM decrease over time was demonstrated by a population sample of 20,306 presumed healthy newborns tested between 0 and 60 days of life and repeat testing of 53 newborns on two separate days. In the population sample, CK-MM concentration was highest in the second 12 h period of life (median = 318 ng/mL) when only 57.6% of newborns tested below 360 ng/mL, the lowest previously published cutoff. By 72 h of age, median CK-MM concentration was 97 ng/mL, and 96.0% of infants had concentrations below 360 ng/mL. Between 72 h and 60 days, median CK-MM concentration ranged from 32 to 37 ng/mL. Establishing age-related cutoffs is crucial for optimizing the sensitivity and specificity of NBS for DMD.

Genes influencing opioid use disorder (OUD) biology have been identified via genome-wide association studies (GWAS), gene expression, and network analyses. These discoveries provide opportunities to identifying existing compounds targeting these genes for drug repurposing studies. However, systematically integrating discovery results and identifying relevant available pharmacotherapies for OUD repurposing studies is challenging. To address this, we’ve constructed a framework that leverages existing results and drug databases to identify candidate pharmacotherapies. For this study, two independent OUD related meta-analyses were used including a GWAS and a differential gene expression (DGE) study of post-mortem human brain. Protein-Protein Interaction (PPI) sub-networks enriched for GWAS risk loci were identified via network analyses. Drug databases Pharos, Open Targets, Therapeutic Target Database (TTD), and DrugBank were queried for clinical status and target selectivity. Cross-omic and drug query results were then integrated to identify candidate compounds. GWAS and DGE analyses revealed 3 and 335 target genes (FDR q<0.05), respectively, while network analysis detected 70 genes in 22 enriched PPI networks. Four selection strategies were implemented, which yielded between 72 and 676 genes with statistically significant support and 110 to 683 drugs targeting these genes, respectively. After filtering out less specific compounds or those targeting well-established psychiatric-related receptors ( OPRM1 and DRD2 ), between 2 and 329 approved drugs remained across the four strategies. By leveraging multiple lines of biological evidence and resources, we identified many FDA approved drugs that target genes associated with OUD. This approach a) allows high-throughput querying of OUD-related genes, b) detects OUD-related genes and compounds not identified using a single domain or resource, and c) produces a succinct summary of FDA approved compounds eligible for efficient expert review. Identifying larger pools of candidate pharmacotherapies and summarizing the supporting evidence bridges the gap between discovery and drug repurposing studies.

Only recently have human postmortem brain studies of differential gene expression (DGE) associated with opioid overdose death (OOD) been published; sample sizes from these studies have been modest (N = 40–153). To increase statistical power to identify OOD-associated genes, we leveraged human prefrontal cortex RNA-seq data from four independent OOD studies and conducted a transcriptome-wide DGE meta-analysis (N = 272). Using a unified gene expression data processing and analysis framework across studies, we meta-analyzed 20, 098 genes and found 335 significant differentially expressed genes (DEGs) by OOD status (false discovery rate < 0.05). Of these, 66 DEGs were among the list of 303 genes reported as OOD-associated in prior prefrontal cortex molecular studies (e.g., genes/gene families OPRK1, NPAS4, DUSP, EGR). The remaining 269 DEGs were not previously reported (e.g., NR4A2, SYT1, HCRTR2, BDNF). There was little evidence of genetic drivers for the observed differences in gene expression between opioid addiction cases and controls. Enrichment analyses for the DEGs across molecular pathway and biological process databases highlight an interconnected set of genes and pathways linked to orexin and tyrosine kinase receptors through MEK/ERK/MAPK signaling to affect neuronal plasticity.