Microglia have emerged as important players in brain aging and pathology. To understand how genetic risk for neurological and psychiatric disorders is related to microglial function, large transcriptome studies are essential. Here, we describe the transcriptome analysis of 255 primary human microglia samples isolated at autopsy from multiple brain regions of 100 human subjects. We performed systematic analyses to investigate various aspects of microglial heterogeneities, including brain region and aging. We mapped expression and splicing quantitative trait loci and showed that many neurological disease susceptibility loci are mediated through gene expression or splicing in microglia. Fine-mapping of these loci nominated candidate causal variants that are within microglia-specific enhancers, finding associations with microglia expression of USP6NL for Alzheimer’s disease and P2RY12 for Parkinson’s disease. We have built the most comprehensive catalog to date of genetic effects on the microglia transcriptome and propose candidate functional variants in neurological and psychiatric disorders.

Accurate reconstruction of ancestral states is a critical evolutionary analysis when studying ancient proteins and comparing biochemical properties between parental or extinct species and their extant relatives. It relies on multiple sequence alignment (MSA) which may introduce biases, and it remains unknown how MSA methodological approaches impact ancestral sequence reconstruction (ASR). Here, we investigate how MSA methodology modulates ASR using a simulation study of various evolutionary scenarios. We evaluate the accuracy of ancestral protein sequence reconstruction for simulated data and compare reconstruction outcomes using different alignment methods. Our results reveal biases introduced not only by aligner algorithms and assumptions, but also tree topology and the rate of insertions and deletions. Under many conditions we find no substantial differences between the MSAs. However, increasing the difficulty for the aligners can significantly impact ASR. The MAFFT consistency aligners and PRANK variants exhibit the best performance, whereas FSA displays limited performance. We also discover a bias towards reconstructed sequences longer than the true ancestors, deriving from a preference for inferring insertions, in almost all MSA methodological approaches. In addition, we find measures of MSA quality generally correlate highly with reconstruction accuracy. Thus, we show MSA methodological differences can affect the quality of reconstructions and propose MSA methods should be selected with care to accurately determine ancestral states with confidence.

The Pirarucu (Arapaima gigas) is one of the world’s largest freshwater fishes and member of the superorder Osteoglossomorpha (bonytongues), one of the oldest lineages of ray-finned fishes. This species is an obligate air-breather found in the basin of the Amazon River with an attractive potential for aquaculture. Its phylogenetic position among bony fishes makes the Pirarucu a relevant subject for evolutionary studies of early teleost diversification. Here, we present, for the first time, a draft genome version of the A. gigas genome, providing useful information for further functional and evolutionary studies. The A. gigas genome was assembled with 103-Gb raw reads sequenced in an Illumina platform. The final draft genome assembly was ∼661 Mb, with a contig N50 equal to 51.23 kb and scaffold N50 of 668 kb. Repeat sequences accounted for 21.69% of the whole genome, and a total of 24,655 protein-coding genes were predicted from the genome assembly, with an average of nine exons per gene. Phylogenomic analysis based on 24 fish species supported the postulation that Osteoglossomorpha and Elopomorpha (eels, tarpons, and bonefishes) are sister groups, both forming a sister lineage with respect to Clupeocephala (remaining teleosts). Divergence time estimations suggested that Osteoglossomorpha and Elopomorpha lineages emerged independently in a period of ∼30 Myr in the Jurassic. The draft genome of A. gigas provides a valuable genetic resource for further investigations of evolutionary studies and may also offer a valuable data for economic applications.