Immunogenetic variation in humans is important in research, clinical diagnosis and increasingly a target for therapeutic intervention. Two highly polymorphic loci play critical roles, namely the human leukocyte antigen (HLA) system, which is the human version of the major histocompatibility complex (MHC), and the Killer-cell immunoglobulin-like receptors (KIR) that are relevant for responses of natural killer (NK) and some subsets of T cells. Their accurate classification has typically required the use of dedicated biological specimens and a combination of in vitro and in silico efforts. Increased availability of next generation sequencing data has led to the development of ancillary computational solutions. Here, we report an evaluation of recently published algorithms to computationally infer complex immunogenetic variation in the form of HLA alleles and KIR haplotypes from whole-genome or whole-exome sequencing data. For both HLA allele and KIR gene typing, we identified tools that yielded >97% overall accuracy for four-digit HLA types, and >99% overall accuracy for KIR gene presence, suggesting the readiness of in silico solutions for use in clinical and high-throughput research settings.

No abstract

Service Email Alerting click here. top right corner of the article or Receive free email alerts when new articles cite this article-sign up in the box at the http://genome.cshlp.org/subscriptions

Human immunogenetic variation in the form of HLA and KIR types has been shown to be strongly associated with a multitude of immune-related phenotypes. However, association studies involving immunogenetic loci most commonly involve simple analyses of classical HLA allelic diversity, resulting in limitations regarding the interpretability and reproducibility of results. We here present MiDAS, a comprehensive R package for immunogenetic data transformation and statistical analysis. MiDAS recodes input data in the form of HLA alleles and KIR types into biologically meaningful variables, allowing HLA amino acid fine mapping, analyses of HLA evolutionary divergence as well as experimentally validated HLA-KIR interactions. Further, MiDAS enables comprehensive statistical association analysis workflows with phenotypes of diverse measurement scales. MiDAS thus closes the gap between the inference of immunogenetic variation and its efficient utilization to make relevant discoveries related to immune and disease biology. It is freely available under a MIT license.

The atrioventricular canal (AVC) is the site where key structures responsible for functional division between heart regions are established, most importantly, the atrioventricular (AV) conduction system and cardiac valves. To elucidate the mechanism underlying AVC development and function, we utilized transgenic zebrafish line sqet31Et expressing EGFP in the AVC to isolate this cell population and profile its transcriptome at 48 and 72 hpf. The zebrafish AVC transcriptome exhibits hallmarks of mammalian AV node, including the expression of genes implicated in its development and those encoding connexins forming low conductance gap junctions. Transcriptome analysis uncovered protein-coding and noncoding transcripts enriched in AVC, which have not been previously associated with this structure, as well as dynamic expression of epithelial-to-mesenchymal transition markers and components of TGF-β, Notch, and Wnt signaling pathways likely reflecting ongoing AVC and valve development. Using transgenic line Tg(myl7:mermaid) encoding voltage-sensitive fluorescent protein, we show that abolishing the pacemaker-containing sinoatrial ring (SAR) through Isl1 loss of function resulted in spontaneous activation in the AVC region, suggesting that it possesses inherent automaticity although insufficient to replace the SAR. The SAR and AVC transcriptomes express partially overlapping species of ion channels and gap junction proteins, reflecting their distinct roles. Besides identifying conserved aspects between zebrafish and mammalian conduction systems, our results established molecular hallmarks of the developing AVC which underlies its role in structural and electrophysiological separation between heart chambers. This data constitutes a valuable resource for studying AVC development and function, and identification of novel candidate genes implicated in these processes.