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High-altitude adaptation of Tibetans represents a remarkable case of natural selection during recent human evolution. Previous genome-wide scans found many non-coding variants under selection, suggesting a pressing need to understand the functional role of non-coding regulatory elements (REs). Here, we generate time courses of paired ATAC-seq and RNA-seq data on cultured HUVECs under hypoxic and normoxic conditions. We further develop a variant interpretation methodology (vPECA) to identify active selected REs (ASREs) and associated regulatory network. We discover three causal SNPs of EPAS1, the key adaptive gene for Tibetans. These SNPs decrease the accessibility of ASREs with weakened binding strength of relevant TFs, and cooperatively down-regulate EPAS1 expression. We further construct the downstream network of EPAS1, elucidating its roles in hypoxic response and angiogenesis. Collectively, we provide a systematic approach to interpret phenotype-associated noncoding variants in proper cell types and relevant dynamic conditions, to model their impact on gene regulation., Tibetan adaptation to the high-altitude environment represents a case of natural selection during recent human evolution. Here the authors investigated the chromatin and transcriptional landscape of umbilical endothelial cells from Tibetan and Han Chinese donors and provide genome-wide characterization of the hypoxia regulatory network associated high-altitude adaptation.

Cranial Neural Crest Cells (CNCC) originate at the cephalic region from forebrain, midbrain and hindbrain, migrate into the developing craniofacial region, and subsequently differentiate into multiple cell types. The entire specification, delamination, migration, and differentiation process is highly regulated and abnormalities during this craniofacial development cause birth defects. To better understand the molecular networks underlying CNCC, we integrate paired gene expression & chromatin accessibility data and reconstruct the genome-wide human Regulatory network of CNCC (hReg-CNCC). Consensus optimization predicts high-quality regulations and reveals the architecture of upstream, core, and downstream transcription factors that are associated with functions of neural plate border, specification, and migration. hReg-CNCC allows us to annotate genetic variants of human facial GWAS and disease traits with associated cis-regulatory modules, transcription factors, and target genes. For example, we reveal the distal and combinatorial regulation of multiple SNPs to core TF ALX1 and associations to facial distances and cranial rare disease. In addition, hReg-CNCC connects the DNA sequence differences in evolution, such as ultra-conserved elements and human accelerated regions, with gene expression and phenotype. hReg-CNCC provides a valuable resource to interpret genetic variants as early as gastrulation during embryonic development. The network resources are available at https://github.com/AMSSwanglab/hReg-CNCC., Zhanying Feng et al. present hReg-CNCC, a high-quality gene regulatory network for human cranial neural crest cells (CNCCs) constructed by consensus optimization modeling. It may be useful in interpreting genetic variants involved in embryonic development by linking the cis-regulatory sequences in this network with GWAS SNPs, disease risk loci, and evolutionarily-conserved regions of the genome.

Motivation Single cell RNA-seq data offers us new resource and resolution to study cell type identity and its conversion. However, data analyses are challenging in dealing with noise, sparsity and poor annotation at single cell resolution. Detecting cell-type-indicative markers is promising to help denoising, clustering and cell type annotation. Results We developed a new method, scTIM, to reveal cell-type-indicative markers. scTIM is based on a multi-objective optimization framework to simultaneously maximize gene specificity by considering gene-cell relationship, maximize gene’s ability to reconstruct cell–cell relationship and minimize gene redundancy by considering gene–gene relationship. Furthermore, consensus optimization is introduced for robust solution. Experimental results on three diverse single cell RNA-seq datasets show scTIM’s advantages in identifying cell types (clustering), annotating cell types and reconstructing cell development trajectory. Applying scTIM to the large-scale mouse cell atlas data identifies critical markers for 15 tissues as ‘mouse cell marker atlas’, which allows us to investigate identities of different tissues and subtle cell types within a tissue. scTIM will serve as a useful method for single cell RNA-seq data mining. Availability and implementation scTIM is freely available at https://github.com/Frank-Orwell/scTIM. Supplementary information Supplementary data are available at Bioinformatics online.