Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits

We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only ≤10% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32–44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having ≥95% probability of contributing ≥0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data.

De Novo Transcriptome Assembly, Functional Annotation and SSR Marker Discovery of Qinling Takin (Budorcas taxicolor bedfordi)

The takin (Budorcas taxicolor) is an endemic ruminant species belonging to the bovine family. The International Union for Conservation of Nature (IUCN) has listed it as an endangered and vulnerable species. However, little is known about its molecular characterization since it lacks a reference genome. This study used RNA sequencing followed by de novo assembly, annotation and simple sequence repeats (SSRs) prediction to assess the transcriptome of Qinling takin (Budorcas taxicolor bedfordi) muscles. In total, 21,648 unigenes with an N50 and mean length of 1388 bp and 817 bp, respectively, were successfully detected and annotated against the public databases (NR, GO, KEGG, and EggNOG). Furthermore, 6222 SSRs were identified using the MIcroSAtellite (MISA) identification tool software. Taken together, these findings will provide valuable information for genetic, genomic, and evolutionary studies on takin.

Tissue Specific Transcriptome Profiling and Genetic Region Marker Discovery of Indian Sesame (Sisemum indicum L.)

Sesame (Sesamum indicum L.), is rich source of oil, protein and potent antioxidants with wide applications. Molecular approach can be of great use for trait improvement as well more availability for this crop. The present work aims at tissue specific transcriptome profiling along with biochemical pathway analysis and genic region putative marker discovery. We report 14389, 9465 and 5490 DEGs in root, leaf and flower-bud tissues, respectively. 135, 113 and 120 cellular metabolic or signaling pathways having common 118 pathways were found in root-leaf (RL), leaf-flower (LF) and flower-root (FR), respectively. 218, 170 and 180 transcription factors were identified in root, leaf and flower transcriptome, respectively. Among DEGs, microRNA targets predicted were 534, 376 and 173 in root, leaf and flower, respectively. Genic region repeat analysis revealed 379 SSR. Further variant analysis revealed 3371, 5439 and 4975 SNPs and 2257, 2403 and 2411 INDELs in root, leaf and flower, respectively. The present study will aid in understanding the major biochemical pathways operating in different tissues. Genic region putative marker discovery can be a valuable genomic resource for future crop improvement program.

Putative markers for the detection of early-stage bladder cancer selected by urine metabolomics

Background Early detection of bladder cancer remains challenging because patients with early-stage bladder cancer usually have no incentive to take cytology or cystoscopy tests if they are asymptomatic. Our goal is to find non-invasive marker candidates that may help us gain insight into the metabolism of early-stage bladder cancer and be examined in routine health checks. Results We acquired urine samples from 124 patients diagnosed with early-stage bladder cancer or hernia (63 cancer patients and 61 controls). In which 100 samples were included in our marker discovery cohort, and the remaining 24 samples were included in our independent test cohort. We obtained metabolic profiles of 922 compounds of the samples by gas chromatography-mass spectrometry. Based on the metabolic profiles of the marker discovery cohort, we selected marker candidates using Wilcoxon rank-sum test with Bonferroni correction and leave-one-out cross-validation; we further excluded compounds detected in less than 60% of the bladder cancer samples. We finally selected eight putative markers. The abundance of all the eight markers in bladder cancer samples was high but extremely low in hernia samples. Moreover, the up-regulation of these markers might be in association with sugars and polyols metabolism. Conclusions In the present study, comparative urine metabolomics selected putative metabolite markers for the detection of early-stage bladder cancer. The suggested relations between early-stage bladder cancer and sugars and polyols metabolism may create opportunities for improving the detection of bladder cancer.