Biology subjects

Hu, Y.

Publications and source records attributed to Hu, Y..

688 records · Page 39Linked to original sources

bioRxiv2017

A powerful approach to estimating annotation-stratified genetic covariance using GWAS summary statistics

Despite the success of large-scale genome-wide association studies (GWASs) on complex traits, our understanding of their genetic architecture is far from complete. Jointly modeling multiple traits genetic profiles has provided insights into the shared genetic basis of many complex traits. However, large-scale inference sets a high bar for both statistical power and biological interpretability. Here we introduce a principled framework to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics. Through theoretical and numerical analyses we demonstrate that our method provides accurate covariance estimates, thus enabling researchers to dissect both the shared and distinct genetic architecture across traits to better understand their etiologies. Among 50 complex traits with publicly accessible GWAS summary statistics (Ntotal {approx} 4.5 million), we identified more than 170 pairs with statistically significant genetic covariance. In particular, we found strong genetic covariance between late-onset Alzheimers disease (LOAD) and amyotrophic lateral sclerosis (ALS), two major neurodegenerative diseases, in single-nucleotide polymorphisms (SNPs) with high minor allele frequencies and in SNPs located in the predicted functional genome. Joint analysis of LOAD, ALS, and other traits highlights LOADs correlation with cognitive traits and hints at an autoimmune component for ALS.

genetics↗

bioRxiv2016

Segregation between the parietal memory network and the default mode network: Effects of spatial smoothing and model order in ICA

A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent fMRI studies. Though it is anatomically adjacent to and spatially overlaps with the default mode network (DMN), its function has been associated with memory processing, and it has been referred to as the parietal memory network (PMN). Independent component analysis (ICA) is the most common data-driven method of extracting PMN and DMN simultaneously. However, the effects of data preprocessing and parameter determination in ICA on PMN-DMN segregation are completely unknown. Here, we employ three typical algorithms of group ICA to assess how spatial smoothing and model order influence the degree of PMN-DMN segregation. Our findings indicate that PMN and DMN can only be stably separated using a combination of low-level spatial smoothing and high-order model across the three ICA algorithms. We thus argue for more considerations on parametric settings for interpreting DMN data.

neuroscience↗

bioRxiv2016

The structured diversity of specialized gut symbionts of the New World army ants

Symbiotic bacteria play important roles in the biology of their arthropod hosts. Yet the microbiota of many diverse and influential groups remain understudied, resulting in a paucity of information on the fidelities and histories of these associations. Motivated by prior findings from a smaller scale, 16S rRNA-based study, we conducted a broad phylogenetic and geographical survey of microbial communities in the ecologically dominant New World army ants (Formicidae: Dorylinae). Amplicon sequencing of the 16S rRNA gene across 28 species spanning the five New World genera showed that the microbial communities of army ants consist of very few common and abundant bacterial species. The two most abundant microbes, referred to as Unclassified Firmicutes and Unclassified Entomoplasmatales, appear to be specialized army ant associates that dominate microbial communities in the gut lumen of three host genera, Eciton, Labidus and Nomamyrmex. Both are present in other army ant genera, including those from the Old World, suggesting that army ant symbioses date back to the Cretaceous. Extensive sequencing of bacterial protein-coding genes revealed multiple strains of these symbionts co-existing within colonies, but seldom within the same individual ant. Bacterial strains formed multiple host species-specific lineages on phylogenies, which often grouped strains from distant geographic locations. These patterns deviate from those seen in other social insects, and raise intriguing questions about the influence of army ant colony swarm-founding and within-colony genetic diversity on strain co-existence, and the effects of hosting a diverse suite of symbiont strains on colony ecology.

evolutionary biology↗

bioRxiv2016

Identification of new bacterial type III secreted effectors with a recursive Hidden Markov Model profile-alignment strategy

To identify new bacterial type III secreted effectors is computationally a big challenge. At least a dozen machine learning algorithms have been developed, but so far have only achieved limited success. Sequence similarity appears important for biologists but is frequently neglected by algorithm developers for effector prediction, although large success was achieved in the field with this strategy a decade ago. In this study, we propose a recursive sequence alignment strategy with Hidden Markov Models, to comprehensively find homologs of known YopJ/P full-length proteins, effector domains and N-terminal signal sequences. Using this method, we identified 155 different YopJ/P-family effectors and 59 proteins with YopJ/P N-terminal signal sequences from 27 genera and more than 70 species. Among these genera, we also identified one type III secretion system (T3SS) from Uliginosibacterium and two T3SSs from Rhizobacter for the first time. Higher conservation of effector domains, N-terminal fusion of signal sequences to other effectors, and the exchange of N-terminal signal sequences between different effector proteins were frequently observed for YopJ/P-family proteins. This made it feasible to identify new effectors based on separate similarity screening for the N-terminal signal peptides and the effector domains of known effectors. This method can also be applied to search for homologues of other known T3SS effectors.

bioinformatics↗