Biology subjects

Kang, H.

Publications and source records attributed to Kang, H..

5 recordsLinked to original sources

bioRxiv2018

Efficient Multivariate Analysis Algorithms for Longitudinal Genome-wide Association Studies

MotivationCurrent dynamic phenotyping system introduces time as an extra dimension to genome-wide association studies (GWAS), which helps to explore the mechanism of dynamical genetic control for complex longitudinal traits. However, existing methods for longitudinal GWAS either ignore the covariance among observations of different time points or encounter computational efficiency issues.\n\nResultsWe herein developed efficient genome-wide multivariate association algorithms (GMA) for longitudinal data. In contrast to existing univariate linear mixed model analyses, the proposed new method has improved statistic power for association detection and computational speed. In addition, the new method can analyze unbalanced longitudinal data with thousands of individuals and more than ten thousand records within a few hours. The corresponding time for balanced longitudinal data is just a few minutes.\n\nAvailability and ImplementationWe wrote a software package to implement the efficient algorithm named GMA (https://github.com/chaoning/GMA), which is available freely for interested users in relevant fields.

bioinformatics↗

bioRxiv2018

Mining unknown porcine protein isoforms by tissue-based map of proteome enhances the pig genome annotation

A lack of the complete pig proteome has left a gap in our knowledge of the pig genome and has restricted the feasibility of using pigs as a biomedical model. We developed the tissue-based proteome maps using 34 major normal pig tissues. A total of 7,319 unknown protein isoforms were identified and systematically characterized, including 3,703 novel protein isoforms, 669 protein isoforms from 460 genes symbolized beginning with LOC, and 2,947 protein isoforms without clear NCBI annotation in current pig reference genome. These newly identified protein isoforms were functionally annotated through profiling the pig transcriptome with high-throughput RNA sequencing (RNA-seq) of the same pig tissues, further improving the genome annotation of corresponding protein coding genes. Combining the well-annotated genes that having parallel expression pattern and subcellular witness, we predicted the tissue related subcellular components and potential function for these unknown proteins. Finally, we mined 3,656 orthologous genes for 49.95% of unknown protein isoforms across multiple species, referring to 65 KEGG pathways and 25 disease signaling pathways. These findings provided valuable insights and a rich resource for enhancing studies of pig genomics and biology as well as biomedical model application to human medicine.

genomics↗

bioRxiv2018

Genome-wide association study reveals sex-specific genetic architecture of facial attractiveness

Facial attractiveness is a complex human trait of great interest in both academia and industry. Literature on sociological and phenotypic factors associated with facial attractiveness is rich, but its genetic basis is poorly understood. In this paper, we conducted a genome-wide association study to discover genetic variants associated with facial attractiveness using 3,928 samples in the Wisconsin Longitudinal Study. We identified two genome-wide significant loci and highlighted a handful of candidate genes, many of which are specifically expressed in human tissues involved in reproduction and hormone synthesis. Additionally, facial attractiveness showed strong and negative genetic correlations with BMI in females and with blood lipids in males. Our analysis also suggested sex-specific selection pressure on variants associated with lower male attractiveness. These results revealed sex-specific genetic architecture of facial attractiveness and provided fundamental new insights into its genetic basis.

genetics↗

bioRxiv2017

Advanced NeuroGPS-Tree: dense reconstruction of brain-wideneuronal population close to ground truth

Recent progresses allow imaging specific neuronal populations at single-axon level across mouse brain. However, digital reconstruction of neurons in large dataset requires months of human labor. Here, we developed a tool to solve this problem. Our tool offers a special error-screening system for fast localization of submicron errors in densely packed neurites and along long projection across the whole brain, thus achieving reconstruction close to the ground-truth. Moreover, our tool equips algorithms that significantly reduce intensive manual interferences and achieve high-level automation, with speed 5 times faster compared to semi-automatic tools. We also demonstrated reconstruction of 35 long projection neurons around one injection site of a mouse brain at an affordable time cost. Our tool is applicable with datasets of 10 TB or higher from various light microscopy, and provides a starting point for the reconstruction of neuronal population for neuroscience studies at a single-cell level.

neuroscience↗

bioRxiv2017

Predicting aging of brain metabolic topography using variational autoencoder

Predicting future brain topography can give insight into neural correlates of aging and neurodegeneration. Due to variability in aging process, it has been challenging to precisely estimate brain topographical change according to aging. Here, we predict age-related brain metabolic change by generating future brain 18F-Fluorodeoxyglucose PET. A cross-sectional PET dataset of cognitively normal subjects with different age was used to develop a generative model. The model generated PET images using age information and characteristic individual features. Predicted regional metabolic changes were correlated with the real changes obtained by follow-up data. This model was applied to produce a brain metabolism aging movie by generating PET at different ages. Normal population distribution of brain metabolic topography at each age was estimated as well. In addition, a generative model using APOE4 status as well as age as inputs revealed a significant effect of APOE4 status on age-related metabolic changes particularly in the calcarine, lingual cortex, hippocampus and amygdala. It suggested APOE4 could be a factor affecting individual variability in age-related metabolic degeneration in normal elderly. This predictive model may not only be extended to understanding cognitive aging process, but apply to development of a preclinical biomarker for various brain disorders.

neuroscience↗