Biology subjects

Kao, C.-H.

Publications and source records attributed to Kao, C.-H..

7 recordsLinked to original sources

bioRxiv2020

A Statistical Framework for QTL Hotspot Detection

Quantitative trait loci (QTL) hotspots (genomic locations enriched in QTL) are a common and notable feature when collecting many QTL for various traits in many areas of biological studies. The QTL hotspots are important and attractive since they are highly informative and may harbor genes for the quantitative traits. So far, the current statistical methods for QTL hotspot detection use either the individual-level data from the genetical genomics experiments or the summarized data from public QTL databases to proceed with the detection analysis. These detection methods attempt to address some of the concerns, including the correlation structure among traits, the magnitude of LOD scores within a hotspot and computational cost, that arise during the process of QTL hotspot detection. In this article, we describe a statistical framework that can handle both types of data as well as address all the concerns at a time for QTL hotspot detection. Our statistical framework directly operates on the QTL matrix and hence has a very cheap computation cost, and is deployed to take advantage of the QTL mapping results for assisting the detection analysis. Two special devices, trait grouping and top {gamma}n, profile, are introduced into the framework. The trait grouping attempts to group the closely linked or pleiotropic traits together to take care of the true linkages and cope with the underestimation of hotspot thresholds due to non-genetic correlations (arising from ignoring the correlation structure among traits), so as to have the ability to obtain much stricter thresholds and dismiss spurious hotspots. The top {gamma}n, profile is designed to outline the LOD-score pattern of a hotspot across the different hotspot architectures, so that it can serve to identify and characterize the types of QTL hotspots with varying sizes and LOD score distributions. Real examples, numerical analysis and simulation study are performed to validate our statistical framework, investigate the detection properties, and also compare with the current methods in QTL hotspot detection. The results demonstrate that the proposed statistical framework can effectively accommodate the correlation structure among traits, identify the types of hotspots and still keep the notable features of easy implementation and fast computation for practical QTL hotspot detection.

genetics↗

bioRxiv2020

Distinct neural encoding of context-dependent error signals and context-dependent changes in behavior during adaptive learning

Effective learning requires using errors in a task-dependent manner, for example adjusting to errors that result from unpredicted environmental changes but ignoring errors that result from environmental stochasticity. Where and how the brain represents errors in a task-dependent manner and uses them to guide behavior are not well understood. We imaged the brains of human participants performing a predictive-inference task with two conditions that had different sources of errors. Their performance was sensitive to this difference, including more choice switches after fundamental changes versus stochastic fluctuations in reward contingencies. Using multi-voxel pattern classification, we identified task-dependent representations of error magnitude and past errors in posterior parietal cortex. These representations were distinct from representations of the resulting behavioral adjustments in dorsomedial frontal, anterior cingulate, and orbitofrontal cortex. The results provide new insights into how the human brain represents errors in a task-dependent manner and guides subsequent adaptive behavior.

neuroscience↗

bioRxiv2020

Genomic, geographic and temporal distributions of SARS-CoV-2 mutations

The COVID-19 pandemic is the most significant public health issue in recent history. Its causal agent, SARS-CoV-2, has evolved rapidly since its first emergence in December 2019. Mutations in the viral genome have critical impacts on the adaptation of viral strains to the local environment, and may alter the characteristics of viral transmission, disease manifestation, and the efficacy of treatment and vaccination. Using the complete sequences of 1,932 SARS-CoV-2 genomes, we examined the genomic, geographic and temporal distributions of aged, new, and frequent mutations of SARS-CoV-2, and identified six phylogenetic clusters of the strains, which also exhibit a geographic preference in different continents. Mutations in the form of single nucleotide variations (SNVs) provide a direct interpretation for the six phylogenetic clusters. Linkage disequilibrium, haplotype structure, evolutionary process, global distribution of mutations unveiled a sketch of the mutational history. Additionally, we found a positive correlation between the average mutation count and case fatality, and this correlation had strengthened with time, suggesting an important role of SNVs on disease outcomes. This study suggests that SNVs may become an important consideration in virus detection, clinical treatment, drug design, and vaccine development to avoid target shifting, and that continued isolation and sequencing is a crucial component in the fight against this pandemic. Significance StatementMutation is the driving force of evolution for viruses like SARS-CoV-2, the causal agent of COVID-19. In this study, we discovered that the genome of SARS-CoV-2 is changing rapidly from the originally isolated form. These mutations have been spreading around the world and caused more than 2.5 million of infected cases and 170 thousands of deaths. We found that fourteen frequent mutations identified in this study can characterize the six main clusters of SARS-CoV-2 strains. In addition, we found the mutation burden is positively correlated with the fatality of COVID-19 patients. Understanding mutations in the SARS-CoV-2 genome will provide useful insight for the design of treatment and vaccination.

microbiology↗

bioRxiv2019

Functional brain network reconfiguration during learning in a dynamic environment

When learning about dynamic and uncertain environments, people should update their beliefs most strongly when new evidence is most informative, such as when the environment undergoes a surprising change or existing beliefs are highly uncertain. Here we show that modulations of surprise and uncertainty are encoded in a particular, temporally dynamic pattern of whole-brain functional connectivity, and this encoding is enhanced in individuals that adapt their learning dynamics more appropriately in response to these factors. The key feature of this whole-brain pattern of functional connectivity is stronger connectivity, or functional integration, between the fronto-parietal and other functional systems. Our results provide new insights regarding the association between dynamic adjustments in learning and dynamic, large-scale changes in functional connectivity across the brain.

neuroscience↗

bioRxiv2019

Growth-regulated Hsp70 phosphorylation regulates stress responses and prion maintenance

Maintenance of protein homeostasis in eukaryotes during normal growth and stress conditions requires the functions of Hsp70 chaperones and associated co-chaperones. Here we investigate an evolutionarily-conserved serine phosphorylation that occurs at the site of communication between the nucleotide-binding and substrate-binding domains of Hsp70. Ser151 phosphorylation in yeast Hsp70 (Ssa1) is promoted by cyclin-dependent kinase (Cdk1) during normal growth and dramatically affects heat shock responses, a function conserved with Hsc70 S153 phosphorylation in human cells. Phospho-mimic forms of Ssa1 (S151D) also fail to relocalize in response to starvation conditions, do not associate in vivo with Hsp40 co-chaperones, Ydj1 and Sis1, and do not catalyze refolding of denatured proteins in vitro in cooperation with Ydj1 and Hsp104. S151 phosphorylation strongly promotes survival of heavy metal exposure and reduces Sup35-dependent [PSI+] prion activity, however, consistent with proposed roles for Ssa1 and Hsp104 in generating self-nucleating seeds of misfolded proteins. Taken together, these results suggest that Cdk1 downregulates Hsp70 function during periods of active growth, reducing propagation of aggregated proteins despite potential costs to overall chaperone efficiency.

molecular biology↗

bioRxiv2018

A Statistical Procedure for Genome-wide Detection of QTL Hotspots Using Public Databases with Application to Rice

Genome-wide detection of quantitative trait loci (QTL) hotspots underlying variation in many molecular and phenotypic traits has been a key step in various biological studies since the QTL hotspots are highly informative and can be linked to the genes for the quantitative traits. Several statistical methods have been proposed to detect QTL hotspots. These hotspot detection methods rely heavily on permutation tests performed on summarized QTL data or individual-level data (with genotypes and phenotypes) from the genetical genomics experiments. In this article, we propose a statistical procedure for QTL hotspot detection by using the summarized QTL (interval) data collected in public web-accessible databases. First, a simple statistical method based on the uniform distribution is derived to convert the QTL interval data into the expected QTL frequency (EQF) matrix. And then, to account for the correlation structure among traits, the QTLs for correlated traits are grouped together into the same categories to form a reduced EQF matrix. Furthermore, a permutation algorithm on the EQF elements or on the QTL intervals is developed to compute a sliding scale of EQF thresholds, ranging from strict to liberal, for assessing the significance of QTL hotspots. With grouping, much stricter thresholds can be obtained to avoid the detection of spurious hotspots. Real example analysis and simulation study are carried out to illustrate our procedure, evaluate the performances and compare with other methods. It shows that our procedure can control the genome-wide error rates at the target levels, provide appropriate thresholds for correlated data and is comparable to the methods using individual-level data in hotspot detection. Depending on the thresholds used, more than 100 hotspots are detected in GRAMENE rice database. We also perform a genome-wide comparative analysis of the detected hotspots and the known genes collected in the Rice Q-TARO database. The comparative analysis reveals that the hotspots and genes are conformable in the sense that they co-localize closely and are functionally related to relevant traits. Our statistical procedure can provide a framework for exploring the networks among QTL hotspots, genes and quantitative traits in biological studies. The R codes that produce both numerical and graphical outputs of QTL hotspot detection in the genome are available on the worldwide web http://www.stat.sinica.edu.tw/~chkao/.

genetics↗

bioRxiv2018

Dynamic combination of sensory and reward information under time pressure

When making choices, collecting more information is beneficial but comes at the cost of sacrificing time that could be allocated to making other potentially rewarding decisions. To investigate how the brain balances these costs and benefits, we conducted a series of novel experiments in humans and simulated various computational models. Under six levels of time pressure, subjects made decisions either by integrating sensory information over time or by dynamically combining sensory and reward information over time. We found that during sensory integration, time pressure reduced performance as the deadline approached, and choice was more strongly influenced by the most recent sensory evidence. By fitting performance and reaction time with various models we found that our experimental results are more compatible with leaky integration of sensory information with an urgency signal or a decision process based on stochastic transitions between discrete states modulated by an urgency signal. When combining sensory and reward information, subjects spent less time on integration than optimally prescribed when reward decreased slowly over time, and the most recent evidence did not have the maximal influence on choice. The suboptimal pattern of reaction time was partially mitigated in an equivalent control experiment in which sensory integration over time was not required, indicating that the suboptimal response time was influenced by the perception of imperfect sensory integration. Meanwhile, during combination of sensory and reward information, performance did not drop as the deadline approached, and response time was not different between correct and incorrect trials. These results indicate a decision process different from what is involved in the integration of sensory information over time. Together, our results not only reveal limitations in sensory integration over time but also illustrate how these limitations influence dynamic combination of sensory and reward information.

neuroscience↗