@article {Park2021-zp,
	title = {Counterfactual inference for single-cell gene expression analysis},
	journal = {medRxiv},
	year = {2021},
	publisher = {Cold Spring Harbor Laboratory Press},
	keywords = {My Papers},
	author = {Park, Yongjin and Kellis, Manolis}
}
@article {He2021-cc,
	title = {Exome-wide age-of-onset analysis reveals exonic variants in ERN1 and SPPL2C associated with Alzheimer{\textquoteright}s disease},
	journal = {Transl. Psychiatry},
	volume = {11},
	number = {1},
	year = {2021},
	month = {feb},
	pages = {146},
	publisher = {Springer Science and Business Media LLC},
	abstract = {Despite recent discoveries in genome-wide association studies (GWAS) of genomic variants associated with Alzheimer{\textquoteright}s disease (AD), its underlying biological mechanisms are still elusive. The discovery of novel AD-associated genetic variants, particularly in coding regions and from APOE ε4 non-carriers, is critical for understanding the pathology of AD. In this study, we carried out an exome-wide association analysis of age-of-onset of AD with  20,000 subjects and placed more emphasis on APOE ε4 non-carriers. Using Cox mixed-effects models, we find that age-of-onset shows a stronger genetic signal than AD case-control status, capturing many known variants with stronger significance, and also revealing new variants. We identified two novel variants, rs56201815, a rare synonymous variant in ERN1, and rs12373123, a common missense variant in SPPL2C in the MAPT region in APOE ε4 non-carriers. Besides, a rare missense variant rs144292455 in TACR3 showed the consistent direction of effect sizes across all studies with a suggestive significant level. In an attempt to unravel their regulatory and biological functions, we found that the minor allele of rs56201815 was associated with lower average FDG uptake across five brain regions in ADNI. Our eQTL analyses based on 6198 gene expression samples from ROSMAP and GTEx revealed that the minor allele of rs56201815 was potentially associated with elevated expression of ERN1, a key gene triggering unfolded protein response (UPR), in multiple brain regions, including the posterior cingulate cortex and nucleus accumbens. Our cell-type-specific eQTL analysis using  80,000 single nuclei in the prefrontal cortex revealed that the protective minor allele of rs12373123 significantly increased the expression of GRN in microglia, and was associated with MAPT expression in astrocytes. These findings provide novel evidence supporting the hypothesis of the potential involvement of the UPR to ER stress in the pathological pathway of AD, and also give more insights into underlying regulatory mechanisms behind the pleiotropic effects of rs12373123 in multiple degenerative diseases including AD and Parkinson{\textquoteright}s disease.},
	keywords = {My Papers},
	author = {He, Liang and Loika, Yury and Park, Yongjin and Genotype Tissue Expression (GTEx) consortium and Bennett, David A and Kellis, Manolis and Kulminski, Alexander M and Alzheimer{\textquoteright}s Disease Neuroimaging Initiative}
}
@article {Xiong2021-uv,
	title = {Genetic drivers of m6A methylation in human brain, lung, heart and muscle},
	journal = {Nat. Genet.},
	year = {2021},
	abstract = {The most prevalent post-transcriptional mRNA modification, N6-methyladenosine (m6A), plays diverse RNA-regulatory roles, but its genetic control in human tissues remains uncharted. Here we report 129 transcriptome-wide m6A profiles, covering 91 individuals and 4 tissues (brain, lung, muscle and heart) from GTEx/eGTEx. We integrate these with interindividual genetic and expression variation, revealing 8,843 tissue-specific and 469 tissue-shared m6A quantitative trait loci (QTLs), which are modestly enriched in, but mostly orthogonal to, expression QTLs. We integrate m6A QTLs with disease genetics, identifying 184 GWAS-colocalized m6A QTL, including brain m6A QTLs underlying neuroticism, depression, schizophrenia and anxiety; lung m6A QTLs underlying expiratory flow and asthma; and muscle/heart m6A QTLs underlying coronary artery disease. Last, we predict novel m6A regulators that show preferential binding in m6A QTLs, protein interactions with known m6A regulators and expression correlation with the m6A levels of their targets. Our results provide important insights and resources for understanding both cis and trans regulation of epitranscriptomic modifications, their interindividual variation and their roles in human disease.},
	keywords = {My Papers},
	author = {Xiong, Xushen and Hou, Lei and Park, Yongjin P and Molinie, Benoit and GTEx Consortium and Gregory, Richard I and Kellis, Manolis}
}
@article {Boix2021-br,
	title = {Regulatory genomic circuitry of human disease loci by integrative epigenomics},
	journal = {Nature},
	volume = {590},
	number = {7845},
	year = {2021},
	pages = {300{\textendash}307},
	abstract = {Annotating the molecular basis of human disease remains an unsolved challenge, as 93\% of disease loci are non-coding and gene-regulatory annotations are highly incomplete1-3. Here we present EpiMap, a compendium comprising 10,000 epigenomic maps across 800 samples, which we used to define chromatin states, high-resolution enhancers, enhancer modules, upstream regulators and downstream target genes. We used this resource to annotate 30,000 genetic loci that were associated with 540 traits4, predicting trait-relevant tissues, putative causal nucleotide variants in enriched tissue enhancers and candidate tissue-specific target genes for each. We partitioned multifactorial traits into tissue-specific contributing factors with distinct functional enrichments and disease comorbidity patterns, and revealed both single-factor monotropic and multifactor pleiotropic loci. Top-scoring loci frequently had multiple predicted driver variants, converging through multiple enhancers with a common target gene, multiple genes in common tissues, or multiple genes and multiple tissues, indicating extensive pleiotropy. Our results demonstrate the importance of dense, rich, high-resolution epigenomic annotations for the investigation of complex traits.},
	keywords = {My Papers},
	author = {Boix, Carles A and James, Benjamin T and Park, Yongjin P and Meuleman, Wouter and Kellis, Manolis}
}
@unpublished {Park2021-mw,
	title = {Single-cell deconvolution of 3,000 post-mortem brain samples for eQTL and GWAS dissection in mental disorders},
	journal = {Cold Spring Harbor Laboratory},
	year = {2021},
	pages = {2021.01.21.426000},
	abstract = {Thousands of genetic variants acting in multiple cell types underlie complex disorders, yet most gene expression studies profile only bulk tissues, making it hard to resolve where genetic and non-genetic contributors act. This is particularly important for psychiatric and neurodegenerative disorders that impact multiple brain cell types with highly-distinct gene expression patterns and proportions. To address this challenge, we develop a new framework, SPLITR, that integrates single-nucleus and bulk RNA-seq data, enabling phenotype-aware deconvolution and correcting for systematic discrepancies between bulk and single-cell data. We deconvolved 3,387 post-mortem brain samples across 1,127 individuals and in multiple brain regions. We find that cell proportion varies across brain regions, individuals, disease status, and genotype, including genetic variants in TMEM106B that impact inhibitory neuron fraction and 4,757 cell-type-specific eQTLs. Our results demonstrate the power of jointly analyzing bulk and single-cell RNA-seq to provide insights into cell-type-specific mechanisms for complex brain disorders. \#\#\# Competing Interest Statement The authors have declared no competing interest.},
	keywords = {1 CRS 2021, My Papers},
	author = {Park, Yongjin and He, Liang and Davila-Velderrain, Jose and Hou, Lei and Mohammadi, Shahin and Mathys, Hansruedi and Peng, Zhuyu and Bennett, David and Tsai, Li-Huei and Kellis, Manolis}
}
@article {Park2019-hy,
	title = {Causal Mediation Analysis Leveraging Multiple Types of Summary Statistics Data},
	year = {2019},
	month = {jan},
	abstract = {Summary statistics of genome-wide association studies (GWAS) teach causal relationship between millions of genetic markers and tens and thousands of phenotypes. However, underlying biological mechanisms are yet to be elucidated. We can achieve necessary interpretation of GWAS in a causal mediation framework, looking to establish a sparse set of mediators between genetic and downstream variables, but there are several challenges. Unlike existing methods rely on strong and unrealistic assumptions, we tackle practical challenges within a principled summary-based causal inference framework. We analyzed the proposed methods in extensive simulations generated from real-world genetic data. We demonstrated only our approach can accurately redeem causal genes, even without knowing actual individual-level data, despite the presence of competing non-causal trails.},
	keywords = {1 CRS 2021, My Papers},
	author = {Park, Yongjin and Sarkar, Abhishek and Nguyen, Khoi and Kellis, Manolis}
}
@unpublished {Park2017-kl,
	title = {A Bayesian approach to mediation analysis predicts 206 causal target genes in Alzheimer{\textquoteright}s disease},
	journal = {bioRxiv},
	year = {2017},
	pages = {219428},
	publisher = {bioRxiv},
	abstract = {Characterizing the intermediate phenotypes, such as gene expression, that mediate genetic effects on complex diseases is a fundamental problem in human genetics. Existing methods utilize genotypic data and summary statistics to identify putative disease genes, but cannot distinguish pleiotropy from causal mediation and are limited by overly strong assumptions about the data. To overcome these limitations, we develop Causal Multivariate Mediation within Extended Linkage disequilibrium (CaMMEL), a novel Bayesian inference framework to jointly model multiple mediated and unmediated effects relying only on summary statistics. We show in simulation that CaMMEL accurately distinguishes between mediating and pleiotropic genes unlike existing methods. We applied CaMMEL to Alzheimer9s disease (AD) and found 206 causal genes in sub-threshold loci (p},
	keywords = {1 CRS 2021, 1 Deconv Paper, mediation, My Papers},
	author = {Park, Yongjin and Sarkar, Abhishek and He, Liang and Davila-Velderrain, Jose and De Jager, Philip L and Kellis, Manolis}
}
@article {Park2017-vv,
	title = {Fast and reliable inference algorithm for hierarchical stochastic block models},
	year = {2017},
	abstract = {Network clustering reveals the organization of a network or corresponding complex system with elements represented as vertices and interactions as edges in a (directed, weighted) graph. Although the notion of clustering can be somewhat loose, network clusters or groups are generally considered as nodes with enriched interactions and edges sharing common patterns. Statistical inference often treats groups as latent variables, with observed networks generated from latent group structure, termed a stochastic block model. Regardless of the definitions, statistical inference can be either translated to modularity maximization, which is provably an NP-complete problem. Here we present scalable and reliable algorithms that recover hierarchical stochastic block models fast and accurately. Our algorithm scales almost linearly in number of edges, and inferred models were more accurate that other scalable methods.},
	keywords = {1 CRS 2021, My Papers, Network},
	author = {Park, Yongjin and Bader, Joel S}
}
@article {Park2017-cb,
	title = {Multi-tissue polygenic models for transcriptome-wide association studies},
	journal = {bioRxiv},
	year = {2017},
	month = {feb},
	pages = {107623},
	publisher = {Cold Spring Harbor Labs Journals},
	abstract = {Transcriptome-wide association studies (TWAS) have proven to be a powerful tool to identify genes associated with human diseases by aggregating cis-regulatory effects on gene expression. However, TWAS relies on building predictive models of gene expression, which are sensitive to the sample size and tissue on which they are trained. The Gene Tissue Expression Project has produced reference transcriptomes across 53 human tissues and cell types; however, the data is highly sparse, making it difficult to build polygenic models in relevant tissues for TWAS. Here, we propose \textbackslashtextquoteleftfQTL\textbackslashtextquoteright, a multi-tissue, multivariate model for mapping expression quantitative trait loci and predicting gene expression. Our model decomposes eQTL effects into SNP-specific and tissue-specific components, pooling information across relevant tissues to effectively boost sample sizes. In simulation, we demonstrate that our multi-tissue approach outperforms single-tissue approaches in identifying causal eQTLs and tissues of action. Using our method, we fit polygenic models for 13,461 genes, characterized the tissue-specificity of the learned cis-eQTLs, and performed TWAS for Alzheimer{\textquoteright}s disease and schizophrenia, identifying 107 and 382 associated genes, respectively.},
	keywords = {My Papers},
	author = {Park, Yongjin and Sarkar, Abhishek K and Bhutani, Kunal and Kellis, Manolis}
}
@unpublished {Park2017-ce,
	title = {Temporal expression divergence of network modules},
	journal = {bioRxiv},
	year = {2017},
	month = {jul},
	pages = {167734},
	abstract = {Here we propose new module-based approaches to identify differentially regulated network sub-modules combining temporal trajectories of expression profiles with static network skeletons. Starting from modules identified by network clustering of static networks, our analysis refines pre-defined genesets by partitioning them into smaller homogeneous sets by non-paramettric Bayesian methods. Especially for case-control time series data we developed multi-time point discriminative models and identified each network module as a mixture or admixture of dynamic discriminative functions. Our results shows that our proposed approach outperformed existing geneset enrichment methods in simulation studies. Moreover we applied the methods to neural stem cell differentiation data, and discovered novel modules differentially perturbed in different developmental stages.},
	keywords = {1 CRS 2021, My Papers},
	author = {Park, Yongjin and Kang, Tae-Hyuk and Friedmann, Theodore and Bader, Joel}
}
@article {Kang2013-mw,
	title = {The housekeeping gene hypoxanthine guanine phosphoribosyltransferase (HPRT) regulates multiple developmental and metabolic pathways of murine embryonic stem cell neuronal differentiation},
	journal = {PLoS One},
	volume = {8},
	number = {10},
	year = {2013},
	pages = {e74967},
	abstract = {The mechanisms by which mutations of the purinergic housekeeping gene hypoxanthine guanine phosphoribosyltransferase (HPRT) cause the severe neurodevelopmental Lesch Nyhan Disease (LND) are poorly understood. The best recognized neural consequences of HPRT deficiency are defective basal ganglia expression of the neurotransmitter dopamine (DA) and aberrant DA neuronal function. We have reported that HPRT deficiency leads to dysregulated expression of multiple DA-related developmental functions and cellular signaling defects in a variety of HPRT-deficient cells, including human induced pluripotent stem (iPS) cells. We now describe results of gene expression studies during neuronal differentiation of HPRT-deficient murine ESD3 embryonic stem cells and report that HPRT knockdown causes a marked switch from neuronal to glial gene expression and dysregulates expression of Sox2 and its regulator, genes vital for stem cell pluripotency and for the neuronal/glial cell fate decision. In addition, HPRT deficiency dysregulates many cellular functions controlling cell cycle and proliferation mechanisms, RNA metabolism, DNA replication and repair, replication stress, lysosome function, membrane trafficking, signaling pathway for platelet activation (SPPA) multiple neurotransmission systems and sphingolipid, sulfur and glycan metabolism. We propose that the neural aberrations of HPRT deficiency result from combinatorial effects of these multi-system metabolic errors. Since some of these aberrations are also found in forms of Alzheimer{\textquoteright}s and Huntington{\textquoteright}s disease, we predict that some of these systems defects play similar neuropathogenic roles in diverse neurodevelopmental and neurodegenerative diseases in common and may therefore provide new experimental opportunities for clarifying pathogenesis and for devising new potential therapeutic targets in developmental and genetic disease.},
	keywords = {My Papers},
	author = {Kang, Tae Hyuk and Park, Yongjin and Bader, Joel S and Friedmann, Theodore}
}
@article {Park2012-fi,
	title = {How networks change with time},
	journal = {Bioinformatics},
	volume = {28},
	number = {12},
	year = {2012},
	pages = {i40{\textendash}8},
	abstract = {MOTIVATION: Biological networks change in response to genetic and environmental cues. Changes are reflected in the abundances of biomolecules, the composition of protein complexes and other descriptors of the biological state. Methods to infer the dynamic state of a cell would have great value for understanding how cells change over time to accomplish biological goals. RESULTS: A new method predicts the dynamic state of protein complexes in a cell, with protein expression inferred from transcription profile time courses and protein complexes inferred by joint analysis of protein co-expression and protein-protein interaction maps. Two algorithmic advances are presented: a new method, DHAC (Dynamical Hierarchical Agglomerative Clustering), for clustering time-evolving networks; and a companion method, MATCH-EM, for matching corresponding clusters across time points. With link prediction as an objective assessment metric, DHAC provides a substantial advance over existing clustering methods. An application to the yeast metabolic cycle demonstrates how waves of gene expression correspond to individual protein complexes. Our results suggest regulatory mechanisms for assembling the mitochondrial ribosome and illustrate dynamic changes in the components of the nuclear pore. AVAILABILITY: All source code and data are available under the Boost Software License as supplementary material, at www.baderzone.org, and at sourceforge.net/projects/dhacdist.},
	keywords = {My Papers},
	author = {Park, Yongjin and Bader, Joel S}
}
@article {Park2011-fz,
	title = {Resolving the structure of interactomes with hierarchical agglomerative clustering},
	journal = {BMC Bioinformatics},
	volume = {12 Suppl 1},
	year = {2011},
	month = {feb},
	pages = {S44},
	abstract = {BACKGROUND: Graphs provide a natural framework for visualizing and analyzing networks of many types, including biological networks. Network clustering is a valuable approach for summarizing the structure in large networks, for predicting unobserved interactions, and for predicting functional annotations. Many current clustering algorithms suffer from a common set of limitations: poor resolution of top-level clusters; over-splitting of bottom-level clusters; requirements to pre-define the number of clusters prior to analysis; and an inability to jointly cluster over multiple interaction types. RESULTS: A new algorithm, Hierarchical Agglomerative Clustering (HAC), is developed for fast clustering of heterogeneous interaction networks. This algorithm uses maximum likelihood to drive the inference of a hierarchical stochastic block model for network structure. Bayesian model selection provides a principled method for collapsing the fine-structure within the smallest groups, and for identifying the top-level groups within a network. Model scores are additive over independent interaction types, providing a direct route for simultaneous analysis of multiple interaction types. In addition to inferring network structure, this algorithm generates link predictions that with cross-validation provide a quantitative assessment of performance for real-world examples. CONCLUSIONS: When applied to genome-scale data sets representing several organisms and interaction types, HAC provides the overall best performance in link prediction when compared with other clustering methods and with model-free graph diffusion kernels. Investigation of performance on genome-scale yeast protein interactions reveals roughly 100 top-level clusters, with a long-tailed distribution of cluster sizes. These are in turn partitioned into 1000 fine-level clusters containing 5 proteins on average, again with a long-tailed size distribution. Top-level clusters correspond to broad biological processes, whereas fine-level clusters correspond to discrete complexes. Surprisingly, link prediction based on joint clustering of physical and genetic interactions performs worse than predictions based on individual data sets, suggesting a lack of synergy in current high-throughput data.},
	keywords = {My Papers},
	author = {Park, Yongjin and Bader, Joel S}
}
@article {Park2010-to,
	title = {Dynamic networks from hierarchical bayesian graph clustering},
	journal = {PLoS One},
	volume = {5},
	number = {1},
	year = {2010},
	month = {jan},
	pages = {e8118},
	abstract = {Biological networks change dynamically as protein components are synthesized and degraded. Understanding the time-dependence and, in a multicellular organism, tissue-dependence of a network leads to insight beyond a view that collapses time-varying interactions into a single static map. Conventional algorithms are limited to analyzing evolving networks by reducing them to a series of unrelated snapshots.Here we introduce an approach that groups proteins according to shared interaction patterns through a dynamical hierarchical stochastic block model. Protein membership in a block is permitted to evolve as interaction patterns shift over time and space, representing the spatial organization of cell types in a multicellular organism. The spatiotemporal evolution of the protein components are inferred from transcript profiles, using Arabidopsis root development (5 tissues, 3 temporal stages) as an example.The new model requires essentially no parameter tuning, out-performs existing snapshot-based methods, identifies protein modules recruited to specific cell types and developmental stages, and could have broad application to social networks and other similar dynamic systems.},
	keywords = {My Papers},
	author = {Park, Yongjin and Moore, Cristopher and Bader, Joel S}
}
