2 Search Results for "Li, Jiajin"

Document
DOI: 10.4230/LIPIcs.WABI.2021.10
An Efficient Linear Mixed Model Framework for Meta-Analytic Association Studies Across Multiple Contexts

Authors: Brandon Jew, Jiajin Li, Sriram Sankararaman, and Jae Hoon Sul

Published in: LIPIcs, Volume 201, 21st International Workshop on Algorithms in Bioinformatics (WABI 2021)

Abstract
Linear mixed models (LMMs) can be applied in the meta-analyses of responses from individuals across multiple contexts, increasing power to detect associations while accounting for confounding effects arising from within-individual variation. However, traditional approaches to fitting these models can be computationally intractable. Here, we describe an efficient and exact method for fitting a multiple-context linear mixed model. Whereas existing exact methods may be cubic in their time complexity with respect to the number of individuals, our approach for multiple-context LMMs (mcLMM) is linear. These improvements allow for large-scale analyses requiring computing time and memory magnitudes of order less than existing methods. As examples, we apply our approach to identify expression quantitative trait loci from large-scale gene expression data measured across multiple tissues as well as joint analyses of multiple phenotypes in genome-wide association studies at biobank scale.
Cite as

Brandon Jew, Jiajin Li, Sriram Sankararaman, and Jae Hoon Sul. An Efficient Linear Mixed Model Framework for Meta-Analytic Association Studies Across Multiple Contexts. In 21st International Workshop on Algorithms in Bioinformatics (WABI 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 201, pp. 10:1-10:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{jew_et_al:LIPIcs.WABI.2021.10,
  author =	{Jew, Brandon and Li, Jiajin and Sankararaman, Sriram and Sul, Jae Hoon},
  title =	{{An Efficient Linear Mixed Model Framework for Meta-Analytic Association Studies Across Multiple Contexts}},
  booktitle =	{21st International Workshop on Algorithms in Bioinformatics (WABI 2021)},
  pages =	{10:1--10:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-200-6},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{201},
  editor =	{Carbone, Alessandra and El-Kebir, Mohammed},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2021.10},
  URN =		{urn:nbn:de:0030-drops-143632},
  doi =		{10.4230/LIPIcs.WABI.2021.10},
  annote =	{Keywords: Meta-analysis, Linear mixed models, multiple-context genetic association}
}
Document
DOI: 10.4230/LIPIcs.ITCS.2020.37
Learning and Testing Variable Partitions

Authors: Andrej Bogdanov and Baoxiang Wang

Published in: LIPIcs, Volume 151, 11th Innovations in Theoretical Computer Science Conference (ITCS 2020)

Abstract
Let F be a multivariate function from a product set Σ^n to an Abelian group G. A k-partition of F with cost δ is a partition of the set of variables V into k non-empty subsets (X_1, ̇s, X_k) such that F(V) is δ-close to F_1(X_1)+ ̇s+F_k(X_k) for some F_1, ̇s, F_k with respect to a given error metric. We study algorithms for agnostically learning k partitions and testing k-partitionability over various groups and error metrics given query access to F. In particular we show that 1) Given a function that has a k-partition of cost δ, a partition of cost O(k n^2)(δ + ε) can be learned in time Õ(n^2 poly 1/ε) for any ε > 0. In contrast, for k = 2 and n = 3 learning a partition of cost δ + ε is NP-hard. 2) When F is real-valued and the error metric is the 2-norm, a 2-partition of cost √(δ^2 + ε) can be learned in time Õ(n^5/ε^2). 3) When F is Z_q-valued and the error metric is Hamming weight, k-partitionability is testable with one-sided error and O(kn^3/ε) non-adaptive queries. We also show that even two-sided testers require Ω(n) queries when k = 2. This work was motivated by reinforcement learning control tasks in which the set of control variables can be partitioned. The partitioning reduces the task into multiple lower-dimensional ones that are relatively easier to learn. Our second algorithm empirically increases the scores attained over previous heuristic partitioning methods applied in this context.
Cite as

Andrej Bogdanov and Baoxiang Wang. Learning and Testing Variable Partitions. In 11th Innovations in Theoretical Computer Science Conference (ITCS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 151, pp. 37:1-37:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{bogdanov_et_al:LIPIcs.ITCS.2020.37,
  author =	{Bogdanov, Andrej and Wang, Baoxiang},
  title =	{{Learning and Testing Variable Partitions}},
  booktitle =	{11th Innovations in Theoretical Computer Science Conference (ITCS 2020)},
  pages =	{37:1--37:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-134-4},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{151},
  editor =	{Vidick, Thomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2020.37},
  URN =		{urn:nbn:de:0030-drops-117221},
  doi =		{10.4230/LIPIcs.ITCS.2020.37},
  annote =	{Keywords: partitioning, agnostic learning, property testing, sublinear-time algorithms, hypergraph cut, reinforcement learning}
}
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