The regulatory landscape of multiple brain regions in outbred heterogeneous stock rats

Munro, Daniel; Wang, Tengfei; Chitre, Apurva S.; Polesskaya, Oksana; Ehsan, Nava; Gao, Jianjun; Gusev, Alexander; Woods, Leah Solberg; Saba, Laura; Chen, Hao; Palmer, Abraham A.; Mohammadi, Pejman

doi:10.1093/nar/gkac912

Cited by 27 publications

(54 citation statements)

References 70 publications

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“…We examined the impact of the upgraded reference assembly on the yield and relative numbers of expression quantitative trait locus (eQTL) using a large RNA-seq dataset for nucleus accumbens. 29 We identified associations that would be labeled as trans-eQTLs using one reference but cis-eQTL using the other, due to relocation of the SNP and/or the TSS. The expectation is that assembly errors will give rise to spurious trans-eQTLs.…”

Section: Resultsmentioning

confidence: 99%

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A revamped rat reference genome improves the discovery of genetic diversity in laboratory rats

Jong¹,

Pan²,

Rastas³

et al. 2023

Preprint

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For over a decade, a large research community has relied on a flawed reference assembly of the genome ofRattus norvegicusknown as Rnor_6.0. The seventh assembly of the rat reference genome4mRatBN7.2, based on the inbred Brown Norway rat, corrects numerous misplaced segments, reduces base-level errors by approximately 9-fold, and increases contiguity by 290-fold, despite some remaining regions of potential misassembly. Gene annotations are now more complete, significantly improving the mapping precision of genomic, transcriptomic, and proteomics data sets. SimpleLiftOverfrom Rnor_6.0 to mRatBN7.2 misses ∼12% of variants. To facilitate the transition to mRatBN7.2, we performed a joint analysis of 163 whole genomes representing 120 strains/substrains. We defined 20.0 million sequence variations, of which 18.7 thousand are predicted to potentially impact the function of 6,677 genes. Phylogenetic analysis confirmed historical records and prior results and refined the ancestral relationship of these strains. Sixteen million polymorphisms segregate in the widely studied heterogeneous stock rat population, and 11313 million variants segregate collectively in the HXB/BXH and FXLE/LEXF strain families. Some inbred strains differ by only 132 M variants, and closely related substrains segregate by even fewer variants. We generated a new rat genetic map based on data from 1,893 heterogeneous stock rats and annotated transcription start sites and alternative polyadenylation sites.

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Section: Resultsmentioning

confidence: 99%

“…RNA-seq data was downloaded from RatGTEx. 29 Brains were extracted from 88 HS rats. Rats were housed under standard laboratory conditions.…”

Section: Methodsmentioning

confidence: 99%

A revamped rat reference genome improves the discovery of genetic diversity in laboratory rats

Jong¹,

Pan²,

Rastas³

et al. 2023

Preprint

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“…CiTruss revealed that the pleiotropic effects of eQTLs, a hierarchy in tissue-specific gene regulation, and local and long-range linkage disequilibrium among eQTLs are structured around local and global subnetworks, and disentangled pleiotropic effects into cis -acting and trans -acting eQTLs for local subnetworks and trans -acting and undetermined eQTLs for global subnetworks. One future direction would be to determine a high-resolution map of hierarchical organization of the global subnetworks across tissue types, by applying CiTruss to eQTL data for a wide variety of tissue types from humans 13 , farm animals 16 , and rats 25 . To further investigate the role of local and long-range linkage disequilibrium of eQTLs in gene regulation, we could compare these eQTLs with Hi-C data 26 to see if long-range chromatin interactions overlap with eQTLs in long-range linkage disequilibrium and if topologically associating domains overlap with the regions of linked eQTLs that contain local subnetworks.…”

Section: Discussionmentioning

confidence: 99%

Learning gene networks under SNP perturbation using SNP and allele-specific expression data

Yoon,

Kim

2023

Preprint

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Allele-specific expression quantification from RNA-seq reads provides opportunities to study the control of gene regulatory networks by cis-acting and trans-acting genetic variants. Many existing methods performed a single-gene and single-SNP association analysis to identify expression quantitative trait loci (eQTLs), and placed the eQTLs against known gene networks for functional interpretation. Instead, we view eQTL data as a capture of the effects of perturbation of gene regulatory system by a large number of genetic variants and reconstruct a gene network perturbed by eQTLs. We introduce a statistical framework called CiTruss for simultaneously learning a gene network and cis-acting and trans-acting eQTLs that perturb this network, given population allele-specific expression and SNP data. CiTruss uses a multi-level conditional Gaussian graphical model to model trans-acting eQTLs perturbing the expression of both alleles in gene network at the top level and cis-acting eQTLs perturbing the expression of each allele at the bottom level. We derive a transformation of this model that allows efficient learning for large-scale human data. Our analysis of the GTEx and LGxSM advanced intercross line mouse data for multiple tissue types with CiTruss provides new insights into genetics of gene regulation. CiTruss revealed that gene networks consist of local subnetworks over proximally located genes and global subnetworks over genes scattered across genome, and that several aspects of gene regulation by eQTLs such as the impact of genetic diversity, pleiotropy, tissue-specific gene regulation, and local and long-range linkage disequilibrium among eQTLs can be explained through these local and global subnetworks.

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“…RNA expression from three brain regions was measured using high-throughput RNA sequencing on the Illumina HiSeq 4000 platform and a poly-A selection protocol (GSE173141). The original data set [58] included tissue from 88 alcohol and drug naïve heterogeneous stock rats and most rats had RNA-Seq libraries from all the regions. The average…”

Section: Plos Computational Biologymentioning

confidence: 99%

RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks

Seal

Basner

et al. 2023

PLoS Comput Biol

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Inferring gene co-expression networks is a useful process for understanding gene regulation and pathway activity. The networks are usually undirected graphs where genes are represented as nodes and an edge represents a significant co-expression relationship. When expression data of multiple (p) genes in multiple (K) conditions (e.g., treatments, tissues, strains) are available, joint estimation of networks harnessing shared information across them can significantly increase the power of analysis. In addition, examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. Condition adaptive fused graphical lasso (CFGL) is an existing method that incorporates condition specificity in a fused graphical lasso (FGL) model for estimating multiple co-expression networks. However, with computational complexity of O(p2K log K), the current implementation of CFGL is prohibitively slow even for a moderate number of genes and can only be used for a maximum of three conditions. In this paper, we propose a faster alternative of CFGL named rapid condition adaptive fused graphical lasso (RCFGL). In RCFGL, we incorporate the condition specificity into another popular model for joint network estimation, known as fused multiple graphical lasso (FMGL). We use a more efficient algorithm in the iterative steps compared to CFGL, enabling faster computation with complexity of O(p2K) and making it easily generalizable for more than three conditions. We also present a novel screening rule to determine if the full network estimation problem can be broken down into estimation of smaller disjoint sub-networks, thereby reducing the complexity further. We demonstrate the computational advantage and superior performance of our method compared to two non-condition adaptive methods, FGL and FMGL, and one condition adaptive method, CFGL in both simulation study and real data analysis. We used RCFGL to jointly estimate the gene co-expression networks in different brain regions (conditions) using a cohort of heterogeneous stock rats. We also provide an accommodating C and Python based package that implements RCFGL.

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The regulatory landscape of multiple brain regions in outbred heterogeneous stock rats

Cited by 27 publications

References 70 publications

A revamped rat reference genome improves the discovery of genetic diversity in laboratory rats

A revamped rat reference genome improves the discovery of genetic diversity in laboratory rats

Learning gene networks under SNP perturbation using SNP and allele-specific expression data

RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks

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