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1. VCSEL: Prioritizing SNP-set by penalized variance component selection

   https://doi.org/10.1214/21-AOAS1491  

   Kim, Juhyun; Shen, Judong; Wang, Anran; Mehrotra, Devan V.; Ko, Seyoon; Zhou, Jin J.; Zhou, Hua (December 2021, The Annals of Applied Statistics)
2. Learning gene networks under SNP perturbation using SNP and allele-specific expression data

   https://doi.org/10.1101/2023.10.23.563661  

   Yoon, Jun Ho; Kim, Seyoung (October 2023, bioRxiv)

   Abstract Allele-specific expression quantification from RNA-seq reads provides opportunities to study the control of gene regulatory networks bycis-acting andtrans-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 andcis-acting andtrans-acting eQTLs that perturb this network, given population allele-specific expression and SNP data. CiTruss uses a multi-level conditional Gaussian graphical model to modeltrans-acting eQTLs perturbing the expression of both alleles in gene network at the top level andcis-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 LG×SM 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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3. Data‐driven guidelines for phylogenomic analyses using SNP data

   https://doi.org/10.1002/aps3.11611  

   Suissa, Jacob S; De_La_Cerda, Gisel Y; Graber, Leland C; Jelley, Chloe; Wickell, David; Phillips, Heather R; Grinage, Ayress D; Moreau, Corrie S; Specht, Chelsea D; Doyle, Jeff J; et al (August 2024, Applications in Plant Sciences)

   There is a general lack of consensus on the best practices for filtering of single‐nucleotide polymorphisms (SNPs) and whether it is better to use SNPs or include flanking regions (full “locus”) in phylogenomic analyses and subsequent comparative methods. Using genotyping‐by‐sequencing data from 22Glycinespecies, we assessed the effects of SNP vs. locus usage and SNP retention stringency. We compared branch length, node support, and divergence time estimation across 16 datasets with varying amounts of missing data and total size. Our results revealed five aspects of phylogenomic data usage that may be generally applicable: (1) tree topology is largely congruent across analyses; (2) filtering strictly for SNP retention (e.g., 90–100%) reduces support and can alter some inferred relationships; (3) absolute branch lengths vary by two orders of magnitude between SNP and locus datasets; (4) data type and branch length variation have little effect on divergence time estimation; and (5) phylograms alter the estimation of ancestral states and rates of morphological evolution. Using SNP or locus datasets does not alter phylogenetic inference significantly, unless researchers want or need to use absolute branch lengths. We recommend against using excessive filtering thresholds for SNP retention to reduce the risk of producing inconsistent topologies and generating low support. 

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4. Effective SNP ranking improves the performance of eQTL mapping

   https://doi.org/10.1002/gepi.22293  

   Jeng, X. Jessie; Rhyne, Jacob; Zhang, Teng; Tzeng, Jung‐Ying (March 2020, Genetic Epidemiology)

   Abstract Genome‐wide expression quantitative trait loci (eQTLs) mapping explores the relationship between gene expression and DNA variants, such as single‐nucleotide polymorphism (SNPs), to understand genetic basis of human diseases. Due to the large number of genes and SNPs that need to be assessed, current methods for eQTL mapping often suffer from low detection power, especially for identifyingtrans‐eQTLs. In this paper, we propose the idea of performing SNP ranking based on the higher criticism statistic, a summary statistic developed in large‐scale signal detection. We illustrate how the HC‐based SNP ranking can effectively prioritize eQTL signals over noise, greatly reduce the burden of joint modeling, and improve the power for eQTL mapping. Numerical results in simulation studies demonstrate the superior performance of our method compared to existing methods. The proposed method is also evaluated in HapMap eQTL data analysis and the results are compared to a database of known eQTLs. 

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5. Modeling allele-specific expression at the gene and SNP levels simultaneously by a Bayesian logistic mixed regression model

   https://doi.org/10.1186/s12859-019-3141-6  

   Xie, Jing; Ji, Tieming; Ferreira, Marco A.; Li, Yahan; Patel, Bhaumik N.; Rivera, Rocio M. (December 2019, BMC Bioinformatics)