A Systems Genetics Approach Implicates USF1, FADS3, and Other Causal Candidate Genes for Familial Combined Hyperlipidemia

Abstract Coronary artery disease (CAD) is a multifactorial disorder, which is partly heritable. Herein, we implemented a mapping of CAD-associated candidate genes by using genome-wide enhancer-promoter conformation (H3K27ac-HiChIP) and expression quantitative trait loci (eQTL). Enhancer-promoter anchor loops from human coronary artery smooth muscle cells (HCASMC) explained 22% of the heritability for CAD. 3D enhancer-promoter genome mapping of CAD-genes in HCASMC was enriched in vascular eQTL genes. By using colocalization and Mendelian randomization analyses, we identified 58 causal candidate vascular genes including some druggable targets (MAP3K11, CAMK1D, PDGFD, IPO9 and CETP). A network analysis of causal candidate genes was enriched in TGF beta and MAPK pathways. The pharmacologic inhibition of causal candidate gene MAP3K11 in vascular SMC reduced the expression of athero-relevant genes and lowered cell migration, a cardinal process in CAD. Genes connected to enhancers are enriched in vascular eQTL and druggable genes causally associated with CAD.

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Identification and fine mapping of qPBR10-1, a novel locus controlling panicle blast resistance in Pigm-containing P/TGMS line

10.21203/rs.3.rs-850447/v1 ◽

2021 ◽

Author(s):

Yunyu Wu ◽

Ning Xiao ◽

Yuhong Li ◽

Qiang Gao ◽

Yuese Ning ◽

...

Keyword(s):

Candidate Genes ◽

Sequence Comparison ◽

Blast Resistance ◽

Rt Pcr ◽

Advanced Backcross ◽

Family Protein ◽

Panicle Blast ◽

Genes Encoding ◽

Causal Candidate ◽

Major Qtl

Abstract Background Rice blast is one of the most widespread and devastating diseases in rice production. Tremendous success has been achieved in identification and characterization of genes and quantitative trait loci (QTLs) conferring seedling blast resistance, however, genetic studies on panicle blast resistance have lagged far behind. Results In this study, two advanced backcross inbred sister lines (MSJ13 and MSJ18) were obtained in the process of introducing Pigm into C134S, and showed significant differences in the panicle blast resistance. One F2 population derived from the crossing MSJ13/MSJ18 was used to QTL mapping for panicle blast resistance using Genotyping by Sequencing (GBS) method. A total of 7 QTLs were identified, including a major QTL qPBR10-1 on chromosome 10 that explaining 24.21% of phenotypic variance with LOD scores of 6.62. Furthermore, qPBR10-1 was verified via the BC1F2 and BC1F3 population and narrowed to a 60.6-kb region with six candidate genes predicted, including two genes encoding exonuclease family protein, two genes encoding hypothetical protein, and two genes encoding transposon protein. The nucleotide variations and the expression patterns of the candidate genes were identified and analyzed between MSJ13 and MSJ18 through sequence comparison and RT-PCR approach, and results indicated that ORF1 and ORF2 encoding exonuclease family protein might be the causal candidate genes for panicle blast resistance in the qPBR10-1 locus. Conclusions A total of 7 QTLs conferring panicle blast resistance was identified from one F2 population derived from the crossing between two advanced backcross inbred sister lines MSJ13 and MSJ18, which harbored the broad-spectrum resistance gene Pigm. A major QTL qPBR10-1 was fine mapped in a 60.6-kb region with six candidate genes predicted, and ORF1 and ORF2 encoding exonuclease family protein might be the causal candidate genes for panicle blast resistance in the qPBR10-1 locus through sequence comparison and RT-PCR approach.

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Identification and fine mapping of qGR6.2, a novel locus controlling rice seed germination under salt stress

BMC Plant Biology ◽

10.1186/s12870-020-02820-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Peng Zeng ◽

Peiwen Zhu ◽

Luofeng Qian ◽

Xumei Qian ◽

Yuxin Mi ◽

...

Keyword(s):

Salt Stress ◽

Seed Germination ◽

Candidate Genes ◽

Germination Rate ◽

Tyrosine Phosphatase ◽

Rice Seed ◽

Microarray Database ◽

Family Protein ◽

Germination Traits ◽

Causal Candidate

Abstract Background Rice growth is frequently affected by salinity. When exposed to high salinity, rice seed germination and seedling establishment are significantly inhibited. With the promotion of direct-seeding in Asia, improving rice seed germination under salt stress is crucial for breeding. Results In this study, an indica landrace Wujiaozhan (WJZ) was identified with high germinability under salt stress. A BC1F2 population derived from the crossing WJZ/Nip (japonica, Nipponbare)//Nip, was used to quantitative trait loci (QTL) mapping for the seed germination rate (GR) and germination index (GI) under H2O and 300 mM NaCl conditions. A total of 13 QTLs were identified, i.e. ten QTLs under H2O conditions and nine QTLs under salt conditions. Six QTLs, qGR6.1, qGR8.1, qGR8.2, qGR10.1, qGR10.2 and qGI10.1 were simultaneously identified under two conditions. Under salt conditions, three QTLs, qGR6.2, qGR10.1 and qGR10.2 for GR were identified at different time points during seed germination, which shared the same chromosomal region with qGI6.2, qGI10.1 and qGI10.2 for GI respectively. The qGR6.2 accounted for more than 20% of phenotypic variation under salt stress, as the major effective QTL. Furthermore, qGR6.2 was verified via the BC2F2 population and narrowed to a 65.9-kb region with eleven candidate genes predicted. Based on the microarray database, five candidate genes were found with high transcript abundances at the seed germination stage, of which LOC_Os06g10650 and LOC_Os06g10710 were differentially expressed after seed imbibition. RT-qPCR results showed the expression of LOC_Os06g10650 was significantly up-regulated in two parents with higher levels in WJZ than Nip during seed germination under salt conditions. Taken together, it suggests that LOC_Os06g10650, encoding tyrosine phosphatase family protein, might be the causal candidate gene for qGR6.2. Conclusions In this study, we identified 13 QTLs from a landrace WJZ that confer seed germination traits under H2O and salt conditions. A major salt-tolerance-specific QTL qGR6.2 was fine mapped to a 65.9-kb region. Our results provide information on the genetic basis of improving rice seed germination under salt stress by marker-assisted selection (MAS).

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A Dual Systems Genetics Approach Identifies Common Genes, Networks, and Pathways for Type 1 and 2 Diabetes in Human Islets

Frontiers in Genetics ◽

10.3389/fgene.2021.630109 ◽

2021 ◽

Vol 12 ◽

Author(s):

Simranjeet Kaur ◽

Aashiq H. Mirza ◽

Anne J. Overgaard ◽

Flemming Pociot ◽

Joachim Størling

Keyword(s):

Candidate Genes ◽

Association Studies ◽

Cell Mass ◽

Interaction Network ◽

Human Islets ◽

Systems Genetics ◽

Genome Wide Association Studies ◽

Dual Systems ◽

Β Cell

Type 1 and 2 diabetes (T1/2D) are complex metabolic diseases caused by absolute or relative loss of functional β-cell mass, respectively. Both diseases are influenced by multiple genetic loci that alter disease risk. For many of the disease-associated loci, the causal candidate genes remain to be identified. Remarkably, despite the partially shared phenotype of the two diabetes forms, the associated loci for T1D and T2D are almost completely separated. We hypothesized that some of the genes located in risk loci for T1D and T2D interact in common pancreatic islet networks to mutually regulate important islet functions which are disturbed by disease-associated variants leading to β-cell dysfunction. To address this, we took a dual systems genetics approach. All genes located in 57 T1D and 243 T2D established genome-wide association studies (GWAS) loci were extracted and filtered for genes expressed in human islets using RNA sequencing data, and then integrated with; (1) human islet expression quantitative trait locus (eQTL) signals in linkage disequilibrium (LD) with T1D- and T2D-associated variants; or (2) with genes transcriptionally regulated in human islets by pro-inflammatory cytokines or palmitate as in vitro models of T1D and T2D, respectively. Our in silico systems genetics approaches created two interaction networks consisting of densely-connected T1D and T2D loci genes. The “T1D-T2D islet eQTL interaction network” identified 9 genes (GSDMB, CARD9, DNLZ, ERAP1, PPIP5K2, TMEM69, SDCCAG3, PLEKHA1, and HEMK1) in common T1D and T2D loci that harbor islet eQTLs in LD with disease-associated variants. The “cytokine and palmitate islet interaction network” identified 4 genes (ASCC2, HIBADH, RASGRP1, and SRGAP2) in common T1D and T2D loci whose expression is mutually regulated by cytokines and palmitate. Functional annotation analyses of the islet networks revealed a number of significantly enriched pathways and molecular functions including cell cycle regulation, inositol phosphate metabolism, lipid metabolism, and cell death and survival. In summary, our study has identified a number of new plausible common candidate genes and pathways for T1D and T2D.

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Systems genetics approach reveals candidate genes for parasite resistance from quantitative trait loci studies in agricultural species

Animal Genetics ◽

10.1111/j.1365-2052.2011.02231.x ◽

2011 ◽

Vol 43 (2) ◽

pp. 190-198 ◽

Cited By ~ 12

Author(s):

B. L. Sayre ◽

G. C. Harris

Keyword(s):

Quantitative Trait Loci ◽

Candidate Genes ◽

Quantitative Trait ◽

Systems Genetics ◽

Parasite Resistance ◽

Trait Loci ◽

Agricultural Species

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Systems genetics: A paradigm to improve discovery of candidate genes and mechanisms underlying complex traits

Plant Science ◽

10.1016/j.plantsci.2014.03.003 ◽

2014 ◽

Vol 223 ◽

pp. 45-48 ◽

Cited By ~ 28

Author(s):

F. Alex Feltus

Keyword(s):

Candidate Genes ◽

Complex Traits ◽

Systems Genetics

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GeneNetwork: a continuously updated tool for systems genetics analyses

10.1101/2020.12.23.424047 ◽

2020 ◽

Author(s):

Pamela M. Watson ◽

David G. Ashbrook

Keyword(s):

Candidate Genes ◽

Quantitative Trait ◽

Predictive Medicine ◽

Systems Genetics ◽

Immune Phenotype ◽

Plausible Candidate ◽

Genetics Research ◽

Omics Analysis

AbstractGeneNetwork and its earlier iteration, WebQTL, have now been an important database and toolkit for quantitative trait genetics research for two decades. Recent improvements to GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi-omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses, demonstrating how a small number of plausible candidate genes can be found for a typical immune phenotype.

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