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An integrative genetics approach to identify candidate genes regulating BMD: combining linkage, gene expression, and association.

ABSTRACT: Numerous quantitative trait loci (QTLs) affecting bone traits have been identified in the mouse; however, few of the underlying genes have been discovered. To improve the process of transitioning from QTL to gene, we describe an integrative genetics approach, which combines linkage analysis, expression QTL (eQTL) mapping, causality modeling, and genetic association in outbred mice. In C57BL/6J x C3H/HeJ (BXH) F(2) mice, nine QTLs regulating femoral BMD were identified. To select candidate genes from within each QTL region, microarray gene expression profiles from individual F(2) mice were used to identify 148 genes whose expression was correlated with BMD and regulated by local eQTLs. Many of the genes that were the most highly correlated with BMD have been previously shown to modulate bone mass or skeletal development. Candidates were further prioritized by determining whether their expression was predicted to underlie variation in BMD. Using network edge orienting (NEO), a causality modeling algorithm, 18 of the 148 candidates were predicted to be causally related to differences in BMD. To fine-map QTLs, markers in outbred MF1 mice were tested for association with BMD. Three chromosome 11 SNPs were identified that were associated with BMD within the Bmd11 QTL. Finally, our approach provides strong support for Wnt9a, Rasd1, or both underlying Bmd11. Integration of multiple genetic and genomic data sets can substantially improve the efficiency of QTL fine-mapping and candidate gene identification.

SUBMITTER: Farber CR

PROVIDER: S-EPMC2661539 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Obesity is a multi-factorial health problem in which genetic factors play an important role. Limited results have been obtained in single-gene studies using either genomic or transcriptomic data. RNA sequencing technology has shown its potential in gaining accurate knowledge about the transcriptome, and may reveal novel genes affecting complex diseases. Integration of genomic and transcriptomic variation (expression quantitative trait loci [eQTL] mapping) has identified causal variants that affect complex diseases. We integrated transcriptomic data from adipose tissue and genomic data from a porcine model to investigate the mechanisms involved in obesity using a systems genetics approach.Using a selective gene expression profiling approach, we selected 36 animals based on a previously created genomic Obesity Index for RNA sequencing of subcutaneous adipose tissue. Differential expression analysis was performed using the Obesity Index as a continuous variable in a linear model. eQTL mapping was then performed to integrate 60 K porcine SNP chip data with the RNA sequencing data. Results were restricted based on genome-wide significant single nucleotide polymorphisms, detected differentially expressed genes, and previously detected co-expressed gene modules. Further data integration was performed by detecting co-expression patterns among eQTLs and integration with protein data.Differential expression analysis of RNA sequencing data revealed 458 differentially expressed genes. The eQTL mapping resulted in 987 cis-eQTLs and 73 trans-eQTLs (false discovery rate?<?0.05), of which the cis-eQTLs were associated with metabolic pathways. We reduced the eQTL search space by focusing on differentially expressed and co-expressed genes and disease-associated single nucleotide polymorphisms to detect obesity-related genes and pathways. Building a co-expression network using eQTLs resulted in the detection of a module strongly associated with lipid pathways. Furthermore, we detected several obesity candidate genes, for example, ENPP1, CTSL, and ABHD12B.To our knowledge, this is the first study to perform an integrated genomics and transcriptomics (eQTL) study using, and modeling, genomic and subcutaneous adipose tissue RNA sequencing data on obesity in a porcine model. We detected several pathways and potential causal genes for obesity. Further validation and investigation may reveal their exact function and association with obesity.

Project description:BACKGROUND:In recent years, the planting area of sweet corn in China has expanded rapidly. Some new varieties with high yields and good adaptabilities have emerged. However, the improvement of edible quality traits, especially through the development of varieties with thin pericarp thickness, has not been achieved to date. Pericarp thickness is a complex trait that is the key factor determining the edible quality of sweet corn. Genetic mapping combined with transcriptome analysis was used to identify candidate genes controlling pericarp thickness. RESULTS:To identify novel quantitative trait loci (QTLs) for pericarp thickness, a sweet corn BC4F3 population of 148 lines was developed using the two sweet corn lines M03 (recurrent parent) and M08 (donor parent). Additionally, a high-density genetic linkage map containing 3876 specific length amplified fragment (SLAF) tags was constructed and used for mapping QTLs for pericarp thickness. Interestingly, 14 QTLs for pericarp thickness were detected, and one stable QTL (qPT10-5) was detected across multiple years, which explained 7.78-35.38% of the phenotypic variation located on chromosome 10 (144,631,242-145,532,401). Forty-two candidate genes were found within the target region of qPT10-5. Moreover, of these 42 genes, five genes (GRMZM2G143402, GRMZM2G143389, GRMZM2G143352, GRMZM6G287947, and AC234202.1_FG004) were differentially expressed between the two parents, as revealed by transcriptome analysis. According to the gene annotation information, three genes might be considered candidates for pericarp thickness. GRMZM2G143352 and GRMZM2G143402 have been annotated as AUX/IAA transcription factor and ZIM transcription factor, respectively, while GRMZM2G143389 has been annotated as FATTY ACID EXPORT 2, chloroplastic. CONCLUSIONS:This study identified a major QTL and candidate genes that could accelerate breeding for the thin pericarp thickness variety of sweet corn, and these results established the basis for map-based cloning and further functional research.

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An integrative genetics approach to identify candidate genes regulating BMD: combining linkage, gene expression, and association.

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