Browsing by Subject "Quantitative Trait Loci"

Now showing 1 - 4 of 4
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    Genetics of gene expression characterizes response to selective breeding for alcohol preference
    (Wiley Blackwell (Blackwell Publishing), 2014-11) Hoffman, P. L.; Saba, L. M.; Flink, S.; Grahame, N. J.; Kechris, K.; Tabakoff, B.; Department of Psychiatry, IU School of Medicine
    Numerous selective breeding experiments have been performed with rodents, in an attempt to understand the genetic basis for innate differences in preference for alcohol consumption. Quantitative trait locus (QTL) analysis has been used to determine regions of the genome that are associated with the behavioral difference in alcohol preference/consumption. Recent work suggests that differences in gene expression represent a major genetic basis for complex traits. Therefore, the QTLs are likely to harbor regulatory regions (eQTLs) for the differentially expressed genes that are associated with the trait. In this study, we examined brain gene expression differences over generations of selection of the third replicate lines of high and low alcohol-preferring (HAP3 and LAP3) mice, and determined regions of the genome that control the expression of these differentially expressed genes (de eQTLs). We also determined eQTL regions (rv eQTLs) for genes that showed a decrease in variance of expression levels over the course of selection. We postulated that de eQTLs that overlap with rv eQTLs, and also with phenotypic QTLs, represent genomic regions that are affected by the process of selection. These overlapping regions controlled the expression of candidate genes (that displayed differential expression and reduced variance of expression) for the predisposition to differences in alcohol consumption by the HAP3/LAP3 mice.
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    Identification of candidate genes for alcohol preference by expression profiling of congenic rat strains
    (Wiley Blackwell (Blackwell Publishing), 2007-07) Carr, Lucinda G.; Kimpel, Mark W.; Liang, Tiebing; McClintick, Jeanette N.; McCall, Kevin; Morse, Melissa; Edenberg, Howard J.; Department of Medicine, IU School of Medicine
    BACKGROUND: A highly significant quantitative trait locus (QTL) on chromosome 4 that influenced alcohol preference was identified by analyzing crosses between the iP and iNP rats. Congenic strains in which the iP chromosome 4 QTL interval was transferred to the iNP (NP.P) exhibited the expected increase in alcohol consumption compared with the iNP background strain. This study was undertaken to identify genes in the chromosome 4 QTL interval that might contribute to the differences in alcohol consumption between the alcohol-naïve congenic and background strains. METHODS: RNA from 5 brain regions from each of 6 NP.P and 6 iNP rats was labeled and analyzed separately on an Affymetrix Rat Genome 230 2.0 microarray to look for both cis-regulated and trans-regulated genes. Expression levels were normalized using robust multi-chip average (RMA). Differential gene expression was validated using quantitative real-time polymerase chain reaction. Five individual brain regions (nucleus accumbens, frontal cortex, amygdala, hippocampus, and striatum) were analyzed to detect differential expression of genes within the introgressed QTL interval, as well as genes outside that region. To increase the power to detect differentially expressed genes, combined analyses (averaging data from the 5 discrete brain regions of each animal) were also carried out. RESULTS: Analyses within individual brain regions that focused on genes within the QTL interval detected differential expression in all 5 brain regions; a total of 35 genes were detected in at least 1 region, ranging from 6 genes in the nucleus accumbens to 22 in the frontal cortex. Analysis of the whole genome detected very few differentially expressed genes outside the QTL. Combined analysis across brain regions was more powerful. Analysis focused on the genes within the QTL interval confirmed 19 of the genes detected in individual regions and detected 15 additional genes. Whole genome analysis detected 1 differentially expressed gene outside the interval. CONCLUSIONS: Cis-regulated candidate genes for alcohol consumption were identified using microarray profiling of gene expression differences in congenic animals carrying a QTL for alcohol preference.
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    A novel structure-aware sparse learning algorithm for brain imaging genetics
    (Springer, 2014) Du, Lei; Yan, Jingwen; Kim, Sungeun; Risacher, Shannon L.; Huang, Heng; Inlow, Mark; Moore, Jason H.; Saykin, Andrew J.; Shen, Li; Department of Radiology and Imaging Sciences, IU School of Medicine
    Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings.
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    Quantitative trait loci identification for brain endophenotypes via new additive model with random networks
    (Oxford University Press, 2018-09) Wang, Xiaoqian; Chen, Hong; Yan, Jingwen; Nho, Kwangsik; Risacher, Shannon L.; Saykin, Andrew J.; Shen, Li; Huang, Heng; Radiology and Imaging Sciences, School of Medicine
    Motivation: The identification of quantitative trait loci (QTL) is critical to the study of causal relationships between genetic variations and disease abnormalities. We focus on identifying the QTLs associated to the brain endophenotypes in imaging genomics study for Alzheimer's Disease (AD). Existing research works mainly depict the association between single nucleotide polymorphisms (SNPs) and the brain endophenotypes via the linear methods, which may introduce high bias due to the simplicity of the models. Since the influence of QTLs on brain endophenotypes is quite complex, it is desired to design the appropriate non-linear models to investigate the associations of genotypes and endophenotypes. Results: In this paper, we propose a new additive model to learn the non-linear associations between SNPs and brain endophenotypes in Alzheimer's disease. Our model can be flexibly employed to explain the non-linear influence of QTLs, thus is more adaptive for the complex distribution of the high-throughput biological data. Meanwhile, as an important computational learning theory contribution, we provide the generalization error analysis for the proposed approach. Unlike most previous theoretical analysis under independent and identically distributed samples assumption, our error bound is based on m-dependent observations, which is more appropriate for the high-throughput and noisy biological data. Experiments on the data from Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort demonstrate the promising performance of our approach for identifying biological meaningful SNPs.