miR-129-5p as a biomarker for pathology and cognitive decline in Alzheimer's disease.

BACKGROUND: Alzheimer's dementia (AD) pathogenesis involves complex mechanisms, including microRNA (miRNA) dysregulation. Integrative network and machine learning analysis of miRNA can provide insights into AD pathology and prognostic/diagnostic biomarkers. METHODS: We performed co-expression network analysis to identify network modules associated with AD, its neuropathology markers, and cognition using brain tissue miRNA profiles from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) (N = 702) as a discovery dataset. We performed association analysis of hub miRNAs with AD, its neuropathology markers, and cognition. After selecting target genes of the hub miRNAs, we performed association analysis of the hub miRNAs with their target genes and then performed pathway-based enrichment analysis. For replication, we performed a consensus miRNA co-expression network analysis using the ROS/MAP dataset and an independent dataset (N = 16) from the Gene Expression Omnibus (GEO). Furthermore, we performed a machine learning approach to assess the performance of hub miRNAs for AD classification. RESULTS: Network analysis identified a glucose metabolism pathway-enriched module (M3) as significantly associated with AD and cognition. Five hub miRNAs (miR-129-5p, miR-433, miR-1260, miR-200a, and miR-221) of M3 had significant associations with AD clinical and/or pathologic traits, with miR129-5p by far the strongest across all phenotypes. Gene-set enrichment analysis of target genes associated with their corresponding hub miRNAs identified significantly enriched biological pathways including ErbB, AMPK, MAPK, and mTOR signaling pathways. Consensus network analysis identified two AD-associated consensus network modules and two hub miRNAs (miR-129-5p and miR-221). Machine learning analysis showed that the AD classification performance (area under the curve (AUC) = 0.807) of age, sex, and APOE ε4 carrier status was significantly improved by 6.3% with inclusion of five AD-associated hub miRNAs. CONCLUSIONS: Integrative network and machine learning analysis identified miRNA signatures, especially miR-129-5p, as associated with AD, its neuropathology markers, and cognition, enhancing our understanding of AD pathogenesis and leading to better performance of AD classification as potential diagnostic/prognostic biomarkers.

Transcriptional risk scores in Alzheimer's disease: From pathology to cognition.

INTRODUCTION: Our previously developed blood-based transcriptional risk scores (TRS) showed associations with diagnosis and neuroimaging biomarkers for Alzheimer's disease (AD). Here, we developed brain-based TRS. METHODS: We integrated AD genome-wide association study summary and expression quantitative trait locus data to prioritize target genes using Mendelian randomization. We calculated TRS using brain transcriptome data of two independent cohorts (N = 878) and performed association analysis of TRS with diagnosis, amyloidopathy, tauopathy, and cognition. We compared AD classification performance of TRS with polygenic risk scores (PRS). RESULTS: Higher TRS values were significantly associated with AD, amyloidopathy, tauopathy, worse cognition, and faster cognitive decline, which were replicated in an independent cohort. The AD classification performance of PRS was increased with the inclusion of TRS up to 16% with the area under the curve value of 0.850. DISCUSSION: Our results suggest brain-based TRS improves the AD classification of PRS and may be a potential AD biomarker. HIGHLIGHTS: Transcriptional risk score (TRS) is developed using brain RNA-Seq data. Higher TRS values are shown in Alzheimer's disease (AD). TRS improves the AD classification power of PRS up to 16%. TRS is associated with AD pathology presence. TRS is associated with worse cognitive performance and faster cognitive decline.

miR-129-5p as a biomarker for pathology and cognitive decline in Alzheimer's disease.

Background: Alzheimer's dementia (AD) pathogenesis involves complex mechanisms, including microRNA (miRNA) dysregulation. Integrative network and machine learning analysis of miRNA can provide insights into AD pathology and prognostic/diagnostic biomarkers. Methods: We performed co-expression network analysis to identify network modules associated with AD, its neuropathology markers, and cognition using brain tissue miRNA profiles from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) (N = 702) as a discovery dataset. We performed association analysis of hub miRNAs with AD, its neuropathology markers, and cognition. After selecting target genes of the hub miRNAs, we performed association analysis of the hub miRNAs with their target genes and then performed pathway-based enrichment analysis. For replication, we performed a consensus miRNA co-expression network analysis using the ROS/MAP dataset and an independent dataset (N = 16) from the Gene Expression Omnibus (GEO). Furthermore, we performed a machine learning approach to assess the performance of hub miRNAs for AD classification. Results: Network analysis identified a glucose metabolism pathway-enriched module (M3) as significantly associated with AD and cognition. Five hub miRNAs (miR-129-5p, miR-433, miR-1260, miR-200a, and miR-221) of M3 had significant associations with AD clinical and/or pathologic traits, with miR129-5p by far the strongest across all phenotypes. Gene-set enrichment analysis of target genes associated with their corresponding hub miRNAs identified significantly enriched biological pathways including ErbB, AMPK, MAPK, and mTOR signaling pathways. Consensus network analysis identified two AD-associated consensus network modules, and two hub miRNAs (miR-129-5p and miR-221). Machine learning analysis showed that the AD classification performance (area under the curve (AUC) = 0.807) of age, sex, and apoE ε4 carrier status was significantly improved by 6.3% with inclusion of five AD-associated hub miRNAs. Conclusions: Integrative network and machine learning analysis identified miRNA signatures, especially miR-129-5p, as associated with AD, its neuropathology markers, and cognition, enhancing our understanding of AD pathogenesis and leading to better performance of AD classification as potential diagnostic/prognostic biomarkers.

Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data.

BACKGROUND: Deep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics. METHODS: The c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional Neural Networks (CNN) were employed for feature selection. Random Forest was used for the final classification. FINDINGS: The application of c-SWAT resulted in a classification accuracy of up to 80.8% and an AUC of 0.808 for distinguishing AD from cognitively normal older adults. This marks a 9.4% improvement in accuracy and a 0.169 increase in AUC compared to methods without c-SWAT. These results were statistically significant, with a p-value of 1.04 × 10ˆ-4. The approach also identified key lipids associated with AD, such as Cer(d16:1/22:0) and PI(37:6). INTERPRETATION: Our results indicate that c-SWAT is effective in improving classification accuracy and in identifying potential lipid biomarkers for AD. These identified lipids offer new avenues for understanding AD and warrant further investigation. FUNDING: The specific funding of this article is provided in the acknowledgements section.

Circulating lipid profiles are associated with cross-sectional and longitudinal changes of central biomarkers for Alzheimer's disease.

Investigating the association of lipidome profiles with central Alzheimer's disease (AD) biomarkers, including amyloid/tau/neurodegeneration (A/T/N), can provide a holistic view between the lipidome and AD. We performed cross-sectional and longitudinal association analysis of serum lipidome profiles with AD biomarkers in the Alzheimer's Disease Neuroimaging Initiative cohort (N=1,395). We identified lipid species, classes, and network modules that were significantly associated with cross-sectional and longitudinal changes of A/T/N biomarkers for AD. Notably, we identified the lysoalkylphosphatidylcholine (LPC(O)) as associated with "A/N" biomarkers at baseline at lipid species, class, and module levels. Also, GM3 ganglioside showed significant association with baseline levels and longitudinal changes of the "N" biomarkers at species and class levels. Our study of circulating lipids and central AD biomarkers enabled identification of lipids that play potential roles in the cascade of AD pathogenesis. Our results suggest dysregulation of lipid metabolic pathways as precursors to AD development and progression.

Novel CYP1B1-RMDN2 Alzheimer's disease locus identified by genome-wide association analysis of cerebral tau deposition on PET.

Determining the genetic architecture of Alzheimer's disease (AD) pathologies can enhance mechanistic understanding and inform precision medicine strategies. Here, we performed a genome-wide association study of cortical tau quantified by positron emission tomography in 3,136 participants from 12 independent studies. The CYP1B1-RMDN2 locus was associated with tau deposition. The most significant signal was at rs2113389, which explained 4.3% of the variation in cortical tau, while APOE4 rs429358 accounted for 3.6%. rs2113389 was associated with higher tau and faster cognitive decline. Additive effects, but no interactions, were observed between rs2113389 and diagnosis, APOE4 , and Aß positivity. CYP1B1 expression was upregulated in AD. rs2113389 was associated with higher CYP1B1 expression and methylation levels. Mouse model studies provided additional functional evidence for a relationship between CYP1B1 and tau deposition but not Aß. These results may provide insight into the genetic basis of cerebral tau and novel pathways for therapeutic development in AD.

Altered cerebral blood flow in older adults with Alzheimer's disease: a systematic review.

The prevalence of Alzheimer's disease is projected to reach 13 million in the U.S. by 2050. Although major efforts have been made to avoid this outcome, so far there are no treatments that can stop or reverse the progressive cognitive decline that defines Alzheimer's disease. The utilization of preventative treatment before significant cognitive decline has occurred may ultimately be the solution, necessitating a reliable biomarker of preclinical/prodromal disease stages to determine which older adults are most at risk. Quantitative cerebral blood flow is a promising potential early biomarker for Alzheimer's disease, but the spatiotemporal patterns of altered cerebral blood flow in Alzheimer's disease are not fully understood. The current systematic review compiles the findings of 81 original studies that compared resting gray matter cerebral blood flow in older adults with mild cognitive impairment or Alzheimer's disease and that of cognitively normal older adults and/or assessed the relationship between cerebral blood flow and objective cognitive function. Individuals with Alzheimer's disease had relatively decreased cerebral blood flow in all brain regions investigated, especially the temporoparietal and posterior cingulate, while individuals with mild cognitive impairment had consistent results of decreased cerebral blood flow in the posterior cingulate but more mixed results in other regions, especially the frontal lobe. Most papers reported a positive correlation between regional cerebral blood flow and cognitive function. This review highlights the need for more studies assessing cerebral blood flow changes both spatially and temporally over the course of Alzheimer's disease, as well as the importance of including potential confounding factors in these analyses.

Integrative Co-methylation Network Analysis Identifies Novel DNA Methylation Signatures and Their Target Genes in Alzheimer's Disease.

BACKGROUND: DNA methylation is a key epigenetic marker, and its alternations may be involved in Alzheimer's disease (AD). CpGs sharing similar biological functions or pathways tend to be co-methylated. METHODS: We performed an integrative network-based DNA methylation analysis on 2 independent cohorts (N = 941) using brain DNA methylation profiles and RNA-sequencing as well as AD pathology data. RESULTS: Weighted co-methylation network analysis identified 6 modules as significantly associated with neuritic plaque burden. In total, 15 hub CpGs including 3 novel CpGs were identified and replicated as being significantly associated with AD pathology. Furthermore, we identified and replicated 4 target genes (ATP6V1G2, VCP, RAD52, and LST1) as significantly regulated by DNA methylation at hub CpGs. In particular, VCP gene expression was also associated with AD pathology in both cohorts. CONCLUSIONS: This integrative network-based multiomics study provides compelling evidence for a potential role of DNA methylation alternations and their target genes in AD.

Immunity gene IFITM3 variant: Relation to cognition and Alzheimer's disease pathology.

Introduction: We investigated single-nucleotide polymorphisms (SNPs) in IFITM3, an innate immunity gene and modulator of amyloid beta in Alzheimer's disease (AD), for association with cognition and AD biomarkers. Methods: We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 1565) and AddNeuroMed (N = 633) as discovery and replication samples, respectively. We performed gene-based association analysis of SNPs in IFITM3 with cognitive performance and SNP-based association analysis with cognitive decline and amyloid, tau, and neurodegeneration biomarkers for AD. Results: Gene-based association analysis showed that IFITM3 was significantly associated with cognitive performance. Particularly, rs10751647 in IFITM3 was associated with less cognitive decline, less amyloid and tau burden, and less brain atrophy in ADNI. The association of rs10751647 with cognitive decline and brain atrophy was replicated in AddNeuroMed. Discussion: This suggests that rs10751647 in IFITM3 is associated with less vulnerability for cognitive decline and AD biomarkers, providing mechanistic insight regarding involvement of immunity and infection in AD. Highlights: IFITM3 is significantly associated with cognitive performance.rs10751647 in IFITM3 is associated with cognitive decline rates with replication.rs10751647 is associated with amyloid beta load, cerebrospinal fluid phosphorylated tau levels, and brain atrophy.rs10751647 is associated with IFITM3 expression levels in blood and brain.rs10751647 in IFITM3 is related to less vulnerability to Alzheimer's disease pathogenesis.

Deep learning-based identification of genetic variants: application to Alzheimer's disease classification.

Deep learning is a promising tool that uses nonlinear transformations to extract features from high-dimensional data. Deep learning is challenging in genome-wide association studies (GWAS) with high-dimensional genomic data. Here we propose a novel three-step approach (SWAT-CNN) for identification of genetic variants using deep learning to identify phenotype-related single nucleotide polymorphisms (SNPs) that can be applied to develop accurate disease classification models. In the first step, we divided the whole genome into nonoverlapping fragments of an optimal size and then ran convolutional neural network (CNN) on each fragment to select phenotype-associated fragments. In the second step, using a Sliding Window Association Test (SWAT), we ran CNN on the selected fragments to calculate phenotype influence scores (PIS) and identify phenotype-associated SNPs based on PIS. In the third step, we ran CNN on all identified SNPs to develop a classification model. We tested our approach using GWAS data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) including (N = 981; cognitively normal older adults (CN) = 650 and AD = 331). Our approach identified the well-known APOE region as the most significant genetic locus for AD. Our classification model achieved an area under the curve (AUC) of 0.82, which was compatible with traditional machine learning approaches, random forest and XGBoost. SWAT-CNN, a novel deep learning-based genome-wide approach, identified AD-associated SNPs and a classification model for AD and may hold promise for a range of biomedical applications.

Dysregulated expression levels of APH1B in peripheral blood are associated with brain atrophy and amyloid-ß deposition in Alzheimer's disease.

BACKGROUND: The interaction between the brain and periphery might play a crucial role in the development of Alzheimer's disease (AD). METHODS: Using blood transcriptomic profile data from two independent AD cohorts, we performed expression quantitative trait locus (cis-eQTL) analysis of 29 significant genetic loci from a recent large-scale genome-wide association study to investigate the effects of the AD genetic variants on gene expression levels and identify their potential target genes. We then performed differential gene expression analysis of identified AD target genes and linear regression analysis to evaluate the association of differentially expressed genes with neuroimaging biomarkers. RESULTS: A cis-eQTL analysis identified and replicated significant associations in seven genes (APH1B, BIN1, FCER1G, GATS, MS4A6A, RABEP1, TRIM4). APH1B expression levels in the blood increased in AD and were associated with entorhinal cortical thickness and global cortical amyloid-ß deposition. CONCLUSION: An integrative analysis of genetics, blood-based transcriptomic profiles, and imaging biomarkers suggests that APH1B expression levels in the blood might play a role in the pathogenesis of AD.

BMI1 is associated with CS8F amyloid-ß and rates of cognitive decline in Alzheimer's disease.

BACKGROUND: Accumulating evidence suggests that BMI1 confers protective effects against Alzheimer's disease (AD). However, the mechanism remains elusive. Based on recent pathophysiological evidence, we sought for the first time to identify genetic variants in BMI1 as associated with AD biomarkers, including amyloid-ß. METHODS: We used genetic, longitudinal cognition, and cerebrospinal fluid (CSF) biomarker data from participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (N = 1565). First, we performed a gene-based association analysis of common single nucleotide polymorphisms (SNPs) (minor allele frequency (MAF) > 5%) located within ± 20 kb of the gene boundary of BMI1, an optimal width for including potential regulatory SNPs in the 5' and 3' untranslated regions (UTR) of BMI1, with CSF Aß1-42 levels. Second, we performed cross-sectional and longitudinal association analyses of SNPs in BMI1 with cognitive performance using linear and mixed-effects models. We replicated association of SNPs in BMI1 with cognitive performance in an independent cohort (N=1084), Religious Orders Study and the Rush Memory and Aging Project (ROS/MAP). RESULTS: Gene-based genetic association analysis showed that BMI1 was significantly associated with CSF Aß1-42 levels after adjusting for multiple testing using permutation (permutation-corrected p value=0.005). rs17415557 in BMI1 showed the most significant association with CSF Aß1-42 levels. Participants with minor alleles of rs17415557 have increased CSF Aß1-42 levels compared to those with no minor alleles. Further analysis identified and replicated the minor allele of rs17415557 as being significantly associated with slower cognitive decline rates in AD. CONCLUSIONS: Our findings provide fundamental evidence that BMI1 rs17415557 may serve as a protective mechanism related to AD pathogenesis, which supports the results of previous studies linking BMI1 to protection against AD.

Estrogen-Dependent Upregulation of Adcyap1r1 Expression in Nucleus Accumbens Is Associated With Genetic Predisposition of Sex-Specific QTL for Alcohol Consumption on Rat Chromosome 4.

Humans show sex differences related to alcohol use disorders (AUD). Animal model research has the potential to provide important insight into how sex differences affect alcohol consumption, particularly because female animals frequently drink more than males. In previous work, inbred strains of the selectively bred alcohol-preferring (P) and non-preferring (NP) rat lines revealed a highly significant quantitative trait locus (QTL) on rat chromosome 4, with a logarithm of the odds score of 9.2 for alcohol consumption. Recently, interval-specific congenic strains (ISCS) were developed by backcrossing the congenic P.NP line to inbred P (iP) rats to further refine the chromosome 4 QTL region. Two ISCS sub-strains, ISCS-A and ISCS-B, were obtained with a narrowed QTL, where the smallest region of overlap consisted of 8.9 Mb in ISCS-B. Interestingly, we found that females from both ISCS lines consumed significantly less alcohol than female iP controls (p < 0.05), while no differences in alcohol consumption were observed between male ISCS and iP controls. RNA-sequencing was performed on the nucleus accumbens of alcohol-naïve female ISCS-B and iP rats, which revealed differentially expressed genes (DEG) with greater than 2-fold change and that were functionally relevant to behavior. These DEGs included down-regulation of Oxt, Asb4, Gabre, Gabrq, Chat, Slc5a7, Slc18a8, Slc10a4, and Ngfr, and up-regulation of Ttr, Msln, Mpzl2, Wnt6, Slc17a7, Aldh1a2, and Gstm2. Pathway analysis identified significant alterations in gene networks controlling nervous system development and function, as well as cell signaling, GABA and serotonin receptor signaling and G-protein coupled receptor signaling. In addition, ß-estradiol was identified as the most significant upstream regulator. The expression levels of estrogen-responsive genes that mapped to the QTL interval and have been previously associated with alcohol consumption were measured using RT-qPCR. We found that expression of the Adcyap1r1 gene, encoding the pituitary adenylate cyclase-activating polypeptide type 1 (PAC1) receptor, was upregulated in female ISCS-B compared to female iP controls, while no differences were exhibited in males. In addition, sequence variants in the Adcyap1r1 promoter region showed a differential response to estrogen stimulation in vitro. These findings demonstrate that rat chromosome 4 QTL contains genetic variants that respond to estrogen and are associated with female alcohol consumption.

Independent investigator incubator (I3): a comprehensive mentorship program to jumpstart productive research careers for junior faculty.

BACKGROUND: In the highly competitive environment of academic medicine, junior faculty investigators face high attrition rates due to challenges in finding effective mentorship, securing grant funding, and obtaining resources to support their career development and research productivity. The purpose of this study was to describe the centralized, cost-sharing design of the Independent Investigator Incubator (I3) program as a novel approach to junior faculty mentoring and to evaluate quantitative outcomes for program improvement. METHODS: In September 2014, the I3 pilot program, a comprehensive mentorship program targeting junior faculty pursuing research careers, was launched. Participants included junior faculty during the crucial first three years of their research careers or during their transition from career development awards to more independent research. Following initial screening, the I3 mentees were paired with a senior faculty "super-mentor" with expertise in either basic science or clinical research. Mentees were provided with robust traditional one-on-one mentoring, targeted feedback from a super-mentor review committee, as well as biostatistician and grant writing support. To assess the effectiveness of the I3 program, we tracked outcome measures via baseline and 12-month mentee surveys. Data collected assessed program diversity, mentee self-assessments, evaluation of the mentoring relationship, scholarship and productivity metrics. Raw data were analyzed using a paired t-test in Excel (P < 0.05). RESULTS: Results of the baseline mentee self-assessment survey found that the I3 mentees indicated common "perceive deficits" including navigating the organizational and institutional culture, clear direction in achieving promotion and tenure, among others. When baseline mentee survey responses were compared to 12-month responses, we identified strong "perceived growth" in categories, such as Research and Interpersonal Skills and Career Development Skills. Further, productivity metrics at 12-months revealed that roughly 80% of I3 mentees successfully published a manuscript(s). The I3 program has helped generate roughly $12.1 million dollars in investigator-initiated funding after two years in the program. CONCLUSION: The I3 program allows for shared costs between institutions and increased availability of successful subject matter experts. Study results imply that the I3 mentoring program provides transformative mentorship for junior faculty. Using our findings, we developed courses and an annual "snapshot" of mentee performance for mentors.

Fine mapping quantitative trait loci that influence alcohol preference behavior in the High and Low Alcohol Preferring (HAP and LAP) mice.

The High Alcohol Preferring (HAP1) and Low Alcohol Preferring (LAP1) mouse lines were selectively bred for differences in alcohol intake. The HAP1 and LAP1 mice are essentially non-inbred lines that originated from an outbred colony of HS/Ibg mice, a heterogeneous stock developed from intercrossing 8 inbred strains of mice. In a former genomewide SNP association study, we identified quantitative trait loci (QTL) on chromosomes 1, 3, 5, and 9 (Bice et al. 2009). Provisional QTL were also identified on chromosomes 8 and X. In the present study, using the same F2 DNA samples, we placed a much denser set of SNPs within each of those QTL regions. Using the same analytical approach employed previously, which utilizes ancestral recombination to fine map the QLT interval, we obtained significant LOD scores on chromosomes 1, 3, and 9, only. Our results using a dense set of SNP markers suggest that there are multiple loci contributing to alcohol preference on those three chromosomes.

Fine mapping and expression of candidate genes within the chromosome 10 QTL region of the high and low alcohol-drinking rats.

The high and low alcohol-drinking (HAD and LAD) rats were selectively bred for differences in alcohol intake. The HAD/LAD rats originated from the N/Nih heterogeneous stock developed from intercrossing eight inbred rat strains. The HAD×LAD F2 were genotyped, and a powerful analytical approach, using ancestral recombination and F2 recombination, was used to narrow a quantitative trait loci (QTL) for alcohol drinking to a 2-cM region on distal chromosome 10 that was in common in the HAD1/LAD1 and HAD2/LAD2 analyses. Quantitative real-time PCR was used to examine mRNA expression of six candidate genes (Crebbp, Trap1, Gnptg, Clcn7, Fahd1, and Mapk8ip3) located within the narrowed QTL region in the HAD1/LAD1 rats. Expression was examined in five brain regions, including the nucleus accumbens, amygdala, caudate putamen, hippocampus, and prefrontal cortex. All six genes showed differential expression in at least one brain region. Of the genes tested in this study, Crebbp and Mapk8ip3 may be the most promising candidates with regard to alcohol drinking.

Genomewide SNP screen to detect quantitative trait loci for alcohol preference in the high alcohol preferring and low alcohol preferring mice.

BACKGROUND: The high and low alcohol preferring (HAP1 and LAP1) mouse lines were selectively bred for differences in alcohol intake. The HAP1 and LAP1 mice are essentially noninbred lines that originated from the outbred colony of HS/Ibg mice, a heterogeneous stock developed from intercrossing 8 inbred strains of mice. METHODS: A total of 867 informative SNPs were genotyped in 989 HAP1 x LAP1 F2, 68 F1s, 14 parents (6 LAP1, 8 HAP1), as well as the 8 inbred strains of mice crossed to generate the HS/Ibg colony. Multipoint genome wide analyses were performed to simultaneously detect linked QTLs and also fine map these regions using the ancestral haplotypes. RESULTS: QTL analysis detected significant evidence of association on 4 chromosomes: 1, 3, 5, and 9. The region on chromosome 9 was previously found linked in a subset of these F2 animals using a whole genome microsatellite screen. CONCLUSIONS: We have detected strong evidence of association to multiple chromosomal regions in the mouse. Several of these regions include candidate genes previously associated with alcohol dependence in humans or other animal models.

Drd2 expression in the high alcohol-preferring and low alcohol-preferring mice.

The high alcohol-preferring (HAP) and low alcohol-preferring (LAP) mice were selectively bred for differences in alcohol preference and consumption. Recently, a large-effect QTL was identified on chromosome 9. The peak for this QTL is near the Drd2 (dopamine receptor 2) locus. The present study examined Drd2 mRNA expression differences between the HAP1 and LAP1 mice in brain regions important in the dopaminergic-reward pathway, including the nucleus accumbens, hippocampus, amygdala, and septum. Results show that alcohol-naïve HAP1 mice exhibited lower levels of Drd2 mRNA expression in the nucleus accumbens and the hippocampus compared to LAP1 mice. No differences were found in the amygdala or septum. To determine if a sequence difference might underlie the expression difference, the Drd2 cDNA was sequenced in each line and one single nucleotide polymorphism (SNP) was identified in the 3' UTR. Both HAP and LAP 3' UTR were cloned in the luc-pGL3-promoter-luc vector. The polymorphism in the Drd2 3' UTR was assessed to determine its functional significance in modulating expression. In vitro expression analysis using neuroblastoma SK-N-SH cells resulted in a significant decrease in expression of the HAP 3' UTR luc construct compared with the LAP 3' UTR construct. This decreased expression is consistent with lower levels of Drd2 expression in the nucleus accumbens and the hippocampus as evidenced by qRT-PCR. These results suggest that the SNP may play a role in the differential expression of Drd2 between the HAP and LAP mice and that the polymorphism in Drd2 may contribute to alcohol preference.