Systemic lupus erythematosus is a genetically complex disease. Currently, the precise allelic polymorphisms associated with this condition remain largely unidentified. In part this reflects the fact ...that multiple genes, each having a relatively minor effect, act in concert to produce disease. Given this complexity, analysis of subclinical phenotypes may aid in the identification of susceptibility alleles. Here, we used flow cytometry to investigate whether some of the immune abnormalities that are seen in the peripheral blood lymphocyte population of lupus patients are seen in their first-degree relatives.
Peripheral blood mononuclear cells were isolated from the subjects, stained with fluorochrome-conjugated monoclonal antibodies to identify various cellular subsets, and analyzed by flow cytometry.
We found reduced proportions of natural killer (NK)T cells among 367 first-degree relatives of lupus patients as compared with 102 control individuals. There were also slightly increased proportions of memory B and T cells, suggesting increased chronic low-grade activation of the immune system in first-degree relatives. However, only the deficiency of NKT cells was associated with a positive anti-nuclear antibody test and clinical autoimmune disease in family members. There was a significant association between mean parental, sibling, and proband values for the proportion of NKT cells, suggesting that this is a heritable trait.
The findings suggest that analysis of cellular phenotypes may enhance the ability to detect subclinical lupus and that genetically determined altered immunoregulation by NKT cells predisposes first-degree relatives of lupus patients to the development of autoimmunity.
Development of efficient analytic methodologies for combining microarray results is a major challenge in gene expression analysis. The widely used effect size models are thought to provide an ...efficient modeling framework for this purpose, where the measures of association for each study and each gene are combined, weighted by the standard errors. A significant disadvantage of this strategy is that the quality of different data sets may be highly variable, but this information is usually neglected during the integration. Moreover, it is widely known that the estimated standard deviations are probably unstable in the commonly used effect size measures (such as standardized mean difference) when sample sizes in each group are small.
We propose a re-parameterization of the traditional mean difference based effect measure by using the log ratio of means as an effect size measure for each gene in each study. The estimated effect sizes for all studies were then combined under two modeling frameworks: the quality-unweighted random effects models and the quality-weighted random effects models. We defined the quality measure as a function of the detection p-value, which indicates whether a transcript is reliably detected or not on the Affymetrix gene chip. The new effect size measure is evaluated and compared under the quality-weighted and quality-unweighted data integration frameworks using simulated data sets, and also in several data sets of prostate cancer patients and controls. We focus on identifying differentially expressed biomarkers for prediction of cancer outcomes.
Our results show that the proposed effect size measure (log ratio of means) has better power to identify differentially expressed genes, and that the detected genes have better performance in predicting cancer outcomes than the commonly used effect size measure, the standardized mean difference (SMD), under both quality-weighted and quality-unweighted data integration frameworks. The new effect size measure and the quality-weighted microarray data integration framework provide efficient ways to combine microarray results.
Mammographic breast density is a highly heritable (h2 > 0.6) and strong risk factor for breast cancer. We conducted a genome-wide linkage study to identify loci influencing mammographic breast ...density (MD).
Epidemiological data were assembled on 1,415 families from the Australia, Northern California and Ontario sites of the Breast Cancer Family Registry, and additional families recruited in Australia and Ontario. Families consisted of sister pairs with age-matched mammograms and data on factors known to influence MD. Single nucleotide polymorphism (SNP) genotyping was performed on 3,952 individuals using the Illumina Infinium 6K linkage panel.
Using a variance components method, genome-wide linkage analysis was performed using quantitative traits obtained by adjusting MD measurements for known covariates. Our primary trait was formed by fitting a linear model to the square root of the percentage of the breast area that was dense (PMD), adjusting for age at mammogram, number of live births, menopausal status, weight, height, weight squared, and menopausal hormone therapy. The maximum logarithm of odds (LOD) score from the genome-wide scan was on chromosome 7p14.1-p13 (LOD = 2.69; 63.5 cM) for covariate-adjusted PMD, with a 1-LOD interval spanning 8.6 cM. A similar signal was seen for the covariate adjusted area of the breast that was dense (DA) phenotype. Simulations showed that the complete sample had adequate power to detect LOD scores of 3 or 3.5 for a locus accounting for 20% of phenotypic variance. A modest peak initially seen on chromosome 7q32.3-q34 increased in strength when only the 513 families with at least two sisters below 50 years of age were included in the analysis (LOD 3.2; 140.7 cM, 1-LOD interval spanning 9.6 cM). In a subgroup analysis, we also found a LOD score of 3.3 for DA phenotype on chromosome 12.11.22-q13.11 (60.8 cM, 1-LOD interval spanning 9.3 cM), overlapping a region identified in a previous study.
The suggestive peaks and the larger linkage signal seen in the subset of pedigrees with younger participants highlight regions of interest for further study to identify genes that determine MD, with the goal of understanding mammographic density and its involvement in susceptibility to breast cancer.
Several methods have recently been proposed for identifying copy number alterations (CNAs) in genomic DNA from tumors, using the signals arising from two-color genotyping technologies. Although copy ...number estimation in normal tissue has been well studied, methods developed for normal tissue tend to perform poorly when applied to tumors, due to normal cell contamination, varying levels of ploidy, and genetic heterogeneity within the tumor. Here we compare the performance of seven methods (DNA-Chip Analyzer software (dCHIP), GenoCNA software, allele-specific copy number analysis of tumors (ASCAT), OncoSNP software, genome alteration print (GAP) visualization, CNVpartition software plug-in for the Genome Studio software, and Partek Genomics Suite software) that have been established for two-color CNA analysis on the Illumina platform, using two ovarian cancer cell lines where spectral karyotyping analysis has also been performed, and two tissue samples, one from a highly malignant ovarian cancer and one from a benign ovarian tumor, all of which harbor significantly different genomic abnormalities. ASCAT shows very stable estimates of CNAs, as does OncoSNP when jointly analyzing paired normal DNA. We found the best performance, in general to be from ASCAT.
Interstitial lung macrophages from tuberculosis-susceptible I/St and tuberculosis-resistant A/Sn mice demonstrated significant constitutive differences in gene expression levels, whereas in vitro ...infection of these cells with Mycobacterium tuberculosis had only a modulatory impact on gene expression. We conclude that intrinsic gene expression profiles are an important determinant of tuberculosis pathogenesis in mice.
Five to 10% of all cases of breast and ovarian cancer are attributed to a heritable genetic predisposition. Transmission of
BRCA1
and
BRCA2
mutations is equally likely through maternal or paternal ...lineage; however, fewer referrals to cancer genetics clinics appear to be made for a paternal, than maternal, family history of breast and/or ovarian cancer. To examine this potential bias, a retrospective review of 315 patient and family charts was conducted by one familial cancer clinic in Toronto, Canada. Referral letters, risk estimates, and family histories were analyzed to identify significant differences between patients referred with maternal and paternal family histories. It was determined that patients are approximately five times more likely to be referred with a maternal family history of breast and/or ovarian cancer as compared to those with a paternal family history (
p
= <.0001). Individuals with a paternal family history were found to have a different, and higher, pattern of risk estimates (
p
= .00064). No significant difference was seen between the type of referrals sent by general practitioners, oncologists, and gynecologists. Recommendations to increase the awareness of paternal family history in assessing cancer risk are provided.
In Genetic Analysis Workshop 18 data, we used a 3-stage approach to explore the benefits of pathway analysis in improving a model to predict 2 diastolic blood pressure phenotypes as a function of ...genetic variation. At stage 1, gene-based tests of association in family data of approximately 800 individuals found over 600 genes associated at p<0.05 for each phenotype. At stage 2, networks and enriched pathways were estimated with Cytoscape for genes from stage 1, separately for the 2 phenotypes, then examining network overlap. This overlap identified 4 enriched pathways, and 3 of these pathways appear to interact, and are likely candidates for playing a role in hypertension. At stage 3, using 157 maximally unrelated individuals, partial least squares regression was used to find associations between diastolic blood pressure and single-nucleotide polymorphisms in genes highlighted by the pathway analyses. However, we saw no improvement in the adjusted cross-validated R (2). Although our pathway-motivated regressions did not improve prediction of diastolic blood pressure, merging gene networks did identify several plausible pathways for hypertension.
Systematic integration of microarrays from different sources increases statistical power of detecting differentially expressed genes and allows assessment of heterogeneity. The challenge, however, is ...in designing and implementing efficient analytic methodologies for combining data generated by different research groups and platforms. The widely used strategy mainly focuses on integrating preprocessed data without having access to the original raw data that yielded the initial results. A main disadvantage of this strategy is that the quality of different data sets may be highly variable, but this information is neglected during the integration.We have recently proposed a quality-weighting strategy to integrate Affymetrix microarrays. The quality measure is a function of the detection p-values, which indicate whether a transcript is reliably detected or not on Affymetrix gene chip. In this study, we compare the proposed quality-weighted strategy with the traditional quality-unweighted strategy, and examine how the quality weights influence two commonly used meta-analysis methods: combining p-values and combining effect size estimates. The methods are compared on a real data set for identifying biomarkers for lung cancer.Our results show that the proposed quality-weighted strategy can lead to larger statistical power for identifying differentially expressed genes when integrating data from Affymetrix microarrays.
Using the Genetic Analysis Workshop 14 simulated datasets we carried out nonparametric linkage analyses and applied a log-linear method for analysis of case-parent-triad data with stratification on ...parental mating type. We proposed and applied a random effect modelling approach to explore the impact of population heterogeneity on tests of association between genetic markers and disease status. The estimated genetic effect may appear to be strongly significant in one population but nonsignificant in another population, leading to confusion about interpretation. However, when results are interpreted in the light of a random effects model, both studies may be making similar statements about a genetic effect that varies depending on environment and background.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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