Psychiatric Disorder Criteria and their Application to Research in Different Racial Groups

BACKGROUND: The advent of standardized classification and assessment of psychiatric disorders, and considerable joint efforts among many countries has led to the reporting of international rates of psychiatric disorders, and inevitably, their comparison between different racial groups. RESULTS: In neurologic diseases with defined genetic etiologies, the same genetic cause has different phenotypes in different racial groups. CONCLUSION: We suggest that genetic differences between races mean that diagnostic criteria refined in one racial group, may not be directly and simply applicable to other racial groups and thus more effort needs to be expended on defining diseases in other groups. Cross-racial confounds (in addition to cultural confounds) make the interpretation of rates in different groups even more hazardous than seems to have been appreciated

Herbivory and eutrophication mediate grassland plant nutrient responses across a global climatic gradient

Plant stoichiometry, the relative concentration of elements, is a key regulator of ecosystem functioning and is also being altered by human activities. In this paper we sought to understand the global drivers of plant stoichiometry and compare the relative contribution of climatic vs. anthropogenic effects. We addressed this goal by measuring plant elemental (C, N, P and K) responses to eutrophication and vertebrate herbivore exclusion at eighteen sites on six continents. Across sites, climate and atmospheric N deposition emerged as strong predictors of plot‐level tissue nutrients, mediated by biomass and plant chemistry. Within sites, fertilization increased total plant nutrient pools, but results were contingent on soil fertility and the proportion of grass biomass relative to other functional types. Total plant nutrient pools diverged strongly in response to herbivore exclusion when fertilized; responses were largest in ungrazed plots at low rainfall, whereas herbivore grazing dampened the plant community nutrient responses to fertilization. Our study highlights (1) the importance of climate in determining plant nutrient concentrations mediated through effects on plant biomass, (2) that eutrophication affects grassland nutrient pools via both soil and atmospheric pathways and (3) that interactions among soils, herbivores and eutrophication drive plant nutrient responses at small scales, especially at water‐limited sites

The Genetic Interpretation of Area under the ROC Curve in Genomic Profiling

Genome-wide association studies in human populations have facilitated the creation of genomic profiles which combine the effects of many associated genetic variants to predict risk of disease. The area under the receiver operator characteristic (ROC) curve is a well established measure for determining the efficacy of tests in correctly classifying diseased and non-diseased individuals. We use quantitative genetics theory to provide insight into the genetic interpretation of the area under the ROC curve (AUC) when the test classifier is a predictor of genetic risk. Even when the proportion of genetic variance explained by the test is 100%, there is a maximum value for AUC that depends on the genetic epidemiology of the disease, i.e. either the sibling recurrence risk or heritability and disease prevalence. We derive an equation relating maximum AUC to heritability and disease prevalence. The expression can be reversed to calculate the proportion of genetic variance explained given AUC, disease prevalence, and heritability. We use published estimates of disease prevalence and sibling recurrence risk for 17 complex genetic diseases to calculate the proportion of genetic variance that a test must explain to achieve AUC = 0.75; this varied from 0.10 to 0.74. We provide a genetic interpretation of AUC for use with predictors of genetic risk based on genomic profiles. We provide a strategy to estimate proportion of genetic variance explained on the liability scale from estimates of AUC, disease prevalence, and heritability (or sibling recurrence risk) available as an online calculator

Prevalence of the most frequent BRCA1 mutations in Polish population

The purpose of our study was to establish the frequency and distribution of the four most common BRCA1 mutations in Polish general population and in a series of breast cancer patients. Analysis of the population frequency of 5382insC (c.5266dupC), 300T >G (p.181T >G), 185delAG (c.68_69delAG) and 3819del5 (c.3700_3704del5) mutations of the BRCA1 gene were performed on a group of respectively 16,849, 13,462, 12,485 and 3923 anonymous samples collected at birth in seven Polish provinces. The patient group consisted of 1845 consecutive female breast cancer cases. The most frequent BRCA1 mutation in the general population was 5382insC found in 29 out of 16,849 samples (0.17%). 300T >G and 3819del5 mutations were found in respectively 11 of 13,462 (0.08%) and four of 3923 (0.1%) samples. The population prevalence for combined Polish founder 5382insC and 300T >G mutations was 0.25% (1/400). The frequencies of 5382insC and 300T >G carriers among consecutive breast cancer cases were, respectively, 1.9% (35/1845) and 1.2% (18/1486). Comparing these data with the population frequency, we calculated the relative risk of breast cancer for 5382insC mutation at OR = 17 and for 300T >G mutation at OR = 26. Our results, based on large population studies, show high frequencies of founder 5382insC and 300T >G BRCA1 mutations in Polish general population. Carriage of one of these mutations is connected with a very high relative risk of breast cancer

Selecting a BRCA risk assessment model for use in a familial cancer clinic

<p>Abstract</p> <p>Background</p> <p>Risk models are used to calculate the likelihood of carrying a <it>BRCA1 </it>or <it>BRCA2 </it>mutation. We evaluated the performances of currently-used risk models among patients from a large familial program using the criteria of high sensitivity, simple data collection and entry and <it>BRCA </it>score reporting.</p> <p>Methods</p> <p>Risk calculations were performed by applying the BRCAPRO, Manchester, Penn II, Myriad II, FHAT, IBIS and BOADICEA models to 200 non-<it>BRCA </it>carriers and 100 <it>BRCA </it>carriers, consecutively tested between August 1995 and March 2006. Areas under the receiver operating characteristic curves (AUCs) were determined and sensitivity and specificity were calculated at the conventional testing thresholds. In addition, subset analyses were performed for low and high risk probands.</p> <p>Results</p> <p>The BRCAPRO, Penn II, Myriad II, FHAT and BOADICEA models all have similar AUCs of approximately 0.75 for <it>BRCA </it>status. The Manchester and IBIS models have lower AUCs (0. and 0.47 respectively). At the conventional testing thresholds, the sensitivities and specificities for a <it>BRCA </it>mutation were, respectively, as follows: BRCAPRO (0.75, 0.62), Manchester (0.58,0.71), Penn II (0.93,0.31), Myriad II (0.71,0.63), FHAT (0.70,0.63), IBIS (0.20,0.74), BOADICEA (0.70, 0.65).</p> <p>Conclusion</p> <p>The Penn II model most closely met the criteria we established and this supports the use of this model for identifying individuals appropriate for genetic testing at our facility. These data are applicable to other familial clinics provided that variations in sample populations are taken into consideration.</p

The NEI/NCBI dbGAP database: Genotypes and haplotypes that may specifically predispose to risk of neovascular age-related macular degeneration

<p>Abstract</p> <p>Background</p> <p>To examine if the significantly associated SNPs derived from the genome wide allelic association study on the AREDS cohort at the NEI (dbGAP) specifically confer risk for neovascular age-related macular degeneration (AMD). We ascertained 134 unrelated patients with AMD who had one sibling with an AREDS classification 1 or less and was past the age at which the affected sibling was diagnosed (268 subjects). Genotyping was performed by both direct sequencing and Sequenom iPLEX system technology. Single SNP analyses were conducted with McNemar's Test (both 2 × 2 and 3 × 3 tests) and likelihood ratio tests (LRT). Conditional logistic regression was used to determine significant gene-gene interactions. LRT was used to determine the best fit for each genotypic model tested (additive, dominant or recessive).</p> <p>Results</p> <p>Before release of individual data, <it>p</it>-value information was obtained directly from the AREDS dbGAP website. Of the 35 variants with <it>P </it>< 10^-6examined, 23 significantly modified risk of neovascular AMD. Many variants located in tandem on 1q32-q22 including those in <it>CFH</it>, <it>CFHR4</it>, <it>CFHR2</it>, <it>CFHR5</it>, <it>F13B</it>, <it>ASPM </it>and <it>ZBTB </it>were significantly associated with AMD risk. Of these variants, single SNP analysis revealed that <it>CFH </it>rs572515 was the most significantly associated with AMD risk (P < 10^-6). Haplotype analysis supported our findings of single SNP association, demonstrating that the most significant haplotype, GATAGTTCTC, spanning <it>CFH</it>, <it>CFHR4</it>, and <it>CFHR2 </it>was associated with the greatest risk of developing neovascular AMD (<it>P </it>< 10^-6). Other than variants on 1q32-q22, only two SNPs, rs9288410 (<it>MAP2</it>) on 2q34-q35 and rs2014307 (<it>PLEKHA1</it>/<it>HTRA1</it>) on 10q26 were significantly associated with AMD status (<it>P </it>= .03 and <it>P </it>< 10^-6respectively). After controlling for smoking history, gender and age, the most significant gene-gene interaction appears to be between rs10801575 (<it>CFH</it>) and rs2014307 (<it>PLEKHA1</it>/<it>HTRA1</it>) (<it>P </it>< 10^-11). The best genotypic fit for rs10801575 and rs2014307 was an additive model based on LRT. After applying a Bonferonni correction, no other significant interactions were identified between any other SNPs.</p> <p>Conclusion</p> <p>This is the first replication study on the NEI dbGAP SNPs, demonstrating that alleles on 1q, 2q and 10q may predispose an individual to AMD.</p

Coordinate up-regulation of TMEM97 and cholesterol biosynthesis genes in normal ovarian surface epithelial cells treated with progesterone: implications for pathogenesis of ovarian cancer

<p>Abstract</p> <p>Background</p> <p>Ovarian cancer (OvCa) most often derives from ovarian surface epithelial (OSE) cells. Several lines of evidence strongly suggest that increased exposure to progesterone (P4) protects women against developing OvCa. However, the underlying mechanisms of this protection are incompletely understood.</p> <p>Methods</p> <p>To determine downstream gene targets of P4, we established short term <it>in vitro </it>cultures of non-neoplastic OSE cells from six subjects, exposed the cells to P4 (10^-6M) for five days and performed transcriptional profiling with oligonucleotide microarrays containing over 22,000 transcripts.</p> <p>Results</p> <p>We identified concordant but modest gene expression changes in cholesterol/lipid homeostasis genes in three of six samples (responders), whereas the other three samples (non-responders) showed no expressional response to P4. The most up-regulated gene was <it>TMEM97 </it>which encodes a transmembrane protein of unknown function (MAC30). Analyses of outlier transcripts, whose expression levels changed most significantly upon P4 exposure, uncovered coordinate up-regulation of 14 cholesterol biosynthesis enzymes, insulin-induced gene 1, low density lipoprotein receptor, <it>ABCG1</it>, endothelial lipase, stearoyl- CoA and fatty acid desaturases, long-chain fatty-acyl elongase, and down-regulation of steroidogenic acute regulatory protein and <it>ABCC6</it>. Highly correlated tissue-specific expression patterns of <it>TMEM97 </it>and the cholesterol biosynthesis genes were confirmed by analysis of the GNF Atlas 2 universal gene expression database. Real-time quantitative RT-PCR analyses revealed 2.4-fold suppression of the <it>TMEM97 </it>gene expression in short-term cultures of OvCa relative to the normal OSE cells.</p> <p>Conclusion</p> <p>These findings suggest that a co-regulated transcript network of cholesterol/lipid homeostasis genes and <it>TMEM97 </it>are downstream targets of P4 in normal OSE cells and that <it>TMEM97 </it>plays a role in cholesterol and lipid metabolism. The P4-induced alterations in cholesterol and lipid metabolism in OSE cells might play a role in conferring protection against OvCa.</p