A gene-model-free method for linkage analysis of a disease-related-trait based on analysis of proband/sibling pairs

In this paper we investigate the power of finding linkage to a disease locus through analysis of the disease-related traits. We propose two family-based gene-model-free linkage statistics. Both involve considering the distribution of the number of alleles identical by descent with the proband and comparing siblings with the disease-related trait to those without the disease-related-trait. The objective is to find linkages to disease-related traits that are pleiotropic for both the disease and the disease-related-traits. The power of these statistics is investigated for Kofendrerd Personality Disorder-related traits a (Joining/founding cults) and trait b (Fear/discomfort with strangers) of the simulated data. The answers were known prior to the execution of the reported analyses. We find that both tests have very high power when applied to the samples created by combining the data of the three cities for which we have nuclear family data

UO2 CORROSION IN HIGH SURFACE-AREA-TO-VOLUME BATCH EXPERIMENTS

Unsaturated drip tests have been used to investigate the alteration of unirradiated UO{sub 2} and spent UO{sub 2} fuel in an unsaturated environment, such as may be expected in the proposed repository at Yucca Mountain. In these tests, simulated groundwater is periodically injected onto a sample at 90 C in a steel vessel. The solids react with the dripping groundwater and water condensed on surfaces to form a suite of U(VI) alteration phases. Solution chemistry is determined from leachate at the bottom of each vessel after the leachate stops interacting with the solids. A more detailed knowledge of the compositional evolution of the leachate is desirable. By providing just enough water to maintain a thin film of water on a small quantity of fuel in batch experiments, we can more closely monitor the compositional changes to the water as it reacts to form alteration phases

Incorporation of genetic model parameters for cost-effective designs of genetic association studies using DNA pooling

<p>Abstract</p> <p>Background</p> <p>Studies of association methods using DNA pooling of single nucleotide polymorphisms (SNPs) have focused primarily on the effects of "machine-error", number of replicates, and the size of the pool. We use the non-centrality parameter (NCP) for the analysis of variance test to compute the approximate power for genetic association tests with DNA pooling data on cases and controls. We incorporate genetic model parameters into the computation of the NCP. Parameters involved in the power calculation are disease allele frequency, frequency of the marker SNP allele in coupling with the disease locus, disease prevalence, genotype relative risk, sample size, genetic model, number of pools, number of replicates of each pool, and the proportion of variance of the pooled frequency estimate due to machine variability. We compute power for different settings of number of replicates and total number of genotypings when the genetic model parameters are fixed. Several significance levels are considered, including stringent significance levels (due to the increasing popularity of 100 K and 500 K SNP "chip" data). We use a factorial design with two to four settings of each parameter and multiple regression analysis to assess which parameters most significantly affect power.</p> <p>Results</p> <p>The power can increase substantially as the genotyping number increases. For a fixed number of genotypings, the power is a function of the number of replicates of each pool such that there is a setting with maximum power. The four most significant parameters affecting power for association are: (1) genotype relative risk, (2) genetic model, (3) sample size, and (4) the interaction term between disease and SNP marker allele probabilities.</p> <p>Conclusion</p> <p>For a fixed number of genotypings, there is an optimal number of replicates of each pool that increases as the number of genotypings increases. Power is not substantially reduced when the number of replicates is close to but not equal to the optimal setting.</p

Characteristics of replicated single-nucleotide polymorphism genotypes from COGA: Affymetrix and Center for Inherited Disease Research

Genetic Analysis Workshop 14 provided re-genotyped single-nucleotide polymorphism (SNP) data. Specifically, both Center for Inherited Disease Research (CIDR) and Affymetrix genotyped the same 11,560 SNPs from the Affymetrix GeneChip Mapping 10K Array marker set on the same 184 individuals from the Collaborative Study on the Genetics of Alcoholism database. While the inconsistency rate between CIDR and Affymetrix (two different genotypes for the same subject) was low (0.2%), the non-replication rate (two different genotypes for the same subject or one identified genotype and one missing genotype) was substantial (9.5%). The missing data could be from no-call regions, which is inconsistent with recent recommendations about the use of no-call regions in association tests. In addition, no-call regions would suggest that the actual inconsistency rate is higher than reported. A high inconsistency rate has significant impact on power in related hypothesis tests. In addition, the data are consistent with assumptions made in a recently proposed likelihood ratio test of association for re-genotyped data

TDT-HET: A new transmission disequilibrium test that incorporates locus heterogeneity into the analysis of family-based association data

<p>Abstract</p> <p>Background</p> <p>Locus heterogeneity is one of the most documented phenomena in genetics. To date, relatively little work had been done on the development of methods to address locus heterogeneity in genetic association analysis. Motivated by Zhou and Pan's work, we present a mixture model of linked and unlinked trios and develop a statistical method to estimate the probability that a heterozygous parent transmits the disease allele at a di-allelic locus, and the probability that any trio is in the linked group. The purpose here is the development of a test that extends the classic transmission disequilibrium test (<it>TDT</it>) to one that accounts for locus heterogeneity.</p> <p>Results</p> <p>Our simulations suggest that, for sufficiently large sample size (1000 trios) our method has good power to detect association even the proportion of unlinked trios is high (75%). While the median difference (<it>TDT-HET </it>empirical power - <it>TDT </it>empirical power) is approximately 0 for all MOI, there are parameter settings for which the power difference can be substantial. Our multi-locus simulations suggest that our method has good power to detect association as long as the markers are reasonably well-correlated and the genotype relative risk are larger. Results of both single-locus and multi-locus simulations suggest our method maintains the correct type I error rate.</p> <p>Finally, the <it>TDT-HET </it>statistic shows highly significant p-values for most of the idiopathic scoliosis candidate loci, and for some loci, the estimated proportion of unlinked trios approaches or exceeds 50%, suggesting the presence of locus heterogeneity.</p> <p>Conclusions</p> <p>We have developed an extension of the <it>TDT </it>statistic (<it>TDT-HET</it>) that allows for locus heterogeneity among coded trios. Benefits of our method include: estimates of parameters in the presence of heterogeneity, and reasonable power even when the proportion of linked trios is small. Also, we have extended multi-locus methods to <it>TDT-HET </it>and have demonstrated that the empirical power may be high to detect linkage. Last, given that we obtain PPBs, we conjecture that the <it>TDT-HET </it>may be a useful method for correctly identifying linked trios. We anticipate that researchers will find this property increasingly useful as they apply next-generation sequencing data in family based studies.</p

Differential Test Performance in the American Educational System: The Impact of Race and Gender

Contrary to Herrnstein and Murray (1994) who claim that racial groups have different cognitive endowments and that these best explain differential test score achievements, our regression analyses document that there is less improvement in test scores per year of education for African-Americans and women. That is, the observed group test score differences do not appear to be due to racial cognitive differences but rather to other factors associated with group-linked experiences in the educational system. We found that 666 of the subjects in the Herrnstein-Murray database had actual IQ scores derived from school records. Using these as independent controls for IQ, we document that each of the test components that were the basis of the Herrnstein-Murray IQ scores was significantly associated with education level (p\u3c .001). Consequently, their IQ score appears to be an education-related measure rather than an IQ test, and thus challenges the validity of their analysis

Mixture modeling of microarray gene expression data

About 28% of genes appear to have an expression pattern that follows a mixture distribution. We use first- and second-order partial correlation coefficients to identify trios and quartets of non-sex-linked genes that are highly associated and that are also mixtures. We identified 18 trio and 35 quartet mixtures and evaluated their mixture distribution concordance. Concordance was defined as the proportion of observations that simultaneously fall in the component with the higher mean or simultaneously in the component with the lower mean based on their Bayesian posterior probabilities. These trios and quartets have a concordance rate greater than 80%. There are 33 genes involved in these trios and quartets. A factor analysis with varimax rotation identifies three gene groups based on their factor loadings. One group of 18 genes has a concordance rate of 56.7%, another group of 8 genes has a concordance rate of 60.8%, and a third group of 7 genes has a concordance rate of 69.6%. Each of these rates is highly significant, suggesting that there may be strong biological underpinnings for the mixture mechanisms of these genes. Bayesian factor screening confirms this hypothesis by identifying six single-nucleotide polymorphisms that are significantly associated with the expression phenotypes of the five most concordant genes in the first group

Using mixture models to characterize disease-related traits

We consider 12 event-related potentials and one electroencephalogram measure as disease-related traits to compare alcohol-dependent individuals (cases) to unaffected individuals (controls). We use two approaches: 1) two-way analysis of variance (with sex and alcohol dependency as the factors), and 2) likelihood ratio tests comparing sex adjusted values of cases to controls assuming that within each group the trait has a 2 (or 3) component normal mixture distribution. In the second approach, we test the null hypothesis that the parameters of the mixtures are equal for the cases and controls. Based on the two-way analysis of variance, we find 1) males have significantly (p < 0.05) lower mean response values than females for 7 of these traits. 2) Alcohol-dependent cases have significantly lower mean response than controls for 3 traits. The mixture analysis of sex-adjusted values of 1 of these traits, the event-related potential obtained at the parietal midline channel (ttth4), found the appearance of a 3-component normal mixture in cases and controls. The mixtures differed in that the cases had significantly lower mean values than controls and significantly different mixing proportions in 2 of the 3 components. Implications of this study are: 1) Sex needs to be taken into account when studying risk factors for alcohol dependency to prevent finding a spurious association between alcohol dependency and the risk factor. 2) Mixture analysis indicates that for the event-related potential "ttth4", the difference observed reflects strong evidence of heterogeneity of response in both the cases and controls

Cell envelope proteins of Staphylococcus epidermidis grown in vivo in a peritoneal chamber implant

Staphylococcus epidermidis was grown in vivo in chambers implanted intraperitoneally in rats. The cell wall and cytoplasmic membrane protein profiles of the in vivo-grown organisms were compared with those of S. epidermidis grown in vitro in nutrient broth (NB), in iron-restricted NB, or in pooled human peritoneal dialysate (HPD). Compared with growth in broth and in common with growth in HPD, growth in vivo in chambers resulted in the repression of many S. epidermidis wall proteins, with proteins of 27, 42, 54, and 70 kDa predominating. Growth in vivo also resulted in the induction of two iron-repressible cytoplasmic membrane proteins of 32 and 36 kDa, which were also present in staphylococci grown in HPD and in iron-restricted NB. Immunoblotting experiments revealed that in sera taken 21 days after inoculation of the intraperitoneal chambers, the predominant antibody response to cell envelope proteins was directed against the 32- and 36-kDa iron-repressible membrane proteins