Epigenetic heterogeneity at imprinted loci in normal populations

Genomic imprinting is the phenomenon by which the two alleles of certain genes are differentially expressed according to their parental origin. Extensive analysis of allelic expression at multiple imprinted loci in a normal population has not performed so far. In the present study, we examined the allelic expression pattern of three imprinted genes in a panel of 262 Japanese normal individuals. We observed differences in the extent of maintenance of allele-specific expression of the three genes. The allelic expression of small nuclear ribonucleoprotein N (SNRPN) was stringently regulated while that of multimembrane-spanning polyspecific transporter-like gene 1 (IMPT1) showed a large degree of variation. Significant biallelic expression of insulin-like growth factor II (IGF2) was observed in about 10% of normal individuals. Our findings add to the accumulating evidence for variable allelic expression at multiple loci in a normal human population. This epigenetic heterogeneity can be a stable trait and potentially influence individual phenotypes.     

A heterogeneity test for fine-scale genetic structure

For organisms with limited vagility and/or occupying patchy habitats, we often encounter nonrandom patterns of genetic affinity over relatively small spatial scales, labelled fine-scale genetic structure. Both the extent and decay rate of that pattern can be expected to depend on numerous interesting demographic, ecological, historical, and mating system factors, and it would be useful to be able to compare different situations. There is, however, no heterogeneity test currently available for fine-scale genetic structure that would provide us with any guidance on whether the differences we encounter are statistically credible. Here, we develop a general nonparametric heterogeneity test, elaborating on standard autocorrelation methods for pairs of individuals. We first develop a 'pooled within-population' correlogram, where the distance classes (lags) can be defined as functions of distance. Using that pooled correlogram as our null-hypothesis reference frame, we then develop a heterogeneity test of the autocorrelations among different populations, lag-by-lag. From these single-lag tests, we construct an analogous test of heterogeneity for multilag correlograms. We illustrate with a pair of biological examples, one involving the Australian bush rat, the other involving toadshade trillium. The Australian bush rat has limited vagility, and sometimes occupies patchy habitat. We show that the autocorrelation pattern diverges somewhat between continuous and patchy habitat types. For toadshade trillium, clonal replication in Piedmont populations substantially increases autocorrelation for short lags, but clonal replication is less pronounced in mountain populations. Removal of clonal replicates reduces the autocorrelation for short lags and reverses the sign of the difference between mountain and Piedmont correlograms.     

Dynamics of Fst for the island model

F(st) is a measure of genetic differentiation in a subdivided population. Sewall Wright observed that F(st)=1/1+2Nm in a haploid diallelic infinite island model, where N is the effective population size of each deme and m is the migration rate. In demonstrating this result, Wright relied on the infinite size of the population. Natural populations are not infinite and therefore they change over time due to genetic drift. In a finite population, F(st) becomes a random variable that evolves over time. In this work we ask, given an initial population state, what are the dynamics of the mean and variance of F(st) under the finite island model? In application both of these quantities are critical in the evaluation of F(st) data. We show that after a time of order N generations the mean of F(st) is slightly biased below 1/1+2Nm. Further we show that the variance of F(st) is of order 1/d where d is the number of demes in the population. We introduce several new mathematical techniques to analyze coalescent genealogies in a dynamic setting.     

A new statistical test for linkage heterogeneity.

A new, statistical test for linkage heterogeneity is described. It is a likelihood-ratio test based on a beta distribution for the prior distribution of the recombination fraction among families (or individuals). The null distribution for this statistic (called the B-test) is derived under a broad range of circumstances. Two other heterogeneity test statistics--the admixture test or A-test first described by Smith and Morton's test (here referred to as the K-test)--are also examined. The probability distribution for the K-test statistic is very sensitive to family size, whereas the other two statistics are not. All three statistics are somewhat sensitive to the magnitude of the recombination fraction theta. Critical values for each of the test statistics are given. A conservative approximation for both the A-test and B-test is given by a chi 2 distribution when P/2 instead of P is used for the observed significance level. In terms of power, the B-test performs best among the three tests over a broad range of alternate heterogeneity hypotheses--except for the specific case of admixture with loose linkage, in which the A-test performs best. Overall, the difference in power among the three tests is not large. An application to some recently published data on the fragile-X syndrome and X-chromosome markers is given.     

A simple test for macromolecular heterogeneity in the analytical ultracentrifuge

A simple check for the presence of heterogeneity in a macromolecular system is proposed, employing comparison of Rayleigh sedimentation-equilibrium patterns for two solutions of the same fringe concentration but differing absolute concentrations. The method is illustrated by application to a bronchial glycoprotein from a cystic-fibrosis patient.     

A combined test of linkage heterogeneity.

This paper focuses on the problem of testing for heterogeneity once linkage is established. In an investigation of genetic linkage, Morton first proposed a general purpose test to detect heterogeneity in the recombination fraction. Two more commonly used tests of linkage heterogeneity are the admixture test (A-test) of Smith, Ott, and Risch and Baron, and the B-test of Risch. All are likelihood-ratio tests, but they differ in the models specifying the heterogeneity. A new test of heterogeneity in the presence of linkage is presented here. I propose a mixture model of heterogeneity, which allows the recombination fraction to vary among families, as does the B-model, yet also allows some families to be unlinked, as the A-model does. This model contains the A and B models as special cases and thus allows a direct test (D-test), which can provide justification for choosing one of these extremes.     

A new test for linkage in the presence of locus heterogeneity.

The detection of linkage in complex traits, although potentially of the greatest value, has proved very difficult. One reason may be the drastic effect that locus heterogeneity has on statistical power. We propose a new test for linkage in the presence of heterogeneity, based upon the sum of individual pedigree maximum lod scores, combined with a bootstrap method for estimating the null-hypothesis distribution. The technique is designed to exploit modern computer capability and to avoid reliance on asymptotic-distribution theory. Numerical comparisons indicate that for small pedigrees this new test can detect linkage with 30%-50% less data than are required by standard methods. A computer program for simulating the distribution and for performing the test of linkage is available from the authors.     

Among-locus variation in Fst: fish, allozymes and the Lewontin-Krakauer test revisited.

Variation among loci in the distribution of allele frequencies among subpopulations is well known; how to tell when the variation exceeds that expected when all loci are subject to uniform evolutionary processes is not well known. If locus-specific effects are important, the ability to detect those effects should vary with the level of gene flow. Populations with low gene flow should exhibit greater variation among loci in Fst than populations with high gene flow, because gene flow acts to homogenize allele frequencies among subpopulations. Here I use Lewontin and Krakauer's k statistic to describe the variance among allozyme loci in 102 published data sets from fishes. As originally proposed, k > 2 was considered evidence that the variation in Fst among loci is greater than expected from neutral evolution. Although that interpretation is invalid, large differences in k in different populations suggest that locus-specific forces may be important in shaping genetic diversity. In these data, k is not greater for populations with expected low levels of gene flow than for populations with expected high levels of gene flow. There is thus no evidence that locus-specific forces are of general importance in shaping the distribution of allele frequencies at enzyme loci among populations of fishes.     

Microaerophilic metabolism of Pachysolen tannophilus at different pH values

Microaerophilic production of xylitol by Pachysolen tannophilus from detoxified hemicellulose hydrolyzate was optimal between pH values 6.0 to 7.5 when about 90% of xylose was utilized for xylitol production, the rest being fermented to ethanol. At pH values of 3.0 and 12.0, respiration became important, consuming up to 30% of available xylose. A graphic procedure suggests that histamine and cysteine are at the active site of xylose reductase in this yeast.     

A genome scan for quantitative trait loci associated with body weight at different developmental stages in chickens

A genome scan to detect quantitative trait loci (QTL) affecting body weight in chickens was conducted on 238 F(2) chickens from a reciprocal cross of Silky Fowl and White Plymouth Rock using 125 microsatellite markers covering 23 autosomes and the Z chromosome. Two types of QTL were considered: static QTL (SQ) and developmental QTL (DQ). Static QTL affected the body weight from hatch to time t, and DQ affected the body weight from time t-1 to time t. Six SQ and nine DQ were detected. Of these QTL, four reached a genome-wide significance of 5% or better, with SQ1 and DQ1 being the most significant QTL. Static QTL1 was on chromosome 1 between GCT0006 and MCW0106 and explained 4.05-9.80% of the phenotypic variation in body weights from 3 to 12 weeks of age. At 9, 10 and 11 weeks, the genome-wide significance thresholds of SQ1 were <1%. Developmental QTL1 was located on chromosome 1 between MCW0168 and GCT0006, and explained 2.75% of the phenotypic variation for body weight from week 7 to 8 with a genome-wide significance level <1%. The results suggest that body weight from hatch to time t and developmental growth from time t-1 to time t may involve two different sets of genes or gene actions.     

A family-based test for correlation between gene expression and trait values

Advances in microarray technology have made it attractive to combine information on clinical traits, marker genotypes, and comprehensive gene expression from family studies to dissect complex disease genetics. Without accounting for family structure, methods that test for association between a trait and gene-expression levels can be misleading. We demonstrate that the standard unstratified test based on Pearson's correlation coefficient can produce spurious results when applied to family data, and we present a stratified family expression association test (FEXAT). We illustrate the utility of the FEXAT via simulation and an application to gene-expression data from lymphoblastoid cell lines from four CEPH families. The FEXAT has a smaller estimated false-discovery rate than the standard test when within-family correlations are of interest, and it detects biologically plausible correlations between beta catenin and genes in the WNT-activation pathway in humans that the standard test does not.     

Antibiotic susceptibility of Salmonella spp. at different pH values

We have examined the effects of acidic pH, in the range of those prevailing within phagosomes and lysosomes, on the growth and the susceptibility to different antibiotics of several strains of Salmonella spp. The minimal inhibitory concentration and the minimal bactericidal concentration of several beta-lactams were increased considerably during culture at pH 5.2. The extent of the increase was a function of: (1) the beta-lactam structure and, more particularly, the hydrophobicity of the side-chain of the molecule; and (2) the bacterial serotype. This phenotypic resistance at acid pH was not due to beta-lactamase activity or to a lower growth rate. In contrast, rifamycin SV was more active at acidic pH than at neutral pH and chloramphenicol, another highly hydrophobic drug, was equally efficacious at both pH values. Membrane lipopolysaccharide mutants, but not porin mutants, cultivated at an acidic pH were inhibited by lower concentrations of the beta-lactams. This suggests that the increased resistance to beta-lactams, and the increased susceptibility to rifamycin SV, at acidic pH, could have resulted from modified permeability of the outer membrane to antibiotics.     

Model-specific tests on variance heterogeneity for detection of potentially interacting genetic loci

ABSTRACT: BACKGROUND: Trait variances among genotype groups at a locus are expected to differ in the presence of an interaction between this locus and another locus or environment. A simple maximum test on variance heterogeneity can thus be used to identify potentially interacting single nucleotide polymorphisms (SNPs). RESULTS: We propose a multiple contrast test for variance heterogeneity that compares the mean of Levene residuals for each genotype group with their average as an alternative to a global Levene test. We applied this test to a Bogalusa Heart Study dataset to screen for potentially interacting SNPs across the whole genome that influence a number of quantitative traits. A user-friendly implementation of this method is available in the R statistical software package multcomp. CONCLUSIONS: We show that the proposed multiple contrast test of model-specific variance heterogeneity can be used to test for potential interactions between SNPs and unknown alleles, loci or covariates and provide valuable additional information compared with traditional tests. Although the test is statistically valid for severely unbalanced designs, care is needed in interpreting the results at loci with low allele frequencies.     

Linkage analysis of quantitative trait loci in the presence of heterogeneity

Variance component modeling for linkage analysis of quantitative traits is a powerful tool for detecting and locating genes affecting a trait of interest, but the presence of genetic heterogeneity will decrease the power of a linkage study and may even give biased estimates of the location of the quantitative trait loci. Many complex diseases are believed to be influenced by multiple genes and therefore genetic heterogeneity is likely to be present for many real applications of linkage analysis. We consider a mixture of multivariate normals to model locus heterogeneity by allowing only a proportion of the sampled pedigrees to segregate trait-influencing allele(s) at a specific locus. However, for mixtures of normals the classical asymptotic distribution theory of the maximum likelihood estimates does not hold, so tests of linkage and/or heterogeneity are evaluated using resampling methods. It is shown that allowing for genetic heterogeneity leads to an increase in power to detect linkage. This increase is more prominent when the genetic effect of the locus is small or when the percentage of pedigrees not segregating trait-influencing allele(s) at the locus is high.