Detection of nonrandom association of alleles from the distribution of the number of heterozygous loci in a sample.

The distribution of the number of heterozygous loci in two randomly chosen gametes or in a random diploid zygote provides information regarding the nonrandom association of alleles among different genetic loci. Two alternative statistics may be employed for detection of nonrandom association of genes of different loci when observations are made on these distributions: observed variance of the number of heterozygous loci (s2k) and a goodness-of-fit criterion (X2) to contrast the observed distribution with that expected under the hypothesis of random association of genes. It is shown, by simulation, that s2k is statistically more efficient than X2 to detect a given extent of nonrandom association. Asymptotic normality of s2k is justified, and X2 is shown to follow a chi-square (chi 2) distribution with partial loss of degrees of freedom arising because of estimation of parameters from the marginal gene frequency data. Whenever direct evaluations of linkage disequilibrium values are possible, tests based on maximum likelihood estimators of linkage disequilibria require a smaller sample size (number of zygotes or gametes) to detect a given level of nonrandom association in comparison with that required if such tests are conducted on the basis of s2k. Summarization of multilocus genotype (or haplotype) data, into the different number of heterozygous loci classes, thus, amounts to appreciable loss of information.

Crystal structure of the alpha(6)beta(6) holoenzyme of propionyl-coenzyme A carboxylase.

Propionyl-coenzyme A carboxylase (PCC), a mitochondrial biotin-dependent enzyme, is essential for the catabolism of the amino acids Thr, Val, Ile and Met, cholesterol and fatty acids with an odd number of carbon atoms. Deficiencies in PCC activity in humans are linked to the disease propionic acidaemia, an autosomal recessive disorder that can be fatal in infants. The holoenzyme of PCC is an alpha(6)beta(6) dodecamer, with a molecular mass of 750 kDa. The alpha-subunit contains the biotin carboxylase (BC) and biotin carboxyl carrier protein (BCCP) domains, whereas the beta-subunit supplies the carboxyltransferase (CT) activity. Here we report the crystal structure at 3.2-A resolution of a bacterial PCC alpha(6)beta(6) holoenzyme as well as cryo-electron microscopy (cryo-EM) reconstruction at 15-A resolution demonstrating a similar structure for human PCC. The structure defines the overall architecture of PCC and reveals unexpectedly that the alpha-subunits are arranged as monomers in the holoenzyme, decorating a central beta(6) hexamer. A hitherto unrecognized domain in the alpha-subunit, formed by residues between the BC and BCCP domains, is crucial for interactions with the beta-subunit. We have named it the BT domain. The structure reveals for the first time the relative positions of the BC and CT active sites in the holoenzyme. They are separated by approximately 55 A, indicating that the entire BCCP domain must translocate during catalysis. The BCCP domain is located in the active site of the beta-subunit in the current structure, providing insight for its involvement in the CT reaction. The structural information establishes a molecular basis for understanding the large collection of disease-causing mutations in PCC and is relevant for the holoenzymes of other biotin-dependent carboxylases, including 3-methylcrotonyl-CoA carboxylase (MCC) and eukaryotic acetyl-CoA carboxylase (ACC).

Transcriptional analysis of liver-specific expression and differential interleukin-6 response of rat kininogen genes

Analyses of rat T1 kininogen gene/chloramphenicol acetyltransferase (T1K/CAT) constructs revealed two regions important for tissue-specific and induced regulation of T1 kininogen.^ Although the T1 kininogen gene is inducible by inflammatory cytokines, a highly homologous K kininogen gene is minimally responsive. Moreover, the basal expression of a KK/CAT construct was 5- to 7-fold higher than that of the analogous T1K/CAT construct. To examine the molecular basis of this differential regulation, a series of promoter swapping experiments was carried out. Our transfection results showed that at least two regions in the K kininogen gene are important for its high basal expression: a distal 19-bp region (C box) constituted a binding site for CCAAT/enhancer binding protein (C/EBP) family proteins and a proximal 66-bp region contained two adjacent binding sites for hepatocyte nuclear factor-3 (HNF-3). The distal HNF-3 binding site from the K kininogen promoter demonstrated a stronger affinity than that from the T1 kininogen promoter. Since C/EBP and HNF-3 are highly enriched in the liver and known to enhance transcription of liver-specific genes, differential binding affinities of these factors accounted for the higher basal expression of the K kininogen gene.^ In contrast to the K kininogen C box, the T1 kininogen C box does not bind C/EBP presumably due to their two-nucleotide divergence. This sequence divergence, however, converts it to a consensus binding sequence for two IL-6-inducible transcription factors--IL-6 response element binding protein and acute-phase response factor. To functionally determine whether C box sequences are important for their differential acute-phase response, T1 and K kininogen C boxes were swapped and analyzed after transfection into Hep3B cells. Our results showed that the T1 kininogen C box is indeed one of the IL-6 response elements in T1 kininogen promoter. Furthermore, its function can be modulated by a 5$\sp\prime$-adjacent C/EBP-binding site (B box) whose mutation significantly reduced the overall induced activity. Moreover, this B box is the target site for binding and transactivation of another IL-6 inducible transcription factor C/EBP$\delta.$ Evolutionary divergence of a few critical nucleotides can either lead to subtle changes in the binding affinities of a given transcription factor or convert a binding sequence for a constitutive factor to a site recognized by an inducible factor. (Abstract shortened by UMI.) ^

Assessment of the effect on statistical power of regression model misspecification by using techniques of mathematical statistics and simulation study

Objectives. This paper seeks to assess the effect on statistical power of regression model misspecification in a variety of situations. ^ Methods and results. The effect of misspecification in regression can be approximated by evaluating the correlation between the correct specification and the misspecification of the outcome variable (Harris 2010).In this paper, three misspecified models (linear, categorical and fractional polynomial) were considered. In the first section, the mathematical method of calculating the correlation between correct and misspecified models with simple mathematical forms was derived and demonstrated. In the second section, data from the National Health and Nutrition Examination Survey (NHANES 2007-2008) were used to examine such correlations. Our study shows that comparing to linear or categorical models, the fractional polynomial models, with the higher correlations, provided a better approximation of the true relationship, which was illustrated by LOESS regression. In the third section, we present the results of simulation studies that demonstrate overall misspecification in regression can produce marked decreases in power with small sample sizes. However, the categorical model had greatest power, ranging from 0.877 to 0.936 depending on sample size and outcome variable used. The power of fractional polynomial model was close to that of linear model, which ranged from 0.69 to 0.83, and appeared to be affected by the increased degrees of freedom of this model.^ Conclusion. Correlations between alternative model specifications can be used to provide a good approximation of the effect on statistical power of misspecification when the sample size is large. When model specifications have known simple mathematical forms, such correlations can be calculated mathematically. Actual public health data from NHANES 2007-2008 were used as examples to demonstrate the situations with unknown or complex correct model specification. Simulation of power for misspecified models confirmed the results based on correlation methods but also illustrated the effect of model degrees of freedom on power.^