Optimizing genomic control in mixed model associations with binary diseases.

Complex computation and approximate solution hinder the application of generalized linear mixed models (GLMM) into genome-wide association studies. We extended GRAMMAR to handle binary diseases by considering genomic breeding values (GBVs) estimated in advance as a known predictor in genomic logit regression, and then reduced polygenic effects by regulating downward genomic heritability to control false negative errors produced in the association tests. Using simulations and case analyses, we showed in optimizing GRAMMAR, polygenic effects and genomic controls could be evaluated using the fewer sampling markers, which extremely simplified GLMM-based association analysis in large-scale data. Further, joint association analysis for quantitative trait nucleotide (QTN) candidates chosen by multiple testing offered significant improved statistical power to detect QTNs over existing methods.

A mixed model reduces spurious genetic associations produced by population stratification in genome-wide association studies.

Population stratification can produce spurious genetic associations in genome-wide association studies (GWASs). Mixed model methodology has been regarded useful for correcting population stratification. This study explored statistical power and false discovery rate (FDR) with the data simulated for dichotomous traits. Empirical FDRs and powers were estimated using fixed models with and without genomic control and using mixed models with and without reflecting loci linked to the candidate marker in genetic relationships. Population stratification with admixture degree ranged from 1% to 10% resulted in inflated FDRs from the fixed model analysis without genomic control and decreased power from the fixed model analysis with genomic control (P<0.05). Meanwhile, population stratification could not change FDR and power estimates from the mixed model analyses (P>0.05). We suggest that the mixed model methodology was useful to reduce spurious genetic associations produced by population stratification in GWAS, even with a high degree of admixture (10%).

How allele frequency and study design affect association test statistics with misrepresentation errors.

We evaluate the effect of genotyping errors on the type-I error of a general association test based on genotypes, showing that, in the presence of errors in the case and control samples, the test statistic asymptotically follows a scaled non-central $\chi ^2$ distribution. We give explicit formulae for the scaling factor and non-centrality parameter for the symmetric allele-based genotyping error model and for additive and recessive disease models. They show how genotyping errors can lead to a significantly higher false-positive rate, growing with sample size, compared with the nominal significance levels. The strength of this effect depends very strongly on the population distribution of the genotype, with a pronounced effect in the case of rare alleles, and a great robustness against error in the case of large minor allele frequency. We also show how these results can be used to correct $p$-values.

Genetic association with multiple traits in the presence of population stratification.

Testing association between a genetic marker and multiple-dependent traits is a challenging task when both binary and quantitative traits are involved. The inverted regression model is a convenient method, in which the traits are treated as predictors although the genetic marker is an ordinal response. It is known that population stratification (PS) often affects population-based association studies. However, how it would affect the inverted regression for pleiotropic association, especially with the mixed types of traits (binary and quantitative), is not examined and the performance of existing methods to correct for PS using the inverted regression analysis is unknown. In this paper, we focus on the methods based on genomic control and principal component analysis, and investigate type I error of pleiotropic association using the inverted regression model in the presence of PS with allele frequencies and the distributions (or disease prevalences) of multiple traits varying across the subpopulations. We focus on common alleles but simulation results for a rare variant are also reported. An application to the HapMap data is used for illustration.

The Centriole Mystique.

Centrioles organize the microtubule network and mitotic spindle and, as basal bodies, nucleate cilia and flagella. They undergo a beguiling process in which one appears to give rise to another and at a baffling orthogonal geometry. Nucleic acid-based replication has been pondered during cycles of zeniths and nadirs of plausibility, the latter now the state. Centrioles can also arise de novo, and thus the longstanding focus on centriole 'replication' may have led us astray from ground truth. We are in an era in which the assembly pathways of most intracellular machines are becoming understood in considerable detail. But apart from knowing the structure and parts list, little in our extant knowledge conveys how centrioles arise. Here the matters at hand are summarized, and a siren call is sounded.

Double genomic control is not effective to correct for population stratification in meta-analysis for genome-wide association studies.

Meta-analysis of genome-wide association studies (GWAS) has become a useful tool to identify genetic variants that are associated with complex human diseases. To control spurious associations between genetic variants and disease that are caused by population stratification, double genomic control (GC) correction for population stratification in meta-analysis for GWAS has been implemented in the software METAL and GWAMA and is widely used by investigators. In this research, we conducted extensive simulation studies to evaluate the double GC correction method in meta-analysis and compared the performance of the double GC correction with that of a principal components analysis (PCA) correction method in meta-analysis. Results show that when the data consist of population stratification, using double GC correction method can have inflated type I error rates at a marker with significant allele frequency differentiation in the subpopulations (such as caused by recent strong selection). On the other hand, the PCA correction method can control type I error rates well and has much higher power in meta-analysis compared to the double GC correction method, even though in the situation that the casual marker does not have significant allele frequency difference between the subpopulations. We applied the double GC correction and PCA correction to meta-analysis of GWAS for two real datasets from the Atherosclerosis Risk in Communities (ARIC) project and the Multi-Ethnic Study of Atherosclerosis (MESA) project. The results also suggest that PCA correction is more effective than the double GC correction in meta-analysis.

Sex differences in brain aromatase activity: genomic and non-genomic controls.

Aromatization of testosterone into estradiol in the preoptic area plays a critical role in the activation of male copulation in quail and in many other vertebrate species. Aromatase expression in quail and in other birds is higher than in rodents and other mammals, which has facilitated the study of the controls and functions of this enzyme. Over relatively long time periods (days to months), brain aromatase activity (AA), and transcription are markedly (four- to sixfold) increased by genomic actions of sex steroids. Initial work indicated that the preoptic AA is higher in males than in females and it was hypothesized that this differential production of estrogen could be a critical factor responsible for the lack of behavioral activation in females. Subsequent studies revealed, however, that this enzymatic sex difference might contribute but is not sufficient to explain the sex difference in behavior. Studies of AA, immunoreactivity, and mRNA concentrations revealed that sex differences observed when measuring enzymatic activity are not necessarily observed when one measures mRNA concentrations. Discrepancies potentially reflect post-translational controls of the enzymatic activity. AA in quail brain homogenates is rapidly inhibited by phosphorylation processes. Similar rapid inhibitions occur in hypothalamic explants maintained in vitro and exposed to agents affecting intracellular calcium concentrations or to glutamate agonists. Rapid changes in AA have also been observed in vivo following sexual interactions or exposure to short-term restraint stress and these rapid changes in estrogen production modulate expression of male sexual behaviors. These data suggest that brain estrogens display most if not all characteristics of neuromodulators if not neurotransmitters. Many questions remain however concerning the mechanisms controlling these rapid changes in estrogen production and their behavioral significance.