Increasing the power of meta-analysis of genome-wide association studies to detect heterogeneous effects.

MOTIVATION: Meta-analysis is essential to combine the results of genome-wide association studies (GWASs). Recent large-scale meta-analyses have combined studies of different ethnicities, environments and even studies of different related phenotypes. These differences between studies can manifest as effect size heterogeneity. We previously developed a modified random effects model (RE2) that can achieve higher power to detect heterogeneous effects than the commonly used fixed effects model (FE). However, RE2 cannot perform meta-analysis of correlated statistics, which are found in recent research designs, and the identified variants often overlap with those found by FE. RESULTS: Here, we propose RE2C, which increases the power of RE2 in two ways. First, we generalized the likelihood model to account for correlations of statistics to achieve optimal power, using an optimization technique based on spectral decomposition for efficient parameter estimation. Second, we designed a novel statistic to focus on the heterogeneous effects that FE cannot detect, thereby, increasing the power to identify new associations. We developed an efficient and accurate p -value approximation procedure using analytical decomposition of the statistic. In simulations, RE2C achieved a dramatic increase in power compared with the decoupling approach (71% vs. 21%) when the statistics were correlated. Even when the statistics are uncorrelated, RE2C achieves a modest increase in power. Applications to real genetic data supported the utility of RE2C. RE2C is highly efficient and can meta-analyze one hundred GWASs in one day. AVAILABILITY AND IMPLEMENTATION: The software is freely available at http://software.buhmhan.com/RE2C . CONTACT: buhm.han@amc.seoul.kr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide association study of NMDA receptor coagonists in human cerebrospinal fluid and plasma.

The N-methyl-D-aspartate receptor (NMDAR) coagonists glycine, D-serine and L-proline play crucial roles in NMDAR-dependent neurotransmission and are associated with a range of neuropsychiatric disorders. We conducted the first genome-wide association study of concentrations of these coagonists and their enantiomers in plasma and cerebrospinal fluid (CSF) of human subjects from the general population (N=414). Genetic variants at chromosome 22q11.2, located in and near PRODH (proline dehydrogenase), were associated with L-proline in plasma (ß=0.29; P=6.38 × 10(-10)). The missense variant rs17279437 in the proline transporter SLC6A20 was associated with L-proline in CSF (ß=0.28; P=9.68 × 10(-9)). Suggestive evidence of association was found for the D-serine plasma-CSF ratio at the D-amino-acid oxidase (DAO) gene (ß=-0.28; P=9.08 × 10(-8)), whereas a variant in SRR (that encodes serine racemase and is associated with schizophrenia) constituted the most strongly associated locus for the L-serine to D-serine ratio in CSF. All these genes are highly expressed in rodent meninges and choroid plexus, anatomical regions relevant to CSF physiology. The enzymes and transporters they encode may be targeted to further construe the nature of NMDAR coagonist involvement in NMDAR gating. Furthermore, the highlighted genetic variants may be followed up in clinical populations, for example, schizophrenia and 22q11 deletion syndrome. Overall, this targeted metabolomics approach furthers the understanding of NMDAR coagonist concentration variability and sets the stage for non-targeted CSF metabolomics projects.

Heritability of periodontal bone loss in mice.

BACKGROUND: Periodontitis is an inflammatory disease of the periodontal tissues that compromises tooth support and can lead to tooth loss. Although bacterial biofilm is central in disease pathogenesis, the host response plays an important role in the progression and severity of periodontitis. Indeed, clinical genetic studies indicate that periodontitis is 50% heritable. In this study, we hypothesized that lipopolysaccharide (LPS) injections lead to a strain-dependent periodontal bone loss pattern. MATERIAL AND METHODS: We utilized five inbred mouse strains that derive the recombinant strains of the hybrid mouse diversity panel. Mice received Porphyromonas gingivalis-LPS injections for 6 wk. RESULTS AND CONCLUSION: Micro-computed tomography analysis demonstrated a statistically significant strain-dependent bone loss. The most susceptible strain, C57BL/6J, had a fivefold higher LPS-induced bone loss compared to the most resistant strain, A/J. More importantly, periodontal bone loss revealed 49% heritability, which closely mimics periodontitis heritability for patients. To evaluate further the functional differences that underlie periodontal bone loss, osteoclast numbers of C57BL/6J and A/J mice were measured in vivo and in vitro. In vitro analysis of osteoclastogenic potential showed a higher number of osteoclasts in C57BL/6J compared to A/J mice. In vivo LPS injections statistically significantly increased osteoclast numbers in both groups. Importantly, the number of osteoclasts was higher in C57BL/6J vs. A/J mice. These data support a significant role of the genetic framework in LPS-induced periodontal bone loss and the feasibility of utilizing the hybrid mouse diversity panel to determine the genetic factors that affect periodontal bone loss. Expanding these studies will contribute in predicting patients genetically predisposed to periodontitis and in identifying the biological basis of disease susceptibility.

Genome-wide association study of monoamine metabolite levels in human cerebrospinal fluid.

Studying genetic determinants of intermediate phenotypes is a powerful tool to increase our understanding of genotype-phenotype correlations. Metabolic traits pertinent to the central nervous system (CNS) constitute a potentially informative target for genetic studies of intermediate phenotypes as their genetic underpinnings may elucidate etiological mechanisms. We therefore conducted a genome-wide association study (GWAS) of monoamine metabolite (MM) levels in cerebrospinal fluid (CSF) of 414 human subjects from the general population. In a linear model correcting for covariates, we identified one locus associated with MMs at a genome-wide significant level (standardized ß=0.32, P=4.92 × 10(-8)), located 20 kb from SSTR1, a gene involved with brain signal transduction and glutamate receptor signaling. By subsequent whole-genome expression quantitative trait locus (eQTL) analysis, we provide evidence that this variant controls expression of PDE9A (ß=0.21; P unadjusted=5.6 × 10(-7); P corrected=0.014), a gene previously implicated in monoaminergic transmission, major depressive disorder and antidepressant response. A post hoc analysis of loci significantly associated with psychiatric disorders suggested that genetic variation at CSMD1, a schizophrenia susceptibility locus, plays a role in the ratio between dopamine and serotonin metabolites in CSF. The presented DNA and mRNA analyses yielded genome-wide and suggestive associations in biologically plausible genes, two of which encode proteins involved with glutamate receptor functionality. These findings will hopefully contribute to an exploration of the functional impact of the highlighted genes on monoaminergic transmission and neuropsychiatric phenotypes.

Genome-wide association study of bipolar disorder in European American and African American individuals.

To identify bipolar disorder (BD) genetic susceptibility factors, we conducted two genome-wide association (GWA) studies: one involving a sample of individuals of European ancestry (EA; n=1001 cases; n=1033 controls), and one involving a sample of individuals of African ancestry (AA; n=345 cases; n=670 controls). For the EA sample, single-nucleotide polymorphisms (SNPs) with the strongest statistical evidence for association included rs5907577 in an intergenic region at Xq27.1 (P=1.6 x 10(-6)) and rs10193871 in NAP5 at 2q21.2 (P=9.8 x 10(-6)). For the AA sample, SNPs with the strongest statistical evidence for association included rs2111504 in DPY19L3 at 19q13.11 (P=1.5 x 10(-6)) and rs2769605 in NTRK2 at 9q21.33 (P=4.5 x 10(-5)). We also investigated whether we could provide support for three regions previously associated with BD, and we showed that the ANK3 region replicates in our sample, along with some support for C15Orf53; other evidence implicates BD candidate genes such as SLITRK2. We also tested the hypothesis that BD susceptibility variants exhibit genetic background-dependent effects. SNPs with the strongest statistical evidence for genetic background effects included rs11208285 in ROR1 at 1p31.3 (P=1.4 x 10(-6)), rs4657247 in RGS5 at 1q23.3 (P=4.1 x 10(-6)), and rs7078071 in BTBD16 at 10q26.13 (P=4.5 x 10(-6)). This study is the first to conduct GWA of BD in individuals of AA and suggests that genetic variations that contribute to BD may vary as a function of ancestry.

Efficient association study design via power-optimized tag SNP selection.

Discovering statistical correlation between causal genetic variation and clinical traits through association studies is an important method for identifying the genetic basis of human diseases. Since fully resequencing a cohort is prohibitively costly, genetic association studies take advantage of local correlation structure (or linkage disequilibrium) between single nucleotide polymorphisms (SNPs) by selecting a subset of SNPs to be genotyped (tag SNPs). While many current association studies are performed using commercially available high-throughput genotyping products that define a set of tag SNPs, choosing tag SNPs remains an important problem for both custom follow-up studies as well as designing the high-throughput genotyping products themselves. The most widely used tag SNP selection method optimizes the correlation between SNPs (r(2)). However, tag SNPs chosen based on an r(2) criterion do not necessarily maximize the statistical power of an association study. We propose a study design framework that chooses SNPs to maximize power and efficiently measures the power through empirical simulation. Empirical results based on the HapMap data show that our method gains considerable power over a widely used r(2)-based method, or equivalently reduces the number of tag SNPs required to attain the desired power of a study. Our power-optimized 100k whole genome tag set provides equivalent power to the Affymetrix 500k chip for the CEU population. For the design of custom follow-up studies, our method provides up to twice the power increase using the same number of tag SNPs as r(2)-based methods. Our method is publicly available via web server at http://design.cs.ucla.edu.

Identification of additional variants within the human dopamine transporter gene provides further evidence for an association with bipolar disorder in two independent samples.

The dopamine transporter (DAT) is the site of action of stimulants, and variations in the human DAT gene (DAT1) have been associated with susceptibility to several psychiatric disorders including attention deficit hyperactivity disorder (ADHD) and bipolar disorder. We have previously reported the association of bipolar disorder to novel SNPs in the 3' end of DAT1. We now report the identification of 20 additional SNPs in DAT1 for a total of 63 variants. We also report evidence for association to bipolar disorder in a second independent sample of families. Eight newly identified SNPs and 14 previously identified SNPs were analyzed in two independent samples of 50 and 70 families each using the transmission disequilibrium test. Two of the eight new SNPs, one in intron 8 and one in intron 13, were found to be moderately associated with bipolar disorder, each in one of the two independent samples. Analysis of haplotypes comprised of all 22 SNPs in sliding windows of five adjacent SNPs revealed an association to the region near introns 7 and 8 in both samples (empirical P-values 0.002 and 0.001, respectively, for the same window). The haplotype block structure observed in the gene in our previous study was confirmed in this sample with greater resolution allowing for discrimination of a third haplotype block in the middle of the gene. Together, these data are consistent with the presence of multiple variants in DAT1 that convey susceptibility to bipolar disorder.

Combining text mining and sequence analysis to discover protein functional regions.

Recently presented protein sequence classification models can identify relevant regions of the sequence. This observation has many potential applications to detecting functional regions of proteins. However, identifying such sequence regions automatically is difficult in practice, as relatively few types of information have enough annotated sequences to perform this analysis. Our approach addresses this data scarcity problem by combining text and sequence analysis. First, we train a text classifier over the explicit textual annotations available for some of the sequences in the dataset, and use the trained classifier to predict the class for the rest of the unlabeled sequences. We then train a joint sequence text classifier over the text contained in the functional annotations of the sequences, and the actual sequences in this larger, automatically extended dataset. Finally, we project the classifier onto the original sequences to determine the relevant regions of the sequences. We demonstrate the effectiveness of our approach by predicting protein sub-cellular localization and determining localization specific functional regions of these proteins.

Using mixtures of common ancestors for estimating the probabilities of discrete events in biological sequences.

Accurately estimating probabilities from observations is important for probabilistic-based approaches to problems in computational biology. In this paper we present a biologically-motivated method for estimating probability distributions over discrete alphabets from observations using a mixture model of common ancestors. The method is an extension of substitution matrix-based probability estimation methods. In contrast to previous such methods, our method has a simple Bayesian interpretation and has the advantage over Dirichlet mixtures that it is both effective and simple to compute for large alphabets. The method is applied to estimate amino acid probabilities based on observed counts in an alignment and is shown to perform comparably to previous methods. The method is also applied to estimate probability distributions over protein families and improves protein classification accuracy.

Protein family classification using sparse Markov transducers.

In this paper we present a method for classifying proteins into families using sparse Markov transducers (SMTs). Sparse Markov transducers, similar to probabilistic suffix trees, estimate a probability distribution conditioned on an input sequence. SMTs generalize probabilistic suffix trees by allowing for wild-cards in the conditioning sequences. Because substitutions of amino acids are common in protein families, incorporating wildcards into the model significantly improves classification performance. We present two models for building protein family classifiers using SMTs. We also present efficient data structures to improve the memory usage of the models. We evaluate SMTs by building protein family classifiers using the Pfam database and compare our results to previously published results.

Increasing accuracy in the diagnosis of acute appendicitis with modern diagnostic techniques.

One hundred consecutive patients admitted to the hospital with the prospective diagnosis of appendicitis were evaluated retrospectively to determine the effect of the use of modern diagnostic techniques upon the accuracy of preoperative diagnosis. Appendectomy was performed on 48 patients. In three of these patients, the diagnostic process was assisted by the use of radiologic or ultrasound techniques. Of the 52 patients who did not undergo appendectomy, 13 patients clearly avoided surgery or were steered towards an other appropriate operative procedure due to findings on ultrasound or barium enema. Ultrasound was particularly useful in differentiating the etiology of right lower quadrant pain in the childbearing aged female. This study demonstrates that the traditionally expected 15% normal appendix rate can be narrowed (6.25% in this group of patients) without increasing morbidity, with the use of sophisticated diagnostic techniques in the evaluation of selected patients who present diagnostic dilemmas.