Association Tests of Multiple Phenotypes: ATeMP

Joint analysis of multiple phenotypes has gained growing attention in genome-wide association studies (GWASs), especially for the analysis of multiple intermediate phenotypes which measure the same underlying complex human disorder. One of the multivariate methods, MultiPhen (O’ Reilly et al. 2012), employs the proportional odds model to regress a genotype on multiple phenotypes, hence ignoring the phenotypic distributions. Despite the flexibilities of MultiPhen, the properties and performance of MultiPhen are not well understood, especially when the phenotypic distributions are non-normal. In fact, it is well known in the statistical literature that the estimation is attenuated when the explanatory variables contain measurement errors. In this study, we first established an equivalence relationship between MultiPhen and the generalized Kendall tau association test, shedding light on why MultiPhen can perform well for joint association analysis of multiple phenotypes. Through the equivalence, we show that MultiPhen may lose power when the phenotypes are non-normal. To maintain the power, we propose two solutions (ATeMP-rn and ATeMP-or) to improve MultiPhen, and demonstrate their effectiveness through extensive simulation studies and a real case study from the Guangzhou Twin Eye Study.     

Butyrylcholinesterase Genetic Variants: Association with Cocaine Dependence and Related Phenotypes

Objective

The search for genetic vulnerability factors in cocaine dependence has focused on the role that neuroplasticity plays in addiction. However, like many other drugs, the ability of an individual to metabolize cocaine can also influence susceptibility to dependence. Butyrylcholinesterase (BChE) metabolizes cocaine, and genetic variants of the BChE gene (BCHE) alter its catalytic activity. Therefore, we hypothesize that cocaine users with polymorphisms in BCHE can show diverse addictive behaviors due to differences in effective plasma concentrations of cocaine. Those polymorphisms might also influence users to prefer one of the two main preparations (crack or powder cocaine), despite having equal access to both. The present work investigates polymorphisms in BCHE and if those genetic variants constitute risk factors for cocaine dependence and for crack cocaine use.

Methods

A total of 1,436 individuals (698 cocaine-dependent patients and 738 controls) were genotyped for three single nucleotide polymorphisms (SNPs) in BCHE: rs1803274, rs4263329, and rs4680662.

Results

For rs4263329, a nominal difference was found between cases and controls. For rs1803274 (the functional SNP), a statistically significant difference was found between patients who used crack cocaine exclusively and those who used only powder cocaine (P = 0.027; OR = 4.36; 95% CI = 1.18–16.04). Allele frequencies and genotypes related to other markers did not differ between cases and controls or between the two cocaine subgroups.

Conclusions

Our findings suggest that the AA genotype of rs1803274 is a risk factor for crack cocaine use, which is more addictive than powder cocaine use. Further studies are needed in order to confirm this preliminary result and clarify the role of BCHE and its variants in cocaine dependence.     

Genome-Wide Pathway Association Studies of Multiple Correlated Quantitative Phenotypes Using Principle Component Analyses

Genome-wide pathway association studies provide novel insight into the biological mechanism underlying complex diseases. Current pathway association studies primarily focus on single important disease phenotype, which is sometimes insufficient to characterize the clinical manifestations of complex diseases. We present a multi-phenotypes pathway association study(MPPAS) approach using principle component analysis(PCA). In our approach, PCA is first applied to multiple correlated quantitative phenotypes for extracting a set of orthogonal phenotypic components. The extracted phenotypic components are then used for pathway association analysis instead of original quantitative phenotypes. Four statistics were proposed for PCA-based MPPAS in this study. Simulations using the real data from the HapMap project were conducted to evaluate the power and type I error rates of PCA-based MPPAS under various scenarios considering sample sizes, additive and interactive genetic effects. A real genome-wide association study data set of bone mineral density (BMD) at hip and spine were also analyzed by PCA-based MPPAS. Simulation studies illustrated the performance of PCA-based MPPAS for identifying the causal pathways underlying complex diseases. Genome-wide MPPAS of BMD detected associations between BMD and KENNY_CTNNB1_TARGETS_UP as well as LONGEVITYPATHWAY pathways in this study. We aim to provide a applicable MPPAS approach, which may help to gain deep understanding the potential biological mechanism of association results for complex diseases.     

Sequential Testing with Recurrent Events over Multiple Treatment Periods

Recurrent event outcomes are adopted increasingly often as a basis for evaluating experimental interventions. In clinical trials involving recurrent events, patients are frequently observed for a baseline period while under standard care, and then randomised to receive either an experimental treatment or continue on standard care. When events are generated according to a mixed Poisson model, having baseline data permits a conditional analysis which can eliminate the subject-specific random effect and yield a more efficient analysis regarding treatment effect. When studies are expected to recruit a large number of patients over an extended period of accrual, or if the period of follow-up is long, sequential testing is desirable to ensure the study is stopped as soon as sufficient data have been collected to establish treatment benefits. We describe methods which facilitate sequential analysis of data arising from trials with recurrent event responses observed over two treatment periods where one is a baseline period of observation. Formulae to help schedule analyses at approximately equal increments of information are given. Simulation studies show that the sequential testing procedures have rejection rates compatible with the nominal error rates under the null and alternative hypotheses. Data from a trial of patients with herpes simplex virus infection are analysed to illustrate the utility of these methods.     

Longitudinal Analysis Is More Powerful than Cross-Sectional Analysis in Detecting Genetic Association with Neuroimaging Phenotypes

Most existing genome-wide association analyses are cross-sectional, utilizing only phenotypic data at a single time point, e.g. baseline. On the other hand, longitudinal studies, such as Alzheimer''s Disease Neuroimaging Initiative (ADNI), collect phenotypic information at multiple time points. In this article, as a case study, we conducted both longitudinal and cross-sectional analyses of the ADNI data with several brain imaging (not clinical diagnosis) phenotypes, demonstrating the power gains of longitudinal analysis over cross-sectional analysis. Specifically, we scanned genome-wide single nucleotide polymorphisms (SNPs) with 56 brain-wide imaging phenotypes processed by FreeSurfer on 638 subjects. At the genome-wide significance level () or a less stringent level (e.g. ), longitudinal analysis of the phenotypic data from the baseline to month 48 identified more SNP-phenotype associations than cross-sectional analysis of only the baseline data. In particular, at the genome-wide significance level, both SNP rs429358 in gene APOE and SNP rs2075650 in gene TOMM40 were confirmed to be associated with various imaging phenotypes in multiple regions of interests (ROIs) by both analyses, though longitudinal analysis detected more regional phenotypes associated with the two SNPs and indicated another significant SNP rs439401 in gene APOE. In light of the power advantage of longitudinal analysis, we advocate its use in current and future longitudinal neuroimaging studies.     

利用分子标记和形态学性状检测的陆地棉栽培品种遗传多样性

利用RAPD,ISSR和SSR3种分子标记方法和2年田间实验对国内外36个陆地棉栽培品种的遗传多样性进行了研究,以3种分子标记在36个品种之间扩增的282条多态性位点所赋值的0,1数据,采用Nei和Li的方法,计算的品种成对相似系数从0．5745到0．9291,其品种平均数从0．6547到0．7524,又以2年品种表现的性状平均数经正态标准化后,采用欧氏距离计算了成对品种的遗传距离。分别以相似系数和传距离矩阵,采用类平均法进行聚类分析,其聚类结果把供试品种大致分为国外品种,新疆品种,早熟类型品种和我国的中熟棉品种等几个类群,类内进一步分组表明,分子标记确定的传关系基本上与品种系谱的种质系统一致,但并不能按系谱或种植生态区域简单地归属,尽管分子标记数据计算的相似系数矩阵和表现型计算的遗传距离矩阵存在极显著的相关关系（r=-0.335),但以遗传距离进行聚类分析的类内分组的组间特征不明显,分子标记是检测类内品种间遗传差异的有效方法,研究结果为棉花育种亲本选配提供了理论依据。     

Impact of Measurement Error on Testing Genetic Association with Quantitative Traits

Measurement error of a phenotypic trait reduces the power to detect genetic associations. We examined the impact of sample size, allele frequency and effect size in presence of measurement error for quantitative traits. The statistical power to detect genetic association with phenotype mean and variability was investigated analytically. The non-centrality parameter for a non-central F distribution was derived and verified using computer simulations. We obtained equivalent formulas for the cost of phenotype measurement error. Effects of differences in measurements were examined in a genome-wide association study (GWAS) of two grading scales for cataract and a replication study of genetic variants influencing blood pressure. The mean absolute difference between the analytic power and simulation power for comparison of phenotypic means and variances was less than 0.005, and the absolute difference did not exceed 0.02. To maintain the same power, a one standard deviation (SD) in measurement error of a standard normal distributed trait required a one-fold increase in sample size for comparison of means, and a three-fold increase in sample size for comparison of variances. GWAS results revealed almost no overlap in the significant SNPs (p<10⁻⁵) for the two cataract grading scales while replication results in genetic variants of blood pressure displayed no significant differences between averaged blood pressure measurements and single blood pressure measurements. We have developed a framework for researchers to quantify power in the presence of measurement error, which will be applicable to studies of phenotypes in which the measurement is highly variable.     

Improving Power for Testing Genetic Association in Case–Control Studies by Reducing the Alternative Space

Summary .  To detect association between a genetic marker and a disease in case–control studies, the Cochran–Armitage trend test is typically used. The trend test is locally optimal when the genetic model is correctly specified. However, in practice, the underlying genetic model, and hence the optimal trend test, are usually unknown. In this case, Pearson's chi-squared test, the maximum of three trend test statistics (optimal for the recessive, additive, and dominant models), and the test based on genetic model selection (GMS) are useful. In this article, we first modify the existing GMS method so that it can be used when the risk allele is unknown. Then we propose a new approach by excluding a genetic model that is not supported by the data. Using either the model selection or exclusion, the alternative space is reduced conditional on the observed data, and hence the power to detect a true association can be increased. Simulation results are reported and the proposed methods are applied to the genetic markers identified from the genome-wide association studies conducted by the Wellcome Trust Case–Control Consortium. The results demonstrate that the genetic model exclusion approach usually performs better than existing methods under its worst situation across scientifically plausible genetic models we considered.     

Association of Genetic Markers with CSF Oligoclonal Bands in Multiple Sclerosis Patients

Objective

to explore the association between genetic markers and Oligoclonal Bands (OCB) in the Cerebro Spinal Fluid (CSF) of Italian Multiple Sclerosis patients.

Methods

We genotyped 1115 Italian patients for HLA-DRB1*15 and HLA-A*02. In a subset of 925 patients we tested association with 52 non-HLA SNPs associated with MS susceptibility and we calculated a weighted Genetic Risk Score. Finally, we performed a Genome Wide Association Study (GWAS) with OCB status on a subset of 562 patients. The best associated SNPs of the Italian GWAS were replicated in silico in Scandinavian and Belgian populations, and meta-analyzed.

Results

HLA-DRB1*15 is associated with OCB+: p = 0.03, Odds Ratio (OR) = 1.6, 95% Confidence Limits (CL) = 1.1–2.4. None of the 52 non-HLA MS susceptibility loci was associated with OCB, except one SNP (rs2546890) near IL12B gene (OR: 1.45; 1.09–1.92). The weighted Genetic Risk Score mean was significantly (p = 0.0008) higher in OCB+ (7.668) than in OCB− (7.412) patients. After meta-analysis on the three datasets (Italian, Scandinavian and Belgian) for the best associated signals resulted from the Italian GWAS, the strongest signal was a SNP (rs9320598) on chromosome 6q (p = 9.4×10⁻⁷) outside the HLA region (65 Mb).

Discussion

genetic factors predispose to the development of OCB.     

Graded Maximal Exercise Testing to Assess Mouse Cardio-Metabolic Phenotypes

Functional assessments of cardiovascular fitness (CVF) are needed to establish animal models of dysfunction, test the effects of novel therapeutics, and establish the cardio-metabolic phenotype of mice. In humans, the graded maximal exercise test (GXT) is a standardized diagnostic for assessing CVF and mortality risk. These tests, which consist of concurrent staged increases in running speed and inclination, provide diagnostic cardio-metabolic parameters, such as, VO_2max, anaerobic threshold, and metabolic crossover. Unlike the human-GXT, published mouse treadmill tests have set, not staged, increases in inclination as speed progress until exhaustion (PXT). Additionally, they often lack multiple cardio-metabolic parameters. Here, we developed a mouse-GXT with the intent of improving mouse-exercise testing sensitivity and developing translatable parameters to assess CVF in healthy and dysfunctional mice. The mouse-GXT, like the human-GXT, incorporated staged increases in inclination, speed, and intensity; and, was designed by considering imitations of the PXT and differences between human and mouse physiology. The mouse-GXT and PXTs were both tested in healthy mice (C57BL/6J, FVBN/J) to determine their ability to identify cardio-metabolic parameters (anaerobic threshold, VO_2max, metabolic crossover) observed in human-GXTs. Next, theses assays were tested on established diet-induced (obese-C57BL/6J) and genetic (cardiac isoform Casq2^-/-) models of cardiovascular dysfunction. Results showed that both tests reported VO_2max and provided reproducible data about performance. Only the mouse-GXT reproducibly identified anaerobic threshold, metabolic crossover, and detected impaired CVF in dysfunctional models. Our findings demonstrated that the mouse-GXT is a sensitive, non-invasive, and cost-effective method for assessing CVF in mice. This new test can be used as a functional assessment to determine the cardio-metabolic phenotype of various animal models or the effects of novel therapeutics.     

A Novel WRN Frameshift Mutation Identified by Multiplex Genetic Testing in a Family with Multiple Cases of Cancer

Next-generation sequencing technology allows simultaneous analysis of multiple susceptibility genes for clinical cancer genetics. In this study, multiplex genetic testing was conducted in a Chinese family with multiple cases of cancer to determine the variations in cancer predisposition genes. The family comprises a mother and her five daughters, of whom the mother and the eldest daughter have cancer and the secondary daughter died of cancer. We conducted multiplex genetic testing of 90 cancer susceptibility genes using the peripheral blood DNA of the mother and all five daughters. WRN frameshift mutation is considered a potential pathogenic variation according to the guidelines of the American College of Medical Genetics. A novel WRN frameshift mutation (p.N1370Tfs*23) was identified in the three cancer patients and in the youngest unaffected daughter. Other rare non-synonymous germline mutations were also detected in DICER and ELAC2. Functional mutations in WRN cause Werner syndrome, a human autosomal recessive disease characterized by premature aging and associated with genetic instability and increased cancer risk. Our results suggest that the WRN frameshift mutation is important in the surveillance of other members of this family, especially the youngest daughter, but the pathogenicity of the novel WRN frameshift mutation needs to be investigated further. Given its extensive use in clinical genetic screening, multiplex genetic testing is a promising tool in clinical cancer surveillance.     

Genetic Mapping of Novel Loci Affecting Canine Blood Phenotypes

Since the publication of the dog genome and the construction of high-quality genome-wide SNP arrays, thousands of dogs have been genotyped for disease studies. For many of these dogs, additional clinical phenotypes are available, such as hematological and clinical chemistry results collected during routine veterinary care. Little is known about the genetic basis of variation in blood phenotypes, but this variation may play an important role in the etiology and progression of many diseases. From a cohort of dogs that had been previously genotyped on a semi-custom Illumina CanineHD array for various genome-wide association studies (GWAS) at Cornell University Hospital for Animals, we chose 353 clinically healthy, adult dogs for our analysis of clinical pathologic test results (14 hematological tests and 25 clinical chemistry tests). After correcting for age, body weight and sex, genetic associations were identified for amylase, segmented neutrophils, urea nitrogen, glucose, and mean corpuscular hemoglobin. Additionally, a strong genetic association (P = 8.1×10⁻¹³) was evident between a region of canine chromosome 13 (CFA13) and alanine aminotransferase (ALT), explaining 23% of the variation in ALT levels. This region of CFA13 encompasses the GPT gene that encodes the transferase. Dogs homozygous for the derived allele exhibit lower ALT activity, making increased ALT activity a less useful marker of hepatic injury in these individuals. Overall, these associations provide a roadmap for identifying causal variants that could improve interpretation of clinical blood tests and understanding of genetic risk factors associated with diseases such as canine diabetes and anemia, and demonstrate the utility of holistic phenotyping of dogs genotyped for disease mapping studies.     

Pseudomonas aeruginosa Displays Multiple Phenotypes during Development as a Biofilm

Complementary approaches were employed to characterize transitional episodes in Pseudomonas aeruginosa biofilm development using direct observation and whole-cell protein analysis. Microscopy and in situ reporter gene analysis were used to directly observe changes in biofilm physiology and to act as signposts to standardize protein collection for two-dimensional electrophoretic analysis and protein identification in chemostat and continuous-culture biofilm-grown populations. Using these approaches, we characterized five stages of biofilm development: (i) reversible attachment, (ii) irreversible attachment, (iii) maturation-1, (iv) maturation-2, and (v) dispersion. Biofilm cells were shown to change regulation of motility, alginate production, and quorum sensing during the process of development. The average difference in detectable protein regulation between each of the five stages of development was 35% (approximately 525 proteins). When planktonic cells were compared with maturation-2 stage biofilm cells, more than 800 proteins were shown to have a sixfold or greater change in expression level (over 50% of the proteome). This difference was higher than when planktonic P. aeruginosa were compared with planktonic cultures of Pseudomonas putida. Las quorum sensing was shown to play no role in early biofilm development but was important in later stages. Biofilm cells in the dispersion stage were more similar to planktonic bacteria than to maturation-2 stage bacteria. These results demonstrate that P. aeruginosa displays multiple phenotypes during biofilm development and that knowledge of stage-specific physiology may be important in detecting and controlling biofilm growth.     

Odor Perception Phenotypes: Multiple, Specific Hyperosmias to Musks

Olfactory detection thresholds for 11 structurally diverse muskodorants and one non-musk odorant were obtained from 32 subjects.Hierarchical cluster analysis produced four groups of subjects.One group (n = 12) was uniformly sensitive to all musks; another(n = 16) was uniformly insensitive. Two groups of subjects containedotherwise insensitive individuals who were exceptionally sensitiveto cyclopentadecanone and musk xylol (n = 2) and to delta9-hexadecenolactoneand tonalid (n = 2) respectively. We propose that the lattertwo groups are odor perception phenotypes (MSHM1 and MSHM2)that consist of multiple, specific hyperosmias to musk odorants.Chem. Senses 21: 411– 416, 1996. ¹Present address: Synesthetics, Inc., Montclair, NJ 07043, USA     

Low-Anxiety Rat Phenotypes Can Be Further Reduced through Genetic Intervention

Background

A previous study using an intercross between the inbred rat strains Lewis (LEW) and Spontaneously Hypertensive Rats (SHR) identified a locus on chromosome 4, named Anxrr16, influencing an experimental index of anxiety and showing a transgressive effect, with alleles from the LEW strain (more anxious) decreasing rather than increasing anxiety.

Objective

To confirm the location and isolate the effect of a rat genome region named Anxrr16 through a planned genomic recombination strategy, where the target locus in SHR rats was replaced with LEW genetic material.

Methods

A new congenic strain, named SHR.LEW-Anxrr16 (SLA16), was developed from a cross between LEW (donor) and SHR (receptor) rats and then evaluated in several anxiety-related tests. The activity and attention levels of the new strain were also evaluated, since hyperactivity was observed during its construction and because SHR is a model of attention deficit hyperactivity disorder.

Results

Significant effects of Anxrr16 were found for open field central locomotion, as well as for other indices of anxiety from the light/dark box, triple test and T-maze. In all cases, the low-anxiety levels of SHR rats were further reduced by the insertion of LEW alleles. Differences in locomotor activity were found only in unfamiliar (hence stressful) environments and no genetic effects were observed in indices of attention.

Conclusion

The SLA16 strain can help in the identification of the molecular pathways involved in experimental anxiety and it demonstrates how apparently extreme phenotypes sometimes hide major opposite-acting genes.