Rank-based Inverse Normal Transformation Research Articles

Detecting a novel locus associated with pellicle peelability in Japanese chestnut populations preselected for a major peelability gene

• The distribution of phenotypic values of peelability in Japanese chestnut populations preselected for a major peelability gene was biased toward maximum values. • Use of rank-based inverse normal transformation and categorization of peelability phenotypes was applied for GWAS to further identify QTLs associated with peelability. • A single QTL for peelability was identified at CCR1.0F_56177061 on chromosome F. Peelability is one of the most important traits in Japanese chestnut ( Castanea crenata Sieb. et Zucc.) breeding. Previous studies revealed that the peelability of Japanese chestnut is controlled by a major recessive peeling gene (MRPG). This locus has been used effectively for marker-assisted selection in chestnut breeding programs, but there are also likely to be minor loci that would further improve the efficiency of breeding for peelability. We conducted a genome-wide association study (GWAS) based on a variational approximation version of BayesB using 27 cultivars and MRPG-preselected populations consisting of 520 F 1 seedlings from 22 families. Using double-digest restriction site-associated DNA sequencing (ddRAD-Seq), we obtained 9164 tag single-nucleotide polymorphisms. Because the phenotypic values of peelability were distributed very asymmetrically, with a peak at the high end due to the preselection for MRPG, we converted the original data set to both inverse normal transformed values and categorical scores, and we analyzed both sets of converted data by GWAS. In both analyses, a quantitative trait locus (QTL) for peelability at CCR1.0F_56177061 was identified with posterior probability values of 0.68 and 1.00, respectively. The average values of genotypes at CCR1.0F_56177061 for cultivars and seedlings in this study were 60.4% (peelability rate) for CC, 63.7% for CT, and 86.5% for TT. This analysis reveals a significant association between CCR1.0F_56177061 genotype and peelability, indicating that this newly identified QTL will be useful for improving peelability in chestnut breeding programs.

Circulating anti-Müllerian hormone levels and markers of subclinical cardiovascular disease in middle-aged and older men

ContextRecent research suggests that higher circulating anti-Müllerian hormone (AMH) levels are associated with less frequent occurrence of (subclinical) cardiovascular disease (CVD) in women, but evidence in men is limited. ObjectiveWe investigated whether circulating AMH levels are associated with measures of subclinical CVD in middle-aged and older men. DesignProspective cohort study with a median follow-up time of 8.7 years. Serum AMH was measured at baseline. We assessed both cross-sectional and longitudinal associations using linear regression models adjusted for confounders. SettingDutch middle-aged and older men from the community. Participants394 men (aged 40–80 years) with an available AMH measurement at baseline. Main outcome measuresAt baseline (2001−2002): carotid intima-media thickness (CIMT), pulse wave velocity (PWV), abdominal aortic diameter, and Framingham risk score (FRS) predictions. At follow-up (2010−2011): CIMT, mean carotid aortic plaque score, PWV, and FRS predictions. All outcomes were transformed using rank-based inverse normal transformation to meet the normality assumption. ResultsHigher AMH levels were associated with lower CIMT at baseline (β = −0.04; 95%CI = 0.07, −0.01), but not with the other measures of subclinical CVD at baseline. Longitudinal analyses suggested that higher baseline AMH levels were associated with lower mean plaque scores at follow-up (β = −0.03, 95%CI = −0.07, 0.00), but not with the other follow-up outcomes. ConclusionsOur results suggest that AMH is associated with current CIMT and future carotid aortic plaque burden in men, implying that circulating AMH levels are potentially associated with local atherosclerosis rather than with total aortic stiffness.

A rank-based normalization method with the fully adjusted full-stage procedure in genetic association studies.

In the area of genetic epidemiology, studies of the genotype-phenotype associations have made significant contributions to human complicated trait genetics. These studies depend on specialized statistical methods for uncover the association between traits and genetic variants, both common and rare variants. Often, in analyzing such studies, potentially confounding factors, such as social and environmental conditions, are required to be involved. Multiple linear regression is the most widely used type of regression analysis when the outcome of interest is quantitative traits. Many statistical tests for identifying genotype-phenotype associations using linear regression rely on the assumption that the traits (or the residuals) of the regression follow a normal distribution. In genomic research, the rank-based inverse normal transformation (INT) is one of the most popular approaches to reach normally distributed traits (or normally distributed residuals). Many researchers believe that applying the INT to the non-normality of the traits (or the non-normality of the residuals) is required for valid inference, because the phenotypic (or residual) outliers and non-normality have the significant influence on both the type I error rate control and statistical power, especially under the situation in rare-variant association testing procedures. Here we propose a test for exploring the association of the rare variant with the quantitative trait by using a fully adjusted full-stage INT. Using simulations we show that the fully adjusted full-stage INT is more appropriate than the existing INT methods, such as the fully adjusted two-stage INT and the INT-based omnibus test, in testing genotype-phenotype associations with rare variants, especially when genotypes are uncorrelated with covariates. The fully adjusted full-stage INT retains the advantages of the fully adjusted two-stage INT and ameliorates the problems of the fully adjusted two-stage INT for analysis of rare variants under non-normality of the trait. We also present theoretical results on these desirable properties. In addition, the two available methods with non-normal traits, the quantile/median regression method and the Yeo-Johnson power transformation, are also included in simulations for comparison with these desirable properties.

Abstract P189: Metabolomic Signatures Of Serum Creatinine Among Hypertensive African Americans.

Background: The prevalence of chronic kidney disease (CKD) in African Americans (AAs) is high. Although ancestry-specific biomarkers have been identified, few translate to the clinic. Metabolomic studies may identify novel biomarkers useful for prevention, diagnosis and treatment. Methods: Plasma from 300 AA Hypertension Genetic Epidemiology Network (HyperGEN) study participants age ≥40 years with serum creatinine (SCr) <2 mg/dL were assayed for an untargeted metabolomic study of SCr using an ultra-high performance liquid chromatography mass spectrometry platform (Metabolon ® ). SCr was measured using a thin-film adaptation of the amidohydrolase enzymatic method on fasting blood samples. Each metabolite was tested for normality and a rank-based inverse normal transformation was performed as appropriate. Multivariable linear regression was used to test for association between the metabolite (outcome) and SCr (exposure of interest) adjusting for age, gender, waist circumference and center. A Bonferroni correction was used to adjust for multiple testing using. The top 25 most significant metabolites were evaluated for replication in 367 participants from the African American-Diabetes Heart Study (AA-DHS). An exploratory pathway analysis was conducted among significant (p< 6.0*10 -5 6.0e -5 metabolites in HyperGEN. Results: Among 300 participants from HyperGEN the mean age was 54.5 years (SD=9.5) and 58.7% were female. Mean SCr was 0.93 (SD=0.25). Eighty percent were treated for hypertension and 28% had diabetes. A total of 809 metabolites were annotated in HyperGEN samples. Of those, 391 were associated with SCr at p<0.05 and 256 metabolites met the Bonferroni significance criteria (p< 6.0*10 -5 ). The top SCr associated metabolites were tartonate(hydroxymalonate)(β[SE]= 2.99[0.07], p=3.98e -117 ), 1-stearoyl-GPC(18:0)(β[SE]= 2.49[0.14], p=2.28e -44 ), 2S-3R-dihydroxybutyrate)(β[SE]= 2.36[0.14],p=1.53e -40 ) and isovalerate(C5)( β[SE]= 2.30[0.15], p = 2.78e -36 ). The association of these metabolites with SCr replicated in AA-DHS [(tartonate(hydroxymalonate)(β[SE]= 0.66[0.19], p=0.001), 1-stearoyl-GPC(18:0)(β[SE]= 1.30[0.20], p=1.61 -10 ), 2S-3R-dihydroxybutyrate)(β[SE]= 1.20[0.18],p=4.69e -11 ) and isovalerate(C5)( β[SE]= 1.24[0.20], p = 1.91e -09 )]. An exploratory pathway analysis highlighted xenobiotics and the lysophospholipid pathway. Significance: Tartronate is a xenobiotic that is related to BMI, insulin resistance and adiponectin. 1-stearoyl-GPC (18:0) is a lysophospholipid metabolite found to be associated with oxidative stress. Conclusion: The current study discovered and replicated metabolites associated with SCr in a population of AAs with a high prevalence of hypertension. If validated, these could provide new insights in kidney disease pathways or aid in the treatment and prevention of CKD in the AA population.

Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies.

Quantitative traits analyzed in Genome-Wide Association Studies (GWAS) are often nonnormally distributed. For such traits, association tests based on standard linear regression are subject to reduced power and inflated type I error in finite samples. Applying the rank-based inverse normal transformation (INT) to nonnormally distributed traits has become common practice in GWAS. However, the different variations on INT-based association testing have not been formally defined, and guidance is lacking on when to use which approach. In this paper, we formally define and systematically compare the direct (D-INT) and indirect (I-INT) INT-based association tests. We discuss their assumptions, underlying generative models, and connections. We demonstrate that the relative powers of D-INT and I-INT depend on the underlying data generating process. Since neither approach is uniformly most powerful, we combine them into an adaptive omnibus test (O-INT). O-INT is robust to model misspecification, protects the type I error, and is well powered against a wide range of nonnormally distributed traits. Extensive simulations were conducted to examine the finite sample operating characteristics of these tests. Our results demonstrate that, for nonnormally distributed traits, INT-based tests outperform the standard untransformed association test, both in terms of power and type I error rate control. We apply the proposed methods to GWAS of spirometry traits in the UK Biobank. O-INT has been implemented in the R package RNOmni, which is available onCRAN.

Reconstruction of Cell-type-Specific Interactomes at Single-Cell Resolution.

The human interactome is instrumental in the systems-level study of the cell and the contextualization of disease-associated gene perturbations. However, reference organismal interactomes do not capture the cell-type-specific context in which proteins and modules preferentially act. Here, we introduce SCINET, a computational framework that reconstructs an ensemble of cell-type-specific interactomes by integrating a global, context-independent reference interactome with a single-cell gene-expression profile. SCINET addresses technical challenges of single-cell data by robustly imputing, transforming, and normalizing the initially noisy and sparse expression of data. Inferred cell-level gene interaction probabilities and group-level interaction strengths define cell-type-specific interactomes. We use SCINET to reconstruct and analyze interactomes of the major human brain and immune cell types, revealing specificity and modularity of perturbations associated with neurodegenerative, neuropsychiatric, and autoimmune disorders. We report cell-type interactomes for brain and immune cell types, together with the SCINET package.

Quantile regression for challenging cases of eQTL mapping.

Mapping of expression quantitative trait loci (eQTLs) facilitates interpretation of the regulatory path from genetic variants to their associated disease or traits. High-throughput sequencing of RNA (RNA-seq) has expedited the exploration of these regulatory variants. However, eQTL mapping is usually confronted with the analysis challenges caused by overdispersion and excessive dropouts in RNA-seq. The heavy-tailed distribution of gene expression violates the assumption of Gaussian distributed errors in linear regression for eQTL detection, which results in increased Type I or Type II errors. Applying rank-based inverse normal transformation (INT) can make the expression values more normally distributed. However, INT causes information loss and leads to uninterpretable effect size estimation. After comprehensive examination of the impact from overdispersion and excessive dropouts, we propose to apply a robust model, quantile regression, to map eQTLs for genes with high degree of overdispersion or large number of dropouts. Simulation studies show that quantile regression has the desired robustness to outliers and dropouts, and it significantly improves eQTL mapping. From a real data analysis, the most significant eQTL discoveries differ between quantile regression and the conventional linear model. Such discrepancy becomes more prominent when the dropout effect or the overdispersion effect is large. All the results suggest that quantile regression provides more reliable and accurate eQTL mapping than conventional linear models. It deserves more attention for the large-scale eQTL mapping.

Are your covariates under control? How normalization can re-introduce covariate effects

Many statistical tests rely on the assumption that the residuals of a model are normally distributed. Rank-based inverse normal transformation (INT) of the dependent variable is one of the most popular approaches to satisfy the normality assumption. When covariates are included in the analysis, a common approach is to first adjust for the covariates and then normalize the residuals. This study investigated the effect of regressing covariates against the dependent variable and then applying rank-based INT to the residuals. The correlation between the dependent variable and covariates at each stage of processing was assessed. An alternative approach was tested in which rank-based INT was applied to the dependent variable before regressing covariates. Analyses based on both simulated and real data examples demonstrated that applying rank-based INT to the dependent variable residuals after regressing out covariates re-introduces a linear correlation between the dependent variable and covariates, increasing type-I errors and reducing power. On the other hand, when rank-based INT was applied prior to controlling for covariate effects, residuals were normally distributed and linearly uncorrelated with covariates. This latter approach is therefore recommended in situations were normality of the dependent variable is required.

A marginal rank-based inverse normal transformation approach to comparing multiple clinical trial endpoints.

The increase in incidence of obesity and chronic diseases and their health care costs have raised the importance of quality diet on the health policy agendas. The healthy eating index is an important measure for diet quality which consists of 12 components derived from ratios of dependent variables with distributions hard to specify, measurement errors and excessive zero observations difficult to model parametrically. Hypothesis testing involving data of such nature poses challenges because the widely used multiple comparison procedures such as Hotelling's T(2) test and Bonferroni correction may suffer from substantial loss of efficiency. We propose a marginal rank-based inverse normal transformation approach to normalizing the marginal distribution of the data before employing a multivariate test procedure. Extensive simulation was conducted to demonstrate the ability of the proposed approach to adequately control the type I error rate as well as increase the power of the test, with data particularly from non-symmetric or heavy-tailed distributions. The methods are exemplified with data from a dietary intervention study for type I diabetic children. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

The effect of phenotypic outliers and non-normality on rare-variant association testing.

Rare-variant association studies (RVAS) have made important contributions to human complex trait genetics. These studies rely on specialized statistical methods for analyzing rare-variant associations, both individually and in aggregate. We investigated the impact that phenotypic outliers and non-normality have on the performance of rare-variant association testing procedures. Ignoring outliers or non-normality can significantly inflate Type I error rates. We found that rank-based inverse normal transformation (INT) and trait winsorisation were both effective at maintaining Type I error control without sacrificing power in the presence of outliers. INT was the optimal method for non-normally distributed traits. For RVAS of quantitative traits with outliers or non-normality, we recommend using INT to transform phenotypic values before association testing.

Testing the significance of a correlation with nonnormal data: Comparison of Pearson, Spearman, transformation, and resampling approaches.

It is well known that when data are nonnormally distributed, a test of the significance of Pearson's r may inflate Type I error rates and reduce power. Statistics textbooks and the simulation literature provide several alternatives to Pearson's correlation. However, the relative performance of these alternatives has been unclear. Two simulation studies were conducted to compare 12 methods, including Pearson, Spearman's rank-order, transformation, and resampling approaches. With most sample sizes (n ≥ 20), Type I and Type II error rates were minimized by transforming the data to a normal shape prior to assessing the Pearson correlation. Among transformation approaches, a general purpose rank-based inverse normal transformation (i.e., transformation to rankit scores) was most beneficial. However, when samples were both small (n ≤ 10) and extremely nonnormal, the permutation test often outperformed other alternatives, including various bootstrap tests.