Simultaneous genome-wide association studies of anti-cyclic citrullinated peptide in rheumatoid arthritis using penalized orthogonal-components regression

Genetic architecture of root and shoot ionomes in rice (Oryza sativa L.)

KEY MESSAGE

Association analysis for ionomic concentrations of 20 elements identified independent genetic factors underlying the root and shoot ionomes of rice, providing a platform for selecting and dissecting causal genetic variants. Understanding the genetic basis of mineral nutrient acquisition is key to fully describing how terrestrial organisms interact with the non-living environment. Rice (Oryza sativa L.) serves both as a model organism for genetic studies and as an important component of the global food system. Studies in rice ionomics have primarily focused on above ground tissues evaluated from field-grown plants. Here, we describe a comprehensive study of the genetic basis of the rice ionome in both roots and shoots of 6-week-old rice plants for 20 elements using a controlled hydroponics growth system. Building on the wealth of publicly available rice genomic resources, including a panel of 373 diverse rice lines, 4.8 M genome-wide single-nucleotide polymorphisms, single- and multi-marker analysis pipelines, an extensive tome of 321 candidate genes and legacy QTLs from across 15 years of rice genetics literature, we used genome-wide association analysis and biparental QTL analysis to identify 114 genomic regions associated with ionomic variation. The genetic basis for root and shoot ionomes was highly distinct; 78 loci were associated with roots and 36 loci with shoots, with no overlapping genomic regions for the same element across tissues. We further describe the distribution of phenotypic variation across haplotypes and identify candidate genes within highly significant regions associated with sulfur, manganese, cadmium, and molybdenum. Our analysis provides critical insight into the genetic basis of natural phenotypic variation for both root and shoot ionomes in rice and provides a comprehensive resource for dissecting and testing causal genetic variants.

Anti-Cyclic Citrullinated Peptide Antibody as a Predictor of Rheumathoid Arthritis Complications

Introduction

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease, with more frequent occurrence in the female gender, it primarily affects the lining of the synovial joints, and is associated with lower quality of life, inability to work, progressive disability, and all of these patients are more likely to develop other comorbidities.

Aim

To display the role of anti-cyclic citrullinated peptide antibody (anti-CCP) in evaluating RA complications during a one-year follow-up, and compare its values with values of rheumatoid factor (RF).

Methods

The study included 40 patients with RA, out of which 6 were excluded during the 1-year follow-up. All patients were treated with anti-rheumatics, methothrexate 15-25mg, occasionally corticosteroids at the same doses.

Results

Anti-CCP values were also significantly higher during the second examination and were 5.0 ± 1.9 (range 0.5-7.6) compared to the first examination when they were 4.2 ± 1.3 (range 0.4-6.2) indicating a higher sensitivity of Anti-CCP in detecting of disease progression (t = -2.064; p = 0.043). Anti-CCP values were statistically significant in patients with complications compared to those without during the first examination and at follow-up after one year (t = 5,382; p = 0.0001).

Conclusion

The positivity of anti-CCP antibodies is a useful marker in terms of predicting the course and prognosis of the RA. A higher titer of anti-CCP antibodies represents a poorer prognosis for the disease. Determination of the presence of anti-CCP antibodies should be performed as a routine examination in all patients with suspected rheumatoid arthritis.

Relation Between Anti-CCP Antibodies and Sharp Score in Rheumatoid Arthritis

Introduction

Rheumatoid arthritis (RA) is a chronic, inflammatory, systemic rheumatic disease, very complex, with many different forms, progressive course, with pronounced changes in the joints, still unknown etiology and poorly understood pathology. Assessing of structural change can be done with proposed scores which observe changes on wrist and wrist joints, as a Sharp score.

Aim

To examine the correlation between Anti-Citrullinated Protein Antibodies (Anti-CCP) values and Sharp score, and to determine the importance of Sharp score in the progression of RA.

Methods

The study had prospective character and followed patients in the period from January 1, 2017 to December 31, 2017. The study included 40 patients with RA. At the beginning of the follow-up of patients, X-ray of hands and feet were performed.

Results

Out of total of 40 patients, 34 or 85% had a follow-up examination after one year. Of these, 14 patients (41.2%) were reported to have complications. The subjects were divided into two groups according to Anti-CCP values. First group included patients with Anti-CCP values <4 and second those who had Anti-CCP> 4. Statistical analysis of the number of patients with complications at first and repeated examination indicated that there were no significant differences and that the sample was consistent between the first and repeated results (p> 0.05). Patients with higher Anti-CCP values also had a higher Sharp score with statistically significant differences during repeated examination (p <0.05). Correlation analysis shows that there was statistically significant (p <0.05) positive correlation with Anti-CCP values, and that an increase in values leads to an increase in the Sharp score (first measurement rho = 0.193, p> 0.05; repeated measurement rho = 0.645, p <0.0001). No statistically significant differences in Sharp score values at the first examination were compared with the repeated examination, but there was a statistically significant difference after one year in patients with complications (X2 = 13,388; p = 0.001), indicating that the Sharp score reflects disease progression.

Conclusion

Anti-CCP values are also directly correlated with the Sharp score, which should be routine in both initial and repeated examination of a patient with RA. Sharp's score represents a marker of progression as well as of therapeutic modality of RA.

Advanced Statistical Methods for NMR-Based Metabolomics

Despite the increasing popularity and applicability of metabolomics for putative biomarker identification, analysis of the data is challenged by low statistical power resulting from the small sample sizes and large numbers of metabolites and other omics information, as well as confounding demographic and clinical variables. To enhance the statistical power and improve reproducibility of the identified metabolite-based biomarkers, we advocate the use of advanced statistical methods that can simultaneously evaluate the relationship between a group of metabolites and various types of variables including other omics profiles, demographic and clinical data, as well as the complex interactions between them. Accordingly, in this chapter, we describe the method of seemingly unrelated regression that can simultaneously analyze multiple metabolites while controlling the confounding effects of demographic and clinical variables (such as gender, age, BMI, smoking status). We also introduce penalized orthogonal components regression as a screening approach that can handle millions of omics predictors in the model.

A two-phase procedure for non-normal quantitative trait genetic association study

BACKGROUND

The nonparametric trend test (NPT) is well suitable for identifying the genetic variants associated with quantitative traits when the trait values do not satisfy the normal distribution assumption. If the genetic model, defined according to the mode of inheritance, is known, the NPT derived under the given genetic model is optimal. However, in practice, the genetic model is often unknown beforehand. The NPT derived from an uncorrected model might result in loss of power. When the underlying genetic model is unknown, a robust test is preferred to maintain satisfactory power.

RESULTS

We propose a two-phase procedure to handle the uncertainty of the genetic model for non-normal quantitative trait genetic association study. First, a model selection procedure is employed to help choose the genetic model. Then the optimal test derived under the selected model is constructed to test for possible association. To control the type I error rate, we derive the joint distribution of the test statistics developed in the two phases and obtain the proper size.

CONCLUSIONS

The proposed method is more robust than existing methods through the simulation results and application to gene DNAH9 from the Genetic Analysis Workshop 16 for associated with Anti-cyclic citrullinated peptide antibody further demonstrate its performance.

Discovery and Replication of Gene Influences on Brain Structure Using LASSO Regression

We implemented least absolute shrinkage and selection operator (LASSO) regression to evaluate gene effects in genome-wide association studies (GWAS) of brain images, using an MRI-derived temporal lobe volume measure from 729 subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Sparse groups of SNPs in individual genes were selected by LASSO, which identifies efficient sets of variants influencing the data. These SNPs were considered jointly when assessing their association with neuroimaging measures. We discovered 22 genes that passed genome-wide significance for influencing temporal lobe volume. This was a substantially greater number of significant genes compared to those found with standard, univariate GWAS. These top genes are all expressed in the brain and include genes previously related to brain function or neuropsychiatric disorders such as MACROD2, SORCS2, GRIN2B, MAGI2, NPAS3, CLSTN2, GABRG3, NRXN3, PRKAG2, GAS7, RBFOX1, ADARB2, CHD4, and CDH13. The top genes we identified with this method also displayed significant and widespread post hoc effects on voxelwise, tensor-based morphometry (TBM) maps of the temporal lobes. The most significantly associated gene was an autism susceptibility gene known as MACROD2. We were able to successfully replicate the effect of the MACROD2 gene in an independent cohort of 564 young, Australian healthy adult twins and siblings scanned with MRI (mean age: 23.8 ± 2.2 SD years). Our approach powerfully complements univariate techniques in detecting influences of genes on the living brain.

Multilocus genetic analysis of brain images

The quest to identify genes that influence disease is now being extended to find genes that affect biological markers of disease, or endophenotypes. Brain images, in particular, provide exquisitely detailed measures of anatomy, function, and connectivity in the living brain, and have identified characteristic features for many neurological and psychiatric disorders. The emerging field of imaging genomics is discovering important genetic variants associated with brain structure and function, which in turn influence disease risk and fundamental cognitive processes. Statistical approaches for testing genetic associations are not straightforward to apply to brain images because the data in brain images is spatially complex and generally high dimensional. Neuroimaging phenotypes typically include 3D maps across many points in the brain, fiber tracts, shape-based analyses, and connectivity matrices, or networks. These complex data types require new methods for data reduction and joint consideration of the image and the genome. Image-wide, genome-wide searches are now feasible, but they can be greatly empowered by sparse regression or hierarchical clustering methods that isolate promising features, boosting statistical power. Here we review the evolution of statistical approaches to assess genetic influences on the brain. We outline the current state of multivariate statistics in imaging genomics, and future directions, including meta-analysis. We emphasize the power of novel multivariate approaches to discover reliable genetic influences with small effect sizes.

Genome-wide association analyses of quantitative traits: the GAW16 experience

The group that formed on the theme of genome-wide association analyses of quantitative traits (Group 2) in the Genetic Analysis Workshop 16 comprised eight sets of investigators. Three data sets were available: one on autoantibodies related to rheumatoid arthritis provided by the North American Rheumatoid Arthritis Consortium; the second on anthropometric, lipid, and biochemical measures provided by the Framingham Heart Study (FHS); and the third a simulated data set modeled after FHS. The different investigators in the group addressed a large set of statistical challenges and applied a wide spectrum of association methods in analyzing quantitative traits at the genome-wide level. While some previously reported genes were validated, some novel chromosomal regions provided significant evidence of association in multiple contributions in the group. In this report, we discuss the different strategies explored by the different investigators with the common goal of improving the power to detect association.

A quantile-based method for association mapping of quantitative phenotypes: an application to rheumatoid arthritis phenotypes

Genetic association of population-based quantitative trait data has traditionally been analyzed using analysis of variance (ANOVA). However, violations of certain statistical assumptions may lead to false-positive association results. In this study, we have explored model-free alternatives to ANOVA using correlations between allele frequencies in the different quantile intervals of the quantitative trait and the quantile values. We performed genome-wide association scans on anti-cyclic citrullinated peptide and rheumatoid factor-immunoglobulin M, two quantitative traits correlated with rheumatoid arthritis, using the data provided in Genetic Analysis Workshop 16. Both the quantitative traits exhibited significant evidence of association on Chromosome 6, although not in the human leukocyte antigen region which is known to harbor a major gene predisposing to rheumatoid arthritis. We found that while a majority of the significant findings using the asymptotic thresholds of ANOVA was not validated using permutations, a relatively higher proportion of the significant findings using the asymptotic cut-offs of the correlation statistic were validated using permutations.

Case-control genome-wide association study of rheumatoid arthritis from Genetic Analysis Workshop 16 using penalized orthogonal-components regression-linear discriminant analysis

Currently, genome-wide association studies (GWAS) are conducted by collecting a massive number of SNPs (i.e., large p) for a relatively small number of individuals (i.e., small n) and associations are made between clinical phenotypes and genetic variation one single-nucleotide polymorphism (SNP) at a time. Univariate association approaches like this ignore the linkage disequilibrium between SNPs in regions of low recombination. This results in a low reliability of candidate gene identification. Here we propose to improve the case-control GWAS approach by implementing linear discriminant analysis (LDA) through a penalized orthogonal-components regression (POCRE), a newly developed variable selection method for large p small n data. The proposed POCRE-LDA method was applied to the Genetic Analysis Workshop 16 case-control data for rheumatoid arthritis (RA). In addition to the two regions on chromosomes 6 and 9 previously associated with RA by GWAS, we identified SNPs on chromosomes 10 and 18 as potential candidates for further investigation.