A Variable-Sized Sliding-Window Approach for Genetic Association Studies via Principal Component Analysis

Recently with the rapid improvements in high-throughout genotyping techniques, researchers are facing the very challenging task of analysing large-scale genetic associations, especially at the whole-genome level, without an optimal solution. In this study, we propose a new approach for genetic association analysis that is based on a variable-sized sliding-window framework and employs principal component analysis to find the optimum window size. With the help of the bisection algorithm in window-size searching, our method is more computationally efficient than available approaches. We evaluate the performance of the proposed method by comparing it with two other methods—a single-marker method and a variable-length Markov chain method. We demonstrate that, in most cases, the proposed method out-performs the other two methods. Furthermore, since the proposed method is based on genotype data, it does not require any computationally intensive phasing program to account for uncertain haplotype phase.     

Detection of Imprinting Effects for Quantitative Traits on X Chromosome Using Nuclear Families with Multiple Daughters

Genomic imprinting is an epigenetic phenomenon in which the expression of an allele copy depends on its parental origin. This mechanism has been found to play an important role in many complex diseases. Statistical tests for imprinting effects have been developed for more than 15 years, but they are only suitable for autosomes. It was not until recently that the parental‐asymmetry test on the X chromosome (XPAT) was proposed to test for imprinting effects. However, this test can only be used for qualitative traits. Therefore, in this article, we propose a class of PAT‐type tests to test for imprinting for quantitative traits on the X chromosome in the presence of association, namely, Q‐XPAT(c), Q‐1‐XPAT(c) and Q‐C‐XPAT(c), where c is a constant. These methods can accommodate complete and incomplete nuclear families with an arbitrary number of daughters. Extensive simulation studies demonstrate that the proposed tests control the size well under the null hypothesis of no imprinting effects and are powerful under various family structures. Moreover, by setting the inbreeding coefficient in females to be nonzero and using the assortative mating pattern in simulations, the proposed tests are shown to be valid under Hardy–Weinberg disequilibrium.     

Identification of Association of Common AGGF1 Variants with Susceptibility for Klippel-Trenaunay Syndrome Using the Structure Association Program

Klippel-Trenaunay syndrome (KTS) is a severe congenital disorder characterized by capillary malformations, venous malformations or varicose veins, and hypertrophy of the affected tissues. The angiogenic factor gene AGGF1 was previously identified as a candidate susceptibility gene for KTS, but further genetic studies are needed to firmly establish the genetic relationship between AGGF1 and KTS. We analyzed HapMap data and identified two tagSNPs, rs13155212 and rs7704267 that capture information for all common variants in AGGF1 . The two SNPs were genotyped in 173 Caucasian KTS patients and 477 Caucasian non-KTS controls, and both significantly associated with susceptibility for KTS ( P = 0.004 and 0.013, respectively). Permutation testing also showed a significant empirical P value for the association (empirical P = 0.006 and 0.015, respectively). To control for potential confounding due to population stratification, the population structure for both cases and controls was characterized by genotyping of 38 ancestry-informative markers (AIMs) and the STRUCTURE program. The association between the AGGF1 SNPs and KTS remained significant after multivariate analysis by incorporating the inferred cluster scores as a covariate or after removal of outlier individuals identified by STRUCTURE. These results suggest that common AGGF1 variants confer risk of KTS.     

Detecting Association of Rare Variants by Testing an Optimally Weighted Combination of Variants for Quantitative Traits in General Families

Although next‐generation sequencing technology allows sequencing the whole genome of large groups of individuals, the development of powerful statistical methods for rare variant association studies is still underway. Even though many statistical methods have been developed for mapping rare variants, most of these methods are for unrelated individuals only, whereas family data have been shown to improve power to detect rare variants. The majority of the existing methods for unrelated individuals is essentially testing the effect of a weighted combination of variants with different weighting schemes. The performance of these methods depends on the weights being used. Recently, researchers proposed a test for Testing the effect of an Optimally Weighted combination of variants (TOW) for unrelated individuals. In this article, we extend our previously developed TOW for unrelated individuals to family‐based data and propose a novel test for Testing the effect of an Optimally Weighted combination of variants for Family‐based designs (TOW‐F). The optimal weights are analytically derived. The results of extensive simulation studies show that TOW‐F is robust to population stratification in a wide range of population structures, is robust to the direction and magnitude of the effects of causal variants, and is relatively robust to the percentage of neutral variants.     

Using principal components analysis to examine resting state EEG in relation to task performance

Brain dynamics research has highlighted the significance of the ongoing EEG in ERP genesis and cognitive functioning. Few studies, however, have assessed the contributions of the intrinsic resting state EEG to these stimulus‐response processes and behavioral outcomes. Principal components analysis (PCA) has increasingly been used to obtain more objective, data‐driven estimates of the EEG and ERPs. PCA was used here to reassess resting state EEG and go/no‐go task ERP data from a previous study (Karamacoska et al., 2017) and the relationships between these measures. Twenty adults had EEG recorded with eyes closed (EC) and eyes open (EO), and as they completed an auditory go/no‐go task. Separate EEG and ERP PCAs were conducted on each resting condition and stimulus type. For each state, seven EEG components were identified within the delta‐beta frequency range, and six ERP components were obtained for go and no‐go stimuli. Within the task, mean reaction time (RT) correlated positively with go P2 amplitude and negatively with P3b positivity. Regressions revealed that greater EC delta‐1 amplitude predicted shorter mean RT, and larger alpha‐3 amplitude predicted go P3b enhancement. These findings demonstrate the immediate P2 and P3b involvement in decision making and response control and the intrinsic EC delta‐1 and alpha‐3 amplitudes that underpin these processes.     

A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: a simulation case

Eye movement artifacts represent a critical issue for quantitative electroencephalography (EEG) analysis and a number of mathematical approaches have been proposed to reduce their contribution in EEG recordings. The aim of this paper was to objectively and quantitatively evaluate the performance of ocular filtering methods with respect to spectral target variables widely used in clinical and functional EEG studies. In particular the following methods were applied: regression analysis and some blind source separation (BSS) techniques based on second-order statistics (PCA, AMUSE and SOBI) and on higher-order statistics (JADE, INFOMAX and FASTICA). Considering blind source decomposition methods, a completely automatic procedure of BSS based on logical rules related to spectral and topographical information was proposed in order to identify the components related to ocular interference. The automatic procedure was applied in different montages of simulated EEG and electrooculography (EOG) recordings: a full montage with 19 EEG and 2 EOG channels, a reduced one with only 6 EEG leads and a third one where EOG channels were not available. Time and frequency results in all of them indicated that AMUSE and SOBI algorithms preserved and recovered more brain activity than the other methods mainly at anterior regions. In the case of full montage: (i) errors were lower than 5% for all spectral variables at anterior sites; and (ii) the highest improvement in the signal-to-artifact (SAR) ratio was obtained up to 40dB at these anterior sites. Finally, we concluded that second-order BSS-based algorithms (AMUSE and SOBI) provided an effective technique for eye movement removal even when EOG recordings were not available or when data length was short.     

Post‐Genome‐Wide Association Study Challenges for Lipid Traits: Describing Age as a Modifier of Gene‐Lipid Associations in the Population Architecture Using Genomics and Epidemiology (PAGE) Study

Numerous common genetic variants that influence plasma high‐density lipoprotein cholesterol, low‐density lipoprotein cholesterol (LDL‐C), and triglyceride distributions have been identified via genome‐wide association studies (GWAS). However, whether or not these associations are age‐dependent has largely been overlooked. We conducted an association study and meta‐analysis in more than 22,000 European Americans between 49 previously identified GWAS variants and the three lipid traits, stratified by age (males: <50 or ≥50 years of age; females: pre‐ or postmenopausal). For each variant, a test of heterogeneity was performed between the two age strata and significant P_het values were used as evidence of age‐specific genetic effects. We identified seven associations in females and eight in males that displayed suggestive heterogeneity by age (P_het < 0.05). The association between rs174547 (FADS1) and LDL‐C in males displayed the most evidence for heterogeneity between age groups (P_het = 1.74E‐03, I² = 89.8), with a significant association in older males (P = 1.39E‐06) but not younger males (P = 0.99). However, none of the suggestive modifying effects survived adjustment for multiple testing, highlighting the challenges of identifying modifiers of modest SNP‐trait associations despite large sample sizes.     

Expansion of plasmablasts and loss of memory B cells in peripheral blood from COVID-19 patients with pneumonia

Studies on the interactions between SARS-CoV-2 and humoral immunity are fundamental to elaborate effective therapies including vaccines. We used polychromatic flow cytometry, coupled with unsupervised data analysis and principal component analysis (PCA), to interrogate B cells in untreated patients with COVID-19 pneumonia. COVID-19 patients displayed normal plasma levels of the main immunoglobulin classes, of antibodies against common antigens or against antigens present in common vaccines. However, we found a decreased number of total and naïve B cells, along with decreased percentages and numbers of memory switched and unswitched B cells. On the contrary, IgM⁺ and IgM⁻ plasmablasts were significantly increased. In vitro cell activation revealed that B lymphocytes showed a normal proliferation index and number of dividing cells per cycle. PCA indicated that B-cell number, naive and memory B cells but not plasmablasts clustered with patients who were discharged, while plasma IgM level, C-reactive protein, D-dimer, and SOFA score with those who died. In patients with pneumonia, the derangement of the B-cell compartment could be one of the causes of the immunological failure to control SARS-Cov2, have a relevant influence on several pathways, organs and systems, and must be considered to develop vaccine strategies.     

Human leukocyte antigen alleles and susceptibility to psoriatic arthritis

Objective

Our purpose was to determine associations between HLA alleles and psoriatic arthritis (PsA).

Methods

678 PsA cases and 688 healthy controls were analyzed in a case–control design. The difference in the proportion of cases and controls with at least 1 copy of HLA alleles were tested for significance using χ² test and Fisher’s exact test. Association analyses of haplotypes inferred by the Expectation–Maximization algorithm were performed. In the family-based association study, data from 283 families were analyzed.

Results

Univariate analysis revealed that cases were more likely to be carriers of HLA-C^∗01, -C^∗02, -C^∗06, -C^∗12, -B^∗27, -B^∗38 and -B^∗57, whereas controls were more likely to be carriers of HLA-C^∗03, -C^∗07, -B^∗07, -B^∗51, -DRB1^∗15 and -DQB1^∗0602. In haplotype analyses, PsA cases were more likely to be carriers of the HLA haplotypes -C^∗01/-B^∗27, -C^∗02/-B^∗27, -C^∗12/-B^∗38, and -C^∗06/-B^∗57, while controls were more likely to be carriers of the haplotypes -C^∗07/-B^∗07 and -C^∗15/-B^∗51. In the family-based association analysis, the HLA alleles -A^∗02, -B^∗27 and -DRB1^∗07 were preferentially transmitted to cases, whereas the alleles -A^∗03, -A^∗28, -B^∗51, -DRB1^∗11 and -DQB1^∗0301 were under transmitted.

Conclusion

This large case–control and family based association study shows that HLA-C^∗12/B^∗38, HLA-B^∗27 and HLA-C^∗06/B^∗57 are haplotypes (alleles) robustly associated with PsA. However, since patients with PsA also have psoriasis it is difficult to determine whether the primary association is with arthritis or psoriasis.