A robust and efficient statistical method for genetic association study using case and control samples from multiple cohorts

BACKGROUND: The theoretical basis of genome-wide association studies (GWAS) is statistical inference of linkage disequilibrium (LD) between any polymorphic marker and a putative disease locus. Most methods widely implemented for such analyses are vulnerable to several key demographic factors and deliver a poor statistical power for detecting genuine associations and also a high false positive rate. Here, we present a likelihood-based statistical approach that accounts properly for non-random nature of case–control samples in regard of genotypic distribution at the loci in populations under study and confers flexibility to test for genetic association in presence of different confounding factors such as population structure, non-randomness of samples etc. RESULTS: We implemented this novel method together with several popular methods in the literature of GWAS, to re-analyze recently published Parkinson’s disease (PD) case–control samples. The real data analysis and computer simulation show that the new method confers not only significantly improved statistical power for detecting the associations but also robustness to the difficulties stemmed from non-randomly sampling and genetic structures when compared to its rivals. In particular, the new method detected 44 significant SNPs within 25 chromosomal regions of size < 1 Mb but only 6 SNPs in two of these regions were previously detected by the trend test based methods. It discovered two SNPs located 1.18 Mb and 0.18 Mb from the PD candidates, FGF20 and PARK8, without invoking false positive risk. CONCLUSIONS: We developed a novel likelihood-based method which provides adequate estimation of LD and other population model parameters by using case and control samples, the ease in integration of these samples from multiple genetically divergent populations and thus confers statistically robust and powerful analyses of GWAS. On basis of simulation studies and analysis of real datasets, we demonstrated significant improvement of the new method over the non-parametric trend test, which is the most popularly implemented in the literature of GWAS

Preoperative radiomic signature based on CT images for noninvasive evaluation of localized nephroblastoma in pediatric patients

BackgroundNephron sparing nephrectomy may not reduce the prognosis of nephroblastoma in the absence of involvement of the renal capsule, sinus vessels, and lymph nodes, However, there is no accurate preoperative noninvasive evaluation method at present.Materials and methods105 nephroblastoma patients underwent contrast-enhanced CT scan between 2013 and 2020 in our hospital were retrospectively collected, including 59 cases with localized stage and 46 cases with non-localized stage, and then were divided into training cohort (n= 73) and validation cohort (n= 32) according to the order of CT scanning time. After lesion segmentation and data preprocessing, radiomic features were extracted from each volume of interest. The multi-step procedure including Pearson correlation analysis and sequential forward floating selection was performed to produce radiomic signature. Prediction model was constructed using the radiomic signature and Logistic Regression classifier for predicting the localized nephroblastoma in the training cohort. Finally, the model performance was validated in the validation cohort.ResultsA total of 1652 radiomic features have been extracted, from which TOP 10 features were selected as the radiomic signature. The area under the receiver operating characteristic curve, accuracy, sensitivity and specificity of the prediction model were 0.796, 0.795, 0.732 and 0.875 for the training cohort respectively, and 0.710, 0.719, 0.611 and 0.857 for the validation cohort respectively. The result comparison with prediction models composed of different machine learning classifiers and different parameters also manifest the effectiveness of our radiomic model.ConclusionA logistic regression model based on radiomic features extracted from preoperative CT images had good ability to noninvasively predict nephroblastoma without renal capsule, sinus vessel, and lymph node involvement

Design considerations and cycling guidance for aluminum foil anodes in lithium-ion rechargeable cells

Al foil is an attractive anode candidate for Li-ion rechargeable batteries, but the systemic problem of fast capacity degradation limits its re-introduction in practical applications. Partial lithiation-delithiation does mitigate the issue to a certain degree, but the cycle life is still tied to the problems associated with the phase transformation between β-LiAl and α-Al. Utilizing the solubility range of β-LiAl has been proven to be a feasible approach to stabilize the β-LiAl grown on an Al foil, i.e., the β-LiAl(Al) anode, but the electrochemically driven ion transport limitations of this electrode remain largely unclear. Herein, we present comprehensive electrochemical analyses of the β-LiAl(Al) electrode to shed light on its kinetic limitations which have intrinsic links to the electrode thickness and total cell capacity. Results show that the β-LiAl(Al) electrode can be charged at a C-rate as high as 2.9 C when a proper prelithiation is done for an Al foil. The superior rate capability is suggested to partly originate from the fast lithium diffusion in β-LiAl: -10−7 cm2·s−1 at room temperature. Furthermore, the cells consisting of β-LiAl(Al) vs. commercial Li4Ti5O12 exhibit promising cycling performances, even at 10 mA·cm−2, giving 300 cycles with a capacity retention of ∼80%. With a systematic investigation of the limiting mechanisms focusing on correlating the prelithiation depth and the cycle life, the cyclability of the β-LiAl(Al) electrode at different current densities (0.5–10 mA·cm−2) is mapped out, providing comprehensive guidance for the practical utilization of the β-LiAl(Al) anode in a range of Li-ion cell types, from all-solid-state batteries to hybrid capacitors

Methods for evaluating gene expression from Affymetrix microarray datasets

<p>Abstract</p> <p>Background</p> <p>Affymetrix high density oligonucleotide expression arrays are widely used across all fields of biological research for measuring genome-wide gene expression. An important step in processing oligonucleotide microarray data is to produce a single value for the gene expression level of an RNA transcript using one of a growing number of statistical methods. The challenge for the researcher is to decide on the most appropriate method to use to address a specific biological question with a given dataset. Although several research efforts have focused on assessing performance of a few methods in evaluating gene expression from RNA hybridization experiments with different datasets, the relative merits of the methods currently available in the literature for evaluating genome-wide gene expression from Affymetrix microarray data collected from real biological experiments remain actively debated.</p> <p>Results</p> <p>The present study reports a comprehensive survey of the performance of all seven commonly used methods in evaluating genome-wide gene expression from a well-designed experiment using Affymetrix microarrays. The experiment profiled eight genetically divergent barley cultivars each with three biological replicates. The dataset so obtained confers a balanced and idealized structure for the present analysis. The methods were evaluated on their sensitivity for detecting differentially expressed genes, reproducibility of expression values across replicates, and consistency in calling differentially expressed genes. The number of genes detected as differentially expressed among methods differed by a factor of two or more at a given false discovery rate (FDR) level. Moreover, we propose the use of genes containing single feature polymorphisms (SFPs) as an empirical test for comparison among methods for the ability to detect true differential gene expression on the basis that SFPs largely correspond to <it>cis</it>-acting expression regulators. The PDNN method demonstrated superiority over all other methods in every comparison, whilst the default Affymetrix MAS5.0 method was clearly inferior.</p> <p>Conclusion</p> <p>A comprehensive assessment of seven commonly used data extraction methods based on an extensive barley Affymetrix gene expression dataset has shown that the PDNN method has superior performance for the detection of differentially expressed genes.</p

Neural Biomarkers Distinguish Severe From Mild Autism Spectrum Disorder Among High-Functioning Individuals.

Several previous studies have reported atypicality in resting-state functional connectivity (FC) in autism spectrum disorder (ASD), yet the relatively small effect sizes prevent us from using these characteristics for diagnostic purposes. Here, canonical correlation analysis (CCA) and hierarchical clustering were used to partition the high-functioning ASD group (i.e., the ASD discovery group) into subgroups. A support vector machine (SVM) model was trained through the 10-fold strategy to predict Autism Diagnostic Observation Schedule (ADOS) scores within the ASD discovery group (r = 0.30, P < 0.001, n = 260), which was further validated in an independent sample (i.e., the ASD validation group) (r = 0.35, P = 0.031, n = 29). The neuroimage-based partition derived two subgroups representing severe versus mild autistic patients. We identified FCs that show graded changes in strength from ASD-severe, through ASD-mild, to controls, while the same pattern cannot be observed in partitions based on ADOS score. We also identified FCs that are specific for ASD-mild, similar to a partition based on ADOS score. The current study provided multiple pieces of evidence with replication to show that resting-state functional magnetic resonance imaging (rsfMRI) FCs could serve as neural biomarkers in partitioning high-functioning autistic individuals based on their symptom severity and showing advantages over traditional partition based on ADOS score. Our results also indicate a compensatory role for a frontocortical network in patients with mild ASD, indicating potential targets for future clinical treatments

Increased brain volume from higher cereal and lower coffee intake : shared genetic determinants and impacts on cognition and metabolism

It is unclear how different diets may affect human brain development and if genetic and environmental factors play a part. We investigated diet effects in the UK Biobank data from 18,879 healthy adults and discovered anticorrelated brain-wide gray matter volume (GMV)-association patterns between coffee and cereal intake, coincidence with their anticorrelated genetic constructs. The Mendelian randomization approach further indicated a causal effect of higher coffee intake on reduced total GMV, which is likely through regulating the expression of genes responsible for synaptic development in the brain. The identified genetic factors may further affect people’s lifestyle habits and body/blood fat levels through the mediation of cereal/coffee intake, and the brain-wide expression pattern of gene CPLX3, a dedicated marker of subplate neurons that regulate cortical development and plasticity, may underlie the shared GMV-association patterns among the coffee/cereal intake and cognitive functions. All the main findings were successfully replicated. Our findings thus revealed that high-cereal and low-coffee diets shared similar brain and genetic constructs, leading to long-term beneficial associations regarding cognitive, body mass index (BMI), and other metabolic measures. This study has important implications for public health, especially during the pandemic, given the poorer outcomes of COVID-19 patients with greater BMIs

Intersecting distributed networks support convergent linguistic functioning across different languages in bilinguals

How bilingual brains accomplish the processing of more than one language has been widely investigated by neuroimaging studies. The assimilation-accommodation hypothesis holds that both the same brain neural networks supporting the native language and additional new neural networks are utilized to implement second language processing. However, whether and how this hypothesis applies at the finer-grained levels of both brain anatomical organization and linguistic functions remains unknown. To address this issue, we scanned Chinese-English bilinguals during an implicit reading task involving Chinese words, English words and Chinese pinyin. We observed broad brain cortical regions wherein interdigitated distributed neural populations supported the same cognitive components of different languages. Although spatially separate, regions including the opercular and triangular parts of the inferior frontal gyrus, temporal pole, superior and middle temporal gyrus, precentral gyrus and supplementary motor areas were found to perform the same linguistic functions across languages, indicating regional-level functional assimilation supported by voxel-wise anatomical accommodation. Taken together, the findings not only verify the functional independence of neural representations of different languages, but show co-representation organization of both languages in most language regions, revealing linguistic-feature specific accommodation and assimilation between first and second languages

GABRB1 Single Nucleotide Polymorphism Associated with Altered Brain Responses (but not Performance) during Measures of Impulsivity and Reward Sensitivity in Human Adolescents.

Variations in genes encoding several GABAA receptors have been associated with human drug and alcohol abuse. Among these, a number of human studies have suggested an association between GABRB1, the gene encoding GABAA receptor β1 subunits, with Alcohol dependence (AD), both on its own and comorbid with other substance dependence and psychiatric illnesses. In the present study, we hypothesized that the GABRB1 genetically-associated increased risk for developing alcoholism may be associated with impaired behavioral control and altered sensitivity to reward, as a consequence of altered brain function. Exploiting the IMAGEN database (Schumann et al., 2010), we explored in a human adolescent population whether possession of the minor (T) variant of the single nucleotide polymorphism (SNP) rs2044081 is associated with performance of tasks measuring aspects of impulsivity, and reward sensitivity that are implicated in drug and alcohol abuse. Allelic variation did not associate with altered performance in either a stop-signal task (SST), measuring one aspect of impulsivity, or a monetary incentive delay (MID) task assessing reward anticipation. However, increased functional magnetic resonance imaging (fMRI) blood-oxygen-level dependent (BOLD) response in the right hemisphere inferior frontal gyrus (IFG), left hemisphere caudate/insula and left hemisphere inferior temporal gyrus (ITG) during MID performance was higher in the minor (T) allelic group. In contrast, during SST performance, the BOLD response found in the right hemisphere supramarginal gyrus, right hemisphere lingual and left hemisphere inferior parietal gyrus indicated reduced responses in the minor genotype. We suggest that β1-containing GABAA receptors may play a role in excitability of brain regions important in controlling reward-related behavior, which may contribute to susceptibility to addictive behavior

A model-based approach to assess reproducibility for large-scale high-throughput MRI-based studies

Magnetic Resonance Imaging (MRI) technology has been increasingly used in neuroscience studies. Reproducibility of statistically significant findings generated by MRI-based studies, especially association studies (phenotype vs. MRI metric) and task-induced brain activation, has been recently heavily debated. However, most currently available reproducibility measures depend on thresholds for the test statistics and cannot be use to evaluate overall study reproducibility. It is also crucial to elucidate the relationship between overall study reproducibility and sample size in an experimental design. In this study, we proposed a model-based reproducibility index to quantify reproducibility which could be used in large-scale high-throughput MRI-based studies including both association studies and task-induced brain activation. We performed the model-based reproducibility assessments for a few association studies and task-induced brain activation by using several recent large sMRI/fMRI databases. For large sample size association studies between brain structure/function features and some basic physiological phenotypes (i.e. Sex, BMI), we demonstrated that the model-based reproducibility of these studies is more than 0.99. For MID task activation, similar results could be observed. Furthermore, we proposed a model-based analytical tool to evaluate minimal sample size for the purpose of achieving a desirable model-based reproducibility. Additionally, we evaluated the model-based reproducibility of gray matter volume (GMV) changes for UK Biobank (UKB) vs. Parkinson Progression Marker Initiative (PPMI) and UK Biobank (UKB) vs. Human Connectome Project (HCP). We demonstrated that both sample size and study-specific experimental factors play important roles in the model-based reproducibility assessments for different experiments. In summary, a systematic assessment of reproducibility is fundamental and important in the current large-scale high-throughput MRI-based studies

Neural network involving medial orbitofrontal cortex and dorsal periaqueductal gray regulation in human alcohol abuse.

Prompted by recent evidence of neural circuitry in rodent models, functional magnetic resonance imaging and functional connectivity analyses were conducted for a large adolescent population at two ages, together with alcohol abuse measures, to characterize a neural network that may underlie the onset of alcoholism. A network centered on the medial orbitofrontal cortex (mOFC), as well as including the dorsal periaqueductal gray (dPAG), central nucleus of the amygdala, and nucleus accumbens, was identified, consistent with the rodent models, with evidence of both inhibitory and excitatory coregulation by the mOFC over the dPAG. Furthermore, significant relationships were detected between raised baseline excitatory coregulation in this network and impulsivity measures, supporting a role for negative urgency in alcohol dependence