FastBiCmrMLM: a fast and powerful compressed variance component mixed logistic model for big genomic case-control genome-wide association study.

Large sample datasets have been regarded as the primary basis for innovative discoveries and the solution to missing heritability in genome-wide association studies. However, their computational complexity cannot consider all comprehensive effects and all polygenic backgrounds, which reduces the effectiveness of large datasets. To address these challenges, we included all effects and polygenic backgrounds in a mixed logistic model for binary traits and compressed four variance components into two. The compressed model combined three computational algorithms to develop an innovative method, called FastBiCmrMLM, for large data analysis. These algorithms were tailored to sample size, computational speed, and reduced memory requirements. To mine additional genes, linkage disequilibrium markers were replaced by bin-based haplotypes, which are analyzed by FastBiCmrMLM, named FastBiCmrMLM-Hap. Simulation studies highlighted the superiority of FastBiCmrMLM over GMMAT, SAIGE and fastGWA-GLMM in identifying dominant, small α (allele substitution effect), and rare variants. In the UK Biobank-scale dataset, we demonstrated that FastBiCmrMLM could detect variants as small as 0.03% and with α ≈ 0. In re-analyses of seven diseases in the WTCCC datasets, 29 candidate genes, with both functional and TWAS evidence, around 36 variants identified only by the new methods, strongly validated the new methods. These methods offer a new way to decipher the genetic architecture of binary traits and address the challenges outlined above.

BLUPmrMLM: A Fast mrMLM Algorithm in Genome-wide Association Studies.

Multilocus genome-wide association study has become the state-of-the-art tool for dissecting the genetic architecture of complex and multiomic traits. However, most existing multilocus methods require relatively long computational time when analyzing large datasets. To address this issue, in this study, we proposed a fast mrMLM method, namely, best linear unbiased prediction multilocus random-SNP-effect mixed linear model (BLUPmrMLM). First, genome-wide single-marker scanning in mrMLM was replaced by vectorized Wald tests based on the best linear unbiased prediction (BLUP) values of marker effects and their variances in BLUPmrMLM. Then, adaptive best subset selection (ABESS) was used to identify potentially associated markers on each chromosome to reduce computational time when estimating marker effects via empirical Bayes. Finally, shared memory and parallel computing schemes were used to reduce the computational time. In simulation studies, BLUPmrMLM outperformed GEMMA, EMMAX, mrMLM, and FarmCPU as well as the control method (BLUPmrMLM with ABESS removed), in terms of computational time, power, accuracy for estimating quantitative trait nucleotide positions and effects, false positive rate, false discovery rate, false negative rate, and F1 score. In the reanalysis of two large rice datasets, BLUPmrMLM significantly reduced the computational time and identified more previously reported genes, compared with the aforementioned methods. This study provides an excellent multilocus model method for the analysis of large-scale and multiomic datasets. The software mrMLM v5.1 is available at BioCode (https://ngdc.cncb.ac.cn/biocode/tool/BT007388) or GitHub (https://github.com/YuanmingZhang65/mrMLM).

Prognostic determination in anoxic-ischemic and traumatic encephalopathies.

Clinical assessment of the unresponsive patient is limited to examination of brainstem reflexes and simple motor responses to stimulation. It is thus difficult, especially if brainstem functions are intact, to give early, accurate prognostic information on comatose patients. Neurochemical tests and imaging have not been validated and have significant limitations. Electrophysiologic investigations provide a window into cerebral function and are tested, clinically useful, safe, available, and inexpensive. Persistent abnormalities of brainstem auditory evoked potentials and short-latency somatosensory evoked potentials reliably indicate the likelihood permanent vegetative state or death. Conversely, the presence of "cognitive" event-related brain potentials (e.g., P300 and mismatch negativity) reflects the functional integrity of higher-level information processing and, therefore, the likelihood of capacity for cognition. A combined clinical and electrophysiologic approach provides optimal prediction of outcome and level of disability.

Prognostic value of evoked responses and event-related brain potentials in coma.

The behaviourally unresponsive patient, unable to exhibit the presence of cognition, constitutes a conundrum for health care specialists. Prognostic uncertainty impedes accurate management decisions and the application of ethical principles. An early, reliable prognosis is highly desirable. In this review investigations studying comatose patients with coma of different etiologies were selected. It is concluded that objective prognostication is enhanced by the use of electrophysiological tests. Persistent abnormalities of brainstem auditory evoked potentials and short-latency somatosensory evoked potentials reliably indicate the likelihood of irreversible neurological deficit or death. Meanwhile, the presence of "cognitive" event-related brain potentials (e.g., P300 and mismatch negativity) reflects the functional integrity of higher level information processing and, therefore, the likelihood of capacity for cognition. An approach that combines clinical and electrophysiological values provides optimal prediction of outcome and level of disability.