Adaptive Strategy for the Statistical Analysis of Connectomes

We study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by considering subnetworks of the global brain network. These subnetworks represent either the inter-connectivity between two brain anatomical regions or by the intra-connectivity within the same brain anatomical region. An appropriate summary statistic, that characterizes a meaningful feature of the subnetwork, is evaluated. Based on this summary statistic, a statistical test is performed to derive the corresponding p-value. The reformulation of the problem in this way reduces the number of statistical tests in an orderly fashion based on our understanding of the problem. Considering the global testing problem, the p-values are corrected to control the rate of false discoveries. Finally, the procedure is followed by a local investigation within the significant subnetworks. We contrast this strategy with the one based on the individual measures in terms of power. We show that this strategy has a great potential, in particular in cases where the subnetworks are well defined and the summary statistics are properly chosen. As an application example, we compare structural brain connection matrices of two groups of subjects with a 22q11.2 deletion syndrome, distinguished by their IQ scores

Analysis of Rare, Exonic Variation amongst Subjects with Autism Spectrum Disorders and Population Controls

We report on results from whole-exome sequencing (WES) of 1,039 subjects diagnosed with autism spectrum disorders (ASD) and 870 controls selected from the NIMH repository to be of similar ancestry to cases. The WES data came from two centers using different methods to produce sequence and to call variants from it. Therefore, an initial goal was to ensure the distribution of rare variation was similar for data from different centers. This proved straightforward by filtering called variants by fraction of missing data, read depth, and balance of alternative to reference reads. Results were evaluated using seven samples sequenced at both centers and by results from the association study. Next we addressed how the data and/or results from the centers should be combined. Gene-based analyses of association was an obvious choice, but should statistics for association be combined across centers (meta-analysis) or should data be combined and then analyzed (mega-analysis)? Because of the nature of many gene-based tests, we showed by theory and simulations that mega-analysis has better power than meta-analysis. Finally, before analyzing the data for association, we explored the impact of population structure on rare variant analysis in these data. Like other recent studies, we found evidence that population structure can confound case-control studies by the clustering of rare variants in ancestry space; yet, unlike some recent studies, for these data we found that principal component-based analyses were sufficient to control for ancestry and produce test statistics with appropriate distributions. After using a variety of gene-based tests and both meta- and mega-analysis, we found no new risk genes for ASD in this sample. Our results suggest that standard gene-based tests will require much larger samples of cases and controls before being effective for gene discovery, even for a disorder like ASD. © 2013 Liu et al

Measurement of the ratios of branching fractions R(D∗)\mathcal{R}(D^{*}) and R(D0)\mathcal{R}(D^{0})

The ratios of branching fractions $\mathcal{R}(D^{*})\equiv\mathcal{B}(\bar{B}\to D^{*}\tau^{-}\bar{\nu}_{\tau})/\mathcal{B}(\bar{B}\to D^{*}\mu^{-}\bar{\nu}_{\mu})$ and $\mathcal{R}(D^{0})\equiv\mathcal{B}(B^{-}\to D^{0}\tau^{-}\bar{\nu}_{\tau})/\mathcal{B}(B^{-}\to D^{0}\mu^{-}\bar{\nu}_{\mu})$ are measured, assuming isospin symmetry, using a sample of proton-proton collision data corresponding to 3.0 fb${ }^{-1}$ of integrated luminosity recorded by the LHCb experiment during 2011 and 2012. The tau lepton is identified in the decay mode $\tau^{-}\to\mu^{-}\nu_{\tau}\bar{\nu}_{\mu}$. The measured values are $\mathcal{R}(D^{*})=0.281\pm0.018\pm0.024$ and $\mathcal{R}(D^{0})=0.441\pm0.060\pm0.066$, where the first uncertainty is statistical and the second is systematic. The correlation between these measurements is $\rho=-0.43$. Results are consistent with the current average of these quantities and are at a combined 1.9 standard deviations from the predictions based on lepton flavor universality in the Standard Model.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-039.html (LHCb public pages

Endurance Exercise Ability in the Horse: A Trait with Complex Polygenic Determinism

Endurance horses are able to run at more than 20 km/h for 160 km (in bouts of 30–40 km). This level of performance is based on intense aerobic metabolism, effective body heat dissipation and the ability to endure painful exercise. The known heritabilities of endurance performance and exercise-related physiological traits in Arabian horses suggest that adaptation to extreme endurance exercise is influenced by genetic factors. The objective of the present genome-wide association study (GWAS) was to identify single nucleotide polymorphisms (SNPs) related to endurance racing performance in 597 Arabian horses. The performance traits studied were the total race distance, average race speed and finishing status (qualified, eliminated or retired). We used three mixed models that included a fixed allele or genotype effect and a random, polygenic effect. Quantile-quantile plots were acceptable, and the regression coefficients for actual vs. expected log10p-values ranged from 0.865 to 1.055. The GWAS revealed five significant quantitative trait loci (QTL) corresponding to 6 SNPs on chromosomes 6, 1, 7, 16, and 29 (two SNPs) with corrected p-values from 1.7 × 10−6 to 1.8 × 10−5. Annotation of these 5 QTL revealed two genes: sortilin-related VPS10-domain-containing receptor 3 (SORCS3) on chromosome 1 is involved in protein trafficking, and solute carrier family 39 member 12 (SLC39A12) on chromosome 29 is active in zinc transport and cell homeostasis. These two coding genes could be involved in neuronal tissues (CNS). The other QTL on chromosomes 6, 7, and 16 may be involved in the regulation of the gene expression through non-coding RNAs, CpG islands and transcription factor binding sites. On chromosome 6, a new candidate equine long non-coding RNA (KCNQ1OT1 ortholog: opposite antisense transcript 1 of potassium voltage-gated channel subfamily Q member 1 gene) was predicted in silico and validated by RT-qPCR in primary cultures of equine myoblasts and fibroblasts. This lncRNA could be one element of the cardiac rhythm regulation. Our GWAS revealed that equine performance during endurance races is a complex polygenic trait, and is partially governed by at least 5 QTL: two coding genes involved in neuronal tissues and three other loci with many regulatory functions such as slowing down heart rate

A dozen years of American Academy of Sleep Medicine (AASM) International Mini-Fellowship: program evaluation and future directions.

UNLABELLED: Sleep medicine remains an underrepresented medical specialty worldwide, with significant geographic disparities with regard to training, number of available sleep specialists, sleep laboratory or clinic infrastructures, and evidence-based clinical practices. The American Academy of Sleep Medicine (AASM) is committed to facilitating the education of sleep medicine professionals to ensure high-quality, evidence-based clinical care and improve access to sleep centers around the world, particularly in developing countries. In 2002, the AASM launched an annual 4-week training program called Mini-Fellowship for International Scholars, designed to support the establishment of sleep medicine in developing countries. The participating fellows were generally chosen from areas that lacked a clinical infrastructure in this specialty and provided with training in AASM Accredited sleep centers. This manuscript presents an overview of the program, summarizes the outcomes, successes, and lessons learned during the first 12 years, and describes a set of programmatic changes for the near-future, as assembled and proposed by the AASM Education Committee and recently approved by the AASM Board of Directors. CITATION: Ioachimescu OC; Wickwire EM; Harrington J; Kristo D; Arnedt JT; Ramar K; Won C; Billings ME; DelRosso L; Williams S; Paruthi S; Morgenthaler TI. A dozen years of American Academy of Sleep Medicine (AASM) international mini-fellowship: program evaluation and future directions

A Dozen Years of American Academy of Sleep Medicine (AASM) International Mini-Fellowship: Program Evaluation and Future Directions

Sleep medicine remains an underrepresented medical specialty worldwide, with significant geographic disparities with regard to training, number of available sleep specialists, sleep laboratory or clinic infrastructures, and evidence-based clinical practices. The American Academy of Sleep Medicine (AASM) is committed to facilitating the education of sleep medicine professionals to ensure high-quality, evidence-based clinical care and improve access to sleep centers around the world, particularly in developing countries. In 2002, the AASM launched an annual 4-week training program called Mini-Fellowship for International Scholars, designed to support the establishment of sleep medicine in developing countries. The participating fellows were generally chosen from areas that lacked a clinical infrastructure in this specialty and provided with training in AASM Accredited sleep centers. This manuscript presents an overview of the program, summarizes the outcomes, successes, and lessons learned during the first 12 years, and describes a set of programmatic changes for the near-future, as assembled and proposed by the AASM Education Committee and recently approved by the AASM Board of Directors. CITATION: Ioachimescu OC; Wickwire EM; Harrington J; Kristo D; Arnedt JT; Ramar K; Won C; Billings ME; DelRosso L; Williams S; Paruthi S; Morgenthaler TI. A dozen years of American Academy of Sleep Medicine (AASM) international mini-fellowship: program evaluation and future directions. J Clin Sleep Med 2014;10(3):331-334

The general outcome of a multiple comparisons.

<p>A total of <i>m</i> null hypotheses are tested. FP is the number of Type I errors or the number of false positives (rejected true hypotheses). Physical significance as indicated in the first column means the existence of a real effect, whereas statistical significance refers to the detection of such effect by means of measurements. FN is the number of Type II errors or the number of false negatives (false hypotheses not rejected). The number <i>R</i> of rejected hypotheses is an observable random variable, while FP, FN, TP and TN are unobservable random variables. The number of true null hypotheses is also unknown in practice. The empirical type I error rate is defined by FP/, while the empirical type II error rate is defined by FN/ and the estimated average power is TP/. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0023009#pone.0023009-Benjamini1" target="_blank">[12]</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0023009#pone.0023009-Dudoit1" target="_blank">[13]</a>.</p

Power of detecting affected atoms and partially affected subsets depending on the proportion .

<p>For the multiplicity correction, the BH95 procedure is used. Three different values of the subsets' size (4, 8 or 16) and two different values of the raw effect Δ (1 or 2) are used. The other parameters are: .</p

Illustration of the different types of subnetworks within a brain network.

<p>In the right side, a connection matrix is presented. In the left side, the connectivity between two groups of node is presented which defines three subnetworks of two types. The first type represents the intra-connection within the same subset of nodes (the red and the green subnetworks) and whose corresponding blocks are localized on the diagonal of the global connection matrix (the red and the green blocks). The second type represents the interconnections between the two subsets of nodes (the yellow subset). Its corresponding block is localized out of the diagonal in the global connection matrix (the yellow block).</p