scholarly journals Family aggregation analysis reveals a heritable background of equine grass sickness (dysautonomia)

2019 ◽  
Author(s):  
Boglárka Vincze ◽  
Márta Varga ◽  
András Gáspárdy ◽  
Orsolya Kutasi ◽  
Petra Zenke ◽  
...  
Keyword(s):  
Large Scale ◽  
Intestinal Microbiome ◽  
Pedigree Data ◽  
Data Set ◽  
Grass Sickness ◽  
Disease Occurrence ◽  
Increased Risk ◽  
Life Threatening ◽  
Family Aggregation ◽  
Close Relatives

AbstractEquine grass sickness (also known as dysautonomia) is a life-threatening polyneuropathic disease affecting horses with approx. 80% mortality. Since it’s first description over a hundred years ago, several factors including phenotypic, environmental, management, climate, and intestinal microbiome) have been associated with increased risk of dysautonomia. But despite the extensive research on dysautonomia, it’s causative factors have yet been identified. A retrospective pedigree and phenotype based genetic epidemiological study was performed to analyze the associations of disease occurrence and the kinship in a Hungarian large scale stud. The pedigree data set containing 1233 horses with 49 affected animals was used in the analysis. The first finding was that among the descendants of some stallions the proportion of affected animals are unexpectedly high, with a maximum of 25% of a stallions descendants affected. Animals with affected siblings have higher odds to be a case (OR: 1.27, 95% CI: 1.01-1.57, p=0.033). Among males in the affected population the odds of dysautonomia is higher than in females (OR: 1.76, 95% CI: 0.95-3.29, p=0.057). Significant familial clustering was observed among the affected animals (GIF p=0.001). Further subgroups were identified with significant (p<0.001) aggregation among close relatives using kinship-based methods. Our analysis of the data and the observed higher disease frequency in males suggests that dysautonomia may have X-linked recessive inheritance as a causal factor. This is the first study providing ancestry data and suggesting a genetic contribution to the likely multifactorial causes of the disease.

scholarly journals Family aggregation analysis shows a possible heritable background of equine grass sickness (dysautonomia) in a Hungarian stud population

2020 ◽  
Author(s):  
Boglárka Vincze ◽  
Márta Varga ◽  
Orsolya Kutasi ◽  
Petra Zenke ◽  
Ottó Szenci ◽  
...  
Keyword(s):  
Genetic Factors ◽  
Intestinal Microbiome ◽  
Environment Management ◽  
Grass Sickness ◽  
Increased Risk ◽  
Familial Clustering ◽  
Life Threatening ◽  
Family Aggregation ◽  
Close Relatives ◽  
Aggregation Analysis

AbstractEquine grass sickness (also known as dysautonomia) is a life-threatening polyneuropathic disease affecting horses with approx. 80% mortality. Since its first description over a century ago, several factors, such as the phenotype, intestinal microbiome, environment, management and climate, have been supposed to be associated with the increased risk of dysautonomia. In this retrospective study, we examined the possible involvement of genetic factors. Medical and pedigree datasets regarding 1,233 horses with 49 affected animals born during a 23-year period were used in the analysis. Among the descendants of some stallions, the proportion of animals diagnosed with dysautonomia was unexpectedly high. Among males, the odds of dysautonomia were found to be higher, albeit not significantly, than among females. Significant familial clustering (genealogical index of familiality, P = 0.001) was observed among the affected animals. Further subgroups were identified with significant (P < 0.001) aggregation among close relatives using kinship-based methods. Our analysis, along with the slightly higher disease frequency in males, suggests that dysautonomia may have a genetic causal factor with an X-linked recessive inheritance pattern. This is the first study providing ancestry data and suggesting a heritable component in the likely multifactorial aetiology of the disease.

scholarly journals Analysis of disease comorbidity patterns in a large-scale China population

2019 ◽  
Vol 12 (S12) ◽  
Author(s):  
Mengfei Guo ◽  
Yanan Yu ◽  
Tiancai Wen ◽  
Xiaoping Zhang ◽  
Baoyan Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Molecular Mechanisms ◽  
Disease Classification ◽  
Psychiatric Disease ◽  
Power Law Distribution ◽  
Data Set ◽  
Chinese Populations ◽  
Diagnosis And Prognosis ◽  
Disease Occurrence ◽  
Anonymized Data

Abstract Background Disease comorbidity is popular and has significant indications for disease progress and management. We aim to detect the general disease comorbidity patterns in Chinese populations using a large-scale clinical data set. Methods We extracted the diseases from a large-scale anonymized data set derived from 8,572,137 inpatients in 453 hospitals across China. We built a Disease Comorbidity Network (DCN) using correlation analysis and detected the topological patterns of disease comorbidity using both complex network and data mining methods. The comorbidity patterns were further validated by shared molecular mechanisms using disease-gene associations and pathways. To predict the disease occurrence during the whole disease progressions, we applied four machine learning methods to model the disease trajectories of patients. Results We obtained the DCN with 5702 nodes and 258,535 edges, which shows a power law distribution of the degree and weight. It further indicated that there exists high heterogeneity of comorbidities for different diseases and we found that the DCN is a hierarchical modular network with community structures, which have both homogeneous and heterogeneous disease categories. Furthermore, adhering to the previous work from US and Europe populations, we found that the disease comorbidities have their shared underlying molecular mechanisms. Furthermore, take hypertension and psychiatric disease as instance, we used four classification methods to predicte the disease occurrence using the comorbid disease trajectories and obtained acceptable performance, in which in particular, random forest obtained an overall best performance (with F1-score 0.6689 for hypertension and 0.6802 for psychiatric disease). Conclusions Our study indicates that disease comorbidity is significant and valuable to understand the disease incidences and their interactions in real-world populations, which will provide important insights for detection of the patterns of disease classification, diagnosis and prognosis.

scholarly journals 331 Efficient quality control methods for genomic and pedigree data used in routine genomic evaluation

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 50-51
Author(s):  
Yutaka Masuda ◽  
Andres Legarra ◽  
Ignacio Aguilar ◽  
Ignacy Misztal
Keyword(s):  
Quality Control ◽  
Large Scale ◽  
Computing Time ◽  
Academic Research ◽  
Snp Markers ◽  
Pedigree Information ◽  
Memory Usage ◽  
Pedigree Data ◽  
Genomic Evaluation ◽  
Data Set

Abstract Quality control and consistency tests on genotypes and historical pedigree data are applied in a routine genomic evaluation and academic research. The quality control takes more time to finish as more genotypes become available, and this step is a bottleneck in a pipeline of routine evaluation. For the efficient quality control, we have developed several algorithms and a computer program to support for large-scale, biallelic, single nucleotide polymorphisms (SNPs). The program is designed to detect unsatisfactory genomic markers and individuals in terms of call rate, marker allele frequencies, duplicate samples, and Mendelian inconsistency in the large genomic data with the pedigree including millions of individuals. Duplicated genotypes can be detected using a set of markers. An SNP genotype is packed into a 2-bit representation in memory that enables bitwise operations with parallel computing to efficiently perform the quality control. The software optionally checks the inconsistency of pedigree information. We compared QCF90 with preGSf90, a preceding program, in terms of memory usage and computing time using a data set including 200,000 genotyped individuals, 50,000 SNP markers per individual, and 216,500 pedigree individuals. In total running time, QCF90 was approximately 6 times faster than PREGSF90 (307 s vs 2075 s) while the memory usage was 30 times less (2 GB vs 75 GB) using only 1 thread. The QCF90 program performed better in speed as more threads were used. A check for genomic duplications took 159 s with 16 threads when 5,000 genotypes were compared with 200,000 genotypes using 2500 SNP markers. The new tool is useful in the routine genomic evaluation and the academic research in which both the genotypes and the pedigree information are used. The QCF90 executable is available at http://nce.ads.uga.edu with a user manual.

scholarly journals ERpred: a web server for the prediction of subtype-specific estrogen receptor antagonists

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11716
Author(s):  
Nalini Schaduangrat ◽  
Aijaz Ahmad Malik ◽  
Chanin Nantasenamat
Keyword(s):  
Large Scale ◽  
Cancer Metastasis ◽  
Model Building ◽  
Hormone Replacement ◽  
Web Server ◽  
Breast Cancer Metastasis ◽  
Data Set ◽  
Therapy Targets ◽  
Subtype Specificity ◽  
Increased Risk

Estrogen receptors alpha and beta (ERα and ERβ) are responsible for breast cancer metastasis through their involvement of clinical outcomes. Estradiol and hormone replacement therapy targets both ERs, but this often leads to an increased risk of breast and endometrial cancers as well as thromboembolism. A major challenge is posed for the development of compounds possessing ER subtype specificity. Herein, we present a large-scale classification structure-activity relationship (CSAR) study of inhibitors from the ChEMBL database which consisted of an initial set of 11,618 compounds for ERα and 7,810 compounds for ERβ. The IC50 was selected as the bioactivity unit for further investigation and after the data curation process, this led to a final data set of 1,593 and 1,281 compounds for ERα and ERβ, respectively. We employed the random forest (RF) algorithm for model building and of the 12 fingerprint types, models built using the PubChem fingerprint was the most robust (Ac of 94.65% and 92.25% and Matthews correlation coefficient (MCC) of 89% and 76% for ERα and ERβ, respectively) and therefore selected for feature interpretation. Results indicated the importance of features pertaining to aromatic rings, nitrogen-containing functional groups and aliphatic hydrocarbons. Finally, the model was deployed as the publicly available web server called ERpred at http://codes.bio/erpred where users can submit SMILES notation as the input query for prediction of the bioactivity against ERα and ERβ.

Hrd in ovarian cancer: Defined today, evolving for the future.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e18052-e18052
Author(s):  
Markus Eckstein ◽  
Kenneth Joel Bloom ◽  
Peter Riccelli ◽  
Frank Policht ◽  
Derry Mae Keeling ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Homologous Recombination ◽  
Genomic Instability ◽  
Large Scale ◽  
Gene Mutations ◽  
Therapy Response ◽  
State Transitions ◽  
Data Set ◽  
Gene Target ◽  
Increased Risk

e18052 Background: Homologous Recombination Repair (HRR) gene mutations result in Homologous Recombination Deficiency (HRD) associated with increased risk of high grade serous ovarian (HGOC) cancer and subsequent response to PARP inhibitors (PARPi). Traditionally, HRD has been determined by testing for germline and/or somatic BRCA1/2 mutations. Today, a growing number of HRR gene mutations are known to result in HRD and genomic instability, thus being a suitable target for PARPi. Therapy response to PARPi is highest in BRCA-mutant followed by HRD+/non-BRCA-mutant HGOC. Today, no standard HRD testing methods exist, causing confusion for physicians, and leading to poor outcomes for missed PARPi eligible patients. Thus, there is need to understand HRD testing utilization and methods in HGOC to inform best practices and optimize HRD testing in the clinic. Methods: We assessed the testing landscape for determining HRD status in ovarian cancer using a data set of 8,400 newly diagnosed and metastatic ovarian cancer patients in the US from Q3-2018 through Q2-2019 identified from Diaceutics’ proprietary Global Diagnostic Index (GDI). Analysis of real-world BRCA1/2 and NGS associated testing data and laboratory profile mapping exercise of 82 US labs was carried out using Diaceutics proprietary methods and data sources to evaluate BRCA1/2 and/or HRD germline/somatic testing rates, test availability, and test panel HRR gene composition. Results: Overall, germline mutation testing rates were 3x greater than somatic testing rates. Excluding BRCA1/2, 67 labs offered comprehensive solid tumor NGS panels capable of measuring HRD with varied HRR gene target composition. Across 34 labs, 5 HRR genes were commonly found on panels: PALB2, ATM, BARD1, BRIP1 and CHEK2. 3 labs currently offering panels explicitly intended for HRD determination only include BRCA1/2 and at least one genomic instability marker (loss of heterozygosity, large-scale state transitions or telomeric allelic imbalance). Conclusions: Lack of standardized HRD panels and low testing rate identifying patients with somatic mutations in BRCA1/2 and other HRR genes is leading to poorer outcomes for missed patients eligible for PARPi’s. As clinical evidence linking HRD status with PARPi efficacy grows in ovarian as well as prostate and pancreatic cancer, Diaceutics recommends organizations such as ASCO, CAP or AMP establish defined universal HRD testing panels including relevant somatic/germline HRR genes and BRCA1/2 as well as genomic instability markers and educate stake holders aiding harmonization and ultimately, better treatment outcomes.

ProGen:Provenance database generator for large-scale data set

2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN
Keyword(s):  
Large Scale ◽  
Data Set ◽  
Large Scale Data ◽  
Scale Data

Study of Biochemical and Clinical Markers in Steatohepatitis Related to Obesity

2018 ◽  
Vol 69 (6) ◽  
pp. 1501-1505
Author(s):  
Roxana Maria Livadariu ◽  
Radu Danila ◽  
Lidia Ionescu ◽  
Delia Ciobanu ◽  
Daniel Timofte
Keyword(s):  
Liver Disease ◽  
Liver Biopsy ◽  
Large Scale ◽  
Biological Data ◽  
Obese Patients ◽  
Clinical Markers ◽  
Mean Values ◽  
Nonalcoholic Fatty Liver ◽  
Obstructive Sleep ◽  
Increased Risk

Nonalcoholic fatty liver disease (NAFLD) is highly associated to obesity and comprises several liver diseases, from simple steatosis to steatohepatitis (NASH) with increased risk of developing progressive liver fibrosis, cirrhosis and hepatocellular carcinoma. Liver biopsy is the gold standard in diagnosing the disease, but it cannot be used in a large scale. The aim of the study was the assessment of some non-invasive clinical and biological markers in relation to the progressive forms of NAFLD. We performed a prospective study on 64 obese patients successively hospitalised for bariatric surgery in our Surgical Unit. Patients with history of alcohol consumption, chronic hepatitis B or C, other chronic liver disease or patients undergoing hepatotoxic drug use were excluded. All patients underwent liver biopsy during sleeve gastrectomy. NAFLD was present in 100% of the patients: hepatic steatosis (38%), NASH with the two forms: with fibrosis (31%) and without fibrosis (20%), cumulating 51%; 7 patients had NASH with vanished steatosis. NASH with fibrosis statistically correlated with metabolic syndrome (p = 0.036), DM II (p = 0.01) and obstructive sleep apnea (p = 0.02). Waist circumference was significantly higher in the steatohepatitis groups (both with and without fibrosis), each 10 cm increase increasing the risk of steatohepatitis (p = 0.007). The mean values of serum fibrinogen and CRP were significantly higher in patients having the progressive forms of NAFLD. Simple clinical and biological data available to the practitioner in medicine can be used to identify obese patients at high risk of NASH, aiming to direct them to specialized medical centers.

Should all Hypertensive Patients be Screened for Primary Aldosteronism?

2019 ◽  
Vol 15 (1) ◽  
pp. 54-56
Author(s):  
Stelina Alkagiet ◽  
Konstantinos Tziomalos
Keyword(s):  
Resistant Hypertension ◽  
Primary Aldosteronism ◽  
Large Scale ◽  
Controlled Trial ◽  
The Other ◽  
Limited Data ◽  
Hypertensive Patients ◽  
Randomized Controlled ◽  
Increased Risk ◽  
Prospective Randomized Controlled Trial

Primary aldosteronism (PA) is not only a leading cause of secondary and resistant hypertension, but is also quite frequent in unselected hypertensive patients. Moreover, PA is associated with increased cardiovascular risk, which is disproportionate to BP levels. In addition, timely diagnosis of PA and prompt initiation of treatment attenuate this increased risk. On the other hand, there are limited data regarding the usefulness of screening for PA in all asymptomatic or normokalemic hypertensive patients. More importantly, until now, no well-organized, large-scale, prospective, randomized controlled trial has proved the effectiveness of screening for PA for improving clinical outcome. Accordingly, until more relevant data are available, screening for PA should be considered in hypertensive patients with spontaneous or diuretic-induced hypokalemia as well as in those with resistant hypertension. However, screening for PA in all hypertensive patients cannot be currently recommended.

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

Sign in / Sign up

Export Citation Format

Share Document