-
Animal : An International Journal of... May 2024Inbreeding plays a crucial role in livestock breeding, influencing genetic diversity and phenotypic traits. Genomic data have helped address limitations posed by...
Inbreeding plays a crucial role in livestock breeding, influencing genetic diversity and phenotypic traits. Genomic data have helped address limitations posed by incomplete pedigrees, providing deeper insights into breed genetic diversity. This study assesses inbreeding levels via pedigree and genomic approaches and analyzes old and recent inbreeding using runs of homozygosity (ROH), and selection signals in Alpine Grey cattle. Pedigree data from 165 575 individuals, analyzed with INBUPGF90 software, computed inbreeding coefficients. Genomic-based coefficients derived from PLINK v1.9. or DetectRUNS R package analyses of 1 180 individuals' genotypes. Common single nucleotide polymorphisms within ROH pinpointed genomic regions, aggregating into "ROH islands" indicative of selection pressure. Overlaps with USCS Genome Browser unveiled gene presence. Moderate correlations (0.20-0.54) existed between pedigree and genomic coefficients, with most genomic estimators having higher (>0.8) correlation values. Inbreeding averaged 0.04 in < 8 Mb ROH segments, and 0.03 in > 16 Mb segments; > 90% of ROHs were < 8 Mb, indicating ancient inbreeding prevalence. Recent inbreeding proved less detrimental than in cosmopolitan breeds. Two major ROH islands on chromosomes 6 and 7 harbored genes linked to immune response, disease resistance (PYURF, HERC3), and fertility (EIF4EBP3, SRA1). This study underscores the need for detailed inbreeding analyses to understand genetic characteristics and historical changes in local breeds like Alpine Grey cattle. Genomic insights, especially from ROH, facilitated overcoming pedigree limitations, illuminating breed genetic diversity. Our findings reveal ancient inbreeding's enduring genetic impact and ROH islands potential for selective sweeps, elucidating traits in Alpine Grey cattle.
Topics: Animals; Cattle; Inbreeding; Pedigree; Polymorphism, Single Nucleotide; Female; Selection, Genetic; Genotype; Male; Homozygote; Genetic Variation; Genomics; Breeding; Genome; Phenotype
PubMed: 38718700
DOI: 10.1016/j.animal.2024.101159 -
Bioinformatics (Oxford, England) Nov 2021The use and functionality of Electronic Health Records (EHR) have increased rapidly in the past few decades. EHRs are becoming an important depository of patient health...
MOTIVATION
The use and functionality of Electronic Health Records (EHR) have increased rapidly in the past few decades. EHRs are becoming an important depository of patient health information and can capture family data. Pedigree analysis is a longstanding and powerful approach that can gain insight into the underlying genetic and environmental factors in human health, but traditional approaches to identifying and recruiting families are low-throughput and labor-intensive. Therefore, high-throughput methods to automatically construct family pedigrees are needed.
RESULTS
We developed a stand-alone application: Electronic Pedigrees, or E-Pedigrees, which combines two validated family prediction algorithms into a single software package for high throughput pedigrees construction. The convenient platform considers patients' basic demographic information and/or emergency contact data to infer high-accuracy parent-child relationship. Importantly, E-Pedigrees allows users to layer in additional pedigree data when available and provides options for applying different logical rules to improve accuracy of inferred family relationships. This software is fast and easy to use, is compatible with different EHR data sources, and its output is a standard PED file appropriate for multiple downstream analyses.
AVAILABILITY AND IMPLEMENTATION
The Python 3.3+ version E-Pedigrees application is freely available on: https://github.com/xiayuan-huang/E-pedigrees.
Topics: Humans; Pedigree; Software; Algorithms; Electronic Health Records
PubMed: 34086863
DOI: 10.1093/bioinformatics/btab419 -
JAMA Neurology Jul 2020Genetic and environmental factors are thought to contribute to cluster headache, and cluster headache can affect multiple members of a family. A thorough understanding...
IMPORTANCE
Genetic and environmental factors are thought to contribute to cluster headache, and cluster headache can affect multiple members of a family. A thorough understanding of its inheritance is critical to understanding the pathogenesis of this debilitating disease.
OBJECTIVE
To systematically review family history rates and inheritance patterns of cluster headache.
EVIDENCE REVIEW
A systematic review was performed in PubMed, Embase, and Cochrane Library. Search criteria were created by a librarian. Articles published between 1985 and 2016, after the publication date of a large review in 1985, were analyzed independently by 2 neurologists to identify family history rates and pedigrees. Pedigrees were analyzed by a genetic counselor.
FINDINGS
A total of 1995 studies were found (1988 through the search criteria and 7 through other means). Forty articles met inclusion criteria: 22 large cohort studies, 1 twin-based study, and 17 case reports or small case series. Across the 22 large cohort studies, the positive family history rate of cluster headache varied between 0% and 22%, with a median of 8.2%. The largest 5 studies, of 1134, 785, 693, 609, and 500 probands each, had a positive family history in 18.0% (numerator not provided), 5.1% (40 of 785 cases), 10.0% (numerator not provided), 2.0% (12 of 609 cases), and 11.2% (56 of 500 cases), respectively. No meta-analysis was performed, given differences in methodologies. Separately, 1 twin-based study examined 37 twin pairs and reported a concordance rate of 5.4% (2 pairs). Finally, 67 pedigrees were identified. Most pedigrees (46 of 67 [69%]) were consistent with an autosomal dominant pattern, but 19 of 67 (28%) were consistent with an autosomal recessive inheritance pattern; 10 pedigrees of probable or atypical cluster headache were identified, and all were consistent with an autosomal dominant inheritance pattern. The sex ratio for cluster headache in identified pedigrees was 1.39 (103:74) in affected men and boys compared with affected women and girls, which is lower than that of the general cluster headache population.
CONCLUSIONS AND RELEVANCE
Cluster headache is an inherited disorder in a subset of families and is associated with multiple hereditary patterns. There is an unexpectedly high preponderance of women and girls with familial cluster headache; genetic subanalyses limited to female participants are necessary to further explore this observation, because these data are otherwise masked by the higher numbers of male participants with cluster headache. Overall, this systematic review supports the notion that familial cluster headache is likely the result of multiple susceptibility genes as well as environmental factors.
Topics: Cluster Headache; Female; Gene-Environment Interaction; Genetic Predisposition to Disease; Humans; Male; Pedigree
PubMed: 32310255
DOI: 10.1001/jamaneurol.2020.0682 -
The Veterinary Record Oct 2023
Topics: Animals; Dogs; Pedigree; Animals, Laboratory; Dog Diseases
PubMed: 37800526
DOI: 10.1002/vetr.3507 -
Genetic Epidemiology Mar 2018Construction of multifactorial disease models from epidemiological findings and their application to disease pedigrees for risk prediction is nontrivial for all but the...
Construction of multifactorial disease models from epidemiological findings and their application to disease pedigrees for risk prediction is nontrivial for all but the simplest of cases. Multifactorial Disease Risk Calculator is a web tool facilitating this. It provides a user-friendly interface, extending a reported methodology based on a liability-threshold model. Multifactorial disease models incorporating all the following features in combination are handled: quantitative risk factors (including polygenic scores), categorical risk factors (including major genetic risk loci), stratified age of onset curves, and the partition of the population variance in disease liability into genetic, shared, and unique environment effects. It allows the application of such models to disease pedigrees. Pedigree-related outputs are (i) individual disease risk for pedigree members, (ii) n year risk for unaffected pedigree members, and (iii) the disease pedigree's joint liability distribution. Risk prediction for each pedigree member is based on using the constructed disease model to appropriately weigh evidence on disease risk available from personal attributes and family history. Evidence is used to construct the disease pedigree's joint liability distribution. From this, lifetime and n year risk can be predicted. Example disease models and pedigrees are provided at the website and are used in accompanying tutorials to illustrate the features available. The website is built on an R package which provides the functionality for pedigree validation, disease model construction, and risk prediction. Website: http://grass.cgs.hku.hk:3838/mdrc/current.
Topics: Disease; Genetic Predisposition to Disease; Humans; Internet; Models, Genetic; Multifactorial Inheritance; Pedigree; Reproducibility of Results; Risk Factors; Software
PubMed: 29178360
DOI: 10.1002/gepi.22101 -
Radiation Research Jul 2022Previous epidemiological studies have demonstrated elevated susceptibility to ionizing radiation in some families, thus suggesting the presence of genetic components...
Previous epidemiological studies have demonstrated elevated susceptibility to ionizing radiation in some families, thus suggesting the presence of genetic components that conferred increased rate of radiation-associated meningioma (RAM). In this study, we exome-sequenced and investigated the segregation pattern of rare deleterious variants in 11 RAM pedigrees. In addition, we performed a rare-variant association analysis in 92 unrelated familial cases of RAM that were ancestry-matched with 88 meningioma-free controls. In the pedigree analysis, we found that each family carried mostly a unique set of rare deleterious variants. A follow-up pathway analysis of the union of the genes that segregated within each of the 11 pedigrees identified a single statistically significant (q value = 7.90E-04) "ECM receptor interaction" set. In the case-control association analysis, we observed no statistically significant variants or genes after multiple testing correction; however, examination of ontological categories of the genes that associated with RAM at nominal P values <0.01 identified biologically relevant pathways such as DNA repair, cell cycle and apoptosis. These results suggest that it is unlikely that a small number of highly penetrant genes are involved in the pathogenesis of RAM. Substantially larger studies are needed to identify genetic risk variants and genes in RAM.
Topics: Case-Control Studies; Exome; Genetic Predisposition to Disease; Humans; Pedigree; Radiation, Ionizing
PubMed: 35405740
DOI: 10.1667/RADE-21-00035.1 -
Journal of Dairy Science Jun 2024Pedigrees used in genetic evaluations contain errors. Because of such errors, assumptions regarding the relatedness among individuals in genetic evaluation models are...
Pedigrees used in genetic evaluations contain errors. Because of such errors, assumptions regarding the relatedness among individuals in genetic evaluation models are wrong. Consequences of that have been investigated in earlier studies focusing on models that did not account for genomic information yet. The objective of this work was to investigate the effects of pedigree errors on the results from genetic evaluations using the single-step model, and the effect of such effects on results from validation studies with forward prediction. We used a real pedigree (n = 361,980) and real genotypes (n = 25,950) of Fleckvieh cattle, sampled in a way to provide a good consistency between pedigree and genomic relationships. Given the real pedigree and genotypes, true breeding values (TBV) were simulated to have a covariance structure equal to the matrix H assumed in a single-step model. Based on TBV, phenotypes were simulated with a heritability of 0.25. Genetic evaluations were conducted with a conventional animal model (i.e., without genomic information) and a single-step animal model under scenarios using either the correct pedigree or a pedigree containing 5%, 10%, or 20% of wrong records. Wrong records were simulated by randomly assigning wrong sires to nongenotyped females. The increasing rates of pedigree errors led to decreasing correlations between TBV and EBV and lower standard deviations of predictions. Less variation was observed because pedigree errors operate actually as a random exchange of daughters among bulls, making them look more similar to each other than they actually are. This occurs of course only when animals have progeny. Therefore, this decreased variation was more pronounced for progeny tested bulls than for young selection candidates. In a forward prediction validation scenario, the stronger decrease in variation when animals get progeny caused an apparent inflation of early predictions. This phenomenon may contribute to the usually observed problem of inflation of early predictions observed in validation studies.
Topics: Animals; Cattle; Pedigree; Female; Genotype; Breeding; Models, Genetic; Phenotype; Male
PubMed: 38135046
DOI: 10.3168/jds.2023-24070 -
Scientific Reports Jul 2023Nowadays, the development of diagnosis and treatment technology is constantly changing the pedigree and classification of nervous system diseases. Analyzing changes in...
Nowadays, the development of diagnosis and treatment technology is constantly changing the pedigree and classification of nervous system diseases. Analyzing changes in earlier disease pedigrees can help us understand the changes involved in disease diagnosis from a macro perspective, as well as predict changes in later disease pedigrees and the direction of diagnosis and treatment. The inpatients of the neurology department from January 2012 to December 2020 in Hunan Children's Hospital were retrospectively analyzed. There were 36,777 patients enrolled in this study. The next analysis was based on factors like age, gender, length of stay (LoS), number of patients per month and per year (MNoP and ANoP, respectively), and average daily hospital cost (ADHE). To evaluate the characteristics of neurological diseases, we applied a series of statistical tools such as numerical characteristics, boxplots, density charts, one-way ANOVA, Kruskal-Wallis tests, time-series plots, and seasonally adjusted indices. The statistical analysis of neurological diseases led to the following conclusions: First, children having neurological illnesses are most likely to develop them between the ages of 4 and 8 years. Benign intracranial hypertension was the youngest mean age of onset among the various neurologic diseases, and most patients with bacterial intracranial infection were young children. Some diseases have a similar mean age of onset, such as seizures (gastroenteritis/diarrhea) and febrile convulsions. Second, women made up most patients with autoimmune diseases of the central nervous system. Treatment options for inherited metabolic encephalopathy and epilepsy are similar, but they differ significantly for viral intracranial infection. Some neurologic diseases were found to have seasonal variations; for example, infectious diseases of the central nervous system were shown to occur more commonly in the warm season, whereas, autoimmune diseases primarily appeared in the autumn and winter months. Additionally, the number of patients admitted to hospitals with intracranial infections and encephalopathy has dramatically dropped recently, but the number of patients with autoimmune diseases of the central nervous system and hereditary metabolic encephalopathy has been rising year over year. Finally, we discovered apparent polycentric distributions in various illnesses' density distributions. The study offered an epidemiological basis for common nervous system diseases, including evidence from age of onset, number of cases, and so on. The pedigree of nervous system diseases has significantly changed. The proportion of patients with neuroimmune diseases and genetic metabolic diseases is rising while the number of patients with infection-related diseases and uncertain diagnoses is decreasing. The existence of a disease multimodal model suggests that there is still a lack of understanding of many diseases' diagnosis and treatment, which needs to be improved further because accurate diagnosis aids in the formulation of individualized treatment plans and the allocation of medical resources; additionally, there is still a lack of effective treatment for most genetic diseases. The seasonal characteristics of nervous system diseases suggest the need for the improvement of sanitation, living conditions, and awareness of daily health care.
Topics: Humans; Child; Female; Child, Preschool; Male; Retrospective Studies; Pedigree; Epilepsy; Hospitalization; Autoimmune Diseases
PubMed: 37407586
DOI: 10.1038/s41598-023-35571-0 -
Journal of Dairy Science Jul 2020Single-step genomic BLUP (ssGBLUP) is a powerful approach for breeding value prediction in populations with a limited number of genotyped animals. However, conflicting...
Single-step genomic BLUP (ssGBLUP) is a powerful approach for breeding value prediction in populations with a limited number of genotyped animals. However, conflicting genomic (G) and pedigree (A) relationship matrices complicate the implementation of ssGBLUP into practice. The metafounder (MF) approach is a recently proposed solution for this problem and has been successfully used on simulated and real multi-breed pig data. Advantages of the method are easily seen across breed evaluations, where pedigrees are traced to several pure breeds, which are thereafter used as MF. Application of the MF method to ruminants is complicated due to multi-breed pedigree structures and the inability to transmit existing unknown parent groups (UPG) to MF. In this study, we apply the MF approach for ssGBLUP evaluation of Finnish Red Dairy cattle treated as a single breed. Relationships among MF were accounted for by a (co)variance matrix (Γ) computed using estimated base population allele frequencies. The attained Γ was used to calculate a relationship matrix A for the genotyped animals. We tested the influence of SNP selection on the Γ matrix by applying a minor allele frequency (MAF) threshold (Γ) where accepted markers had an MAF ≥0.05. Elements in the Γ matrix were slightly lower than in the Γ matrix. Correlation between diagonal elements of the genomic and pedigree relationship matrices increased from 0.53 (A) to 0.76 ( A and [Formula: see text] ). Average diagonal elements of A and [Formula: see text] matrices increased to the same level as in the G matrix. The ssGBLUP breeding values (GEBV) were solved using either the original 236 or redefined 8 UPG, or 8 MF computed with or without the MAF threshold. For bulls, the GEBV validation test results for the 8 UPG and 8 MF gave the same validation reliability (R; 0.31) and over-dispersion (0.73, measured by regression coefficient b). No significant R increase was observed in cows. Thus, the MF greatly influenced the pedigree relationship matrices but not the GEBV. Selection of SNP according to MAF had a notable effect on the Γ matrix and made the A and G matrices more similar.
Topics: Animals; Cattle; Female; Food, Formulated; Gene Frequency; Genome; Genomics; Genotype; Male; Models, Genetic; Pedigree; Reproducibility of Results; Selective Breeding
PubMed: 32418688
DOI: 10.3168/jds.2019-17483 -
Methods in Molecular Biology (Clifton,... 2017Data used to study human genetics are often not obtained by simple random sampling, which is assumed by many statistical methods, especially those that are based on...
Data used to study human genetics are often not obtained by simple random sampling, which is assumed by many statistical methods, especially those that are based on likelihood for making inferences. There is a well-developed theory to correct likelihoods based on sibship data whether or not the exact mode of ascertainment is known. In the case of larger pedigrees, however, the problem is much more difficult unless they are recruited into the sample by single ascertainment. There is no one piece of software that analyzes ascertainment in general, so most of this chapter is devoted to theory. A general method by which one general genetic analysis software package corrects pedigree data for ascertainment is briefly described.
Topics: Genetic Testing; Humans; Likelihood Functions; Models, Genetic; Pedigree; Probability; Software
PubMed: 28980248
DOI: 10.1007/978-1-4939-7274-6_11