No exact method for determining genotypic and identity-by-descent probabilities is available for large complex pedigrees. Approximate methods for such pedigrees cannot be guaranteed to be unbiased. A new method is proposed that uses the Metropolis-Hastings algorithm to sample a Markov chain of descent graphs which fit the pedigree and known genotypes. Unknown genotypes are determined from each descent graph. Genotypic probabilities are estimated as their means. The algorithm is shown to be unbiased for small complex pedigrees and feasible and consistent for moderately large complex pedigrees.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1017/s0016672303006232 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A Japanese Family with a Novel Pathogenic Variant in KIF1A Presenting with Spastic Paraparesis, Cerebellar Ataxia, and Intellectual Disability.
Cerebellum
December 2024
Department of Neurology, International University of Health and Welfare Mita Hospital, Mita 1-4-3, Minato-ku, Tokyo, 108-8329, Japan.
Variants in KIF1A are associated with hereditary spastic paraplegia (SPG30), which can manifest in both pure and complex forms. We describe a Japanese family with a novel KIF1A variant presenting with a complex form of SPG30. Patient 1, a 69-year-old woman, experienced progressive gait disturbance due to spastic paraparesis and cerebellar atrophy, and intellectual disability.
View Article and Find Full Text PDFIntegrating Gene Expression Data into Single-Step Method (ssBLUP) Improves Genomic Prediction Accuracy for Complex Traits of Duroc × Erhualian F Pig Population.
Curr Issues Mol Biol
December 2024
The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan 430070, China.
The development of multi-omics has increased the likelihood of further improving genomic prediction (GP) of complex traits. Gene expression data can directly reflect the genotype effect, and thus, they are widely used for GP. Generally, the gene expression data are integrated into multiple random effect models as independent data layers or used to replace genotype data for genomic prediction.
View Article and Find Full Text PDFUnraveling ptosis: a comprehensive review of clinical manifestations, genetics, and treatment.
Prog Retin Eye Res
December 2024
Health Management Center, the Third Xiangya Hospital, Central South University, Changsha 410013, China; Research Center of Medical Experimental Technology, the Third Xiangya Hospital, Central South University, Changsha 410013, China; Center for Experimental Medicine, the Third Xiangya Hospital, Central South University, Changsha 410013, China; Disease Genome Research Center, Central South University, Changsha 410013, China. Electronic address:
Ptosis is defined as an abnormally low-lying upper eyelid margin on the primary gaze, generally resulting from a congenital or acquired abnormality of the nerves or muscles that control the eyelid. Ptosis can occur alone or concurrently as an ocular or systemic syndrome, and the prevalence of ptosis varies among different countries and populations. Isolated ptosis typically causes aesthetic problems in patients and can lead to functional ophthalmic problems in severe cases.
View Article and Find Full Text PDFEnhancing patient representation learning with inferred family pedigrees improves disease risk prediction.
J Am Med Inform Assoc
December 2024
Statistical Modeling, Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riβ 88400, Germany.
Background: Machine learning and deep learning are powerful tools for analyzing electronic health records (EHRs) in healthcare research. Although family health history has been recognized as a major predictor for a wide spectrum of diseases, research has so far adopted a limited view of family relations, essentially treating patients as independent samples in the analysis.
Methods: To address this gap, we present ALIGATEHR, which models inferred family relations in a graph attention network augmented with an attention-based medical ontology representation, thus accounting for the complex influence of genetics, shared environmental exposures, and disease dependencies.
Genetic analysis of patients with low-frequency non-syndromic hearing loss.
Mol Genet Genomics
December 2024
ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, 83 Fen Yang Road, Shanghai, 200031, China.
Low-frequency non-syndromic hearing loss (LFNSHL) is a rare auditory disorder affecting frequencies ≤ 2000 Hz. To elucidate its genetic basis, we conducted whole-exome sequencing on nine Chinese families (31 affected individuals) with LFNSHL. Four heterozygous pathogenic variants, including two novel variants, were identified in common LFNSHL-related genes (WFS1, DIAPH1) and less common genes (TNC, EYA4), achieving a 44% genetic diagnosis rate.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!