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Results for “trait imputation”

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41 records · Page 3Linked to original sources

PubMed2004

A new method for computing the multipoint posterior probability of linkage.

The posterior probability of linkage (PPL) is a Bayesian statistic which directly measures the probability of linkage between a trait locus and a marker (in the 2-point case) or a genomic region (in the multipoint case). It has several benefits, including ease of interpretation, the ability to incorporate prior genomic information, and a mathematically rigorous and robust procedure for accumulating linkage information across multiple heterogeneous datasets. To date, the majority of work on the PPL has focused on the development of the 2-point statistic, with only preliminary attempts at the development of an equivalent multipoint version. In this paper we present a new way of computing of the multipoint PPL. This new version imputes to each genomic point an estimate of the 2-point PPL we would have obtained from a fully informative marker giving similar evidence for linkage. This version, which we call the imputed PPL, is shown to be superior to previously developed versions.

Bayes Theorem↗

PubMed2003

Genome-wide linkage analysis of the tracking of systolic blood pressure using a mixed model.

BACKGROUND: Elevated blood pressure in middle age is a major risk factor for subsequent cardiovascular complications. An important longitudinal characteristic of blood pressure is the "tracking phenomenon". Tracking is defined as the persistence of the rank of a person's blood pressure level in a group over a long period of time. In this analysis, we used the Framingham data to investigate whether there are some genes responsible for this phenomenon. RESULTS: Both two-point and multipoint linkage analyses were applied to family members with complete data only and to all family data with missing values imputed by a Gaussian model. The results of two-point linkage analysis indicated that two loci for linkage with the intercept were on chromosomes 10 and 13, and two loci for linkage with both slope and intercept were on chromosomes 1 and 3. Multipoint linkage analysis indicated only one region, 200-240 cM on chromosome 1, to be linked with both intercept and slope. For the intercept of SBP, the highest LOD (4.43) was found at 214 cM when missing data were imputed, and the highest LOD (2.81) was at 231 cM for the complete case data. For the slope of SBP, the highest multipoint LODs were 3.63 at 227 cM and 2.02 at 234 cM for the complete case data and imputation data, respectively. CONCLUSION: One or more genes in the range of 200-240 cM on chromosome 1 may be related to the tracking phenomenon of SBP.

Adult↗

PubMed2025

Inference and visualization of complex genotype-phenotype maps with gpmap-tools.

Understanding how biological sequences give rise to observable traits, that is, how genotype maps to phenotype, is a central goal in biology. Yet our knowledge of genotype-phenotype maps in natural systems is limited due to the high dimensionality of sequence space and the context-dependent effects of mutations. The emergence of Multiplex assays of variant effect (MAVEs), along with large collections of natural sequences, offer new opportunities to empirically characterize these maps at an unprecedented scale. However, tools for statistical and exploratory analysis of these high-dimensional data are still needed. To address this gap, we developed gpmap-tools (https://github.com/cmarti/gpmap-tools), a python library that integrates a series of models for inference, phenotypic imputation, and error estimation from MAVE data or collections of natural sequences in the presence of genetic interactions of every possible order. gpmap-tools also provides methods for summarizing patterns of epistasis and visualization of genotype-phenotype maps containing up to millions of genotypes. To demonstrate its utility, we used gpmap-tools to infer genotype-phenotype maps containing 262,144 variants of the Shine-Dalgarno sequence from both genomic 5'UTR sequences and experimental MAVE data. Visualization of the inferred landscapes consistently revealed high-fitness ridges that link core motifs at different distances from the start codon. In summary, gpmap-tools provides a flexible, interpretable framework for studying complex genotype-phenotype maps, opening new avenues for understanding the architecture of genetic interactions and their evolutionary consequences.

Gaussian process↗

PubMed2005

Effect of item redundancy on Rasch item and person estimates.

One of the assumptions of many latent trait models is local independence. This assumption specifies that, after controlling for the underlying trait, item responses are independent. Given the lack of studies of model robustness against such violations, it appears that this assumption is frequently taken for granted. Therefore, this study investigated the robustness of Rasch item and person estimates with simulated data under varying number of items, sample sizes, and levels of item redundancy. Item and person reliabilities, the standard deviations of the person and item estimates, the root mean squared differences and mean signed differences among person and item estimates, the correlations between person estimates, and the percentage of person estimates shifting by more than .50 logits were used to evaluate the impact of item redundancy. Both norm and criterion-reference interpretations may be influenced by the imputation of redundancy into the data. However, it appears that the amount of redundancy needs to be considerable before such interpretations would be adversely impacted. Suggestions for further simulation research are provided.

Humans↗

PubMed2023

Towards mechanistic models of mutational effects: Deep learning on Alzheimer's Aβ peptide.

Deep Mutational Scanning (DMS) has enabled multiplexed measurement of mutational effects on protein properties, including kinematics and self-organization, with unprecedented resolution. However, potential bottlenecks of DMS characterization include experimental design, data quality, and depth of mutational coverage. Here, we apply deep learning to comprehensively model the mutational effect of the Alzheimer's Disease associated peptide Aβ42 on aggregation-related biochemical traits from DMS measurements. Among tested neural network architectures, Convolutional Neural Networks and Recurrent Neural Networks are found to be the most cost-effective models with high performance even under insufficiently-sampled DMS studies. While sequence features are essential for satisfactory prediction from neural networks, geometric-structural features further enhance the prediction performance. Notably, we demonstrate how mechanistic insights into phenotype may be extracted from the neural networks themselves suitably designed. This methodological benefit is particularly relevant for biochemical systems displaying a strong coupling between structure and phenotype such as the conformation of Aβ42 aggregate and nucleation, as shown here using a Graph Convolutional Neural Network (GCN) developed from the protein atomic structure input. In addition to accurate imputation of missing values (which here ranged up to 55% of all phenotype values at key residues), the mutationally-defined nucleation phenotype generated from a GCN shows improved resolution for identifying known disease-causing mutations relative to the original DMS phenotype. Our study suggests that neural network derived sequence-phenotype mapping can be exploited not only to provide direct support for protein engineering or genome editing but also to facilitate therapeutic design with the gained perspectives from biological modeling.

Alzheimer's disease↗