Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...

Phylogenetic Trees

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.

Phylogenetic Trees

Phylogenetic Trees

Phylogeny

Phylogeny

Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.

Genetic Drift

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

TIDEST: post-imputation differential expression testing for spatial transcriptomics data.

bioRxiv : the preprint server for biology·2026

Same author

Spatiotemporal analysis of autism gene enrichment implicates cortex, thalamus, and hypothalamus.

bioRxiv : the preprint server for biology·2026

Same author

Spatial transcriptomics implicates the thalamus and cortex in autism and schizophrenia.

bioRxiv : the preprint server for biology·2026

Same author

Modeling rare coding variation on chromosome X provides insight into the genetics and differential sex prevalence of autism spectrum disorder.

medRxiv : the preprint server for health sciences·2026

Same author

Estimating protein isoform abundances with PAQu.

bioRxiv : the preprint server for biology·2026

Same author

A framework to infer de novo exonic variants when parental genotypes are missing enhances association studies of autism.

Bioinformatics (Oxford, England)·2026

Same journal

Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention.

The annals of applied statistics·2026

Same journal

A NOVEL BAYESIAN FRAMEWORK UNCOVERING BRAIN CONNECTIVITY-TO-SHAPE RELATIONSHIP IN PRECLINICAL ALZHEIMER'S DISEASE.

The annals of applied statistics·2026

Same journal

EVALUATING MULTIPLEX DIAGNOSTIC TEST USING PARTIALLY ORDERED BAYES CLASSIFIER.

The annals of applied statistics·2026

Same journal

BRIDGING THE GAP: ENHANCING THE GENERALIZABILITY OF EPIGENETIC CLOCKS THROUGH TRANSFER LEARNING.

The annals of applied statistics·2026

Same journal

TREATMENT EFFECT HETEROGENEITY AND IMPORTANCE MEASURES FOR MULTIVARIATE CONTINUOUS TREATMENTS.

The annals of applied statistics·2026

Same journal

FEDERATED LEARNING OF ROBUST INDIVIDUALIZED DECISION RULES WITH APPLICATION TO HETEROGENEOUS MULTIHOSPITAL SEPSIS POPULATION.

The annals of applied statistics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 2, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

REFINING GENETICALLY INFERRED RELATIONSHIPS USING TREELET COVARIANCE SMOOTHING.

Andrew Crossett¹, Ann B Lee¹, Lambertus Klei¹

¹West Chester University, Carnegie Mellon University, University of Pittsburgh School of Medicine, University of Pittsburgh School of Medicine and Carnegie Mellon University.

The Annals of Applied Statistics

|March 4, 2014

Summary

This summary is machine-generated.

We developed Treelet Covariance Smoothing to improve genetic relationship estimates, especially for distant relatives. This method enhances heritability calculations using population data, advancing genetic studies.

Keywords:

Covariance estimation cryptic relatedness genome-wide association heritability kinship

More Related Videos

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

Published on: October 16, 2018

Related Experiment Videos

Last Updated: May 2, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

Published on: October 16, 2018

Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Technological advances and large datasets reveal genetic factors for traits and diseases.
Estimating familial relationships, especially distant ones, from genetic markers can be imprecise.
Accurate relationship inference is crucial for understanding genetic contributions to complex traits.

Purpose of the Study:

To introduce a novel method, Treelet Covariance Smoothing, for denoising genetically inferred relationship matrices.
To improve the accuracy of estimating pairwise genetic relatedness, particularly for distant relatives.
To enable more precise heritability estimation in population-based samples.

Main Methods:

Treelet Covariance Smoothing utilizes multiscale decomposition of covariance matrices.
The method exploits hierarchical structures within correlated individuals.
Applied to both simulated and real genetic data for relationship matrix refinement.

Main Results:

Smoothed relationship matrices yield more accurate estimates of relatedness among distant relatives.
Demonstrated accurate heritability estimation for body mass index using population data.
The method shows significant improvements over traditional approaches for noisy relationship data.

Conclusions:

Treelet Covariance Smoothing effectively refines genetic relationship matrices and improves heritability estimates.
Enables heritability estimation from population-based samples, expanding traditional methods.
The approach has broad statistical applications, including regularization of structured matrices.