Musings on Visscher et al. (2006)

2020 ◽  
Vol 23 (2) ◽  
pp. 107-108
Author(s):  
Peter M. Visscher
Keyword(s):  
Identity By Descent ◽  
Twin Design ◽  
Sibling Pairs ◽  
Environmental Similarity ◽  
Genome Wide ◽  
Trait Similarity ◽  
Classical Twin Design ◽  
Equal Environments Assumption ◽  
Unbiased Estimates ◽  
Equal Environments

AbstractThe classical twin design relies on a number of strong number of assumptions in order to yield unbiased estimates of heritability. This includes the equal environments assumption — that monozygotic and dizygotic twins experience similar degrees of environmental similarity — an assumption that is likely to be violated in practice for many traits of interest. An alternative method of estimating heritability that does not suffer from many of these limitations is to model trait similarity between sibling pairs as a function of their empirical genome-wide identity by descent sharing, estimated from genetic markers. In this review, I recount the story behind Nick Martin’s and my development of this method, our first attempts at applying it in a human population and more recent studies using the original and related methods to estimate trait heritability.

scholarly journals Genomics of ageing in twins

2014 ◽  
Vol 73 (4) ◽  
pp. 526-531 ◽  
Author(s):  
Massimo Mangino
Keyword(s):  
Molecular Mechanisms ◽  
Twin Studies ◽  
Later Life ◽  
Twin Design ◽  
The Past ◽  
Human Ageing ◽  
Classical Twin Design ◽  
Environmental Component ◽  
Early Life Events ◽  
Generation Sequencing

Ageing is a complex multifactorial process, reflecting the progression of all degenerative pathways within an organism. Due to the increase of life expectancy, in recent years, there is a pressing need to identify early-life events and risk factors that determine health outcomes in later life. So far, genetic variation only explains ~20–25 % of the variability of human survival to age 80+. This clearly implies that other factors (environmental, epigenetic and lifestyle) contribute to lifespan and the rate of healthy ageing within an individual. Twin studies in the past two decades proved to be a very powerful tool to discriminate the genetic from the environmental component. The aim of this review is to describe the basic concepts of the twin study design and to report some of the latest studies in which high-throughput technologies (e.g. genome/epigenome-wide assay, next generation sequencing, MS metabolic profiling) combined with the classical twin design have been applied to the analysis of novel ‘omics’ to further understand the molecular mechanisms of human ageing.

Twins: a test of the equal environments assumption

1990 ◽  
Vol 81 (4) ◽  
pp. 322-326 ◽  
Author(s):  
A. Morris-Yates ◽  
G. Andrews ◽  
P. Howie ◽  
S. Henderson
Keyword(s):  
Equal Environments Assumption ◽  
Equal Environments

scholarly journals Heritability and the Equal Environments Assumption: Evidence from Multiple Samples of Misclassified Twins

2013 ◽  
Vol 43 (5) ◽  
pp. 415-426 ◽  
Author(s):  
Dalton Conley ◽  
Emily Rauscher ◽  
Christopher Dawes ◽  
Patrik K. E. Magnusson ◽  
Mark L. Siegal
Keyword(s):  
Equal Environments Assumption ◽  
Multiple Samples ◽  
Equal Environments

Genome-wide linkage scan for exercise stroke volume and cardiac output in the HERITAGE Family Study

2002 ◽  
Vol 10 (2) ◽  
pp. 57-62 ◽  
Author(s):  
Tuomo Rankinen ◽  
Ping An ◽  
Louis Pérusse ◽  
Treva Rice ◽  
Yvon C. Chagnon ◽  
...  
Keyword(s):  
Cardiac Output ◽  
Stroke Volume ◽  
Black Family ◽  
Potential Candidate ◽  
Nuclear Families ◽  
Sibling Pairs ◽  
Suggestive Evidence ◽  
Genome Wide ◽  
A Genome ◽  
Analytical Strategies

A genome-wide linkage scan was performed for genes affecting submaximal exercise cardiac output (Q) and stroke volume (SV) in the sedentary state and their responses to a standardized 20-wk endurance training program. A total of 509 polymorphic markers were used, and 328 pairs of siblings from 99 white nuclear families and 102 sibling pairs from 105 black family units were available. Q and SV were measured in relative steady state during exercise at 50 W (Q50 and SV50, respectively). Baseline phenotypes were adjusted for age, sex, and body surface area (BSA), and the training responses (post-training − baseline, Δ) were adjusted for age, sex, baseline BSA, and baseline value of the phenotype. Three analytical strategies were used: a multipoint variance components linkage analysis using all the family data, and regression-based single- and multipoint linkage analyses using pairs of siblings. In whites, baseline SV50 and ΔSV50 showed promising linkages ( P < 0.0023) with markers on chromosomes 14q31.1 and 10p11.2, respectively. Suggestive evidence of linkage (0.01 > P > 0.0023) for ΔSV50 and Δ Q50 was detected on chromosome 2q31.1 and for baseline SV50 and Q50 on chromosome 9q32-q33. In blacks, markers on 18q11.2 showed promising linkages with baseline Q50. Suggestive evidence of linkage was found in three regions for baseline SV50 (1p21.3, 3q13.3, 12q13.2) and one for baseline SV50 and Q50 (10p14). All these chromosomal regions include several potential candidate genes and therefore warrant further studies in the HERITAGE cohort and other studies.

scholarly journals HAPPI GWAS: Holistic Analysis with Pre and Post Integration GWAS

2020 ◽  
Author(s):  
Marianne L. Slaten ◽  
Yen On Chan ◽  
Vivek Shrestha ◽  
Alexander E. Lipka ◽  
Ruthie Angelovici
Keyword(s):  
Association Studies ◽  
Phenotypic Traits ◽  
Genome Wide Association Studies ◽  
Sequencing Data ◽  
Gwas Analysis ◽  
Genome Wide ◽  
Large Populations ◽  
Unbiased Estimates ◽  
Best Linear Unbiased ◽  
Automated Pipeline

AbstractMotivationAdvanced publicly available sequencing data from large populations have enabled in-formative genome-wide association studies (GWAS) that associate SNPs with phenotypic traits of interest. Many publicly available tools able to perform GWAS have been developed in response to increased demand. However, these tools lack a comprehensive pipeline that includes both pre-GWAS analysis such as outlier removal, data transformation, and calculation of Best Linear Unbiased Predictions (BLUPs) or Best Linear Unbiased Estimates (BLUEs). In addition, post-GWAS analysis such as haploblock analysis and candidate gene identification are lacking.ResultsHere, we present HAPPI GWAS, an open-source GWAS tool able to perform pre-GWAS, GWAS, and post-GWAS analysis in an automated pipeline using the command-line interface.AvailabilityHAPPI GWAS is written in R for any Unix-like operating systems and is available on GitHub (https://github.com/Angelovici-Lab/HAPPI.GWAS.git)[email protected]

Classical twin design in modern pharmacogenomics studies

2010 ◽  
Vol 11 (2) ◽  
pp. 215-226 ◽  
Author(s):  
Nilüfer Rahmioğlu ◽  
Kourosh R Ahmadi
Keyword(s):  
Twin Design ◽  
Classical Twin Design

scholarly journals Gene-Environment Interplay in Twin Models

2013 ◽  
Vol 21 (3) ◽  
pp. 368-389 ◽  
Author(s):  
Brad Verhulst ◽  
Peter K. Hatemi
Keyword(s):  
Genetic Similarity ◽  
Behavior Genetics ◽  
Empirical Studies ◽  
Environment Interaction ◽  
Twin Design ◽  
Gene Environment Interaction ◽  
Twin Model ◽  
Gene Environment ◽  
Twin Models ◽  
Classical Twin Design

In this article, we respond to Shultziner's critique that argues that identical twins are more alike not because of genetic similarity, but because they select into more similar environments and respond to stimuli in comparable ways, and that these effects bias twin model estimates to such an extent that they are invalid. The essay further argues that the theory and methods that undergird twin models, as well as the empirical studies which rely upon them, are unaware of these potential biases. We correct this and other misunderstandings in the essay and find that gene-environment (GE) interplay is a well-articulated concept in behavior genetics and political science, operationalized as gene-environment correlation and gene-environment interaction. Both are incorporated into interpretations of the classical twin design (CTD) and estimated in numerous empirical studies through extensions of the CTD. We then conduct simulations to quantify the influence of GE interplay on estimates from the CTD. Due to the criticism's mischaracterization of the CTD and GE interplay, combined with the absence of any empirical evidence to counter what is presented in the extant literature and this article, we conclude that the critique does not enhance our understanding of the processes that drive political traits, genetic or otherwise.

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