Heterogeneity in Statistical Genetics: How to Assess, Address, and Account for Mixtures in Association Studies Series: Statistics for Biology and Health, by DerekGordon, Stephen J.Finch, WonkukKim2020. Heterogeneity in Statistical Genetics: How to Assess, Address, and Account for Mixtures in Association Studies (1st ed.). Cham: Springer (2020). Springer Series Statistics for Biology and Health. 352 pages. ISBN: 978‐3‐030‐61120‐0. https://doi.org/10.1007/978-3-030-61121-7

2021 ◽  
Author(s):  
Michael Nothnagel
Keyword(s):  
Association Studies ◽  
Statistical Genetics ◽  
Springer Series

Principals about principal components in statistical genetics

2018 ◽  
Vol 20 (6) ◽  
pp. 2200-2216 ◽  
Author(s):  
Fentaw Abegaz ◽  
Kridsadakorn Chaichoompu ◽  
Emmanuelle Génin ◽  
David W Fardo ◽  
Inke R König ◽  
...  
Keyword(s):  
Principal Components ◽  
Rare Variants ◽  
Association Studies ◽  
Meta Analysis ◽  
Principal Component ◽  
Statistical Genetics ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Study Results

Abstract Principal components (PCs) are widely used in statistics and refer to a relatively small number of uncorrelated variables derived from an initial pool of variables, while explaining as much of the total variance as possible. Also in statistical genetics, principal component analysis (PCA) is a popular technique. To achieve optimal results, a thorough understanding about the different implementations of PCA is required and their impact on study results, compared to alternative approaches. In this review, we focus on the possibilities, limitations and role of PCs in ancestry prediction, genome-wide association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detection. We also describe several variations of classic PCA that deserve increased attention in statistical genetics applications.

scholarly journals VarExp: Estimating variance explained by Genome-Wide GxE summary statistics

2017 ◽  
Author(s):  
Vincent Laville ◽  
Amy R. Bentley ◽  
Florian Privé ◽  
Xiafoeng Zhu ◽  
Jim Gauderman ◽  
...  
Keyword(s):  
Association Studies ◽  
Statistical Genetics ◽  
Interaction Effects ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Ethical Challenges ◽  
Gxe Interaction ◽  
Genome Wide ◽  
Variance Explained

AbstractMany genomic analyses, such as genome-wide association studies (GWAS) or genome-wide screening for Gene-Environment (GxE) interactions have been performed to elucidate the underlying mechanisms of human traits and diseases. When the analyzed outcome is quantitative, the overall contribution of identified genetic variants to the outcome is often expressed as the percentage of phenotypic variance explained. In practice, this is commonly estimated using individual genotype data. However, using individual-level data faces practical and ethical challenges when the GWAS results are derived in large consortia through meta-analysis of results from multiple cohorts. In this work, we present a R package, “VarExp”, that allows for the estimation of the percentage of phenotypic variance explained by variants of interest using summary statistics only. Our package allows for a range of models to be evaluated, including marginal genetic effects, GxE interaction effects, and main genetic and interaction effects jointly. Its implementation integrates all recent methodological developments on the topic and does not need external data to be uploaded by users.The R source code, tutorial and associated example are available at https://gitlab.pasteur.fr/statistical-genetics/VarExp.git.

scholarly journals RAISS: Robust and Accurate imputation from Summary Statistics

2018 ◽  
Author(s):  
Hanna Julienne ◽  
Huwenbo Shi ◽  
Bogdan Pasaniuc ◽  
Hugues Aschard
Keyword(s):  
Effect Size ◽  
Association Studies ◽  
Real Data ◽  
Statistical Genetics ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Link Type ◽  
Small Effect Size ◽  
Python Package

AbstractMotivationMulti-trait analyses using public summary statistics from genome-wide association studies (GWAS) are becoming increasingly popular. A constraint of multi-trait methods is that they require complete summary data for all traits. While methods for the imputation of summary statistics exist, they lack precision for genetic variants with small effect size. This is benign for univariate analyses where only variants with large effect size are selected a posteriori. However, it can lead to strong p-value inflation in multi-trait testing. Here we present a new approach that improve the existing imputation methods and reach a precision suitable for multi-trait analyses.ResultsWe fine-tuned parameters to obtain a very high accuracy imputation from summary statistics. We demonstrate this accuracy for small size-effect variants on real data of 28 GWAS. We implemented the resulting methodology in a python package specially designed to efficiently impute multiple GWAS in parallel.AvailabilityThe python package is available at: https://gitlab.pasteur.fr/statistical-genetics/raiss, its accompanying documentation is accessible here http://statistical-genetics.pages.pasteur.fr/raiss/[email protected]

Serotonergic Genes and Suicidality

Crisis ◽  
2001 ◽  
Vol 22 (2) ◽  
pp. 54-60 ◽  
Author(s):  
Lisheng Du ◽  
Gabor Faludi ◽  
Miklos Palkovits ◽  
David Bakish ◽  
Pavel D. Hrdina
Keyword(s):  
Suicidal Ideation ◽  
Suicidal Behavior ◽  
Tryptophan Hydroxylase ◽  
Association Studies ◽  
Receptor Gene ◽  
Brain Regions ◽  
Depressive Illness ◽  
Control Group ◽  
Serotonergic Activity ◽  
Completed Suicide

Summary: Several lines of evidence indicate that abnormalities in the functioning of the central serotonergic system are involved in the pathogenesis of depressive illness and suicidal behavior. Studies have shown that the number of brain and platelet serotonin transporter binding sites are reduced in patients with depression and in suicide victims, and that the density of 5-HT2A receptors is increased in brain regions of depressed in suicide victims and in platelets of depressed suicidal patients. Genes that code for proteins, such as tryptophan hydroxylase, 5-HT transporter, and 5-HT2A receptor, involved in regulating serotonergic neurotransmission, have thus been major candidate genes for association studies of suicide and suicidal behavior. Recent studies by our group and by others have shown that genetic variations in the serotonin-system-related genes might be associated with suicidal ideation and completed suicide. We have shown that the 102 C allele in 5-HT2A receptor gene was significantly associated with suicidal ideation (χ2 = 8.5, p < .005) in depressed patients. Patients with a 102 C/C genotype had a significantly higher mean HAMD item #3 score (indication of suicidal ideation) than T/C or T/T genotype patients. Our results suggest that the 102T/C polymorphism in 5-HT2A receptor gene is primarily associated with suicidal ideation in patients with major depression and not with depression itself. We also found that the 5-HT transporter gene S/L polymorphism was significantly associated with completed suicide. The frequency of the L/L genotype in depressed suicide victims was almost double of that found in control group (48.6% vs. 26.2%). The odds ratio for the L allele was 2.1 (95% CI 1.2-3.7). The association between polymorphism in serotonergic genes and suicidality supports the hypothesis that genetic factors can modulate suicide risk by influencing serotonergic activity.

Sign in / Sign up

Export Citation Format

Share Document