On selecting markers for association studies: Patterns of linkage disequilibrium between two and three diallelic loci

2002 ◽  
Vol 24 (1) ◽  
pp. 57-67 ◽  
Author(s):  
Chad Garner ◽  
Montgomery Slatkin
Keyword(s):  
Linkage Disequilibrium ◽  
Association Studies

scholarly journals Challenges of Adjusting Single-Nucleotide Polymorphism Effect Sizes for Linkage Disequilibrium

2021 ◽  
pp. 1-11
Author(s):  
Valentina Escott-Price ◽  
Karl Michael Schmidt
Keyword(s):  
Linkage Disequilibrium ◽  
Association Studies ◽  
Statistical Significance ◽  
Ordinary Least Squares ◽  
Effect Sizes ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Tikhonov Regularisation

<b><i>Background:</i></b> Genome-wide association studies (GWAS) were successful in identifying SNPs showing association with disease, but their individual effect sizes are small and require large sample sizes to achieve statistical significance. Methods of post-GWAS analysis, including gene-based, gene-set and polygenic risk scores, combine the SNP effect sizes in an attempt to boost the power of the analyses. To avoid giving undue weight to SNPs in linkage disequilibrium (LD), the LD needs to be taken into account in these analyses. <b><i>Objectives:</i></b> We review methods that attempt to adjust the effect sizes (β<i>-</i>coefficients) of summary statistics, instead of simple LD pruning. <b><i>Methods:</i></b> We subject LD adjustment approaches to a mathematical analysis, recognising Tikhonov regularisation as a framework for comparison. <b><i>Results:</i></b> Observing the similarity of the processes involved with the more straightforward Tikhonov-regularised ordinary least squares estimate for multivariate regression coefficients, we note that current methods based on a Bayesian model for the effect sizes effectively provide an implicit choice of the regularisation parameter, which is convenient, but at the price of reduced transparency and, especially in smaller LD blocks, a risk of incomplete LD correction. <b><i>Conclusions:</i></b> There is no simple answer to the question which method is best, but where interpretability of the LD adjustment is essential, as in research aiming at identifying the genomic aetiology of disorders, our study suggests that a more direct choice of mild regularisation in the correction of effect sizes may be preferable.

scholarly journals A Novel Method for Mendelian Randomization Analyses With Pleiotropy and Linkage Disequilibrium in Genetic Variants From Individual Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuquan Wang ◽  
Tingting Li ◽  
Liwan Fu ◽  
Siqian Yang ◽  
Yue-Qing Hu
Keyword(s):  
Linkage Disequilibrium ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Random Effect ◽  
Pleiotropic Effect ◽  
Genome Wide Association Studies ◽  
The Novel ◽  
Novel Method

Mendelian randomization makes use of genetic variants as instrumental variables to eliminate the influence induced by unknown confounders on causal estimation in epidemiology studies. However, with the soaring genetic variants identified in genome-wide association studies, the pleiotropy, and linkage disequilibrium in genetic variants are unavoidable and may produce severe bias in causal inference. In this study, by modeling the pleiotropic effect as a normally distributed random effect, we propose a novel mixed-effects regression model-based method PLDMR, pleiotropy and linkage disequilibrium adaptive Mendelian randomization, which takes linkage disequilibrium into account and also corrects for the pleiotropic effect in causal effect estimation and statistical inference. We conduct voluminous simulation studies to evaluate the performance of the proposed and existing methods. Simulation results illustrate the validity and advantage of the novel method, especially in the case of linkage disequilibrium and directional pleiotropic effects, compared with other methods. In addition, by applying this novel method to the data on Atherosclerosis Risk in Communications Study, we conclude that body mass index has a significant causal effect on and thus might be a potential risk factor of systolic blood pressure. The novel method is implemented in R and the corresponding R code is provided for free download.

scholarly journals A New Diversity Panel for Winter Rapeseed (Brassica napus L.) Genome-Wide Association Studies

Agronomy ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 2006
Author(s):  
David P. Horvath ◽  
Michael Stamm ◽  
Zahirul I. Talukder ◽  
Jason Fiedler ◽  
Aidan P. Horvath ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Brassica Napus ◽  
Association Studies ◽  
Decay Rates ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
High Quality ◽  
Brassica Napus L ◽  
Genome Wide ◽  
Quality Markers

A diverse population (429 member) of canola (Brassica napus L.) consisting primarily of winter biotypes was assembled and used in genome-wide association studies. Genotype by sequencing analysis of the population identified and mapped 290,972 high-quality markers ranging from 18.5 to 82.4% missing markers per line and an average of 36.8%. After interpolation, 251,575 high-quality markers remained. After filtering for markers with low minor allele counts (count > 5), we were left with 190,375 markers. The average distance between these markers is 4463 bases with a median of 69 and a range from 1 to 281,248 bases. The heterozygosity among the imputed population ranges from 0.9 to 11.0% with an average of 5.4%. The filtered and imputed dataset was used to determine population structure and kinship, which indicated that the population had minimal structure with the best K value of 2–3. These results also indicated that the majority of the population has substantial sequence from a single population with sub-clusters of, and admixtures with, a very small number of other populations. Analysis of chromosomal linkage disequilibrium decay ranged from ~7 Kb for chromosome A01 to ~68 Kb for chromosome C01. Local linkage decay rates determined for all 500 kb windows with a 10kb sliding step indicated a wide range of linkage disequilibrium decay rates, indicating numerous crossover hotspots within this population, and provide a resource for determining the likely limits of linkage disequilibrium from any given marker in which to identify candidate genes. This population and the resources provided here should serve as helpful tools for investigating genetics in winter canola.

scholarly journals MR-LDP: a two-sample Mendelian randomization for GWAS summary statistics accounting for linkage disequilibrium and horizontal pleiotropy

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Qing Cheng ◽  
Yi Yang ◽  
Xingjie Shi ◽  
Kar-Fu Yeung ◽  
Can Yang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Linkage Disequilibrium ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Alternative Methods ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Causal Relationships ◽  
Disease Outcomes

Abstract The proliferation of genome-wide association studies (GWAS) has prompted the use of two-sample Mendelian randomization (MR) with genetic variants as instrumental variables (IVs) for drawing reliable causal relationships between health risk factors and disease outcomes. However, the unique features of GWAS demand that MR methods account for both linkage disequilibrium (LD) and ubiquitously existing horizontal pleiotropy among complex traits, which is the phenomenon wherein a variant affects the outcome through mechanisms other than exclusively through the exposure. Therefore, statistical methods that fail to consider LD and horizontal pleiotropy can lead to biased estimates and false-positive causal relationships. To overcome these limitations, we proposed a probabilistic model for MR analysis in identifying the causal effects between risk factors and disease outcomes using GWAS summary statistics in the presence of LD and to properly account for horizontal pleiotropy among genetic variants (MR-LDP) and develop a computationally efficient algorithm to make the causal inference. We then conducted comprehensive simulation studies to demonstrate the advantages of MR-LDP over the existing methods. Moreover, we used two real exposure–outcome pairs to validate the results from MR-LDP compared with alternative methods, showing that our method is more efficient in using all-instrumental variants in LD. By further applying MR-LDP to lipid traits and body mass index (BMI) as risk factors for complex diseases, we identified multiple pairs of significant causal relationships, including a protective effect of high-density lipoprotein cholesterol on peripheral vascular disease and a positive causal effect of BMI on hemorrhoids.

Sign in / Sign up

Export Citation Format

Share Document