Mixed Models for Case-Control Genome-Wide Association Studies: Major Challenges and Partial Solutions

Golan, David E.; Lin, Dan-Yu

doi:10.1201/9781315154084-27

Cited by 6 publications

(6 citation statements)

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“…Importantly, not only does LMM provide a control for confounding due to sample structure, but it also increases the statistical power to detect causal variants and enables estimation of heritability explained by genotyped markers [25]. This is achieved by applying a correction that is specific to a given type of sample structure [26,27].…”

Section: Box 1: Statistical Methods In Host Gwasmentioning

confidence: 99%

Tantalizing dilemma in risk prediction from disease scoring statistics

Awany

Allali

Chimusa

2019

Briefings in Functional Genomics

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Over the past decade, human host genome-wide association studies (GWASs) have contributed greatly to our understanding of the impact of host genetics on phenotypes. Recently, the microbiome has been recognized as a complex trait in host genetic variation, leading to microbiome GWAS (mGWASs). For these, many different statistical methods and software tools have been developed for association mapping. Applications of these methods and tools have revealed several important findings; however, the establishment of causal factors and the direction of causality in the interactive role between human genetic polymorphisms, the microbiome and the host phenotypes are still a huge challenge. Here, we review disease scoring approaches in host and mGWAS and their underlying statistical methods and tools. We highlight the challenges in pinpointing the genetic-associated causal factors in host and mGWAS and discuss the role of multi-omic approach in disease scoring statistics that may provide a better understanding of human phenotypic variation by enabling further system biological experiment to establish causality.

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Section: Box 1: Statistical Methods In Host Gwasmentioning

confidence: 99%

Tantalizing dilemma in risk prediction from disease scoring statistics

Awany

Allali

Chimusa

2019

Briefings in Functional Genomics

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“…. , ) is a vector of random effects and is a vector of random errors, where ~ 0, and ~ (0, ) [2,3]. The formulation of this model for continuous variation in human population has been useful in advancing the human genetics variation [4,5].…”

Section: Overviewmentioning

confidence: 99%

“…In genetic association studies, extension of this LMM has been studied in order to estimate heritability of traits, capture genetic relatedness and breed values of individuals and location of quantitative trait loci (QTL) [3,7]. This LMM has gained popularity in testing for association in genome-wide association studies (GWAS) because of their demonstrated effectiveness in accounting for relatedness among samples and in controlling population stratification 2 and other confounding factors [8,9,10]. It tackles confounders using measures of genetic similarity to capture the probabilities that the pairs of individuals have causative alleles in common [11], and such measures include those based on identity by descent and the realised relationship matrix (RRM) [12].…”

Section: Overviewmentioning

confidence: 99%

Computational Generalization of Mixed Models on Large-Scale Data with Applications to Genetic Studies

Wanyonyi

Mbete

Chimusa

2018

AJPAS

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Aims: To discuss different LMM-based approaches applied in GWAS and software packages implementation and Classify different computational tools that applies LMM approaches according to their applicability and performance. To identify possible SNPs associated to a particular disease using different computational tools based on LMM approaches. To estimate genetic and residual variance parameters that account phenotypic variation of the disease. Study Design: Case control study Place and Duration of Study: The research was carried out in Tanzania at African Institute of Mathematical Science for six months. Methodology: Linear Mixed Models (LMMs) are widely applied in genomic wide associations studies (GWAS) owing to their effectiveness of correcting hidden relationship, population structure and family structure. This essay is aimed at exploring different mathematical approaches of LMMs in GWAS. These approaches are linear mixed model with inclusion of all markers (LMMi) and linear mixed model with exclusion of all markers (LMMe) when calculating genetic relationship matrix. LMMi is more efficient as compared to LMMe when applied in studies of randomly ascertained quantitative traits. The LMM approaches are classified based on their applicability and performance. Two computational GWAS tools namely, PLINK and EMMAX were used which were based on LMM approaches to analyze unpublished real data from West Africa (Gambia and Ghana). Genetic and residual variance parameters were estimated that accounted for the phenotypic variation of the disease to be 0.0594 and 0.0723. A total of 338408 variants and 959 people (484 males, 405 females and 70 missing phenotypes) pass filters and quality control using PLINK was used in the study. Among the remaining phenotypes, 864 are cases and 95 are controls. The performance of different mathematical approaches of LMMs and their software implementation, including EMMAX and Plink via the application to a GWAS of tuberculosis (TB) in 959 individuals in West Africa (Ghana and Gambia) was compared. Of these 864 cases of TB and 95 healthy individuals retained after quality control (QC) using Plink, and 329601 autosome single nucleotide polymorphisms (from chromosome 1 to chromosome 22) included in the analysis after 288 duplicands ID individuals removed after QC. The LMM approaches are classified based on their applicability and performance. Two computational GWAS tools, namely Plink and EMMAX were used in the analysis of data. Genetic and residual variance parameters were estimated that accounted for the phenotypic variation of the disease to be 0.0594 and 0.0723. Results: Result showed that SNPs associated with tuberculosis were on chromosome 17 and SNP on chromosome 13 with both having false discovery rate with step up significance value. Plink failed to correct hidden relatedness. Although EMMAX reduced the false positive rate, it still exhibited very low presence of stratification. Conclusion: This study aimed at understanding and exploring different approaches of mixed models as applied in genetic studies. Overview of genetic variation, advantages, successes and application of mixed models and current challenges of mixed models in GWAS were discussed. Moreover, the study showed that SNPs was associated with a particular disease using computational tools that applies LMM approaches. The summary statistics from PLINK and EMMAX found two causal SNPs associated with the TB. These SNPs were rs7225581 on chromosome 17 and SNP rs4941412 on chromosome 13 with both having 0.69% FDR H. However, PLINK failed to correct hidden relatedness. This phenotypic variation showed that all common single nucleotide polymorphisms (SNPs) expressed approximately 18.52% of phenotypic variation of the disease.

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“…Traditional approaches to estimating genetic correlation estimation are based on exploring a linear mixed-effect model framework in which the effects of genetic variants are assumed to be random (usually assumed to be normally distributed with 0-mean). This is opposite to the fixed effect assumption embedded in the framework of quantitative genetics theory [Falconer, 1960, Lynch and Walsh, 1998, Lee et al, 2018, Gorfine et al, 2017, Janson et al, 2017, Golan and Rosset, 2018. Some popular approaches for making inference about genetic correlation in linear mixed model are: maximum likelihood estimation , Lee et al, 2012, 2013, moment method [Golan et al, 2014, Lu et al, 2017 and linkage disequilibrium score regression [Bulik-Sullivan et al, 2015, Speed andBalding, 2019].…”

Section: Introductionmentioning

confidence: 99%

Reliable genetic correlation estimation via multiple sample splitting and smoothing

Mai

2023

Preprint

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In this paper, we aim to investigate the problem of estimating the genetic correlation between two traits. Instead of making assumptions about the distribution of effect sizes of the genetic factors, we propose the use of a high-dimensional linear model to relate a trait to genetic factors. To estimate the genetic correlation, we develop a generic strategy that combines the use of sparse penalization methods and multiple sample splitting approaches. The final estimate is determined by taking the median of the calculations, resulting in a smoothed and reliable estimate. Through simulations, we demonstrate that our proposed approach is reliable and accurate in comparison to naive plug-in methods. To further illustrate the advantages of our method, we apply it to a real-world example of a bacterial GWAS dataset, specifically to estimate the genetic correlation between antibiotic resistant traits in {\it Streptococus pneumoniae}. This application not only validates the effectiveness of our method but also highlights its potential in real-world applications.

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Mixed Models for Case-Control Genome-Wide Association Studies: Major Challenges and Partial Solutions

Cited by 6 publications

References 1 publication

Tantalizing dilemma in risk prediction from disease scoring statistics

Tantalizing dilemma in risk prediction from disease scoring statistics

Computational Generalization of Mixed Models on Large-Scale Data with Applications to Genetic Studies

Reliable genetic correlation estimation via multiple sample splitting and smoothing

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