A new approach of dissecting genetic effects for complex traits

AbstractDuring the past decades, genome-wide association studies (GWAS) have been used to successfully identify tens of thousands of genetic variants associated with complex traits included in humans, animals, and plants. All common genome-wide association (GWA) methods rely on population structure correction to avoid false genotype and phenotype associations. However, population structure correction is a stringent penalization, which also impedes the identification of real associations. Here, we used recent statistical advances and proposed iterative screen regression (ISR), which enables simultaneous multiple marker associations and shown to appropriately correction population stratification and cryptic relatedness in GWAS. Results from analyses of simulated suggest that the proposed ISR method performed well in terms of power (sensitivity) versus FDR (False Discovery Rate) and specificity, also less bias (higher accuracy) in effect (PVE) estimation than the existing multi-loci (mixed) model and the single-locus (mixed) model. We also show the practicality of our approach by applying it to rice, outbred mice, and A.thaliana datasets. It identified several new causal loci that other methods did not detect. Our ISR provides an alternative for multi-loci GWAS, and the implementation was computationally efficient, analyzing large datasets practicable (n>100,000).

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GWAS-Flow: A GPU accelerated framework for efficient permutation based genome-wide association studies

10.1101/783100 ◽

2019 ◽

Cited By ~ 2

Author(s):

Jan A. Freudenthal ◽

Markus J. Ankenbrand ◽

Dominik G. Grimm ◽

Arthur Korte

Keyword(s):

Complex Traits ◽

Mixed Model ◽

Linear Mixed Model ◽

Association Studies ◽

Large Datasets ◽

Genome Wide Association ◽

Small Data ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Non Gaussian

AbstractMotivationGenome-wide association studies (GWAS) are one of the most commonly used methods to detect associations between complex traits and genomic polymorphisms. As both genotyping and phenotyping of large populations has become easier, typical modern GWAS have to cope with massive amounts of data. Thus, the computational demand for these analyses grew remarkably during the last decades. This is especially true, if one wants to implement permutation-based significance thresholds, instead of using the naïve Bonferroni threshold. Permutation-based methods have the advantage to provide an adjusted multiple hypothesis correction threshold that takes the underlying phenotypic distribution into account and will thus remove the need to find the correct transformation for non Gaussian phenotypes. To enable efficient analyses of large datasets and the possibility to compute permutation-based significance thresholds, we used the machine learning framework TensorFlow to develop a linear mixed model (GWAS-Flow) that can make use of the available CPU or GPU infrastructure to decrease the time of the analyses especially for large datasets.ResultsWe were able to show that our application GWAS-Flow outperforms custom GWAS scripts in terms of speed without loosing accuracy. Apart from p-values, GWAS-Flow also computes summary statistics, such as the effect size and its standard error for each individual marker. The CPU-based version is the default choice for small data, while the GPU-based version of GWAS-Flow is especially suited for the analyses of big data.AvailabilityGWAS-Flow is freely available on GitHub (https://github.com/Joyvalley/GWAS_Flow) and is released under the terms of the MIT-License.

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Impacts of Population Structure and Analytical Models in Genome-Wide Association Studies of Complex Traits in Forest Trees: A Case Study in Eucalyptus globulus

PLoS ONE ◽

10.1371/journal.pone.0081267 ◽

2013 ◽

Vol 8 (11) ◽

pp. e81267 ◽

Cited By ~ 40

Author(s):

Eduardo P. Cappa ◽

Yousry A. El-Kassaby ◽

Martín N. Garcia ◽

Cintia Acuña ◽

Nuno M. G. Borralho ◽

...

Keyword(s):

Population Structure ◽

Complex Traits ◽

Eucalyptus Globulus ◽

Association Studies ◽

Genome Wide Association ◽

Analytical Models ◽

Genome Wide Association Studies ◽

Forest Trees ◽

Genome Wide

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Variable Selection in Heterogeneous Datasets: A Truncated-rank Sparse Linear Mixed Model with Applications to Genome-wide Association Studies

10.1101/228106 ◽

2017 ◽

Cited By ~ 2

Author(s):

Haohan Wang ◽

Bryon Aragam ◽

Eric P. Xing

Keyword(s):

Population Structure ◽

Variable Selection ◽

Mixed Model ◽

Linear Mixed Model ◽

Association Studies ◽

Genome Wide Association ◽

Low Rank ◽

Genome Wide Association Studies ◽

Unified Framework ◽

Genome Wide

AbstractA fundamental and important challenge in modern datasets of ever increasing dimensionality is variable selection, which has taken on renewed interest recently due to the growth of biological and medical datasets with complex, non-i.i.d. structures. Naïvely applying classical variable selection methods such as the Lasso to such datasets may lead to a large number of false discoveries. Motivated by genome-wide association studies in genetics, we study the problem of variable selection for datasets arising from multiple subpopulations, when this underlying population structure is unknown to the researcher. We propose a unified framework for sparse variable selection that adaptively corrects for population structure via a low-rank linear mixed model. Most importantly, the proposed method does not require prior knowledge of sample structure in the data and adaptively selects a covariance structure of the correct complexity. Through extensive experiments, we illustrate the effectiveness of this framework over existing methods. Further, we test our method on three different genomic datasets from plants, mice, and human, and discuss the knowledge we discover with our method.

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Multiplex Confounding Factor Correction for Genomic Association Mapping with Squared Sparse Linear Mixed Model

10.1101/228114 ◽

2017 ◽

Author(s):

Haohan Wang ◽

Xiang Liu ◽

Yunpeng Xiao ◽

Ming Xu ◽

Eric P. Xing

Keyword(s):

Population Structure ◽

Association Mapping ◽

Complex Traits ◽

Association Studies ◽

Phenotypic Variability ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Confounding Factors ◽

Genetic Loci ◽

Genome Wide

AbstractGenome-wide Association Study has presented a promising way to understand the association between human genomes and complex traits. Many simple polymorphic loci have been shown to explain a significant fraction of phenotypic variability. However, challenges remain in the non-triviality of explaining complex traits associated with multifactorial genetic loci, especially considering the confounding factors caused by population structure, family structure, and cryptic relatedness. In this paper, we propose a Squared-LMM (LMM2) model, aiming to jointly correct population and genetic confounding factors. We offer two strategies of utilizing LMM2 for association mapping: 1) It serves as an extension of univariate LMM, which could effectively correct population structure, but consider each SNP in isolation. 2) It is integrated with the multivariate regression model to discover association relationship between complex traits and multifactorial genetic loci. We refer to this second model as sparse Squared-LMM (sLMM2). Further, we extend LMM2/sLMM2 by raising the power of our squared model to the LMMn/sLMMn model. We demonstrate the practical use of our model with synthetic phenotypic variants generated from genetic loci of Arabidopsis Thaliana. The experiment shows that our method achieves a more accurate and significant prediction on the association relationship between traits and loci. We also evaluate our models on collected phenotypes and genotypes with the number of candidate genes that the models could discover. The results suggest the potential and promising usage of our method in genome-wide association studies.

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Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies

BMC Bioinformatics ◽

10.1186/s12859-019-3300-9 ◽

2019 ◽

Vol 20 (S23) ◽

Cited By ~ 1

Author(s):

Haohan Wang ◽

Tianwei Yue ◽

Jingkang Yang ◽

Wei Wu ◽

Eric P. Xing

Keyword(s):

Neural Network ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Complex Traits ◽

Population Stratification ◽

Mixed Model ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

Abstract Background Genome-wide Association Studies (GWAS) have contributed to unraveling associations between genetic variants in the human genome and complex traits for more than a decade. While many works have been invented as follow-ups to detect interactions between SNPs, epistasis are still yet to be modeled and discovered more thoroughly. Results In this paper, following the previous study of detecting marginal epistasis signals, and motivated by the universal approximation power of deep learning, we propose a neural network method that can potentially model arbitrary interactions between SNPs in genetic association studies as an extension to the mixed models in correcting confounding factors. Our method, namely Deep Mixed Model, consists of two components: 1) a confounding factor correction component, which is a large-kernel convolution neural network that focuses on calibrating the residual phenotypes by removing factors such as population stratification, and 2) a fixed-effect estimation component, which mainly consists of an Long-short Term Memory (LSTM) model that estimates the association effect size of SNPs with the residual phenotype. Conclusions After validating the performance of our method using simulation experiments, we further apply it to Alzheimer’s disease data sets. Our results help gain some explorative understandings of the genetic architecture of Alzheimer’s disease.

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301 Methods of genome-wide association studies and their applications in dairy cattle

Journal of Animal Science ◽

10.1093/jas/skaa278.055 ◽

2020 ◽

Vol 98 (Supplement_4) ◽

pp. 31-31

Author(s):

Li Ma

Keyword(s):

Dairy Cattle ◽

Complex Traits ◽

Mixed Model ◽

Association Studies ◽

Snp Markers ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genetics Research ◽

Genome Wide ◽

Dairy Cattle Breeding

Abstract Genome-wide association studies (GWAS) has been widely used to map quantitative trait loci (QTL) of complex traits and diseases since 2007. To date, the human GWAS catalog has accumulated 4,410 publications and 172,351 associations, and the animal QTLdb has curated 983 publications and 130,407 QTLs for cattle, largest in livestock species. During the past 13 years of development, GWAS methods has evolved from simple linear regression, using principal components to address sample relatedness, mixed models, to Bayesian full model approaches. These methods have their advantages and limitations, so it is important to choose an appropriate method, especially for studies in livestock where sample size is often limited. Note that the most popular GWAS approach, the mixed model method, originated from animal breeding and genetics research. Leveraging the national cattle genomic database at the Council on Dairy Cattle Breeding (CDCB), we have conducted GWAS analyses of various dairy traits to identify QTLs and SNP markers of importance. Combining with sequence and functional annotation data, we seek to understand the genetic basis of complex traits and to reveal useful knowledge that can be incorporated into more accurate genomic predictions in the future.

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Genetic Studies: The Linear Mixed Models in Genome-wide Association Studies

The Open Bioinformatics Journal ◽

10.2174/1875036201307010027 ◽

2013 ◽

Vol 7 (1) ◽

pp. 27-33 ◽

Cited By ~ 7

Author(s):

Gengxin Li ◽

Hongjiang Zhu

Keyword(s):

Population Structure ◽

Mixed Model ◽

Linear Mixed Model ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Model Based ◽

Genome Wide ◽

A Genome ◽

Sample Structure

With the availability of high-density genomic data containing millions of single nucleotide polymorphisms and tens or hundreds of thousands of individuals, genetic association study is likely to identify the variants contributing to complex traits in a genome-wide scale. However, genome-wide association studies are confounded by some spurious associations due to not properly interpreting sample structure (containing population structure, family structure and cryptic relatedness). The absence of complete genealogy of population in the genome-wide association studies model greatly motivates the development of new methods to correct the inflation of false positive. In this process, linear mixed model based approaches with the advantage of capturing multilevel relatedness have gained large ground. We summarize current literatures dealing with sample structure, and our review focuses on the following four areas: (i) The approaches handling population structure in genome-wide association studies; (ii) The linear mixed model based approaches in genome-wide association studies; (iii) The performance of linear mixed model based approaches in genome-wide association studies and (iv) The unsolved issues and future work of linear mixed model based approaches.

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