A-MBED: Adaptive Markov Blanket Method for Epitasis Detection in Genome-Wide Association Studies

With the completion of the international HapMap project and the development of high-throughput technologies, designing more effective epistasis detection algorithm for genome-wide data poses a significant challenge. This paper proposes a new method based on the Markov blanket to solve the limitations of the existing algorithm, such as a large false-positive proportion and low accuracy. The algorithm uses G2 to judge the strength of correlation between variables of self-adaptive remove strategy and SNP matching method; to effectively eliminate variables that are unrelated to the target, as well as weak correlation between variables; to significantly reduce the search space and time; to prevent unnecessary retrieval analysis; and to improve the accuracy of the detection algorithm to a certain extent.

Download Full-text

Genome-Wide Association Study of Coronary Artery Disease

International Journal of Hypertension ◽

10.4061/2010/790539 ◽

2010 ◽

Vol 2010 ◽

pp. 1-8 ◽

Cited By ~ 7

Author(s):

Naomi Ogawa ◽

Yasushi Imai ◽

Hiroyuki Morita ◽

Ryozo Nagai

Keyword(s):

Coronary Artery Disease ◽

Coronary Artery ◽

Association Studies ◽

Genome Wide Association ◽

Candidate Gene Approach ◽

International Hapmap Project ◽

Genome Wide Association Studies ◽

Genetic Determinants ◽

Genome Wide ◽

Artery Disease

Coronary artery disease (CAD) is a multifactorial disease with environmental and genetic determinants. The genetic determinants of CAD have previously been explored by the candidate gene approach. Recently, the data from the International HapMap Project and the development of dense genotyping chips have enabled us to perform genome-wide association studies (GWAS) on a large number of subjects without bias towards any particular candidate genes. In 2007, three chip-based GWAS simultaneously revealed the significant association between common variants on chromosome 9p21 and CAD. This association was replicated among other ethnic groups and also in a meta-analysis. Further investigations have detected several other candidate loci associated with CAD. The chip-based GWAS approach has identified novel and unbiased genetic determinants of CAD and these insights provide the important direction to better understand the pathogenesis of CAD and to develop new and improved preventive measures and treatments for CAD.

Download Full-text

Genetic substructure and complex demographic history of South African Bantu speakers

Nature Communications ◽

10.1038/s41467-021-22207-y ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Dhriti Sengupta ◽

◽

Ananyo Choudhury ◽

Cesar Fortes-Lima ◽

Shaun Aron ◽

...

Keyword(s):

South Africa ◽

Population Structure ◽

Iron Age ◽

Association Studies ◽

Demographic History ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Scale Population ◽

Genome Wide Data ◽

Genetic Substructure

AbstractSouth Eastern Bantu-speaking (SEB) groups constitute more than 80% of the population in South Africa. Despite clear linguistic and geographic diversity, the genetic differences between these groups have not been systematically investigated. Based on genome-wide data of over 5000 individuals, representing eight major SEB groups, we provide strong evidence for fine-scale population structure that broadly aligns with geographic distribution and is also congruent with linguistic phylogeny (separation of Nguni, Sotho-Tswana and Tsonga speakers). Although differential Khoe-San admixture plays a key role, the structure persists after Khoe-San ancestry-masking. The timing of admixture, levels of sex-biased gene flow and population size dynamics also highlight differences in the demographic histories of individual groups. The comparisons with five Iron Age farmer genomes further support genetic continuity over ~400 years in certain regions of the country. Simulated trait genome-wide association studies further show that the observed population structure could have major implications for biomedical genomics research in South Africa.

Download Full-text

networkGWAS: A network-based approach for genome-wide association studies in structured populations

10.1101/2021.11.11.468206 ◽

2021 ◽

Author(s):

Giulia Muzio ◽

Leslie O'Bray ◽

Laetitia Meng-Papaxanthos ◽

Juliane Klatt ◽

Karsten Borgwardt

Keyword(s):

Genetic Markers ◽

Complex Traits ◽

Multiple Testing ◽

Association Studies ◽

Search Space ◽

Structured Populations ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Multiple Testing Correction ◽

Genome Wide

While the search for associations between genetic markers and complex traits has discovered tens of thousands of trait-related genetic variants, the vast majority of these only explain a tiny fraction of observed phenotypic variation. One possible strategy to detect stronger associations is to aggregate the effects of several genetic markers and to test entire genes, pathways or (sub)networks of genes for association to a phenotype. The latter, network-based genome-wide association studies, in particular suffers from a huge search space and an inherent multiple testing problem. As a consequence, current approaches are either based on greedy feature selection, thereby risking that they miss relevant associations, and/or neglect doing a multiple testing correction, which can lead to an abundance of false positive findings. To address the shortcomings of current approaches of network-based genome-wide association studies, we propose <tt>networkGWAS</tt>, a computationally efficient and statistically sound approach to gene-based genome-wide association studies based on mixed models and neighborhood aggregation. It allows for population structure correction and for well-calibrated p-values, which we obtain through a block permutation scheme. <tt>networkGWAS</tt> successfully detects known or plausible associations on simulated rare variants from H. sapiens data as well as semi-simulated and real data with common variants from A. thaliana and enables the systematic combination of gene-based genome-wide association studies with biological network information.

Download Full-text

ClusterMI: Detecting High-Order SNP Interactions Based on Clustering and Mutual Information

International Journal of Molecular Sciences ◽

10.3390/ijms19082267 ◽

2018 ◽

Vol 19 (8) ◽

pp. 2267 ◽

Cited By ~ 6

Author(s):

Xia Cao ◽

Guoxian Yu ◽

Jie Liu ◽

Lianyin Jia ◽

Jun Wang

Keyword(s):

Mutual Information ◽

Clustering Algorithm ◽

Association Studies ◽

Case Control ◽

High Order ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Genome Wide Data ◽

Optimization Search

Identifying single nucleotide polymorphism (SNP) interactions is considered as a popular and crucial way for explaining the missing heritability of complex diseases in genome-wide association studies (GWAS). Many approaches have been proposed to detect SNP interactions. However, existing approaches generally suffer from the high computational complexity resulting from the explosion of candidate high-order interactions. In this paper, we propose a two-stage approach (called ClusterMI) to detect high-order genome-wide SNP interactions based on significant pairwise SNP combinations. In the screening stage, to alleviate the huge computational burden, ClusterMI firstly applies a clustering algorithm combined with mutual information to divide SNPs into different clusters. Then, ClusterMI utilizes conditional mutual information to screen significant pairwise SNP combinations in each cluster. In this way, there is a higher probability of identifying significant two-locus combinations in each group, and the computational load for the follow-up search can be greatly reduced. In the search stage, two different search strategies (exhaustive search and improved ant colony optimization search) are provided to detect high-order SNP interactions based on the cardinality of significant two-locus combinations. Extensive simulation experiments show that ClusterMI has better performance than other related and competitive approaches. Experiments on two real case-control datasets from Wellcome Trust Case Control Consortium (WTCCC) also demonstrate that ClusterMI is more capable of identifying high-order SNP interactions from genome-wide data.

Download Full-text

Genetic-substructure and complex demographic history of South African Bantu speakers

10.1101/2020.08.11.243840 ◽

2020 ◽

Author(s):

Dhriti Sengupta ◽

Ananyo Choudhury ◽

Cesar Fortes-Lima ◽

Shaun Aron ◽

Gavin Whitelaw ◽

...

Keyword(s):

South Africa ◽

Population Structure ◽

Iron Age ◽

Association Studies ◽

Demographic History ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Scale Population ◽

Genome Wide Data ◽

Genetic Substructure

AbstractSouth Eastern Bantu-speaking (SEB) groups constitute more than 80% of the population in South Africa. Despite clear linguistic and geographic diversity, the genetic differences between these groups have not been systematically investigated. Based on genome-wide data of over 5000 individuals, representing eight major SEB groups, we provide strong evidence for fine-scale population structure that broadly aligns with geographic distribution and is also congruent with linguistic phylogeny (separation of Nguni, Sotho-Tswana and Tsonga speakers). Although differential Khoe-San admixture plays a key role, the structure persists after Khoe-San ancestry-masking. The timing of admixture, levels of sex-biased gene flow and population size dynamics also highlight differences in the demographic histories of individual groups. The comparisons with five Iron Age farmer genomes further support genetic continuity over ∼400 years in certain regions of the country. Simulated trait genome-wide association studies further show that the observed population structure could have major implications for biomedical genomics research in South Africa.

Download Full-text

Investigation of common, low-frequency and rare genome-wide variation in anorexia nervosa

Molecular Psychiatry ◽

10.1038/mp.2017.88 ◽

2017 ◽

Vol 23 (5) ◽

pp. 1169-1180 ◽

Cited By ~ 16

Author(s):

L M Huckins ◽

◽

K Hatzikotoulas ◽

L Southam ◽

L M Thornton ◽

...

Keyword(s):

Anorexia Nervosa ◽

Rare Variants ◽

De Novo ◽

Association Studies ◽

Low Frequency ◽

Search Space ◽

Effect Sizes ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Genome Wide Significance

Abstract Anorexia nervosa (AN) is a complex neuropsychiatric disorder presenting with dangerously low body weight, and a deep and persistent fear of gaining weight. To date, only one genome-wide significant locus associated with AN has been identified. We performed an exome-chip based genome-wide association studies (GWAS) in 2158 cases from nine populations of European origin and 15 485 ancestrally matched controls. Unlike previous studies, this GWAS also probed association in low-frequency and rare variants. Sixteen independent variants were taken forward for in silico and de novo replication (11 common and 5 rare). No findings reached genome-wide significance. Two notable common variants were identified: rs10791286, an intronic variant in OPCML (P=9.89 × 10−6), and rs7700147, an intergenic variant (P=2.93 × 10−5). No low-frequency variant associations were identified at genome-wide significance, although the study was well-powered to detect low-frequency variants with large effect sizes, suggesting that there may be no AN loci in this genomic search space with large effect sizes.

Download Full-text