Dataset Information

Prediction of genetic contributions to complex traits using whole genome sequencing data.

ABSTRACT: Although markers identified by genome-wide association studies have individually strong statistical significance, their performance in prediction remains limited. Our goal was to use animal breeding genomic prediction models to predict additive genetic contributions for systolic blood pressure (SBP) using whole genome sequencing data with different validation designs. The additive genetic contributions of SBP were estimated via linear mixed model. Rare variants (MAF<0.05) were collapsed through the k-means method to create a "collapsed single-nucleotide polymorphisms." Prediction of the additive genomic contributions of SBP was conducted using genomic Best Linear Unbiased Predictor (GBLUP) and BayesCπ. Estimates of predictive accuracy were compared using common single-nucleotide polymorphisms (SNPs) versus common and collapsed SNPs, and for prediction within and across families. The additive genetic variance of SBP contributed to 18% of the phenotypic variance (h(2) = 0.18). BayesCπ had slightly better prediction accuracies than GBLUP. In both models, within-family predictions had higher accuracies both in the training and testing set than didacross-family design. Collapsing rare variants via the k-means method and adding to the common SNPs did not improve prediction accuracies. The prediction model, including both pedigree and genomic information, achieved a slightly higher accuracy than using either source of information alone. Prediction of genetic contributions to complex traits is feasible using whole genome sequencing and statistical methods borrowed from animal breeding. The relatedness of individuals between the training and testing set strongly affected the performance of prediction models. Methods for inclusion of rare variants in these models need more development.

SUBMITTER: Yao C

PROVIDER: S-EPMC4143683 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Prediction of genetic contributions to complex traits using whole genome sequencing data.

Yao Chen C Leng Ning N Weigel Kent A KA Lee Kristine E KE Engelman Corinne D CD Meyers Kristin J KJ

BMC proceedings 20140617 Suppl 1

Although markers identified by genome-wide association studies have individually strong statistical significance, their performance in prediction remains limited. Our goal was to use animal breeding genomic prediction models to predict additive genetic contributions for systolic blood pressure (SBP) using whole genome sequencing data with different validation designs. The additive genetic contributions of SBP were estimated via linear mixed model. Rare variants (MAF<0.05) were collapsed through ...[more]

PMID: 25519339

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Project description:AimsThis study established blood group analysis methods using whole-genome sequencing (WGS) data and conducted blood group analyses to determine the domestic allele frequency using public data from the Korean whole sequence analysis of the Korean Reference Genome Project conducted by the Korea Disease Control and Prevention Agency (KDCA).Materials and methodsWe analyzed the differences between the human reference sequences (hg19) and the conventional reference cDNA sequences of blood group genes using the Clustal Omega website, and established blood group analysis methods using WGS data for 41 genes, including 39 blood group genes involved in 36 blood group antigens, as well as the GATA1 and KLF1 genes, which are erythrocyte-specific transcription factor genes. Using CLC genomics Workbench 11.0 (Qiagen, Aarhus, Denmark), variant analysis was performed on these 41 genes in 250 Korean WGS data sets, and each blood group's genotype was predicted. The frequencies for major alleles were also investigated and compared with data from the Korean rare blood program (KRBP) and the Erythrogene database (East Asian and all races).ResultsAmong the 41 blood group-related genes, hg19 showed variants in the following genes compared to the conventional reference cDNA: GYPA, RHD, RHCE, FUT3, ACKR1, SLC14A1, ART4, CR1, and GCNT2. Among 250 WGS data sets from the Korean Reference Genome Project, 70.6 variants were analyzed in 205 samples; 45 data samples were excluded due to having no variants. In particular, the FUT3, GNCT2, B3GALNT1, CR1, and ACHE genes contained numerous variants, with averages of 21.1, 13.9, 13.4, 9.6, and 7.0, respectively. Except for some blood groups, such as ABO and Lewis, for which it was difficult to predict the alleles using only WGS data, most alleles were successfully predicted in most blood groups. A comparison of allele frequencies showed no significant differences compared to the KRBP data, but there were differences compared to the Erythrogene data for the Lutheran, Kell, Duffy, Yt, Scianna, Landsteiner-Wiener, and Cromer blood group systems. Numerous minor blood group systems that were not available in the KRBP data were also included in this study.ConclusionsWe successfully established and performed blood group analysis using Korean public WGS data. It is expected that blood group analysis using WGS data will be performed more frequently in the future and will contribute to domestic data on blood group allele frequency and eventually the supply of safe blood products.

Project description:BackgroundHigh-throughput genotype (HTG) data has been used primarily in genome-wide association (GWA) studies; however, GWA results explain only a limited part of the complete genetic variation of traits. In systems genetics, network approaches have been shown to be able to identify pathways and their underlying causal genes to unravel the biological and genetic background of complex diseases and traits, e.g., the Weighted Gene Co-expression Network Analysis (WGCNA) method based on microarray gene expression data. The main objective of this study was to develop a scale-free weighted genetic interaction network method using whole genome HTG data in order to detect biologically relevant pathways and potential genetic biomarkers for complex diseases and traits.ResultsWe developed the Weighted Interaction SNP Hub (WISH) network method that uses HTG data to detect genome-wide interactions between single nucleotide polymorphism (SNPs) and its relationship with complex traits. Data dimensionality reduction was achieved by selecting SNPs based on its: 1) degree of genome-wide significance and 2) degree of genetic variation in a population. Network construction was based on pairwise Pearson's correlation between SNP genotypes or the epistatic interaction effect between SNP pairs. To identify modules the Topological Overlap Measure (TOM) was calculated, reflecting the degree of overlap in shared neighbours between SNP pairs. Modules, clusters of highly interconnected SNPs, were defined using a tree-cutting algorithm on the SNP dendrogram created from the dissimilarity TOM (1-TOM). Modules were selected for functional annotation based on their association with the trait of interest, defined by the Genome-wide Module Association Test (GMAT). We successfully tested the established WISH network method using simulated and real SNP interaction data and GWA study results for carcass weight in a pig resource population; this resulted in detecting modules and key functional and biological pathways related to carcass weight.ConclusionsWe developed the WISH network method which is a novel 'systems genetics' approach to study genetic networks underlying complex trait variation. The WISH network method reduces data dimensionality and statistical complexity in associating genotypes with phenotypes in GWA studies and enables researchers to identify biologically relevant pathways and potential genetic biomarkers for any complex trait of interest.

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Prediction of genetic contributions to complex traits using whole genome sequencing data.

Publications

Prediction of genetic contributions to complex traits using whole genome sequencing data.

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