Simultaneous fine mapping of closely linked epistatic quantitative trait loci using combined linkage disequilibrium and linkage with a general pedigree

Causal mutations and their intra- and inter-locus interactions play a critical role in complex trait variation. It is often not easy to detect epistatic quantitative trait loci (QTL) due to complicated population structure requirements for detecting epistatic effects in linkage analysis studies and due to main effects often being hidden by interaction effects. Mapping their positions is even harder when they are closely linked. The data structure requirement may be overcome when information on linkage disequilibrium is used. We present an approach using a mixed linear model nested in an empirical Bayesian approach, which simultaneously takes into account additive, dominance and epistatic effects due to multiple QTL. The covariance structure used in the mixed linear model is based on combined linkage disequilibrium and linkage information. In a simulation study where there are complex epistatic interactions between QTL, it is possible to simultaneously map interacting QTL into a small region using the proposed approach. The estimated variance components are accurate and less biased with the proposed approach compared with traditional models

An efficient variance component approach implementing an average information REML suitable for combined LD and linkage mapping with a general complex pedigree

Variance component (VC) approaches based on restricted maximum likelihood (REML) have been used as an attractive method for positioning of quantitative trait loci (QTL). Linkage disequilibrium (LD) information can be easily implemented in the covariance structure among QTL effects (e.g. genotype relationship matrix) and mapping resolution appears to be high. Because of the use of LD information, the covariance structure becomes much richer and denser compared to the use of linkage information alone. This makes an average information (AI) REML algorithm based on mixed model equations and sparse matrix techniques less useful. In addition, (near-) singularity problems often occur with high marker densities, which is common in fine-mapping, causing numerical problems in AIREML based on mixed model equations. The present study investigates the direct use of the variance covariance matrix of all observations in AIREML for LD mapping with a general complex pedigree. The method presented is more efficient than the usual approach based on mixed model equations and robust to numerical problems caused by near-singularity due to closely linked markers. It is also feasible to fit multiple QTL simultaneously in the proposed method whereas this would drastically increase computing time when using mixed model equation-based methods

Using an evolutionary algorithm and parallel computing for haplotyping in a general complex pedigree with multiple marker loci

<p>Abstract</p> <p>Background</p> <p>Haplotype reconstruction is important in linkage mapping and association mapping of quantitative trait loci (QTL). One widely used statistical approach for haplotype reconstruction is simulated annealing (SA), implemented in SimWalk2. However, the algorithm needs a very large number of sequential iterations, and it does not clearly show if convergence of the likelihood is obtained.</p> <p>Results</p> <p>An evolutionary algorithm (EA) is a good alternative whose convergence can be easily assessed during the process. It is feasible to use a powerful parallel-computing strategy with the EA, increasing the computational efficiency. It is shown that the EA can be ~4 times faster and gives more reliable estimates than SimWalk2 when using 4 processors. In addition, jointly updating dependent variables can increase the computational efficiency up to ~2 times. Overall, the proposed method with 4 processors increases the computational efficiency up to ~8 times compared to SimWalk2. The efficiency will increase more with a larger number of processors.</p> <p>Conclusion</p> <p>The use of the evolutionary algorithm and the joint updating method can be a promising tool for haplotype reconstruction in linkage and association mapping of QTL.</p

Prognostic Value of Metastatic Tumoral Caveolin-1 Expression in Patients with Resected Gastric Cancer

Objective. Caveolin-1 (Cav-1), as the main component of caveolae, has complex roles in tumourigenesis in human malignancies. We investigated Cav-1 in primary and metastatic tumor cells of gastric cancer (GC) and its association with clinical outcomes. Methods. We retrieved 145 cases of GC who had undergone curative gastrectomy. The expression levels of Cav-1 was evaluated by immunohistochemistry, and its association with clinicopathological parameters and patient survival was analyzed. Results. High expression of Cav-1 protein of the GC in the stomach and metastatic lymph node was 12.4% (18/145) and 16.5% (15/91). In the multivariate analysis, tumoral Cav-1 protein in metastatic lymph node showed prognostic significance for relapse-free survival (RFS, HR, 3.934; 95% CI, 1.882–8.224; P=0.001) and cancer-specific survival outcome (CSS, HR, 2.681; 95% CI, 1.613–8.623; P=0.002). Among the GCs with metastatic lymph node, it remained as a strong indicator of poor prognosis for RFS (HR, 3.136; 95% CI, 1.444–6.810; P=0.004) and CSS (HR, 2.509; 95% CI, 1.078–5.837; P=0.032). Conclusion. High expression of tumoral Cav-1 protein in metastatic lymph node is associated with unfavorable prognosis of curative resected GC, indicating the potential of novel prognostic markers

Detection of genomic regions underlying resistance to gastrointestinal parasites in Australian sheep

International audienceAbstractBackgroundThis study aimed at identifying genomic regions that underlie genetic variation of worm egg count, as an indicator trait for parasite resistance in a large population of Australian sheep, which was genotyped with the high-density 600 K Ovine single nucleotide polymorphism array. This study included 7539 sheep from different locations across Australia that underwent a field challenge with mixed gastrointestinal parasite species. Faecal samples were collected and worm egg counts for three strongyle species, i.e. Teladorsagia circumcincta, Haemonchus contortus and Trichostrongylus colubriformis were determined. Data were analysed using genome-wide association studies (GWAS) and regional heritability mapping (RHM).ResultsBoth RHM and GWAS detected a region on Ovis aries (OAR) chromosome 2 that was highly significantly associated with parasite resistance at a genome-wise false discovery rate of 5%. RHM revealed additional significant regions on OAR6, 18, and 24. Pathway analysis revealed 13 genes within these significant regions (SH3RF1, HERC2, MAP3K, CYFIP1, PTPN1, BIN1, HERC3, HERC5, HERC6, IBSP, SPP1, ISG20, and DET1), which have various roles in innate and acquired immune response mechanisms, as well as cytokine signalling. Other genes involved in haemostasis regulation and mucosal defence were also detected, which are important for protection of sheep against invading parasites.ConclusionsThis study identified significant genomic regions on OAR2, 6, 18, and 24 that are associated with parasite resistance in sheep. RHM was more powerful in detecting regions that affect parasite resistance than GWAS. Our results support the hypothesis that parasite resistance is a complex trait and is determined by a large number of genes with small effects, rather than by a few major genes with large effects

Equilibrium and Dynamical Evolution of Self-Gravitating System Embedded in a Potential Well

Isothermal and self-gravitating systems bound by non-conducting and conducting walls are known to be unstable if the density contrast between the center and the boundary exceeds critical values. We investigate the equilibrium and dynamical evolution of isothermal and self-gravitating system embedded in potential well, which can be the situation of many astrophysical objects such as the central parts of the galaxies, or clusters of galaxies with potential dominated by dark matter, but is still limited to the case where the potential well is fixed during the evolution. As the ratio between the depth of surrounding potential well and potential of embedded system becomes large, the potential well becomes effectively the same boundary condition as conducting wall, which behaves like a thermal heat bath. We also use the direct N-body simulation code, NBODY6 to simulate the dynamical evolution of stellar system embedded in potential wells and propose the equilibrium models for this system. In deep potential well, which is analogous to the heat bath with high temperature, the embedded self-gravitating system is dynamically hot, and loosely bound or can be unbound since the kinetic energy increases due to the heating by the potential well. On the other hand, the system undergoes core collapse by self-gravity when potential well is shallow. Binary heating can stop the collapse and leads to the expansion, but the evolution is very slow because the potential as a heat bath can absorb the energy generated by the binaries. The system can be regarded as quasi-static. Density and velocity dispersion profiles from the N-body simulations in the final quasi-equilibrium state are similar to our equilibrium models assumed to be in thermal equilibrium with the potential well.Comment: 12 pages, 12 figures, Submitted to MNRA

Estimation of genomic prediction accuracy from reference populations with varying degrees of relationship

Genomic prediction is emerging in a wide range of fields including animal and plant breeding, risk prediction in human precision medicine and forensic. It is desirable to establish a theoretical framework for genomic prediction accuracy when the reference data consists of information sources with varying degrees of relationship to the target individuals. A reference set can contain both close and distant relatives as well as `unrelated' individuals from the wider population in the genomic prediction. The various sources of information were modeled as different populations with different effective population sizes (Nₑ). Both the effective number of chromosome segments (Mₑ) and Nₑ are considered to be a function of the data used for prediction. We validate our theory with analyses of simulated as well as real data, and illustrate that the variation in genomic relationships with the target is a predictor of the information content of the reference set. With a similar amount of data available for each source, we show that close relatives can have a substantially larger effect on genomic prediction accuracy than lesser related individuals. We also illustrate that when prediction relies on closer relatives, there is less improvement in prediction accuracy with an increase in training data or marker panel density. We release software that can estimate the expected prediction accuracy and power when combining different reference sources with various degrees of relationship to the target, which is useful when planning genomic prediction (before or after collecting data) in animal, plant and human genetics

Detecting Genotype-Population Interaction Effects by Ancestry Principal Components

Heterogeneity in the phenotypic mean and variance across populations is often observed for complex traits. One way to understand heterogeneous phenotypes lies in uncovering heterogeneity in genetic effects. Previous studies on genetic heterogeneity across populations were typically based on discrete groups in populations stratified by different countries or cohorts, which ignored the difference of population characteristics for the individuals within each group and resulted in loss of information. Here, we introduce a novel concept of genotype-by-population (G × P) interaction where population is defined by the first and second ancestry principal components (PCs), which are less likely to be confounded with country/cohort-specific factors. We applied a reaction norm model fitting each of 70 complex traits with significant SNP-heritability and the PCs as covariates to examine G × P interactions across diverse populations including white British and other white Europeans from the UK Biobank (N = 22,229). Our results demonstrated a significant population genetic heterogeneity for behavioral traits such as age at first sexual intercourse and academic qualification. Our approach may shed light on the latent genetic architecture of complex traits that underlies the modulation of genetic effects across different populations

Using imputed whole-genome sequence data to improve the accuracy of genomic prediction for parasite resistance in Australian sheep

International audienceAbstractBackgroundThis study aimed at (1) comparing the accuracies of genomic prediction for parasite resistance in sheep based on whole-genome sequence (WGS) data to those based on 50k and high-density (HD) single nucleotide polymorphism (SNP) panels; (2) investigating whether the use of variants within quantitative trait loci (QTL) regions that were selected from regional heritability mapping (RHM) in an independent dataset improved the accuracy more than variants selected from genome-wide association studies (GWAS); and (3) comparing the prediction accuracies between variants selected from WGS data to variants selected from the HD SNP panel.ResultsThe accuracy of genomic prediction improved marginally from 0.16 ± 0.02 and 0.18 ± 0.01 when using all the variants from 50k and HD genotypes, respectively, to 0.19 ± 0.01 when using all the variants from WGS data. Fitting a GRM from the selected variants alongside a GRM from the 50k SNP genotypes improved the prediction accuracy substantially compared to fitting the 50k SNP genotypes alone. The gain in prediction accuracy was slightly more pronounced when variants were selected from WGS data compared to when variants were selected from the HD panel. When sequence variants that passed the GWAS -log10(pvalue)\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$- log_{10} (p\,value)$$\end{document} threshold of 3 across the entire genome were selected, the prediction accuracy improved by 5% (up to 0.21 ± 0.01), whereas when selection was limited to sequence variants that passed the same GWAS -log10(pvalue)\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$- log_{10} (p\,value)$$\end{document} threshold of 3 in regions identified by RHM, the accuracy improved by 9% (up to 0.25 ± 0.01).ConclusionsOur results show that through careful selection of sequence variants from the QTL regions, the accuracy of genomic prediction for parasite resistance in sheep can be improved. These findings have important implications for genomic prediction in sheep