A two-phase Bayesian methodology for the analysis of binary phenotypes in genome-wide association studies

Recent advances in sequencing and genotyping technologies are contributing to a data revolution in genome-wide association studies that is characterized by the challenging large p small n problem in statistics. That is, given these advances, many such studies now consider evaluating an extremely large number of genetic markers (p) genotyped on a small number of subjects (n). Given the dimension of the data, a joint analysis of the markers is often fraught with many challenges, while a marginal analysis is not sufficient. To overcome these obstacles, herein, we propose a Bayesian two-phase methodology that can be used to jointly relate genetic markers to binary traits while controlling for confounding. The first phase of our approach makes use of a marginal scan to identify a reduced set of candidate markers that are then evaluated jointly via a hierarchical model in the second phase. Final marker selection is accomplished through identifying a sparse estimator via a novel and computationally efficient maximum a posteriori estimation technique. We evaluate the performance of the proposed approach through extensive numerical studies, and consider a genome-wide application involving colorectal cancer.     

羊重要性状全基因组关联分析研究进展

全基因组关联分析(genome-wide association study, GWAS)是一种复杂性状功能基因鉴定的分析策略,已成为挖掘畜禽重要经济性状候选基因的重要手段。随着绵羊和山羊基因组完成和公布,以及不同密度的SNP (single nucleotide polymorphism)芯片的推出并进行商业化推广,不仅大大丰富了羊标记辅助选择可利用的分子标记,而且还为开展重要性状的分子机理的探索提供了重要技术支撑。本文主要针对羊角、羊毛、羊奶、生长发育、肉质、繁殖和疾病等重要性状的GWAS研究所用的群体、主要研究方法和研究结果进行了综述,并对GWAS方法研究现状进行了归纳,以期为进一步利用GWAS进行羊的各种性状的遗传基础研究提供参考。     

Polygenic transcriptome risk scores for COPD and lung function improve cross-ethnic portability of prediction in the NHLBI TOPMed program

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Testing prediction accuracy in short-term ecological studies

Applied ecology is based on an assumption that a management action will result in a predicted outcome. Testing the prediction accuracy of ecological models is the most powerful way of evaluating the knowledge implicit in this cause-effect relationship, however, the prevalence of predictive modeling and prediction testing are spreading slowly in ecology. The challenge of prediction testing is particularly acute for small-scale studies, because withholding data for prediction testing (e.g., via k-fold cross validation) can reduce model precision. However, by necessity small-scale studies are common. We use one such study that explored small mammal abundance along an elevational gradient to test prediction accuracy of models with varying degrees of information content. For each of three small mammal species, we conducted 5000 iterations of the following process: (1) randomly selected 75 % of the data to develop generalized linear models of species abundance that used detailed site measurements as covariates, (2) used an information theoretic approach to compare the top model with detailed covariates to habitat type-only and null models constructed with the same data, (3) tested those models’ ability to predict the 25 % of the randomly withheld data, and (4) evaluated prediction accuracy with a quadratic loss function. Detailed models fit the model-evaluation data best but had greater expected prediction error when predicting out-of-sample data relative to the habitat type models. Relationships between species and detailed site variables may be evident only within the framework of explicitly hierarchical analyses. We show that even with a small but relatively typical dataset (n = 28 sampling locations across 125 km over two years), researchers can effectively compare models with different information content and measure models’ predictive power, thus evaluating their own ecological understanding and defining the limits of their inferences. Identifying the appropriate scope of inference through prediction testing is ecologically valuable and is attainable even with small datasets.     

Genomic consequences of selection and genome-wide association mapping in soybean

Background

Crop improvement always involves selection of specific alleles at genes controlling traits of agronomic importance, likely resulting in detectable signatures of selection within the genome of modern soybean (Glycine max L. Merr.). The identification of these signatures of selection is meaningful from the perspective of evolutionary biology and for uncovering the genetic architecture of agronomic traits.

Results

To this end, two populations of soybean, consisting of 342 landraces and 1062 improved lines, were genotyped with the SoySNP50K Illumina BeadChip containing 52,041 single nucleotide polymorphisms (SNPs), and systematically phenotyped for 9 agronomic traits. A cross-population composite likelihood ratio (XP-CLR) method was used to screen the signals of selective sweeps. A total of 125 candidate selection regions were identified, many of which harbored genes potentially involved in crop improvement. To further investigate whether these candidate regions were in fact enriched for genes affected by selection, genome-wide association studies (GWAS) were conducted on 7 selection traits targeted in soybean breeding (grain yield, plant height, lodging, maturity date, seed coat color, seed protein and oil content) and 2 non-selection traits (pubescence and flower color). Major genomic regions associated with selection traits overlapped with candidate selection regions, whereas no overlap of this kind occurred for the non-selection traits, suggesting that the selection sweeps identified are associated with traits of agronomic importance. Multiple novel loci and refined map locations of known loci related to these traits were also identified.

Conclusions

These findings illustrate that comparative genomic analyses, especially when combined with GWAS, are a promising approach to dissect the genetic architecture of complex traits.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1872-y) contains supplementary material, which is available to authorized users.     

Deciphering genetic basis of developmental and agronomic traits by integrating high-throughput optical phenotyping and genome-wide association studies in wheat

Dissecting the genetic basis of complex traits such as dynamic growth and yield potential is a major challenge in crops. Monitoring the growth throughout growing season in a large wheat population to uncover the temporal genetic controls for plant growth and yield-related traits has so far not been explored. In this study, a diverse wheat panel composed of 288 lines was monitored by a non-invasive and high-throughput phenotyping platform to collect growth traits from seedling to grain filling stage and their relationship with yield-related traits was further explored. Whole genome re-sequencing of the panel provided 12.64 million markers for a high-resolution genome-wide association analysis using 190 image-based traits and 17 agronomic traits. A total of 8327 marker-trait associations were detected and clustered into 1605 quantitative trait loci (QTLs) including a number of known genes or QTLs. We identified 277 pleiotropic QTLs controlling multiple traits at different growth stages which revealed temporal dynamics of QTLs action on plant development and yield production in wheat. A candidate gene related to plant growth that was detected by image traits was further validated. Particularly, our study demonstrated that the yield-related traits are largely predictable using models developed based on i-traits and provide possibility for high-throughput early selection, thus to accelerate breeding process. Our study explored the genetic architecture of growth and yield-related traits by combining high-throughput phenotyping and genotyping, which further unravelled the complex and stage-specific contributions of genetic loci to optimize growth and yield in wheat.     

Single step genome-wide association studies based on genotyping by sequence data reveals novel loci for the litter traits of domestic pigs

In this study, data genotyping by sequence (GBS) was used to perform single step GWAS (ssGWAS) to identify SNPs associated with the litter traits in domestic pigs and search for candidate genes in the region of significant SNPs. After quality control, 167,355 high-quality SNPs from 532 pigs were obtained. Phenotypic traits on 2112 gilt litters from 532 pigs were recorded including total number born (TNB), number born alive (NBA), and litter weight born alive (LWB). A single-step genomic BLUP approach (ssGBLUP) was used to implement the genome-wide association analysis at a 5% genome-wide significance level. A total of 8, 23 and 20 significant SNPs were associated with TNB, NBA, and LWB, respectively, and these significant SNPs accounted for 62.78%, 79.75%, and 58.79% of genetic variance. Furthermore, 1 (SSC14: 16314857), 4 (SSC1: 81986236, SSC1: 66599775, SSC1: 161999013, and SSC1: 267883107), and 5 (SSC9: 29030061, SSC2: 32368561, SSC5: 110375350, SSC13: 45619882 and SSC13: 45647829) significant SNPs for TNB, NBA, and LWB were inferred to be novel loci. At SSC1, the AIM1 and FOXO3 genes were found to be associated with NBA; these genes increase ovarian reproductive capacity and follicle number and decrease gonadotropin levels. The genes SLC36A4 and INTU are involved in cell growth, cytogenesis and development were found to be associated with LWB. These significant SNPs can be used as an indication for regions in the Sus scrofa genome for variability in litter traits, but further studies are expected to confirm causative mutations.     

Identifying disease related sub-pathways for analysis of genome-wide association studies

Most methods for genome-wide association studies (GWAS) focus on discovering a single genetic variant, but the pathogenesis of complex diseases is thought to arise from the joint effect of multiple genetic variants. Information about pathway structure, such as the interactions and distances between gene products within pathways, can help us learn more about the functions and joint effect of genes associated with disease risk. We developed a novel sub-pathway based approach to study the joint effect of multiple genetic variants that are modestly associated with disease. The approach prioritized sub-pathways based on the significance values of single nucleotide polymorphisms (SNPs) and the interactions and distances between gene products within pathways. We applied the method to seven complex diseases. The result showed that our method can efficiently identify statistically significant sub-pathways associated with the pathogenesis of complex diseases. The approach identified sub-pathways that may inform the interpretation of GWAS data.     

Genome‐wide association studies identify susceptibility loci affecting respiratory disease in Chinese Erhualian pigs under natural conditions

Prevalence of swine respiratory disease causes poor growth performance in and serious economic losses to the swine industry. In this study, a categorical trait of enzootic pneumonia‐like (EPL) score representing the infection gradient of a respiratory disease, more likely enzootic pneumonia, was recorded in a herd of 332 Chinese Erhualian pigs. According to their EPL scores and the disease effect on weight gains, these pigs were grouped into controls (EPL score ≤ 1) and cases (EPL score > 1). The weight gain of the case group reduced significantly at days 180, 210, 240 and 300 as compared to the control group. The heritability of EPL score was estimated to be 0.24 based on the pedigree information using a linear mixed model. All 332 Erhualian pigs and their nine sire parents were genotyped with Illumina Porcine 60K SNP chips. Two genome‐wide association studies were performed under a generalized linear mixed model and a case–control model respectively. In total, five loci surpassed the suggestive significance level (P = 2.98 × 10^?5) on chromosomes 2, 8, 12 and 14. CXCL6, CXCL8, KIT and CTBP2 were highlighted as candidate genes that might play important roles in determining resistance/susceptibility to swine EP‐like respiratory disease. The findings advance understanding of the genetic basis of resistance/susceptibility to respiratory disease in pigs.     

Genetic dissection of days to flowering via genome-wide association studies in Turkish common bean germplasm

Common bean is a nutrient‐dense legume crop serving as a source of food for millions of people. Characterization of unexplored common bean germplasm to unlock the phenotypic and genetic variations is still needed to explore the breeding potential of this crop. The current study aimed to dissect the genetic basis having association for days to flowering (DF). A total of 188 common bean accessions collected from 19 provinces of Turkey were used as plant material under five environments and two locations. Analysis of variance (ANOVA) revealed that genotypes and genotype by environment interaction have significant effects on DF. A total of 10 most stable accessions were evaluated from stability analysis. Overall maximum (75) and minimum (54) DF were observed for Hakkari-51 and Mus-46 accessions, respectively. The implemented constellation plot divided studied germplasm according to their DF and growth habit. A total of 7900 DArTseq markers were used for association analysis. Mixed linear model using the Q + K Model resulted a total of 18 DArTseq markers from five environments. DArT-8668385 marker identified in Bolu during 2016 was also associated with DF in Sivas during 2017. Combined data of five years resulted a total of four markers (DArT-22346534, DArT-3369768, DArT-3374613, and DArT-3370801) having significant association ( p  <  0.01 ) for DF. DArT-22346534 present on Pv 08 accounted a maximum of 9.89% variation to the studied trait. A total of four putative candidate genes were predicted from sequences reflecting homology to identified four DArTseq markers. We envisage that exploitation of identified DArTseq markers will hopefully beneficial for the development of new common bean varieties having better adaptation ability to changing climatic conditions.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12298-021-01029-8.