Overview of techniques to account for confounding due to population stratification and cryptic relatedness in genomic data association analyses

Quantitative genomics-enabled selection for simultaneous improvement of lint yield and seed traits in cotton (Gossypium hirsutum L.)

KEY MESSAGE

A Bayesian linkage disequilibrium-based multiple-locus mixed model identified QTLs for fibre, seed and oil traits and predicted breeding worthiness of test lines, enabling their simultaneous improvement in cotton. Improving cotton seed and oil yields has become increasingly important while continuing to breed for higher lint yield. In this study, a novel Bayesian linkage disequilibrium-based multiple-locus mixed model was developed for QTL identification and genomic prediction (GP). A multi-parent population consisting of 256 recombinant inbred lines, derived from four elite cultivars with distinct combinations of traits, was used in the analysis of QTLs for lint percentage, seed index, lint index and seed oil content and their interrelations. All four traits were moderately heritable and correlated but with no large influence of genotype × environment interactions across multiple seasons. Seven to ten major QTLs were identified for each trait with many being adjacent or overlapping for different trait pairs. A fivefold cross-validation of the model indicated prediction accuracies of 0.46-0.62. GP results based on any two-season phenotypes were strongly correlated with phenotypic means of a pooled analysis of three-season experiments (r = 0.83-0.92). When used for selection of improvement in lint, seed and oil yields, GP captured 40-100% of individuals with comparable lint yields of those selected based on the three-season phenotypic results. Thus, this quantitative genomics-enabled approach can not only decipher the genomic variation underlying lint, seed and seed oil traits and their interrelations, but can provide predictions for their simultaneous improvement. We discuss future breeding strategies in cotton that will enhance the entire value of the crop, not just its fibre.

Polygenic Prediction of Education and Its Role in the Intergenerational Transmission of Education: Cohort Changes Among Finnish Men and Women Born in 1925-1989

Major changes in the educational distribution of the population and in institutions over the past century have affected the societal barriers to educational attainment. These changes can possibly result in stronger genetic associations. Using genetically informed, population-representative Finnish surveys linked to administrative registers, we investigated the polygenic associations and intergenerational transmission of education for those born between 1925 and 1989. First, we found that a polygenic index (PGI) designed to capture genetic predisposition to education strongly increased the predictiveness of educational attainment in pre-1950s cohorts, particularly among women. When decomposing the total contribution of PGI across different educational transitions, the transition between the basic and academic secondary tracks was the most important. This transition accounted for 60-80% of the total PGI-education association among most cohorts. The transition between academic secondary and higher tertiary levels increased its contribution across cohorts. Second, for cohorts born between 1955 and 1984, we observed that one eighth of the association between parental and one's own education is explained by the PGI. There was also an increase in the intergenerational correlation of education among these cohorts, which was partly explained by an increasing association between family education of origin and the PGI.

Direct inference and control of genetic population structure from RNA sequencing data

RNAseq data can be used to infer genetic variants, yet its use for estimating genetic population structure remains underexplored. Here, we construct a freely available computational tool (RGStraP) to estimate RNAseq-based genetic principal components (RG-PCs) and assess whether RG-PCs can be used to control for population structure in gene expression analyses. Using whole blood samples from understudied Nepalese populations and the Geuvadis study, we show that RG-PCs had comparable results to paired array-based genotypes, with high genotype concordance and high correlations of genetic principal components, capturing subpopulations within the dataset. In differential gene expression analysis, we found that inclusion of RG-PCs as covariates reduced test statistic inflation. Our paper demonstrates that genetic population structure can be directly inferred and controlled for using RNAseq data, thus facilitating improved retrospective and future analyses of transcriptomic data.

Transmission disequilibrium analysis of whole genome data in childhood-onset systemic lupus erythematosus

Childhood-onset systemic lupus erythematosus (cSLE) patients are unique, with hallmarks of Mendelian disorders (early-onset and severe disease) and thus are an ideal population for genetic investigation of SLE. In this study, we use the transmission disequilibrium test (TDT), a family-based genetic association analysis that employs robust methodology, to analyze whole genome sequencing data. We aim to identify novel genetic associations in an ancestrally diverse, international cSLE cohort. Forty-two cSLE patients and 84 unaffected parents from 3 countries underwent whole genome sequencing. First, we performed TDT with single nucleotide variant (SNV)-based (common variants) using PLINK 1.9, and gene-based (rare variants) analyses using Efficient and Parallelizable Association Container Toolbox (EPACTS) and rare variant TDT (rvTDT), which applies multiple gene-based burden tests adapted for TDT, including the burden of rare variants test. Applying the GWAS standard threshold (5.0 × 10-8) to common variants, our SNV-based analysis did not return any genome-wide significant SNVs. The rare variant gene-based TDT analysis identified many novel genes significantly enriched in cSLE patients, including HNRNPUL2, a DNA repair protein, and DNAH11, a ciliary movement protein, among others. Our approach identifies several novel SLE susceptibility genes in an ancestrally diverse childhood-onset lupus cohort.

Genetic Markers Predisposing to Nonalcoholic Steatohepatitis

The growing prevalence of nonalcoholic fatty liver disease (NAFLD) has sparked interest in understanding genetics and epigenetics associated with the development and progression of the disease. A better understanding of the genetic factors related to progression will be beneficial in the risk stratification of patients. These genetic markers can also serve as potential therapeutic targets in the future. In this review, we focus on the genetic markers associated with the progression and severity of NAFLD.

Inferring feature importance with uncertainties with application to large genotype data

Estimating feature importance, which is the contribution of a prediction or several predictions due to a feature, is an essential aspect of explaining data-based models. Besides explaining the model itself, an equally relevant question is which features are important in the underlying data generating process. We present a Shapley-value-based framework for inferring the importance of individual features, including uncertainty in the estimator. We build upon the recently published model-agnostic feature importance score of SAGE (Shapley additive global importance) and introduce Sub-SAGE. For tree-based models, it has the advantage that it can be estimated without computationally expensive resampling. We argue that for all model types the uncertainties in our Sub-SAGE estimator can be estimated using bootstrapping and demonstrate the approach for tree ensemble methods. The framework is exemplified on synthetic data as well as large genotype data for predicting feature importance with respect to obesity.

Genetic diversity and selective sweeps in historical and modern Canadian spring wheat cultivars using the 90K SNP array

Previous molecular characterization studies conducted in Canadian wheat cultivars shed some light on the impact of plant breeding on genetic diversity, but the number of varieties and markers used was small. Here, we used 28,798 markers of the wheat 90K single nucleotide polymorphisms to (a) assess the extent of genetic diversity, relationship, population structure, and divergence among 174 historical and modern Canadian spring wheat varieties registered from 1905 to 2018 and 22 unregistered lines (hereinafter referred to as cultivars), and (b) identify genomic regions that had undergone selection. About 91% of the pairs of cultivars differed by 20-40% of the scored alleles, but only 7% of the pairs had kinship coefficients of < 0.250, suggesting the presence of a high proportion of redundancy in allelic composition. Although the 196 cultivars represented eight wheat classes, our results from phylogenetic, principal component, and the model-based population structure analyses revealed three groups, with no clear structure among most wheat classes, breeding programs, and breeding periods. F_ST statistics computed among different categorical variables showed little genetic differentiation (< 0.05) among breeding periods and breeding programs, but a diverse level of genetic differentiation among wheat classes and predicted groups. Diversity indices were the highest and lowest among cultivars registered from 1970 to 1980 and from 2011 to 2018, respectively. Using two outlier detection methods, we identified from 524 to 2314 SNPs and 41 selective sweeps of which some are close to genes with known phenotype, including plant height, photoperiodism, vernalization, gluten strength, and disease resistance.

Human Genetics to Identify Therapeutic Targets for NAFLD: Challenges and Opportunities

Non-alcoholic fatty liver disease (NAFLD) is a continuous progression of pathophysiologic stages that is challenging to diagnose due to its inherent heterogeneity and poor standardization across a wide variety of diagnostic measures. NAFLD is heritable, and several loci have been robustly associated with various stages of disease. In the past few years, larger genetic association studies using new methodology have identified novel genes associated with NAFLD, some of which have shown therapeutic promise. This mini-review provides an overview of the heterogeneity in NAFLD phenotypes and diagnostic methods, discusses genetic associations in relation to the specific stages for which they were identified, and offers a perspective on the design of future genetic mapping studies to accelerate therapeutic target identification.

Associations of age, sex, sexual abuse, and genotype with monoamine oxidase a gene methylation

Epigenome-wide studies report higher methylation among women than men with decreasing levels with age. Little is known about associations of sex and age with methylation of monoamine oxidase A (MAOA). Methylation of the first exonic and partial first intronic region of MAOA has been shown to strengthen associations of interactions of MAOA-uVNTR genotypes and adversity with aggression and substance misuse. Our study examined associations of sex and age with MAOA first exon and intron methylation levels in 252 women and 157 men aged 14–73 years. Participants included adolescents recruited at a substance misuse clinic, their siblings and parents, and healthy women. Women showed ~ 50% higher levels of exonic, and ~ 15% higher intronic, methylation than men. Methylation levels were similar between younger (M = 22.7 years) and older (M = 46.1 years) participants, and stable across age. Age modified few associations of methylation levels with sex. MAOA genotypes modified few associations of methylation with sex and age. Higher methylation levels among women were not explained by genotype, nor interaction of genotype and sexual abuse. Findings were similar after adjusting for lifetime diagnoses of substance dependence (women = 24.3%; men = 34.2%). Methylation levels were higher among women who experienced sexual abuse than women who did not. Results extend on prior studies by showing that women display higher levels of methylation than men within first intronic/exonic regions of MAOA, which did not decrease with age in either sex. Findings were not conditioned by genotype nor interactions of genotype and trauma, and indicate X-chromosome inactivation.

Importance of correcting genomic relationships in single-locus QTL mapping model with an advanced backcross population

Advanced backcross (AB) populations have been widely used to identify and utilize beneficial alleles in various crops such as rice, tomato, wheat, and barley. For the development of an AB population, a controlled crossing scheme is used and this controlled crossing along with the selection (both natural and artificial) of agronomically adapted alleles during the development of AB population may lead to unbalanced allele frequencies in the population. However, it is commonly believed that interval mapping of traits in experimental crosses such as AB populations is immune to the deviations from the expected frequencies under Mendelian segregation. Using two AB populations and simulated data sets as examples, we describe the severity of the problem caused by unbalanced allele frequencies in quantitative trait loci mapping and demonstrate how it can be corrected using the linear mixed model having a polygenic effect with the covariance structure (genomic relationship matrix) calculated from molecular markers.

Penalized linear mixed models for structured genetic data

Many genetic studies that aim to identify genetic variants associated with complex phenotypes are subject to unobserved confounding factors arising from environmental heterogeneity. This poses a challenge to detecting associations of interest and is known to induce spurious associations when left unaccounted for. Penalized linear mixed models (LMMs) are an attractive method to correct for unobserved confounding. These methods correct for varying levels of relatedness and population structure by modeling it as a random effect with a covariance structure estimated from observed genetic data. Despite an extensive literature on penalized regression and LMMs separately, the two are rarely discussed together. The aim of this review is to do so while examining the statistical properties of penalized LMMs in the genetic association setting. Specifically, the ability of penalized LMMs to accurately estimate genetic effects in the presence of environmental confounding has not been well studied. To clarify the important yet subtle distinction between population structure and environmental heterogeneity, we present a detailed review of relevant concepts and methods. In addition, we evaluate the performance of penalized LMMs and competing methods in terms of estimation and selection accuracy in the presence of a number of confounding structures.

A new method for exploring gene-gene and gene-environment interactions in GWAS with tree ensemble methods and SHAP values

Background

The identification of gene–gene and gene–environment interactions in genome-wide association studies is challenging due to the unknown nature of the interactions and the overwhelmingly large number of possible combinations. Parametric regression models are suitable to look for prespecified interactions. Nonparametric models such as tree ensemble models, with the ability to detect any unspecified interaction, have previously been difficult to interpret. However, with the development of methods for model explainability, it is now possible to interpret tree ensemble models efficiently and with a strong theoretical basis.

Results

We propose a tree ensemble- and SHAP-based method for identifying as well as interpreting potential gene–gene and gene–environment interactions on large-scale biobank data. A set of independent cross-validation runs are used to implicitly investigate the whole genome. We apply and evaluate the method using data from the UK Biobank with obesity as the phenotype. The results are in line with previous research on obesity as we identify top SNPs previously associated with obesity. We further demonstrate how to interpret and visualize interaction candidates.

Conclusions

The new method identifies interaction candidates otherwise not detected with parametric regression models. However, further research is needed to evaluate the uncertainties of these candidates. The method can be applied to large-scale biobanks with high-dimensional data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04041-7.

DNA methylation and gene expression integration in cardiovascular disease

BACKGROUND

The integration of different layers of omics information is an opportunity to tackle the complexity of cardiovascular diseases (CVD) and to identify new predictive biomarkers and potential therapeutic targets. Our aim was to integrate DNA methylation and gene expression data in an effort to identify biomarkers related to cardiovascular disease risk in a community-based population. We accessed data from the Framingham Offspring Study, a cohort study with data on DNA methylation (Infinium HumanMethylation450 BeadChip; Illumina) and gene expression (Human Exon 1.0 ST Array; Affymetrix). Using the MOFA2 R package, we integrated these data to identify biomarkers related to the risk of presenting a cardiovascular event.

RESULTS

Four independent latent factors (9, 19, 21-only in women-and 27), driven by DNA methylation, were associated with cardiovascular disease independently of classical risk factors and cell-type counts. In a sensitivity analysis, we also identified factor 21 as associated with CVD in women. Factors 9, 21 and 27 were also associated with coronary heart disease risk. Moreover, in a replication effort in an independent study three of the genes included in factor 27 were also present in a factor identified to be associated with myocardial infarction (CDC42BPB, MAN2A2 and RPTOR). Factor 9 was related to age and cell-type proportions; factor 19 was related to age and B cells count; factor 21 pointed to human immunodeficiency virus infection-related pathways and inflammation; and factor 27 was related to lifestyle factors such as alcohol consumption, smoking and body mass index. Inclusion of factor 21 (only in women) improved the discriminative and reclassification capacity of the Framingham classical risk function and factor 27 improved its discrimination.

CONCLUSIONS

Unsupervised multi-omics data integration methods have the potential to provide insights into the pathogenesis of cardiovascular diseases. We identified four independent factors (one only in women) pointing to inflammation, endothelium homeostasis, visceral fat, cardiac remodeling and lifestyles as key players in the determination of cardiovascular risk. Moreover, two of these factors improved the predictive capacity of a classical risk function.

Principals about principal components in statistical genetics

Principal components (PCs) are widely used in statistics and refer to a relatively small number of uncorrelated variables derived from an initial pool of variables, while explaining as much of the total variance as possible. Also in statistical genetics, principal component analysis (PCA) is a popular technique. To achieve optimal results, a thorough understanding about the different implementations of PCA is required and their impact on study results, compared to alternative approaches. In this review, we focus on the possibilities, limitations and role of PCs in ancestry prediction, genome-wide association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detection. We also describe several variations of classic PCA that deserve increased attention in statistical genetics applications.

Parentage and relatedness reconstruction in Pinus sylvestris using genotyping-by-sequencing

Estimating kinship is fundamental for studies of evolution, conservation, and breeding. Genotyping-by-sequencing (GBS) and other restriction based genotyping methods have become widely applied in these applications in non-model organisms. However, sequencing errors, depth, and reproducibility between library preps could potentially hinder accurate genetic inferences. In this study, we tested different sets of parameters in data filtering, different reference populations and eight estimation methods to obtain a robust procedure for relatedness estimation in Scots pine (Pinus sylvestris L.). We used a seed orchard as our study system, where candidate parents are known and pedigree reconstruction can be compared with theoretical expectations. We found that relatedness estimates were lower than expected for all categories of kinship estimated if the proportion of shared SNPs was low. However, estimates reached expected values if loci showing an excess of heterozygotes were removed and genotyping error rates were considered. The genetic variance-covariance matrix (G-matrix) estimation, however, performed poorly in kinship estimation. The reduced relatedness estimates are likely due to false heterozygosity calls. We analyzed the mating structure in the seed orchard and identified a selfing rate of 3% (including crosses between clone mates) and external pollen contamination of 33.6%. Little genetic structure was observed in the sampled Scots pine natural populations, and the degree of inbreeding in the orchard seed crop is comparable to natural stands. We illustrate that under our optimized data processing procedure, relatedness, and genetic composition, including level of pollen contamination within a seed orchard crop, can be established consistently by different estimators.

An efficient integrative resampling method for gene-trait association analysis

Genetic association studies are popular for identifying genetic variants, such as single nucleotide polymorphisms (SNPs), that are associated with complex traits. Statistical tests are commonly performed one SNP at a time with an assumed mode of inheritance such as recessive, additive, or dominant genetic model. Such analysis can result in inadequate power when the employed model deviates from the underlying true genetic model. We propose an integrative association test procedure under a generalized linear model framework to flexibly model the data from the above three common genetic models and beyond. A computationally efficient resampling procedure is adopted to estimate the null distribution of the proposed test statistic. Simulation results show that our methods maintain the Type I error rate irrespective of the existence of confounding covariates and achieve adequate power compared to the methods with the true genetic model. The new methods are applied to two genetic studies on the resistance of severe malaria and sarcoidosis.

Genome-wide association study of milk and reproductive traits in dual-purpose Xinjiang Brown cattle

Background

Dual-purpose cattle are more adaptive to environmental challenges than single-purpose dairy or beef cattle. Balance among milk, reproductive, and mastitis resistance traits in breeding programs is therefore more critical for dual-purpose cattle to increase net income and maintain well-being. With dual-purpose Xinjiang Brown cattle adapted to the Xinjiang Region in northwestern China, we conducted genome-wide association studies (GWAS) to dissect the genetic architecture related to milk, reproductive, and mastitis resistance traits. Phenotypic data were collected for 2410 individuals measured during 1995–2017. By adding another 445 ancestors, a total of 2855 related individuals were used to derive estimated breeding values for all individuals, including the 2410 individuals with phenotypes. Among phenotyped individuals, we genotyped 403 cows with the Illumina 150 K Bovine BeadChip.

Results

GWAS were conducted with the FarmCPU (Fixed and random model circulating probability unification) method. We identified 12 markers significantly associated with six of the 10 traits under the threshold of 5% after a Bonferroni multiple test correction. Seven of these SNPs were in QTL regions previously identified to be associated with related traits. One identified SNP, BovineHD1600006691, was significantly associated with both age at first service and age at first calving. This SNP directly overlapped a QTL previously reported to be associated with calving ease. Within 160 Kb upstream and downstream of each significant SNP identified, we speculated candidate genes based on functionality. Four of the SNPs were located within four candidate genes, including CDH2, which is linked to milk fat percentage, and GABRG2, which is associated with milk protein yield.

Conclusions

These findings are beneficial not only for breeding through marker-assisted selection, but also for genome editing underlying the related traits to enhance the overall performance of dual-purpose cattle.

Population Substructure Has Implications in Validating Next-Generation Cancer Genomics Studies with TCGA

In the era of large genetic and genomic datasets, it has become crucially important to validate results of individual studies using data from publicly available sources, such as The Cancer Genome Atlas (TCGA). However, how generalizable are results from either an independent or a large public dataset to the remainder of the population? The study presented here aims to answer that question. Utilizing next generation sequencing data from endometrial and ovarian cancer patients from both the University of Iowa and TCGA, genomic admixture of each population was analyzed using STRUCTURE and ADMIXTURE software. In our independent data set, one subpopulation was identified, whereas in TCGA 4⁻6 subpopulations were identified. Data presented here demonstrate how different the genetic substructures of the TCGA and University of Iowa populations are. Validation of genomic studies between two different population samples must be aware of, account for and be corrected for background genetic substructure.

Association between IL1 gene polymorphism and human African trypanosomiasis in populations of sleeping sickness foci of southern Cameroon

BACKGROUND

Human African Trypanosomiasis (HAT) is a neglected tropical disease caused by infections due to Trypanosoma brucei subspecies. In addition to the well-established environmental and behavioural risks of becoming infected, there is evidence for a genetic component to the response to trypanosome infection. We undertook a candidate gene case-control study to investigate genetic associations further.

METHODOLOGY

We genotyped one polymorphism in each of seven genes (IL1A, IL1RN, IL4RN, IL6, HP, HPR, and HLA-G) in 73 cases and 250 controls collected from 19 ethno-linguistic subgroups stratified into three major ethno-linguistic groups, 2 pooled ethno-linguistic groups and 11 ethno-linguistic subgroups from three Cameroonian HAT foci. The seven polymorphic loci tested consisted of three SNPs, three variable numbers of tandem repeat (VNTR) and one INDEL.

RESULTS

We found that the genotype (TT) and minor allele (T) of IL1A gene as well as the genotype 1A3A of IL1RN were associated with an increased risk of getting Trypanosoma brucei gambiense and develop HAT when all data were analysed together and also when stratified by the three major ethno-linguistic groups, 2 pooled ethno-linguistic subgroups and 11 ethno-linguistic subgroups.

CONCLUSION

This study revealed that one SNP rs1800794 of IL1A and one VNTR rs2234663 of IL1RN were associated with the increased risk to be infected by Trypanosoma brucei gambiense and develop sleeping sickness in southern Cameroon. The minor allele T and the genotype TT of SNP rs1800794 in IL1A as well as the genotype 1A3A of IL1RN rs2234663 VNTR seem to increase the risk of getting Trypanosoma brucei gambiense infections and develop sleeping sickness in southern Cameroon.

Genome wide association study of body weight and feed efficiency traits in a commercial broiler chicken population, a re-visitation

Genome wide association study was conducted using a mixed linear model (MLM) approach that accounted for family structure to identify single nucleotide polymorphisms (SNPs) and candidate genes associated with body weight (BW) and feed efficiency (FE) traits in a broiler chicken population. The results of the MLM approach were compared with the results of a general linear model approach that does not take family structure in to account. In total, 11 quantitative trait loci (QTL) and 21 SNPs, were identified to be significantly associated with BW traits and 5 QTL and 5 SNPs were found associated with FE traits using MLM approach. Besides some overlaps between the results of the two GWAS approaches, there are considerable differences in the detected QTL. Even though the genomic inflation factor (λ) values indicate that there is no strong family structure in this population, using models that account for the existing family structure may reduce bias and increase accuracy of the estimated SNP effects in the association analysis. The SNPs and candidate genes identified in this study provide information on the genetic background of BW and FE traits in broiler chickens and might be used as prior information for genomic selection.

Genome-wide mapping of quantitative trait loci in admixed populations using mixed linear model and Bayesian multiple regression analysis

Background

Population stratification and cryptic relationships have been the main sources of excessive false-positives and false-negatives in population-based association studies. Many methods have been developed to model these confounding factors and minimize their impact on the results of genome-wide association studies. In most of these methods, a two-stage approach is applied where: (1) methods are used to determine if there is a population structure in the sample dataset and (2) the effects of population structure are corrected either by modeling it or by running a separate analysis within each sub-population. The objective of this study was to evaluate the impact of population structure on the accuracy and power of genome-wide association studies using a Bayesian multiple regression method.

Methods

We conducted a genome-wide association study in a stochastically simulated admixed population. The genome was composed of six chromosomes, each with 1000 markers. Fifteen segregating quantitative trait loci contributed to the genetic variation of a quantitative trait with heritability of 0.30. The impact of genetic relationships and breed composition (BC) on three analysis methods were evaluated: single marker simple regression (SMR), single marker mixed linear model (MLM) and Bayesian multiple-regression analysis (BMR). Each method was fitted with and without BC. Accuracy, power, false-positive rate and the positive predictive value of each method were calculated and used for comparison.

Results

SMR and BMR, both without BC, were ranked as the worst and the best performing approaches, respectively. Our results showed that, while explicit modeling of genetic relationships and BC is essential for models SMR and MLM, BMR can disregard them and yet result in a higher power without compromising its false-positive rate.

Conclusions

This study showed that the Bayesian multiple-regression analysis is robust to population structure and to relationships among study subjects and performs better than a single marker mixed linear model approach.

Fuzzy set-based generalized multifactor dimensionality reduction analysis of gene-gene interactions

BACKGROUND

Gene-gene interactions (GGIs) are a known cause of missing heritability. Multifactor dimensionality reduction (MDR) is one of most commonly used methods for GGI detection. The generalized multifactor dimensionality reduction (GMDR) method is an extension of MDR method that is applicable to various types of traits, and allows covariate adjustments. Our previous Fuzzy MDR (FMDR) is another extension for overcoming simple binary classification. FMDR uses continuous member-ship values instead of binary membership values 0 and 1, improving power for detecting causal SNPs and more intuitive interpretations in real data analysis. Here, we propose the fuzzy generalized multifactor dimensionality reduction (FGMDR) method, as a combined analysis of fuzzy set-based analysis and GMDR method, to detect GGIs associated with diseases using fuzzy set theory.

RESULTS

Through simulation studies for different types of traits, the proposed FGMDR showed a higher detection ratio of causal SNPs, compared to GMDR. We then applied FGMDR to two real data: Crohn's disease (CD) data from the Wellcome Trust Case Control Consortium (WTCCC) with a binary phenotype and the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) data from Korean population with a continuous phenotype. The interactions derived by our method include the pre-reported interactions associated with phenotypes.

CONCLUSIONS

The proposed FGMDR performs well for GGI detection with covariate adjustments. The program written in R for FGMDR is available at http://statgen.snu.ac.kr/software/FGMDR .

Reducing cryptic relatedness in genomic data sets via a central node exclusion algorithm

Cryptic relatedness is a confounding factor in genetic diversity and genetic association studies. Development of strategies to reduce cryptic relatedness in a sample is a crucial step for downstream genetic analyses. This study uses a node selection algorithm, based on network degrees of centrality, to evaluate its applicability and impact on evaluation of genetic diversity and population stratification. 1,036 Guzerá (Bos indicus) females were genotyped using Illumina Bovine SNP50 v2 BeadChip. Four strategies were compared. The first and second strategies consist on a iterative exclusion of most related individuals based on PLINK kinship coefficient (φij) and VanRaden's φij, respectively. The third and fourth strategies were based on a node selection algorithm. The fourth strategy, Network G matrix, preserved the larger number of individuals with a better diversity and representation from the initial sample. Determining the most probable number of populations was directly affected by the kinship metric. Network G matrix was the better strategy for reducing relatedness due to producing a larger sample, with more distant individuals, a more similar distribution when compared with the full data set in the MDS plots and keeping a better representation of the population structure. Resampling strategies using VanRaden's φij as a relationship metric was better to infer the relationships among individuals. Moreover, the resampling strategies directly impact the genomic inflation values in genomewide association studies. The use of the node selection algorithm also implies better selection of the most central individuals to be removed, providing a more representative sample.

A novel linkage-disequilibrium corrected genomic relationship matrix for SNP-heritability estimation and genomic prediction

Single nucleotide polymorphism (SNP)-heritability estimation is an important topic in several research fields, including animal, plant and human genetics, as well as in ecology. Linear mixed model estimation of SNP-heritability uses the structures of genomic relationships between individuals, which is constructed from genome-wide sets of SNP-markers that are generally weighted equally in their contributions. Proposed methods to handle dependence between SNPs include, “thinning” the marker set by linkage disequilibrium (LD)-pruning, the use of haplotype-tagging of SNPs, and LD-weighting of the SNP-contributions. For improved estimation, we propose a new conceptual framework for genomic relationship matrix, in which Mahalanobis distance-based LD-correction is used in a linear mixed model estimation of SNP-heritability. The superiority of the presented method is illustrated and compared to mixed-model analyses using a VanRaden genomic relationship matrix, a matrix used by GCTA and a matrix employing LD-weighting (as implemented in the LDAK software) in simulated (using real human, rice and cattle genotypes) and real (maize, rice and mice) datasets. Despite of the computational difficulties, our results suggest that by using the proposed method one can improve the accuracy of SNP-heritability estimates in datasets with high LD.

Genome-wide association analyses of 54 traits identified multiple loci for the determination of floret fertility in wheat

Increasing grain yield is still the main target of wheat breeding; yet today's wheat plants utilize less than half of their yield potential. Owing to the difficulty of determining grain yield potential in a large population, few genetic factors regulating floret fertility (i.e. the difference between grain yield potential and grain number) have been reported to date. In this study, we conducted a genome-wide association study (GWAS) by quantifying 54 traits (16 floret fertility traits and 38 traits for assimilate partitioning and spike morphology) in 210 European winter wheat accessions. The results of this GWAS experiment suggested potential associations between floret fertility, assimilate partitioning and spike morphology revealed by shared quantitative trait loci (QTLs). Several candidate genes involved in carbohydrate metabolism, phytohormones or floral development colocalized with such QTLs, thereby providing potential targets for selection. Based on our GWAS results we propose a genetic network underlying floret fertility and related traits, nominating determinants for improved yield performance.

A genome-wide association study of seed composition traits in wild soybean (Glycine soja)

BACKGROUND

Cultivated soybean (Glycine max) is a major agricultural crop that provides a crucial source of edible protein and oil. Decreased amounts of saturated palmitic acid and increased amounts of unsaturated oleic acid in soybean oil are considered optimal for human cardiovascular health and therefore there has considerable interest by breeders in discovering genes affecting the relative concentrations of these fatty acids. Using a genome-wide association (GWA) approach with nearly 30,000 single nucleotide polymorphisms (SNPs), we investigated the genetic basis of protein, oil and all five fatty acid levels in seeds from a sample of 570 wild soybeans (Glycine soja), the progenitor of domesticated soybean, to identify quantitative trait loci (QTLs) affecting these seed composition traits.

RESULTS

We discovered 29 SNPs located on ten different chromosomes that are significantly associated with the seven seed composition traits in our wild soybean sample. Eight SNPs co-localized with QTLs previously uncovered in linkage or association mapping studies conducted with cultivated soybean samples, while the remaining SNPs appeared to be in novel locations. Twenty-four of the SNPs significantly associated with fatty acid variation, with the majority located on chromosomes 14 (6 SNPs) and seven (8 SNPs). Two SNPs were common for two or more fatty acids, suggesting loci with pleiotropic effects. We also identified some candidate genes that are involved in fatty acid metabolism and regulation. For each of the seven traits, most of the SNPs produced differences between the average phenotypic values of the two homozygotes of about one-half standard deviation and contributed over 3% of their total variability.

CONCLUSIONS

This is the first GWA study conducted on seed composition traits solely in wild soybean populations, and a number of QTLs were found that have not been previously discovered. Some of these may be useful to breeders who select for increased protein/oil content or altered fatty acid ratios in the seeds. The results also provide additional insight into the genetic architecture of these traits in a large sample of wild soybean, and suggest some new candidate genes whose molecular effects on these traits need to be further studied.

Design Considerations for Genetic Linkage and Association Studies

This chapter describes the main issues that genetic epidemiologists usually consider in the design of linkage and association studies. For linkage, we briefly consider the situation of rare highly penetrant alleles showing a disease pattern consistent with Mendelian inheritance investigated through parametric methods in large pedigrees, or with autozygosity mapping in inbred families, and we then turn our focus to the most common design, the affected sibling pair design that is of more relevance for common, complex diseases. Power and sample size calculations are provided as a function of the strength of the genetic effect being investigated. We also discuss the impact of other determinants of statistical power such as disease heterogeneity, pedigree and genotyping errors and the effect of the type and density of genetic markers. For association studies, we consider the popular case-control design for dichotomous phenotypes and we provide power and sample size calculations for one-stage and multistage designs. For candidate genes, guidelines are given on the prioritization of genetic variants, and for genome-wide association studies (GWAS) the issue of choosing an appropriate SNP array is discussed. A warning is issued regarding the danger of designing an underpowered replication study following an initial GWAS. The risk of finding spurious association due to population stratification, cryptic relatedness, and differential bias is underlined.

Genetic heterogeneity underlying variation in a locally adaptive clinal trait in Pinus sylvestris revealed by a Bayesian multipopulation analysis

Local adaptation is a common feature of plant and animal populations. Adaptive phenotypic traits are genetically differentiated along environmental gradients, but the genetic basis of such adaptation is still poorly known. Genetic association studies of local adaptation combine data over populations. Correcting for population structure in these studies can be problematic since both selection and neutral demographic events can create similar allele frequency differences between populations. Correcting for demography with traditional methods may lead to eliminating some true associations. We developed a new Bayesian approach for identifying the loci underlying an adaptive trait in a multipopulation situation in the presence of possible double confounding due to population stratification and adaptation. With this method we studied the genetic basis of timing of bud set, a surrogate trait for timing of yearly growth cessation that confers local adaptation to the populations of Scots pine (Pinus sylvestris). Population means of timing of bud set were highly correlated with latitude. Most effects at individual loci were small. Interestingly, we found genetic heterogeneity (that is, different sets of loci associated with the trait) between the northern and central European parts of the cline. We also found indications of stronger stabilizing selection toward the northern part of the range. The harsh northern conditions may impose greater selective pressure on timing of growth cessation, and the relative importance of different environmental cues used for tracking the seasons might differ depending on latitude of origin.

Prediction of complex human diseases from pathway-focused candidate markers by joint estimation of marker effects: case of chronic fatigue syndrome

Background

The current practice of using only a few strongly associated genetic markers in regression models results in generally low power in prediction or accounting for heritability of complex human traits.

Purpose

We illustrate here a Bayesian joint estimation of single nucleotide polymorphism (SNP) effects principle to improve prediction of phenotype status from pathway-focused sets of SNPs. Chronic fatigue syndrome (CFS), a complex disease of unknown etiology with no laboratory methods for diagnosis, was chosen to demonstrate the power of this Bayesian method. For CFS, such a genetic predictive model in combination with clinical evidence might lead to an earlier diagnosis than one based solely on clinical findings.

Methods

One of our goals is to model disease status using Bayesian statistics which perform variable selection and parameter estimation simultaneously and which can induce the sparseness and smoothness of the SNP effects. Smoothness of the SNP effects is obtained by explicit modeling of the covariance structure of the SNP effects.

Results

The Bayesian model achieved perfect goodness of fit when tested within the sampled data. Tenfold cross-validation resulted in 80 % accuracy, one of the best so far for CFS in comparison to previous prediction models. Model reduction aspects were investigated in a computationally feasible manner. Additionally, genetic variation estimates provided by the model identified specific genetic markers for their biological role in the disease pathophysiology.

Conclusions

This proof-of-principle study provides a powerful approach combining Bayesian methods, SNPs representing multiple pathways and rigorous case ascertainment for accurate genetic risk prediction modeling of complex diseases like CFS and other chronic diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s40246-015-0030-6) contains supplementary material, which is available to authorized users.

Evaluation of multi-locus models for genome-wide association studies: a case study in sugar beet

Association mapping has become a widely applied genomic approach to dissect the genetic architecture of complex traits. A major issue for association mapping is the need to control for the confounding effects of population structure, which is commonly done by mixed models incorporating kinship information. In this case study, we employed experimental data from a large sugar beet population to evaluate multi-locus models for association mapping. As in linkage mapping, markers are selected as cofactors to control for population structure and genetic background variation. We compared different biometric models with regard to important quantitative trait locus (QTL) mapping parameters like the false-positive rate, the QTL detection power and the predictive power for the proportion of explained genotypic variance. Employing different approaches we show that the multi-locus model, that is, incorporating cofactors, outperforms the other models, including the mixed model used as a reference model. Thus, multi-locus models are an attractive alternative for association mapping to efficiently detect QTL for knowledge-based breeding.

Genome-Wide Association in Tomato Reveals 44 Candidate Loci for Fruit Metabolic Traits

Genome-wide association studies have been successful in identifying genes involved in polygenic traits and are valuable for crop improvement. Tomato (Solanum lycopersicum) is a major crop and is highly appreciated worldwide for its health value. We used a core collection of 163 tomato accessions composed of S. lycopersicum, S. lycopersicum var cerasiforme, and Solanum pimpinellifolium to map loci controlling variation in fruit metabolites. Fruits were phenotyped for a broad range of metabolites, including amino acids, sugars, and ascorbate. In parallel, the accessions were genotyped with 5,995 single-nucleotide polymorphism markers spread over the whole genome. Genome-wide association analysis was conducted on a large set of metabolic traits that were stable over 2 years using a multilocus mixed model as a general method for mapping complex traits in structured populations and applied to tomato. We detected a total of 44 loci that were significantly associated with a total of 19 traits, including sucrose, ascorbate, malate, and citrate levels. These results not only provide a list of candidate loci to be functionally validated but also a powerful analytical approach for finding genetic variants that can be directly used for crop improvement and deciphering the genetic architecture of complex traits.

Haplotypes of the inducible nitric oxide synthase gene are strongly associated with exhaled nitric oxide levels in adults: a population-based study

BACKGROUND

Previous genetic association studies have reported evidence for association of single-nucleotide polymorphisms (SNPs) in the NOS2 gene, encoding inducible nitric oxide synthase (iNOS), to variation in levels of fractional exhaled nitric oxide (FENO) in children and adults. In this study, we evaluated 10 SNPs in the region of chromosome 17 from 26.07 Mb to 26.13 Mb to further understand the contribution of NOS2 to variation in levels of FENO.

METHODS

In a cohort of 5912 adults 25-75 years of age, we investigated the relationship between NOS2 haplotypes and FENO, and effect modification by asthma.

RESULTS

Seven common (frequency ≥5%) haplotypes (H1-H7) were inferred from all possible haplotype combinations. One haplotype (H3) was significantly associated with lower levels of FENO: -5.8% (95% CI -9.8 to -1.7; p=0.006) compared with the most common baseline haplotype H1. Two haplotypes (H5 and H6) were significantly associated with higher levels of FENO: +10.7% (95% CI 5.0 to 16.7; p=0.0002) and +14.9% (95% CI 10.6 to 19.3; p=7.8×10(-13)), respectively. The effect of haplotype H3 was mainly seen in subjects with asthma (-21.6% (95% CI -33.5 to -5.9)) and was not significant in subjects without asthma (-4.2% (95% CI -8.4 to 0.2)). The p value for interaction between H3 and asthma status was 0.004.

CONCLUSIONS

Our findings suggest that several common haplotypes in the NOS2 gene contribute to variation in FENO in adults. We also saw some evidence of effect modification by asthma status on haplotype H3.

The maize leaf lipidome shows multilevel genetic control and high predictive value for agronomic traits

Although the plant lipidome show an enormous level of structural and functional diversity, our knowledge about its genetic control and its connection to whole-plant phenotypes is very limited. Here, we profiled 563 lipid species with UPLC-FT-MS in 289 field-grown inbred lines genotyped with 56,110 SNPs. Genome-wide association study identified 174 associations for 76 lipids explaining up to 31.4% of the genetic variance (P-value 8.4 × 10(-18)). Candidate genes were found for lipid synthesis, breakdown, transfer, and protection against peroxidation. The detected SNP-lipid associations could be grouped into associations with 1) individual lipids, 2) lipids from one biochemical class, and 3) lipids from several classes, suggesting a multilevel genetic control architecture. We further found a strong connection between the lipidome and agronomic traits in field-evaluated hybrid progeny. A cross-validated prediction model yielded correlations of up to 0.78 suggesting that the lipidome accurately predicts agronomic traits relevant in hybrid maize breeding.

Cross-validation in association mapping and its relevance for the estimation of QTL parameters of complex traits

Association mapping has become a widely applied genomic approach to identify quantitative trait loci (QTL) and dissect the genetic architecture of complex traits. However, approaches to assess the quality of the obtained QTL results are lacking. We therefore evaluated the potential of cross-validation in association mapping based on a large sugar beet data set. Our results show that the proportion of the population that should be used as estimation and validation sets, respectively, depends on the size of the mapping population. Generally, a fivefold cross-validation, that is, 20% of the lines as independent validation set, appears appropriate for commonly used population sizes. The predictive power for the proportion of genotypic variance explained by QTL was overestimated by on average 38% indicating a strong bias in the estimated QTL effects. The cross-validated predictive power ranged between 4 and 50%, which are more realistic estimates of this parameter for complex traits. In addition, QTL frequency distributions can be used to assess the precision of QTL position estimates and the robustness of the detected QTL. In summary, cross-validation can be a valuable tool to assess the quality of QTL parameters in association mapping.

Evaluation of the lasso and the elastic net in genome-wide association studies

The number of publications performing genome-wide association studies (GWAS) has increased dramatically. Penalized regression approaches have been developed to overcome the challenges caused by the high dimensional data, but these methods are relatively new in the GWAS field. In this study we have compared the statistical performance of two methods (the least absolute shrinkage and selection operator—lasso and the elastic net) on two simulated data sets and one real data set from a 50 K genome-wide single nucleotide polymorphism (SNP) panel of 5570 Fleckvieh bulls. The first simulated data set displays moderate to high linkage disequilibrium between SNPs, whereas the second simulated data set from the QTLMAS 2010 workshop is biologically more complex. We used cross-validation to find the optimal value of regularization parameter λ with both minimum MSE and minimum MSE + 1SE of minimum MSE. The optimal λ values were used for variable selection. Based on the first simulated data, we found that the minMSE in general picked up too many SNPs. At minMSE + 1SE, the lasso didn't acquire any false positives, but selected too few correct SNPs. The elastic net provided the best compromise between few false positives and many correct selections when the penalty weight α was around 0.1. However, in our simulation setting, this α value didn't result in the lowest minMSE + 1SE. The number of selected SNPs from the QTLMAS 2010 data was after correction for population structure 82 and 161 for the lasso and the elastic net, respectively. In the Fleckvieh data set after population structure correction lasso and the elastic net identified from 1291 to 1966 important SNPs for milk fat content, with major peaks on chromosomes 5, 14, 15, and 20. Hence, we can conclude that it is important to analyze GWAS data with both the lasso and the elastic net and an alternative tuning criterion to minimum MSE is needed for variable selection.

Combined linkage disequilibrium and linkage mapping: Bayesian multilocus approach

Quantitative trait loci (QTL) affecting the phenotype of interest can be detected using linkage analysis (LA), linkage disequilibrium (LD) mapping or a combination of both (LDLA). The LA approach uses information from recombination events within the observed pedigree and LD mapping from the historical recombinations within the unobserved pedigree. We propose the Bayesian variable selection approach for combined LDLA analysis for single-nucleotide polymorphism (SNP) data. The novel approach uses both sources of information simultaneously as is commonly done in plant and animal genetics, but it makes fewer assumptions about population demography than previous LDLA methods. This differs from approaches in human genetics, where LDLA methods use LA information conditional on LD information or the other way round. We argue that the multilocus LDLA model is more powerful for the detection of phenotype-genotype associations than single-locus LDLA analysis. To illustrate the performance of the Bayesian multilocus LDLA method, we analyzed simulation replicates based on real SNP genotype data from small three-generational CEPH families and compared the results with commonly used quantitative transmission disequilibrium test (QTDT). This paper is intended to be conceptual in the sense that it is not meant to be a practical method for analyzing high-density SNP data, which is more common. Our aim was to test whether this approach can function in principle.

Chemical genomics for studying parasite gene function and interaction

With the development of new technologies in genome sequencing, gene expression profiling, genotyping, and high-throughput screening of chemical compound libraries, small molecules are playing increasingly important roles in studying gene expression regulation, gene-gene interaction, and gene function. Here we briefly review and discuss some recent advancements in drug target identification and phenotype characterization using combinations of high-throughput screening of small-molecule libraries and various genome-wide methods such as whole-genome sequencing, genome-wide association studies (GWAS), and genome-wide expression analysis. These approaches can be used to search for new drugs against parasite infections, to identify drug targets or drug resistance genes, and to infer gene function.

Genome-wide association mapping for wood characteristics in Populus identifies an array of candidate single nucleotide polymorphisms

Establishing links between phenotypes and molecular variants is of central importance to accelerate genetic improvement of economically important plant species. Our work represents the first genome-wide association study to the inherently complex and currently poorly understood genetic architecture of industrially relevant wood traits. Here, we employed an Illumina Infinium 34K single nucleotide polymorphism (SNP) genotyping array that generated 29,233 high-quality SNPs in c. 3500 broad-based candidate genes within a population of 334 unrelated Populus trichocarpa individuals to establish genome-wide associations. The analysis revealed 141 significant SNPs (α ≤ 0.05) associated with 16 wood chemistry/ultrastructure traits, individually explaining 3-7% of the phenotypic variance. A large set of associations (41% of all hits) occurred in candidate genes preselected for their suggested a priori involvement with secondary growth. For example, an allelic variant in the FRA8 ortholog explained 21% of the total genetic variance in fiber length, when the trait's heritability estimate was considered. The remaining associations identified SNPs in genes not previously implicated in wood or secondary wall formation. Our findings provide unique insights into wood trait architecture and support efforts for population improvement based on desirable allelic variants.

PedWiz: a web-based tool for pedigree informatics

A novel web-based tool PedWiz that pipelines the informatics process for pedigree data is introduced. PedWiz is designed to assist researchers in the analysis of pedigree data. It provides a convenient tool for pedigree informatics: descriptive statistics, relative pairs, genetic similarity coefficients, the variance-covariance matrix for three estimated coefficients of allele identical-by-descent sharing as well as mean allele sharing, a plot of the pedigree structures, and a visualization of the identity coefficients. With a renewed interest in linkage and other family based methods, PedWiz will be a valuable tool for the analysis of family data.

Maximizing genetic differentiation in core collections by PCA-based clustering of molecular marker data

Developing genetically diverse core sets is key to the effective management and use of crop genetic resources. Core selection increasingly uses molecular marker-based dissimilarity and clustering methods, under the implicit assumption that markers and genes of interest are genetically correlated. In practice, low marker densities mean that genome-wide correlations are mainly caused by genetic differentiation, rather than by physical linkage. Although of central concern, genetic differentiation per se is not specifically targeted by most commonly employed dissimilarity and clustering methods. Principal component analysis (PCA) on genotypic data is known to effectively describe the inter-locus correlations caused by differentiation, but to date there has been no evaluation of its application to core selection. Here, we explore PCA-based clustering of marker data as a basis for core selection, with the aim of demonstrating its use in capturing genetic differentiation in the data. Using simulated datasets, we show that replacing full-rank genotypic data by the subset of genetically significant PCs leads to better description of differentiation and improves assignment of genotypes to their population of origin. We test the effectiveness of differentiation as a criterion for the formation of core sets by applying a simple new PCA-based core selection method to simulated and actual data and comparing its performance to one of the best existing selection algorithms. We find that although gains in genetic diversity are generally modest, PCA-based core selection is equally effective at maximizing diversity at non-marker loci, while providing better representation of genetically differentiated groups.

Detecting immigrants in a highly genetically homogeneous spiny lobster population (Palinurus elephas) in the northwest Mediterranean Sea

We investigated the genetic structure of early benthic juveniles of the spiny lobster Palinurus elephas in the northwest Mediterranean Sea by means of ten polymorphic microsatellite markers. Non-metric Multidimensional Scaling coupled with assignment tests were used as a new approach to further delimit a reference population inside a genetically homogeneous pool of individuals and test for the presence of long distance immigrants. From this approach, we found that most early benthic juveniles collected while settling in the northwest Mediterranean Sea originated from a common larval pool. However, 4.2% of the individuals were classified as immigrants from other genetically differentiated populations, with more immigrants in the south than in the north of the sampled basin. Given currents in the northwest Mediterranean Sea and the long pelagic larval phase of P. elephas that lasts several months, this result suggest a restricted homogenized zone in the studied basin with some individuals probably coming from more differentiated populations through the Almeria-Oran Front or the Strait of Sicily.

Genome-wide association study heterogeneous cohort homogenization via subject weight knock-down

Population structure can be a source of both false-positive and false-negative findings in a genome-wide association study. This article proposes an approach that helps to reduce the false-positives. It consists of homogenizing the diseased/healthy phenotype ratio across the cohort, by decreasing the statistical weight of selected individuals. After homogenization, the cohort is statistically handled as if originating from a single well-mixed population. The method was applied to homogenize a Parkinson's disease genome-wide association study cohort.

Genome-wide association mapping including phenotypes from relatives without genotypes

A common problem for genome-wide association analysis (GWAS) is lack of power for detection of quantitative trait loci (QTLs) and precision for fine mapping. Here, we present a statistical method, termed single-step GBLUP (ssGBLUP), which increases both power and precision without increasing genotyping costs by taking advantage of phenotypes from other related and unrelated subjects. The procedure achieves these goals by blending traditional pedigree relationships with those derived from genetic markers, and by conversion of estimated breeding values (EBVs) to marker effects and weights. Additionally, the application of mixed model approaches allow for both simple and complex analyses that involve multiple traits and confounding factors, such as environmental, epigenetic or maternal environmental effects. Efficiency of the method was examined using simulations with 15,800 subjects, of which 1500 were genotyped. Thirty QTLs were simulated across genome and assumed heritability was 0·5. Comparisons included ssGBLUP applied directly to phenotypes, BayesB and classical GWAS (CGWAS) with deregressed proofs. An average accuracy of prediction 0·89 was obtained by ssGBLUP after one iteration, which was 0·01 higher than by BayesB. Power and precision for GWAS applications were evaluated by the correlation between true QTL effects and the sum of m adjacent single nucleotide polymorphism (SNP) effects. The highest correlations were 0·82 and 0·74 for ssGBLUP and CGWAS with m=8, and 0·83 for BayesB with m=16. Standard deviations of the correlations across replicates were several times higher in BayesB than in ssGBLUP. The ssGBLUP method with marker weights is faster, more accurate and easier to implement for GWAS applications without computing pseudo-data.

Robustness of Bayesian multilocus association models to cryptic relatedness

Population-based association analyses are more powerful than within-family analyses in identifying genetic loci associated with a phenotype of interest. However, if the population or sample structure is omitted from the model, population stratification and cryptic relatedness may lead to false positive and negative signals caused by relatedness between individuals, rather than association due to close linkage of the marker and the trait loci. Therefore it is important to correct or account for these confounders in population-based association analyses. However, there is cumulative evidence that when fitting a multilocus association model, the genetic relationships between the individuals can be captured by the markers themselves, bringing about a possibility to use the models without an additional correction for the population or sample structure. In this work we have further investigated this possibility in the Bayesian multilocus association model context using the extended Bayesian LASSO and the indicator-based variable selection. In particular, we have studied whether these multilocus models benefit from an insertion of an additional polygenic term representing the genetic variation not captured by the markers and taking account of the residual dependencies between the individuals. We have found that although the models may benefit from the insertion of the polygenic component, omitting the component does not damage the model performance severely.

Comparison of biometrical approaches for QTL detection in multiple segregating families

Detection of QTL in multiple segregating families possesses many advantages over the classical QTL mapping in biparental populations. It has thus become increasingly popular, and different biometrical approaches are available to analyze such data sets. We empirically compared an approach based on linkage mapping methodology with an association mapping approach. To this end, we used a large population of 788 elite maize lines derived from six biparental families genotyped with 857 SNP markers. In addition, we constructed genetic maps with reduced marker densities to assess the dependency of the performance of both mapping approaches on the marker density. We used cross-validation and resample model averaging and found that while association mapping performed better under high marker densities, this was reversed under low marker densities. In addition to main effect QTL, we also detected epistatic interactions. Our results suggest that both approaches will profit from a further increase in marker density and that a cross-validation should be applied irrespective of the biometrical approach.