Genetic Analysis Of Complex Traits Research Articles

Expanded utility of the R package, qgg, with applications within genomic medicine.

Here, we present an expanded utility of the R package qgg for genetic analyses of complex traits and diseases. One of the major updates of the package is, that it now includes Bayesian linear regression modeling procedures, which provide a unified framework for mapping of genetic variants, estimation of heritability and genomic prediction from either individual level data or from genome-wide association study summary data. With this release, the qgg package now provides a wealth of the commonly used methods in analysis of complex traits and diseases, without the need to switch between software and data formats. The methodologies are implemented in the publicly available R software package, qgg, using fast and memory efficient algorithms in C++ and is available on CRAN or as a developer version at our GitHub page (https://github.com/psoerensen/qgg). Notes on the implemented statistical genetic models, tutorials and example scripts are available at our GitHub page https://psoerensen.github.io/qgg/.

Pervasive Downward Bias in Estimates of Liability-Scale Heritability in Genome-wide Association Study Meta-analysis: A Simple Solution

BackgroundSingle nucleotide polymorphism–based heritability is a fundamental quantity in the genetic analysis of complex traits. For case-control phenotypes, for which the continuous distribution of risk in the population is unobserved, observed-scale heritability estimates must be transformed to the more interpretable liability scale. This article describes how the field standard approach incorrectly performs the liability correction in that it does not appropriately account for variation in the proportion of cases across the cohorts comprising the meta-analysis. We propose a simple solution that incorporates cohort-specific ascertainment using the summation of effective sample sizes across cohorts. This solution is applied at the stage of single nucleotide polymorphism–based heritability estimation and does not require generating updated meta-analytic genome-wide association study summary statistics. MethodsWe began by performing a series of simulations to examine the ability of the standard approach and our proposed approach to recapture liability-scale heritability in the population. We went on to examine the differences in estimates obtained from these 2 approaches for real data for 12 major case-control genome-wide association studies of psychiatric and neurologic traits. ResultsWe found that the field standard approach for performing the liability conversion can downwardly bias estimates by as much as approximately 50% in simulation and approximately 30% in real data. ConclusionsPrior estimates of liability-scale heritability for genome-wide association study meta-analysis may be drastically underestimated. To this end, we strongly recommend using our proposed approach of using the sum of effective sample sizes across contributing cohorts to obtain unbiased estimates.

Whole-Genome Sequencing of 117 Chromosome Segment Substitution Lines for Genetic Analyses of Complex Traits in Rice

Rice is one of the most important food crops in Asia. Genetic analyses of complex traits and molecular breeding studies in rice greatly rely on the construction of various genetic populations. Chromosome segment substitution lines (CSSLs) serve as a powerful genetic population for quantitative trait locus (QTL) mapping in rice. Moreover, CSSLs containing target genomic regions can be used as improved varieties in rice breeding. In this study, we developed a set of CSSLs consisting of 117 lines derived from the recipient ‘Huanghuazhan’ (HHZ) and the donor ‘Basmati Surkb 89–15’ (BAS). The 117 lines were extensively genotyped by whole-genome resequencing, and a high-density genotype map was constructed for the CSSL population. The 117 CSSLs covered 99.78% of the BAS genome. Each line contained a single segment, and the average segment length was 6.02 Mb. Using the CSSL population, we investigated three agronomic traits in Shanghai and Hangzhou, China, and a total of 25 QTLs were detected in both environments. Among those QTLs, we found that RFT1 was the causal gene for heading date variance between HHZ and BAS. RFT1 from BAS was found to contain a loss-of-function allele based on yeast two-hybrid assay, and its causal variation was a P to S change in the 94th amino acid of the RFT1 protein. The combination of high-throughput genotyping and marker-assisted selection (MAS) is a highly efficient way to construct CSSLs in rice, and extensively genotyped CSSLs will be a powerful tool for the genetic mapping of agronomic traits and molecular breeding for target QTLs/genes.

Analysis of independent cohorts of outbred CFW mice reveals novel loci for behavioral and physiological traits and identifies factors determining reproducibility.

Combining samples for genetic association is standard practice in human genetic analysis of complex traits, but is rarely undertaken in rodent genetics. Here, using 23 phenotypes and genotypes from two independent laboratories, we obtained a sample size of 3076 commercially available outbred mice and identified 70 loci, more than double the number of loci identified in the component studies. Fine-mapping in the combined sample reduced the number of likely causal variants, with a median reduction in set size of 51%, and indicated novel gene associations, including Pnpo, Ttll6, and GM11545 with bone mineral density, and Psmb9 with weight. However, replication at a nominal threshold of 0.05 between the two component studies was low, with less than one-third of loci identified in one study replicated in the second. In addition to overestimates in the effect size in the discovery sample (Winner’s Curse), we also found that heterogeneity between studies explained the poor replication, but the contribution of these two factors varied among traits. Leveraging these observations, we integrated information about replication rates, study-specific heterogeneity, and Winner’s Curse corrected estimates of power to assign variants to one of four confidence levels. Our approach addresses concerns about reproducibility and demonstrates how to obtain robust results from mapping complex traits in any genome-wide association study.

Whole-Exome Sequencing Analysis of Human Semen Quality in Russian Multiethnic Population

The global trend toward the reduction of human spermatogenic function observed in many countries, including Russia, raised the problem of extensive screening and monitoring of male fertility and elucidation of its genetic and ethnic mechanisms. Recently, whole-exome sequencing (WES) was developed as a powerful tool for genetic analysis of complex traits. We present here the first Russian WES study for identification of new genes associated with semen quality. The experimental 3 × 2 design of the WES study was based on the analysis of 157 samples including three ethnic groups—Slavs (59), Buryats (n = 49), and Yakuts (n = 49), and two different semen quality groups—pathozoospermia (n = 95) and normospermia (n = 62). Additionally, our WES study group was negative for complete AZF microdeletions of the Y-chromosome. The normospermia group included men with normal sperm parameters in accordance with the WHO-recommended reference limit. The pathozoospermia group included men with impaired semen quality, namely, with any combined parameters of sperm concentration <15 × 106/ml, and/or progressive motility <32%, and/or normal morphology <4%. The WES was performed for all 157 samples. Subsequent calling and filtering of variants were carried out according to the GATK Best Practices recommendations. On the genotyping stage, the samples were combined into four cohorts: three sets corresponded to three ethnic groups, and the fourth set contained all the 157 whole-exome samples. Association of the obtained polymorphisms with semen quality parameters was investigated using the χ2 test. To prioritize the obtained variants associated with pathozoospermia, their effects were determined using Ensembl Variant Effect Predictor. Moreover, polymorphisms located in genes expressed in the testis were revealed based on the genomic annotation. As a result, the nine potential SNP markers rs6971091, rs557806, rs610308, rs556052, rs1289658, rs278981, rs1129172, rs12268007, and rs17228441 were selected for subsequent verification on our previously collected population sample (about 1,500 males). The selected variants located in seven genes FAM71F1, PPP1R15A, TRIM45, PRAME, RBM47, WDFY4, and FSIP2 that are expressed in the testis and play an important role in cell proliferation, meiosis, and apoptosis.

Brain imaging genomics: influences of genomic variability on the structure and function of the human brain

Abstract Brain imaging genomics is an emerging discipline in which genomic and brain imaging data are integrated in order to elucidate the molecular mechanisms that underly brain phenotypes and diseases, including neuropsychiatric disorders. As with all genetic analyses of complex traits and diseases, brain imaging genomics has evolved from small, individual candidate gene investigations towards large, collaborative genome-wide association studies. Recent investigations, mostly population-based, have studied well-powered cohorts comprising tens of thousands of individuals and identified multiple robust associations of single-nucleotide polymorphisms and copy number variants with structural and functional brain phenotypes. Such systematic genomic screens of millions of genetic variants have generated initial insights into the genetic architecture of brain phenotypes and demonstrated that their etiology is polygenic in nature, involving multiple common variants with small effect sizes and rare variants with larger effect sizes. Ongoing international collaborative initiatives are now working to obtain a more complete picture of the underlying biology. As in other complex phenotypes, novel approaches – such as gene–gene interaction, gene–environment interaction, and epigenetic analyses – are being implemented in order to investigate their contribution to the observed phenotypic variability. An important consideration for future research will be the translation of brain imaging genomics findings into clinical practice.

Reference Trait Analysis Reveals Correlations Between Gene Expression and Quantitative Traits in Disjoint Samples.

Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease-related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTL. We propose a simple and powerful alternative strategy for correlating independently obtained sets of complex traits and molecular features. In contrast to TWAS, our approach gains precision by correlating complex traits through a common set of continuous phenotypes instead of genetic predictors, and can identify transcript-trait correlations for which the regulation is not genetic. In our approach, a set of multiple quantitative "reference" traits is measured across all individuals, while measures of the complex trait of interest and transcriptional profiles are obtained in disjoint subsamples. A conventional multivariate statistical method, canonical correlation analysis, is used to relate the reference traits and traits of interest to identify gene expression correlates. We evaluate power and sample size requirements of this methodology, as well as performance relative to other methods, via extensive simulation and analysis of a behavioral genetics experiment in 258 Diversity Outbred mice involving two independent sets of anxiety-related behaviors and hippocampal gene expression. After splitting the data set and hiding one set of anxiety-related traits in half the samples, we identified transcripts correlated with the hidden traits using the other set of anxiety-related traits and exploiting the highest canonical correlation (R = 0.69) between the trait data sets. We demonstrate that this approach outperforms TWAS in identifying associated transcripts. Together, these results demonstrate the validity, reliability, and power of reference trait analysis for identifying relations between complex traits and their molecular substrates.

The molecular genetics of hand preference revisited

Hand preference is a prominent behavioural trait linked to human brain asymmetry. A handful of genetic variants have been reported to associate with hand preference or quantitative measures related to it. Most of these reports were on the basis of limited sample sizes, by current standards for genetic analysis of complex traits. Here we performed a genome-wide association analysis of hand preference in the large, population-based UK Biobank cohort (N = 331,037). We used gene-set enrichment analysis to investigate whether genes involved in visceral asymmetry are particularly relevant to hand preference, following one previous report. We found no evidence supporting any of the previously suggested variants or genes, nor that genes involved in visceral laterality have a role in hand preference. It remains possible that some of the previously reported genes or pathways are relevant to hand preference as assessed in other ways, or else are relevant within specific disorder populations. However, some or all of the earlier findings are likely to be false positives, and none of them appear relevant to hand preference as defined categorically in the general population. Our analysis did produce a small number of novel, significant associations, including one implicating the microtubule-associated gene MAP2 in handedness.

Can Deep Learning Improve Genomic Prediction of Complex Human Traits?

The genetic analysis of complex traits does not escape the current excitement around artificial intelligence, including a renewed interest in "deep learning" (DL) techniques such as Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). However, the performance of DL for genomic prediction of complex human traits has not been comprehensively tested. To provide an evaluation of MLPs and CNNs, we used data from distantly related white Caucasian individuals (n ∼100k individuals, m ∼500k SNPs, and k = 1000) of the interim release of the UK Biobank. We analyzed a total of five phenotypes: height, bone heel mineral density, body mass index, systolic blood pressure, and waist-hip ratio, with genomic heritabilities ranging from ∼0.20 to 0.70. After hyperparameter optimization using a genetic algorithm, we considered several configurations, from shallow to deep learners, and compared the predictive performance of MLPs and CNNs with that of Bayesian linear regressions across sets of SNPs (from 10k to 50k) that were preselected using single-marker regression analyses. For height, a highly heritable phenotype, all methods performed similarly, although CNNs were slightly but consistently worse. For the rest of the phenotypes, the performance of some CNNs was comparable or slightly better than linear methods. Performance of MLPs was highly dependent on SNP set and phenotype. In all, over the range of traits evaluated in this study, CNN performance was competitive to linear models, but we did not find any case where DL outperformed the linear model by a sizable margin. We suggest that more research is needed to adapt CNN methodology, originally motivated by image analysis, to genetic-based problems in order for CNNs to be competitive with linear models.

QTL Identification for Cooking and Eating Quality in indica Rice Using Multi-Parent Advanced Generation Intercross (MAGIC) Population.

Association mapping using a multi-parent advanced generation intercross (MAGIC) population provides a promising tool in genetic dissection of rice cooking and eating quality (CEQ). In this study, QTLs were identified for ten physicochemical properties related to CEQ using 508 F6 MAGIC lines. The whole population and eight founder lines were genotyped with 6K Illumina Infinium HD Assay. All traits had high heritability estimates and showed a large genetic variation in the MAGIC population. Highly significant phenotypic correlations were present between traits. AC was significantly positively correlated with PKT, TV, FV, SBV, PKT, and RT but significantly negatively correlated with GC and BDV. Seventeen QTLs were identified for all traits. GBSSI locus was hosted or closely to nine QTLs, qAC6, qGC6.1, qPKT6.1, qPKV6, qBDV6.1, qTV6.1, qFV6, qSBV6, and qRT6, suggesting that GBSSI impacts the overall CEQ. Another locus closed to SSIIa, located at 6.99 Mb, affects five traits, GC, PKT, BDV, SBV, and PT. The identified QTLs revealed small to modest effects where the highest percentage of phenotypic variance explained was 17.18%. These QTLs are directly relevant and useful in breeding for CEQ in indica rice. These results also confirmed that QTL mapping via association mapping using a MAGIC population is a powerful method in genetic analysis of complex traits.

Accuracy of genotype imputation in Labrador Retrievers.

SummaryThe dog is a valuable model species for the genetic analysis of complex traits, and the use of genotype imputation in dogs will be an important tool for future studies. It is of particular interest to analyse the effect of factors like single nucleotide polymorphism (SNP) density of genotyping arrays and relatedness between dogs on imputation accuracy due to the acknowledged genetic and pedigree structure of dog breeds. In this study, we simulated different genotyping strategies based on data from 1179 Labrador Retriever dogs. The study involved 5826 SNPs on chromosome 1 representing the high density (HighD) array; the low‐density (LowD) array was simulated by masking different proportions of SNPs on the HighD array. The correlations between true and imputed genotypes for a realistic masking level of 87.5% ranged from 0.92 to 0.97, depending on the scenario used. A correlation of 0.92 was found for a likely scenario (10% of dogs genotyped using HighD, 87.5% of HighD SNPs masked in the LowD array), which indicates that genotype imputation in Labrador Retrievers can be a valuable tool to reduce experimental costs while increasing sample size. Furthermore, we show that genotype imputation can be performed successfully even without pedigree information and with low relatedness between dogs in the reference and validation sets. Based on these results, the impact of genotype imputation was evaluated in a genome‐wide association analysis and genomic prediction in Labrador Retrievers.

Construction of chromosome segment substitution lines of Chinese cabbage (Brassica rapa L. ssp. pekinensis) in the background of RcBr (B. rapa L. ssp. dichotoma) and characterization of segments representing the bolting trait

Chromosome segment substitution lines (CSSLs) are useful tools for precise mapping of quantitative trait loci (QTLs) and evaluation of gene actions or interactions in theoretical studies. To facilitate the genetic analysis of complex traits and use of marker-assisted breeding in Brassica rapa, we developed a set of CSSLs using the extremely early-flowering inbred line RcBr as the recipient and extremely late-bolting Chinese cabbage variety 08A061 as the donor parent. The CSSL population consisted of 120 lines containing a total of 275 substituted segments, of which 47 lines contained only one substituted segment. The total length of the substituted segments spanned 2489.75 cM with an average length of 9.05 cM, representing 91.0% of the B. rapa genome and each line consisting of an average 98.1% of the recurrent parent genome. A total of 15 putative additive QTLs and 10 pairwised epistatic QTLs for three bolting indices were subsequently detected by QTL IciMapping 3.2 under two growth environments. Phenotypic variation explained by the additive QTLs, with the total variation ranging from 3.31 to 31.29% and 10.04 to 75.83%, respectively. Overall, the epistatic QTLs explained only a small amount of the phenotypic variation. The CSSLs and analyses presented here will help enhance our understanding of the genetics of bolting in B. rapa, thereby facilitating future molecular breeding programs.

Cognitive genomics: Searching for the genetic roots of neuropsychological functioning.

Human cognition has long been known to be under substantial genetic control. With the complete mapping of the human genome, genome-wide association studies for many complex traits have proliferated; however, the highly polygenic nature of intelligence has made the identification of the precise genes that influence both global and specific cognitive abilities more difficult than anticipated. Here, we review the latest developments in the genomics of cognition, including a discussion of methodological advances in the genetic analysis of complex traits, and shared genetic contributions to cognitive abilities and neuropsychiatric disorders. A wealth of twin and family studies have provided compelling evidence for a strong heritable component of both global and specific cognitive abilities, and for the existence of "generalist genes" responsible for a large portion of the variance in diverse cognitive abilities. Increasingly sophisticated analytic tools and ever-larger sample sizes are now facilitating the identification of specific genetic and molecular underpinnings of cognitive abilities, leading to optimism regarding possibilities for novel treatments for illnesses related to cognitive function. We conclude with a set of future directions for the field, which will further accelerate discoveries regarding the biological pathways relevant to cognitive abilities. These, in turn, may be further interrogated in order to link biological mechanisms to behavior. (PsycINFO Database Record

Naturally Segregating Variation at Ugt86Dd Contributes to Nicotine Resistance in Drosophila melanogaster.

Identifying the sequence polymorphisms underlying complex trait variation is a key goal of genetics research, since knowing the precise causative molecular events allows insight into the pathways governing trait variation. Genetic analysis of complex traits in model systems regularly starts by constructing QTL maps, but generally fails to identify causative sequence polymorphisms. Previously we mapped a series of QTL contributing to resistance to nicotine in a Drosophila melanogaster multiparental mapping resource and here use a battery of functional tests to resolve QTL to the molecular level. One large-effect QTL resided over a cluster of UDP-glucuronosyltransferases, and quantitative complementation tests using deficiencies eliminating subsets of these detoxification genes revealed allelic variation impacting resistance. RNAseq showed that Ugt86Dd had significantly higher expression in genotypes that are more resistant to nicotine, and anterior midgut-specific RNA interference (RNAi) of this gene reduced resistance. We discovered a segregating 22-bp frameshift deletion in Ugt86Dd, and accounting for the InDel during mapping largely eliminates the QTL, implying the event explains the bulk of the effect of the mapped locus. CRISPR/Cas9 editing of a relatively resistant genotype to generate lesions in Ugt86Dd that recapitulate the naturally occurring putative loss-of-function allele, leads to a large reduction in resistance. Despite this major effect of the deletion, the allele appears to be very rare in wild-caught populations and likely explains only a small fraction of the natural variation for the trait. Nonetheless, this putatively causative coding InDel can be a launchpad for future mechanistic exploration of xenobiotic detoxification.

A Pilot Genome-Wide Association Study Identifies Potential Metabolic Pathways Involved in Tinnitus.

Tinnitus, the perception of an auditory phantom sound in the form of ringing, buzzing, roaring, or hissing in the absence of an external sound source, is perceived by ~15% of the population and 2.5% experiences a severely bothersome tinnitus. The contribution of genes on the development of tinnitus is still under debate. The current manuscript reports a pilot Genome Wide Association Study (GWAS) into tinnitus, in a small cohort of 167 independent tinnitus subjects, and 749 non-tinnitus controls, who were collected as part of a cross-sectional study. After genotyping, imputation, and quality checking, the association between the tinnitus phenotype and 4,000,000 single-nucleotide polymorphisms (SNPs) was tested followed by gene set enrichment analysis. None of the SNPs reached the threshold for genome-wide significance (p < 5.0e–8), with the most significant SNPs, situated outside coding genes, reaching a p-value of 3.4e–7. By using the Genetic Analysis of Complex Traits (GACT) software, the percentage of the variance explained by all SNPs in the GWAS was estimated to be 3.2%, indicating that additive genetic effects explain only a small fraction of the tinnitus phenotype. Despite the lack of genome-wide significant SNPs, which is, at least in part, due to the limited sample size of the current study, evidence was found for a genetic involvement in tinnitus. Gene set enrichment analysis showed several metabolic pathways to be significantly enriched with SNPs having a low p-value in the GWAS. These pathways are involved in oxidative stress, endoplasmatic reticulum (ER) stress, and serotonin reception mediated signaling. These results are a promising basis for further research into the genetic basis of tinnitus, including GWAS with larger sample sizes and considering tinnitus subtypes for which a greater genetic contribution is more likely.

Commentary: Fundamental problems with candidate gene-by-environment interaction studies - reflections on Moore and Thoemmes (2016).

Moore and Thoemmes elaborate on one particular source of difficulty in the study of candidate gene-by-environment interactions (cG×E): how different biologically plausible configurations of gene-environment covariation can bias estimates of cG×E when not explicitly modeled. However, even if cG×E investigators were able to account for the sources of bias Moore and Thoemmes elaborate, it is unlikely that conventional approaches would yield reliable results. Published cG×E findings to date have generally employed inadequate analytic procedures, have relied on samples orders of magnitude too small to detect plausible effects, and have relied on a particular candidate gene approach that has been unfruitful and largely jettisoned in mainstream genetic analyses of complex traits. Analytic procedures for the study of gene-environment interplay must evolve to meet the challenges that the genetic architecture of complex traits presents, and investigators must collaborate on grander scales if we hope to begin to understand how specific genes and environments combine to affect behavior.

Genome-wide association of multiple complex traits in outbred mice by ultra-low-coverage sequencing.

Two bottlenecks impeding the genetic analysis of complex traits in rodents are access to mapping populations able to deliver gene-level mapping resolution, and the need for population specific genotyping arrays and haplotype reference panels. Here we combine low coverage sequencing (0.15X) with a novel method to impute the ancestral haplotype space in 1,887 commercially available outbred mice. We mapped 156 unique quantitative trait loci for 92 phenotypes at 5% false discovery rate. Gene-level mapping resolution was achieved at about a fifth of loci, implicating Unc13c and Pgc1-alpha at loci for the quality of sleep, Adarb2 for home cage activity, Rtkn2 for intensity of reaction to startle, Bmp2 for wound healing, Il15 and Id2 for several T-cell measures and Prkca for bone mineral content. These findings have implications for diverse areas of mammalian biology and demonstrate how GWAS can be extended via low-coverage sequencing to species with highly recombinant outbred populations.

A Flexible Computational Framework Using R and Map-Reduce for Permutation Tests of Massive Genetic Analysis of Complex Traits.

In quantitative trait locus (QTL) mapping significance of putative QTL is often determined using permutation testing. The computational needs to calculate the significance level are immense, 104 up to 108 or even more permutations can be needed. We have previously introduced the PruneDIRECT algorithm for multiple QTL scan with epistatic interactions. This algorithm has specific strengths for permutation testing. Here, we present a flexible, parallel computing framework for identifying multiple interacting QTL using the PruneDIRECT algorithm which uses the map-reduce model as implemented in Hadoop. The framework is implemented in R, a widely used software tool among geneticists. This enables users to rearrange algorithmic steps to adapt genetic models, search algorithms, and parallelization steps to their needs in a flexible way. Our work underlines the maturity of accessing distributed parallel computing for computationally demanding bioinformatics applications through building workflows within existing scientific environments. We investigate the PruneDIRECT algorithm, comparing its performance to exhaustive search and DIRECT algorithm using our framework on a public cloud resource. We find that PruneDIRECT is vastly superior for permutation testing, and perform 2 ×105 permutations for a 2D QTL problem in 15 hours, using 100 cloud processes. We show that our framework scales out almost linearly for a 3D QTL search.

MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information.

Summary: We have developed an algorithm for genetic analysis of complex traits using genome-wide SNPs in a linear mixed model framework. Compared to current standard REML software based on the mixed model equation, our method is substantially faster. The advantage is largest when there is only a single genetic covariance structure. The method is particularly useful for multivariate analysis, including multi-trait models and random regression models for studying reaction norms. We applied our proposed method to publicly available mice and human data and discuss the advantages and limitations.Availability and implementation: MTG2 is available in https://sites.google.com/site/honglee0707/mtg2.Contact: hong.lee@une.edu.auSupplementary information: Supplementary data are available at Bioinformatics online.

Integrating Nonadditive Genomic Relationship Matrices into the Study of Genetic Architecture of Complex Traits.

The study of genetic architecture of complex traits has been dramatically influenced by implementing genome-wide analytical approaches during recent years. Of particular interest are genomic prediction strategies which make use of genomic information for predicting phenotypic responses instead of detecting trait-associated loci. In this work, we present the results of a simulation study to improve our understanding of the statistical properties of estimation of genetic variance components of complex traits, and of additive, dominance, and genetic effects through best linear unbiased prediction methodology. Simulated dense marker information was used to construct genomic additive and dominance matrices, and multiple alternative pedigree- and marker-based models were compared to determine if including a dominance term into the analysis may improve the genetic analysis of complex traits. Our results showed that a model containing a pedigree- or marker-based additive relationship matrix along with a pedigree-based dominance matrix provided the best partitioning of genetic variance into its components, especially when some degree of true dominance effects was expected to exist. Also, we noted that the use of a marker-based additive relationship matrix along with a pedigree-based dominance matrix had the best performance in terms of accuracy of correlations between true and estimated additive, dominance, and genetic effects.