Genetic Variation Of Complex Traits Research Articles

Correction for Verweij et al. (2012)

EvolutionVolume 67, Issue 5 p. 1537-1537 ERRATUMFree Access Correction for Verweij et al. (†) This article corrects the following: MAINTENANCE OF GENETIC VARIATION IN HUMAN PERSONALITY: TESTING EVOLUTIONARY MODELS BY ESTIMATING HERITABILITY DUE TO COMMON CAUSAL VARIANTS AND INVESTIGATING THE EFFECT OF DISTANT INBREEDING Karin J.H. Verweij, Jian Yang, Jari Lahti, Juha Veijola, Mirka Hintsanen, Laura Pulkki-Råback, Kati Heinonen, Anneli Pouta, Anu-Katriina Pesonen, Elisabeth Widen, Anja Taanila, Matti Isohanni, Jouko Miettunen, Aarno Palotie, Lars Penke, Susan K. Service, Andrew C. Heath, Grant W. Montgomery, Olli Raitakari, Mika Kähönen, Jorma Viikari, Katri Räikkönen, Johan G Eriksson, Liisa Keltikangas-Järvinen, Terho Lehtimäki, Nicholas G. Martin, Marjo-Riitta Järvelin, Peter M. Visscher, Matthew C. Keller, Brendan P. Zietsch, Volume 66Issue 10Evolution pages: 3238-3251 First Published online: May 21, 2012 First published: 12 March 2013 https://doi.org/10.1111/evo.12095AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat In Verweij et al. (2012), there is a typographical error in Table 1. The heading of the 3rd column in Table 1 should read ‘% VA due to common variants (MAF > .01)’. The published version reads ‘… (MAF > .1)’. The correct table is below: Table 1. Predictions from evolutionary models for maintenance of genetic variation in complex traits No. of causal % VA due to common variants Inbreeding Useful Model variantsaa Previous research strongly suggests a highly polygenic basis to personality (Verweij et al. 2010) (MAF > .01) VNA/VGbb VNA/VG is expected to be fairly low under neutrality (Hill et al. 2008), though no specific level can be predicted; ‘higher’ predicted levels are in comparison this baseline h2(SNPs)/H2 affects trait references Selective neutrality No prediction 98% Low High No (Eyre-Walker 2010) Mutation-selection balance Many <<98% Higher Low Possiblycc Yes under directional selection, no under stabilising selection (Eyre-Walker 2010) Balancing selection pleiotropic balancing selection No prediction >98% Low High No (Turelli and Barton 2004) environmental heterogeneity No prediction >98% Low High No (Turelli and Barton 2004) negative frequency-dependent selection Few >98% Low High No (Mani et al. 1990; Kopp and Hermisson 2006) sex-dependent selection Up to two >98% Low High No (Turelli and Barton 2004) overdominance for fitness, resulting from antagonistic pleiotropydd These predictions appear to approximately generalise to overdominance in general (Burger 2000) Few >98% Higher Low Possiblyee Inbreeding is expected to decrease fitness, but does not necessarily affect the trait in question (Curtsinger et al. 1994; Hedrick 1999; Burger 2000) VA additive genetic variation; VNA: nonadditive genetic variation; MAF: minor allele frequency a Previous research strongly suggests a highly polygenic basis to personality (Verweij et al. 2010) b VNA/VG is expected to be fairly low under neutrality (Hill et al. 2008), though no specific level can be predicted; ‘higher’ predicted levels are in comparison this baseline c Yes under directional selection, no under stabilising selection d These predictions appear to approximately generalise to overdominance in general (Burger 2000) e Inbreeding is expected to decrease fitness, but does not necessarily affect the trait in question LITERATURE CITED Verweij, K. J. H., Yang, J., Lahti, J., Veijola, J., Hintsanen, M., Pulkki-Råback, L., Heinonen, K., Pouta, A., Pesonen, A.-K., Widen, E., Taanila, A., Isohanni, M., Miettunen, J., Palotie, A., Penke, L., Service, S. K., Heath, A. C., Montgomery, G. W., Raitakari, O., Kähönen, M., Viikari, J., Räikkönen, K., Eriksson, J. G., Keltikangas-Järvinen, L., Lehtimäki, T., Martin, N. G., Järvelin, M.-R., Visscher, P. M., Keller, M. C. and Zietsch, B. P. 2012. Maintenance of variation in human personality: testing evolutionary models by estimating heritability due to common causal variants and investigating the effect of distant inbreeding. Evolution, 66: 3238– 3251. doi: 10.1111/j.1558-5646.2012.01679.x. Wiley Online LibraryPubMedWeb of Science®Google Scholar Volume67, Issue5May 2013Pages 1537-1537 ReferencesRelatedInformation

Interpreting noncoding genetic variation in complex traits and human disease

Association studies provide genome-wide information about the genetic basis of complex disease, but medical research has primarily focused on protein-coding variants, due to the difficulty of interpreting non-coding mutations. This picture has changed with advances in the systematic annotation of functional non-coding elements. Evolutionary conservation, functional genomics, chromatin state, sequence motifs, and molecular quantitative trait loci all provide complementary information about non-coding function. These functional maps can help prioritize variants on risk haplotypes, filter mutations encountered in the clinic, and perform systems-level analyses to reveal processes underlying disease associations. Advances in predictive modeling can enable dataset integration to reveal pathways shared across loci and alleles, and richer regulatory models can guide the search for epistatic interactions. Lastly, new massively parallel reporter experiments can systematically validate regulatory predictions. Ultimately, advances in regulatory and systems genomics can help unleash the value of whole-genome sequencing for personalized genomic risk assessment, diagnosis, and treatment.

Estimating Additive and Non-Additive Genetic Variances and Predicting Genetic Merits Using Genome-Wide Dense Single Nucleotide Polymorphism Markers

Non-additive genetic variation is usually ignored when genome-wide markers are used to study the genetic architecture and genomic prediction of complex traits in human, wild life, model organisms or farm animals. However, non-additive genetic effects may have an important contribution to total genetic variation of complex traits. This study presented a genomic BLUP model including additive and non-additive genetic effects, in which additive and non-additive genetic relation matrices were constructed from information of genome-wide dense single nucleotide polymorphism (SNP) markers. In addition, this study for the first time proposed a method to construct dominance relationship matrix using SNP markers and demonstrated it in detail. The proposed model was implemented to investigate the amounts of additive genetic, dominance and epistatic variations, and assessed the accuracy and unbiasedness of genomic predictions for daily gain in pigs. In the analysis of daily gain, four linear models were used: 1) a simple additive genetic model (MA), 2) a model including both additive and additive by additive epistatic genetic effects (MAE), 3) a model including both additive and dominance genetic effects (MAD), and 4) a full model including all three genetic components (MAED). Estimates of narrow-sense heritability were 0.397, 0.373, 0.379 and 0.357 for models MA, MAE, MAD and MAED, respectively. Estimated dominance variance and additive by additive epistatic variance accounted for 5.6% and 9.5% of the total phenotypic variance, respectively. Based on model MAED, the estimate of broad-sense heritability was 0.506. Reliabilities of genomic predicted breeding values for the animals without performance records were 28.5%, 28.8%, 29.2% and 29.5% for models MA, MAE, MAD and MAED, respectively. In addition, models including non-additive genetic effects improved unbiasedness of genomic predictions.

Identification and Dissection of Four Major QTL Affecting Milk Fat Content in the German Holstein-Friesian Population

Milk composition traits exhibit a complex genetic architecture with a small number of major quantitative trait loci (QTL) explaining a large fraction of the genetic variation and numerous QTL with minor effects. In order to identify QTL for milk fat percentage (FP) in the German Holstein-Friesian (HF) population, a genome-wide association study (GWAS) was performed. The study population consisted of 2327 progeny-tested bulls. Genotypes were available for 44,280 SNPs. Phenotypes in the form of estimated breeding values (EBVs) for FP were used as highly heritable traits. A variance components-based approach was used to account for population stratification. The GWAS identified four major QTL regions explaining 46.18% of the FP EBV variance. Besides two previously known FP QTL on BTA14 (P = 8.91×10−198) and BTA20 (P = 7.03×10−12) within DGAT1 and GHR, respectively, we uncovered two additional QTL regions on BTA5 (P = 2.00×10−13) and BTA27 (P = 9.83×10−5) encompassing EPS8 and GPAT4, respectively. EPS8 and GPAT4 are involved in lipid metabolism in mammals. Re-sequencing of EPS8 and GPAT4 revealed 50 polymorphisms. Genotypes for five of them were inferred for the entire study population. Two polymorphisms affecting potential transcription factor binding sites of EPS8 (P = 1.40×10−12) and GPAT4 (P = 5.18×10−5), respectively, were highly significantly associated with the FP EBV. Our results provide evidence that alteration of regulatory sites is an important aspect of genetic variation of complex traits in cattle.

Presence of Multiple Independent Effects in Risk Loci of Common Complex Human Diseases

Many genetic loci and SNPs associated with many common complex human diseases and traits are now identified. The total genetic variance explained by these loci for a trait or disease, however, has often been very small. Much of the "missing heritability" has been revealed to be hidden in the genome among the large number of variants with small effects. Several recent studies have reported the presence of multiple independent SNPs and genetic heterogeneity in trait-associated loci. It is therefore reasonable to speculate that such a phenomenon could be common among loci known to be associated with a complex trait or disease. For testing this hypothesis, a total of 117 loci known to be associated with rheumatoid arthritis (RA), Crohn disease (CD), type 1 diabetes (T1D), or type 2 diabetes (T2D) were selected. The presence of multiple independent effects was assessed in the case-control samples genotyped by the Wellcome Trust Case Control Consortium study and imputed with SNP genotype information from the HapMap Project and the 1000 Genomes Project. Eleven loci with evidence of multiple independent effects were identified in the study, and the number was expected to increase at larger sample sizes and improved statistical power. The variance explained by the multiple effects in a locus was much higher than the variance explained by the single reported SNP effect. The results thus significantly improve our understanding of the allelic structure of these individual disease-associated loci, as well as our knowledge of the general genetic mechanisms of common complex traits and diseases.

Genetic Effects at Pleiotropic Loci Are Context-Dependent with Consequences for the Maintenance of Genetic Variation in Populations

Context-dependent genetic effects, including genotype-by-environment and genotype-by-sex interactions, are a potential mechanism by which genetic variation of complex traits is maintained in populations. Pleiotropic genetic effects are also thought to play an important role in evolution, reflecting functional and developmental relationships among traits. We examine context-dependent genetic effects at pleiotropic loci associated with normal variation in multiple metabolic syndrome (MetS) components (obesity, dyslipidemia, and diabetes-related traits). MetS prevalence is increasing in Western societies and, while environmental in origin, presents substantial variation in individual response. We identify 23 pleiotropic MetS quantitative trait loci (QTL) in an F16 advanced intercross between the LG/J and SM/J inbred mouse strains (Wustl:LG,SM-G16; n = 1002). Half of each family was fed a high-fat diet and half fed a low-fat diet; and additive, dominance, and parent-of-origin imprinting genotypic effects were examined in animals partitioned into sex, diet, and sex-by-diet cohorts. We examine the context-dependency of the underlying additive, dominance, and imprinting genetic effects of the traits associated with these pleiotropic QTL. Further, we examine sequence polymorphisms (SNPs) between LG/J and SM/J as well as differential expression of positional candidate genes in these regions. We show that genetic associations are different in different sex, diet, and sex-by-diet settings. We also show that over- or underdominance and ecological cross-over interactions for single phenotypes may not be common, however multidimensional synthetic phenotypes at loci with pleiotropic effects can produce situations that favor the maintenance of genetic variation in populations. Our findings have important implications for evolution and the notion of personalized medicine.

Genome partitioning of genetic variation for complex traits using common SNPs

We estimate and partition genetic variation for height, body mass index (BMI), von Willebrand factor and QT interval (QTi) using 586,898 SNPs genotyped on 11,586 unrelated individuals. We estimate that ∼45%, ∼17%, ∼25% and ∼21% of the variance in height, BMI, von Willebrand factor and QTi, respectively, can be explained by all autosomal SNPs and a further ∼0.5-1% can be explained by X chromosome SNPs. We show that the variance explained by each chromosome is proportional to its length, and that SNPs in or near genes explain more variation than SNPs between genes. We propose a new approach to estimate variation due to cryptic relatedness and population stratification. Our results provide further evidence that a substantial proportion of heritability is captured by common SNPs, that height, BMI and QTi are highly polygenic traits, and that the additive variation explained by a part of the genome is approximately proportional to the total length of DNA contained within genes therein.

Dissecting Genetic Networks Underlying Complex Phenotypes: The Theoretical Framework

Great progress has been made in genetic dissection of quantitative trait variation during the past two decades, but many studies still reveal only a small fraction of quantitative trait loci (QTLs), and epistasis remains elusive. We integrate contemporary knowledge of signal transduction pathways with principles of quantitative and population genetics to characterize genetic networks underlying complex traits, using a model founded upon one-way functional dependency of downstream genes on upstream regulators (the principle of hierarchy) and mutual functional dependency among related genes (functional genetic units, FGU). Both simulated and real data suggest that complementary epistasis contributes greatly to quantitative trait variation, and obscures the phenotypic effects of many ‘downstream’ loci in pathways. The mathematical relationships between the main effects and epistatic effects of genes acting at different levels of signaling pathways were established using the quantitative and population genetic parameters. Both loss of function and “co-adapted” gene complexes formed by multiple alleles with differentiated functions (effects) are predicted to be frequent types of allelic diversity at loci that contribute to the genetic variation of complex traits in populations. Downstream FGUs appear to be more vulnerable to loss of function than their upstream regulators, but this vulnerability is apparently compensated by different FGUs of similar functions. Other predictions from the model may account for puzzling results regarding responses to selection, genotype by environment interaction, and the genetic basis of heterosis.

Common SNPs explain a large proportion of the heritability for human height

SNPs discovered by genome-wide association studies (GWASs) account for only a small fraction of the genetic variation of complex traits in human populations. Where is the remaining heritability? We estimated the proportion of variance for human height explained by 294,831 SNPs genotyped on 3,925 unrelated individuals using a linear model analysis, and validated the estimation method with simulations based on the observed genotype data. We show that 45% of variance can be explained by considering all SNPs simultaneously. Thus, most of the heritability is not missing but has not previously been detected because the individual effects are too small to pass stringent significance tests. We provide evidence that the remaining heritability is due to incomplete linkage disequilibrium between causal variants and genotyped SNPs, exacerbated by causal variants having lower minor allele frequency than the SNPs explored to date.

Variants in TF and HFE Explain ∼40% of Genetic Variation in Serum-Transferrin Levels

Only a small proportion of genetic variation in complex traits has been explained by SNPs from genome-wide association studies (GWASs). We report the results from two GWASs for serum markers of iron status (serum iron, serum transferrin, transferrin saturation with iron, and serum ferritin), which are important in iron overload (e.g., hemochromatosis) and deficiency (e.g., anemia) conditions. We performed two GWASs on samples of Australians of European descent. In the first GWAS, 411 adolescent twins and their siblings were genotyped with 100K SNPs. rs1830084, 10.8 kb 3' of TF, was significantly associated with serum transferrin (p total association test = 1.0 x 10(-9); p within-family test = 2.2 x 10(-5)). In the second GWAS on an independent sample of 459 female monozygotic (MZ) twin pairs genotyped with 300K SNPs, we found rs3811647 (within intron 11 of TF, HapMap CEU r(2) with rs1830084 = 0.86) was significantly associated with serum transferrin (p = 3.0 x 10(-15)). In the second GWAS, we found two additional and independent SNPs on TF (rs1799852 and rs2280673) and confirmed the known C282Y mutation in HFE to be independently associated with serum transferrin. The three variants in TF (rs3811647, rs1799852 and rs2280673) plus the HFE C282Y mutation explained approximately 40% of genetic variation in serum transferrin (p = 7.8 x 10(-25)). These findings are potentially important for our understanding of iron metabolism and of regulation of hepatic protein secretion, and also strongly support the hypothesis that the genetic architecture of some endophenotypes may be simpler than that of disease.

Dominant male-sterile populations for association mapping and introgression of exotic wheat germplasm

The introgression of exotic wheat germplasm such as synthetic hexaploid (tetraploid × diploid amphiploid) wheat (Triticum aestivum L.) into an adapted gene pool has the potential to amplify the genetic variation for complex traits. The dominant male-sterile gene can be used to facilitate recurrent selection in wheat, thus increasing the opportunity for genetic recombination between exotic and cultivated genomes and the identification and retention of desirable alleles. Our dominant male-sterile recurrent selection project began in 1983 with the intermating of the source germplasm (Chris hard red spring wheat in Triticum tauschii cytoplasm) with 34 soft winter lines and varieties, intermated without selection for 3 generations, and then selecting male-sterile plants for agronomic type each season. After 20 generations of recurrent selection, we extracted 94 inbred lines without selection during inbreeding except for fertility. Those lines were evaluated for linkage disequilibrium (LD) on the 5A chromosome for comparison with an earlier study involving 95 elite soft winter wheat varieties of similar genetic background. For the male-sterile population-derived lines, LD decayed within 1 cM (r2 &lt; 0.1) compared with 5 cM in the previous report for soft winter wheat varieties. This approach to introgression facilitates the potential use of synthetic wheat and other unadapted germplasm for expanding the gene pool and provides a potential resource for high-resolution association mapping of genes controlling traits of interest.

Genotype Matrix Mapping: Searching for Quantitative Trait Loci Interactions in Genetic Variation in Complex Traits

In order to reveal quantitative trait loci (QTL) interactions and the relationship between various interactions in complex traits, we have developed a new QTL mapping approach, named genotype matrix mapping (GMM), which searches for QTL interactions in genetic variation. The central approach in GMM is the following. (1) Each tested marker is given a virtual matrix, named a genotype matrix (GM), containing intersecting lines and rows equal to the total allele number for that marker in the population analyzed. (2) QTL interactions are then estimated and compared through virtual networks among the GMs. To evaluate the contribution of marker combinations to a quantitative phenotype, the GMM method divides the samples into two non-overlapping subclasses, S0 and S1; the former contains the samples that have a specific genotype pattern to be evaluated, and the latter contains samples that do not. Based on this division, the F-measure is calculated as an index of significance. With the GMM method, we extracted significant marker combinations consisting of one to three interacting markers. The results indicated there were multiple QTL interactions affecting the phenotype (flowering date). GMM will be a valuable approach to identify QTL interactions in genetic variation of a complex trait within a variety of organisms.

Quantitative trait analysis of seed yield and other complex traits in hybrid spring rapeseed (Brassica napus L.): 1. Identification of genomic regions from winter germplasm

The introgression of winter germplasm into spring canola (Brassica napus L.) represents a novel approach to improve seed yield of hybrid spring canola. In this study, quantitative trait loci (QTL) for seed yield and other traits were genetically mapped to determine the effects of genomic regions introgressed from winter germplasm into spring canola. Plant materials used comprised of two populations of doubled haploid (DH) lines having winter germplasm introgression from two related French winter cultivars and their testcrosses with a spring line used in commercial hybrids. These populations were evaluated for 2 years at two locations (Wisconsin, USA and Saskatchewan, Canada). Genetic linkage maps based on RFLP loci were constructed for each DH population. Six QTL were detected in the testcross populations for which the winter alleles increased seed yield. One of these QTL explained 11 and 19% of the phenotypic variation in the two Canadian environments. The winter allele for another QTL that increased seed yield was linked in coupling to a QTL allele for high glucosinolate content, suggesting that the transition of rapeseed into canola could have resulted in the loss of favorable seed yield alleles. Most QTL for which the introgressed allele decreased seed yield of hybrids mapped to genomic regions having homoeologous non-reciprocal transpositions. This suggests that allelic configurations created by these rearrangements might make an important contribution to genetic variation for complex traits in oilseed B. napus and could account for a portion of the heterotic effects in hybrids.

Genetic Polymorphisms in Monoamine Neurotransmitter Systems Show Only Weak Association with Acute Post-Surgical Pain in Humans

BackgroundCandidate gene studies on the basis of biological hypotheses have been a practical approach to identify relevant genetic variation in complex traits. Based on previous reports and the roles in pain pathways, we have examined the effects of variations of loci in the genes of monoamine neurotransmitter systems including metabolizing enzymes, receptors and transporters on acute clinical pain responses in humans.ResultsVariations in the catecholamine metabolizing enzyme genes (MAOA and COMT) showed significant associations with the maximum post-operative pain rating while the serotonin transporter gene (SLC6A4) showed association with the onset time of post-operative pain. Analgesic onset time after medication was significantly associated with the norepinephrine transporter gene (SLC6A2). However, the association between COMT genetic variation and pain sensitivity in our study differ from previous studies with small sample sizes, population stratification and pain phenotype derived from combining different types of pain stimuli. Correcting for multiple comparisons did not sustain these genetic associations between monoamine neurotransmitter systems and pain sensitivity even in this large and homogeneous sample.ConclusionThese results suggest that the previously reported associations between genetic polymorphisms in the monoamine neurotransmitter systems and the interindividual variability in pain responses cannot be replicated in a clinically relevant pain phenotype.

Epistasis and balanced polymorphism influencing complex trait variation

Complex traits such as human disease, growth rate, or crop yield are polygenic, or determined by the contributions from numerous genes in a quantitative manner. Although progress has been made in identifying major quantitative trait loci (QTL), experimental constraints have limited our knowledge of small-effect QTL, which may be responsible for a large proportion of trait variation. Here, we identified and dissected a one-centimorgan chromosome interval in Arabidopsis thaliana without regard to its effect on growth rate, and examined the signature of historical sequence polymorphism among Arabidopsis accessions. We found that the interval contained two growth rate QTL within 210 kilobases. Both QTL showed epistasis; that is, their phenotypic effects depended on the genetic background. This amount of complexity in such a small area suggests a highly polygenic architecture of quantitative variation, much more than previously documented. One QTL was limited to a single gene. The gene in question displayed a nucleotide signature indicative of balancing selection, and its phenotypic effects are reversed depending on genetic background. If this region typifies many complex trait loci, then non-neutral epistatic polymorphism may be an important contributor to genetic variation in complex traits.