Relevance of genetic relationship in GWAS and genomic prediction

The objective of this study was to analyze the relevance of relationship information on the identification of low heritability quantitative trait loci (QTLs) from a genome-wide association study (GWAS) and on the genomic prediction of complex traits in human, animal and cross-pollinating populations. The simulation-based data sets included 50 samples of 1000 individuals of seven populations derived from a common population with linkage disequilibrium. The populations had non-inbred and inbred progeny structure (50 to 200) with varying number of members (5 to 20). The individuals were genotyped for 10,000 single nucleotide polymorphisms (SNPs) and phenotyped for a quantitative trait controlled by 10 QTLs and 90 minor genes showing dominance. The SNP density was 0.1 cM and the narrow sense heritability was 25%. The QTL heritabilities ranged from 1.1 to 2.9%. We applied mixed model approaches for both GWAS and genomic prediction using pedigree-based and genomic relationship matrices. For GWAS, the observed false discovery rate was kept below the significance level of 5%, the power of detection for the low heritability QTLs ranged from 14 to 50%, and the average bias between significant SNPs and a QTL ranged from less than 0.01 to 0.23 cM. The QTL detection power was consistently higher using genomic relationship matrix. Regardless of population and training set size, genomic prediction provided higher prediction accuracy of complex trait when compared to pedigree-based prediction. The accuracy of genomic prediction when there is relatedness between individuals in the training set and the reference population is much higher than the value for unrelated individuals.     

High-Resolution Genetic Mapping of Complex Traits from a Combined Analysis of F2 and Advanced Intercross Mice

Genetic influences on anxiety disorders are well documented; however, the specific genes underlying these disorders remain largely unknown. To identify quantitative trait loci (QTL) for conditioned fear and open field behavior, we used an F₂ intercross (n = 490) and a 34th-generation advanced intercross line (AIL) (n = 687) from the LG/J and SM/J inbred mouse strains. The F₂ provided strong support for several QTL, but within wide chromosomal regions. The AIL yielded much narrower QTL, but the results were less statistically significant, despite the larger number of mice. Simultaneous analysis of the F₂ and AIL provided strong support for QTL and within much narrower regions. We used a linear mixed-model approach, implemented in the program QTLRel, to correct for possible confounding due to familial relatedness. Because we recorded the full pedigree, we were able to empirically compare two ways of accounting for relatedness: using the pedigree to estimate kinship coefficients and using genetic marker estimates of “realized relatedness.” QTL mapping using the marker-based estimates yielded more support for QTL, but only when we excluded the chromosome being scanned from the marker-based relatedness estimates. We used a forward model selection procedure to assess evidence for multiple QTL on the same chromosome. Overall, we identified 12 significant loci for behaviors in the open field and 12 significant loci for conditioned fear behaviors. Our approach implements multiple advances to integrated analysis of F₂ and AILs that provide both power and precision, while maintaining the advantages of using only two inbred strains to map QTL.     

Additive by Additive Variance with Inbreeding and Linkage

Two-locus coancestries, which provide the coefficients of the additive by additive component in the variance and covariance of relatives for a quantitative trait, were formulated generally in terms of two-locus probabilities of identity by descent for combinations of parental and recombinant gametes. Explicit expressions, with linkage, were developed for all relatives of self-fertilization, for selfed and outbred relatives and for full and half sibs from unrelated inbred parents. The relative effect of linkage on the two-locus coancestry decreases rapidly as inbreeding and relatedness of the relatives increase. It was concluded that the error caused by ignoring linkage would probably be small in the estimation of genetic variances or in the projection of selection response with self-fertilization.     

Plasticity genes and plasticity costs: a new approach using an Arabidopsis recombinant inbred population

Earlier flowering is triggered by vernalization in some but not all Arabidopsis ecotypes, often reflecting allelic variation at the FRIGIDA (FRI) locus. Using a recombinant inbred (RI) population polymorphic at FRI, we examined fitness consequences of variation for plasticity. Flowering and fitness were scored for 68 RI genotypes following full and partial vernalization treatments. Within-environment and mixed-model anovas estimated variance components for a genotype effect and a G x E term, respectively. Selection analyses examined whether delayed bolting increases fitness; a plasticity costs analysis asked whether increased plasticity lowers fitness. We also explored whether trait QTL had environment-specific effects, colocated in the immediate vicinity of FRI, or overlapped with fitness QTL. Selection may favor fri alleles and constitutive early flowering, especially in conditions that only partially vernalize plants. Plasticity costs, detected only after partial vernalization and only marginally significant, were nonetheless consistent with FRI-FLC function. We discuss how information about QTL with environment-specific effects, fitness QTL, and knowledge about plasticity genes can improve interpretation of selection or plasticity cost analyses.     

Efficiency of genome-wide association studies in random cross populations

Genome-wide association studies (GWAS) with plant species have employed inbred lines panels. We evaluated the efficiency of GWAS in non-inbred and inbred populations and assessed factors affecting GWAS. Fifty samples of 800 individuals from populations with linkage disequilibrium were simulated. Individuals were genotyped for 10,000 single nucleotide polymorphisms (SNPs) and phenotyped for traits controlled by ten quantitative trait loci (QTLs) and 90 minor genes, assuming different degrees of dominance and broad sense heritabilities of 40 and 80%. The average SNP density was 0.1 centiMorgan (cM) and the QTL heritabilities ranged from 3.2 to 11.8%. The results for random cross populations evidenced that to increase the QTL detection power, the additive-dominance model must be fitted for traits controlled by dominance effects but must not be fitted for traits showing no dominance. The power of detection was maximized by increasing the sample size to 400 and the false discovery rate (FDR) to 5%. The average power of detection for the low, intermediate, and high heritability QTLs achieved 52.4, 87.0, and 100.0%, respectively. Assuming sample sizes of 400 and 800, the observed FDR was equal to or lower than the specified level of significance. The association mapping was highly precise, since at least 97% of the declared QTLs were detected by the SNP inside it (average bias of 0.4 cM). Besides controlling the FDR, relatedness (and identity by state) efficiently controls the number of significant associations outside the QTL interval (not all false positive associations). The analysis of the inbred random cross population provided essentially the same results as the non-inbred populations.     

Detection and integration of quantitative trait loci for grain yield components and oil content in two connected recombinant inbred line populations of high-oil maize

Improvement in grain yield is an important objective in high-oil maize breeding. In this study, one high-oil maize inbred was crossed with two normal maize inbreds to produce two connected recombinant inbred line (RIL) populations with 282 and 263 F_7:8 families, respectively. The field experiments were conducted under four environments, and eight grain yield components and grain oil content were evaluated. Two genetic linkage maps were constructed using 216 and 208 polymorphic SSR markers. Quantitative trait loci (QTL) were detected for all traits under each environment and in combined analysis. Meta-analysis was used to integrate genetic maps and detected QTL in both populations. A total of 199 QTL were detected, 122 in population 1 and 87 in population 2. Seven, 11 and 19 QTL showed consistency across five environments, across two RIL populations and with respective F_2:3 generations, respectively. 183 QTL were integrated in 28 meta-QTL (mQTL). QTL with contributions over 15% were consistently detected in 3–4 cases and integrated in mQTL. Each mQTL included 3–19 QTL related to 1–4 traits, reflecting remarkable QTL co-location for grain yield components and oil content. Further research and marker-assisted selection (MAS) should be concentrated on 37 consistent QTL and four genetic regions of mQTL with more than 10 QTL at bins 3.04–3.05, 7.02, 8.04–8.05 and 9.04–9.05. Near-isogenic lines for 100-grain-weight QTL at bin 7.02–7.03, for ear-length QTL at bin 7.02–7.03 and for rows-per-ear QTL at bin 3.08 are now in construction using MAS. Co-located candidate genes could facilitate the identification of candidate genes for grain yield in maize.     

GENETIC ARCHITECTURE OF SKELETAL EVOLUTION IN EUROPEAN LAKE AND STREAM STICKLEBACK

Advances in genomic techniques are greatly facilitating the study of molecular signatures of selection in diverging natural populations. Connecting these signatures to phenotypes under selection remains challenging, but benefits from dissections of the genetic architecture of adaptive divergence. We here perform quantitative trait locus (QTL) mapping using 488 F₂ individuals and 2011 single nucleotide polymorphisms (SNPs) to explore the genetic architecture of skeletal divergence in a lake‐stream stickleback system from Central Europe. We find QTLs for gill raker, snout, and head length, vertebral number, and the extent of lateral plating (plate number and height). Although two large‐effect loci emerge, QTL effect sizes are generally small. Examining the neighborhood of the QTL‐linked SNPs identifies several genes involved in bone formation, which emerge as strong candidate genes for skeletal evolution. Finally, we use SNP data from the natural source populations to demonstrate that some SNPs linked to QTLs in our cross also exhibit striking allele frequency differences in the wild, suggesting a causal role of these QTLs in adaptive population divergence. Our study paves the way for comparative analyses across other (lake‐stream) stickleback populations, and for functional investigations of the candidate genes.     

Tagging and mapping candidate loci for vernalization and flower initiation in hexaploid oat

Flowering time is a decisive factor in the adaptation of oat. Some oat varieties require low temperatures for floral initiation, a process called vernalization. The objectives of this study were to clone, characterize, and map genes associated with vernalization in oat, and to identify markers linked to quantitative trait loci (QTL) that affect vernalization response. Genetic linkage maps were developed using Diversity Arrays Technology markers in recombinant inbred lines from the oat populations UFRGS 8?×?UFRGS 930605 and UFRGS 881971?×?Pc68/5*Starter. Flowering time and response to vernalization were characterized using field trials and controlled greenhouse experiments, and QTL were identified in two genetic regions on each of the two maps. PCR primer pairs anchored in the conserved coding regions of the Vrn1, Vrn2, and Vrn3 genes from wheat, barley, and Lolium were used to amplify and clone corresponding oat sequences. Cloned sequences corresponding to the targeted genes were recovered for both Vrn1 and Vrn3. A copy of the Vrn3 gene was mapped using a PCR amplicon, and an oat Vrn1 fragment was mapped by restriction fragment length polymorphism analysis. The location of the mapped Vrn1 locus was homologous to major QTL affecting flowering time in other work, and homoeologous to major QTL affecting response to vernalization in this study.     

Characterization of a global germplasm collection and its potential utilization for analysis of complex quantitative traits in maize

Association mapping is a powerful approach for exploring the molecular basis of phenotypic variations in plants. A maize (Zea mays L.) association mapping panel including 527 inbred lines with tropical, subtropical and temperate backgrounds, representing the global maize diversity, was genotyped using 1,536 single nucleotide polymorphisms (SNPs). In total, 926 SNPs with minor allele frequencies of ≥0.1 were used to estimate the pattern of genetic diversity and relatedness among individuals. The analysis revealed broad phenotypic diversity and complex genetic relatedness in the maize panel. Two different Bayesian approaches identified three specific subpopulations, which were then reconfirmed by principal component analysis (PCA) and tree-based analyses. Marker–trait associations were performed to assess the suitability of different models for false-positive correction by population structure (Q matrix/PCA) and familial kinship (K matrix) alone or in combination in this panel. The K, Q + K and PCA + K models could reduce the false positives, and the Q + K model performed slightly better for flowering time, ear height and ear diameter. Our findings suggest that this maize panel is suitable for association mapping in order to understand the relationship between genotypic and phenotypic variations for agriculturally complex quantitative traits using optimal statistical methods.     

A multiparental cross population for mapping QTL for agronomic traits in durum wheat (Triticum turgidum ssp. durum)

Multiparental cross designs for mapping quantitative trait loci (QTL) provide an efficient alternative to biparental populations because of their broader genetic basis and potentially higher mapping resolution. We describe the development and deployment of a recombinant inbred line (RIL) population in durum wheat (Triticum turgidum ssp. durum) obtained by crossing four elite cultivars. A linkage map spanning 2664 cM and including 7594 single nucleotide polymorphisms (SNPs) was produced by genotyping 338 RILs. QTL analysis was carried out by both interval mapping on founder haplotype probabilities and SNP bi‐allelic tests for heading date and maturity date, plant height and grain yield from four field experiments. Sixteen QTL were identified across environments and detection methods, including two yield QTL on chromosomes 2BL and 7AS, with the former mapped independently from the photoperiod response gene Ppd‐B1, while the latter overlapped with the vernalization locus VRN‐A3. Additionally, 21 QTL with environment‐specific effects were found. Our results indicated a prevalence of environment‐specific QTL with relatively small effect on the control of grain yield. For all traits, functionally different QTL alleles in terms of direction and size of genetic effect were distributed among parents. We showed that QTL results based on founder haplotypes closely matched functional alleles at known heading date loci. Despite the four founders, only 2.1 different functional haplotypes were estimated per QTL, on average. This durum wheat population provides a mapping resource for detailed genetic dissection of agronomic traits in an elite background typical of breeding programmes.     

Quantitative trait loci (QTL) detection in multicross inbred designs: recovering QTL identical-by-descent status information from marker data

Mapping quantitative trait loci in plants is usually conducted using a population derived from a cross between two inbred lines. The power of such QTL detection and the parameter estimates depend largely on the choice of the two parental lines. Thus, the QTL detected in such populations represent only a small part of the genetic architecture of the trait. In addition, the effects of only two alleles are characterized, which is of limited interest to the breeder, while common pedigree breeding material remains unexploited for QTL mapping. In this study, we extend QTL mapping methodology to a generalized framework, based on a two-step IBD variance component approach, applicable to any type of breeding population obtained from inbred parents. We then investigate with simulated data mimicking conventional breeding programs the influence of different estimates of the IBD values on the power of QTL detection. The proposed method would provide an alternative to the development of specifically designed recombinant populations, by utilizing the genetic variation actually managed by plant breeders. The use of these detected QTL in assisting breeding would thus be facilitated.     

Mapping quantitative trait loci in selected breeding populations: A segregation distortion approach

Quantitative trait locus (QTL) mapping is often conducted in line-crossing experiments where a sample of individuals is randomly selected from a pool of all potential progeny. QTLs detected from such an experiment are important for us to understand the genetic mechanisms governing a complex trait, but may not be directly relevant to plant breeding if they are not detected from the breeding population where selection is targeting for. QTLs segregating in one population may not necessarily segregate in another population. To facilitate marker-assisted selection, QTLs must be detected from the very population which the selection is targeting. However, selected breeding populations often have depleted genetic variation with small population sizes, resulting in low power in detecting useful QTLs. On the other hand, if selection is effective, loci controlling the selected trait will deviate from the expected Mendelian segregation ratio. In this study, we proposed to detect QTLs in selected breeding populations via the detection of marker segregation distortion in either a single population or multiple populations using the same selection scheme. Simulation studies showed that QTL can be detected in strong selected populations with selected population sizes as small as 25 plants. We applied the new method to detect QTLs in two breeding populations of rice selected for high grain yield. Seven QTLs were identified, four of which have been validated in advanced generations in a follow-up study. Cloned genes in the vicinity of the four QTLs were also reported in the literatures. This mapping-by-selection approach provides a new avenue for breeders to improve breeding progress. The new method can be applied to breeding programs not only in rice but also in other agricultural species including crops, trees and animals.     

Optimum allocation of resources for QTL detection using a nested association mapping strategy in maize

In quantitative trait locus (QTL) mapping studies, it is mandatory that the available financial resources are spent in such a way that the power for detection of QTL is maximized. The objective of this study was to optimize for three different fixed budgets the power of QTL detection 1 − β* in recombinant inbred line (RIL) populations derived from a nested design by varying (1) the genetic complexity of the trait, (2) the costs for developing, genotyping, and phenotyping RILs, (3) the total number of RILs, and (4) the number of environments and replications per environment used for phenotyping. Our computer simulations were based on empirical data of 653 single nucleotide polymorphism markers of 26 diverse maize inbred lines which were selected on the basis of 100 simple sequence repeat markers out of a worldwide sample of 260 maize inbreds to capture the maximum genetic diversity. For the standard scenario of costs, the optimum number of test environments (E _opt) ranged across the examined total budgets from 7 to 19 in the scenarios with 25 QTL. In comparison, the E _opt values observed for the scenarios with 50 and 100 QTL were slightly higher. Our finding of differences in 1 − β* estimates between experiments with optimally and sub-optimally allocated resources illustrated the potential to improve the power for QTL detection without increasing the total resources necessary for a QTL mapping experiment. Furthermore, the results of our study indicated that also in studies using the latest genomics tools to dissect quantitative traits, it is required to evaluate the individuals of the mapping population in a high number of environments with a high number of replications per environment.     

Use of a novel outbred by inbred F1 cross to detect genetic markers for growth

A unique outbred by inbred F1 resource population was established. The population structure facilitated the unique opportunity of examining gene by genetic background interaction through crossing two modern broiler sires with dams from two unrelated inbred lines, with no selection for growth rate, to produce about 600 F1 chicks. Pools of DNA were generated from the phenotypic extremes (20% high and low) for 8-week body weight for each of the four combinations of sire and dam line. For one sire family, pools were also separately generated for each sex. The pools were genoyped with 25 informative (segregating) microsatellites. This unique F1 cross between outbred and inbred populations allowed use of the inbred alleles as an 'internal control' for polymerase chain reaction amplification quality in DNA pools. Ten microsatellites showed marked differences (P < 0.05) in allele frequencies between high and low pools, suggesting an association between marker and quantitative trait loci (QTL). These differences were verified using selective genotyping. For many markers, differences in allele frequencies between the high and the low pools, or marker effect, varied between the two dam lines and the two sexes, suggesting an interaction between some genes and the genetic background as represented by different dam lines or sexes. The suggestive marker-QTL associations identified in this F1 population demonstrate the efficiency of this population design while different QTL effects in different genetic line crosses and sexes highlight the importance of gene by genetic background interaction in QTL detection.     

Identification of agronomically important QTL in tetraploid potato cultivars using a marker–trait association analysis

Key message

Nineteen tuber quality traits in potato were phenotyped in 205 cultivars and 299 breeder clones. Association analysis using 3364 AFLP loci and 653 SSR-alleles identified QTL for these traits.

Abstract

Two association mapping panels were analysed for marker–trait associations to identify quantitative trait loci (QTL). The first panel comprised 205 historical and contemporary tetraploid potato cultivars that were phenotyped in field trials at two locations with two replicates (the academic panel). The second panel consisted of 299 potato cultivars and included recent breeds obtained from five Dutch potato breeding companies and reference cultivars (the industrial panel). Phenotypic data for the second panel were collected during subsequent clonal selection generations at the individual breeding companies. QTL were identified for 19 agro-morphological and quality traits. Two association mapping models were used: a baseline model without, and a more advanced model with correction for population structure and genetic relatedness. Correction for population structure and genetic relatedness was performed with a kinship matrix estimated from marker information. The detected QTL partly not only confirmed previous studies, e.g. for tuber shape and frying colour, but also new QTL were found like for after baking darkening and enzymatic browning. Pleiotropic effects could be discerned for several QTL.     

A genome-wide association study for feed efficiency-related traits in a crossbred pig population

Feed efficiency (FE) is one of the most important traits in pig production. However, it is difficult and costly to measure it, limiting the collection of large amount of data for an accurate selection for better FE. Therefore, the identification of single-nucleotide polymorphisms (SNPs) associated with FE-related traits to be used in the genetic evaluation is of great interest of pig breeding programs for increasing the prediction accuracy and the genetic progress of these traits. The objective of this study was to identify SNPs significantly associated with FE-related traits: average daily gain (ADG), average daily feed intake (ADFI) and feed conversion ratio (FCR). We also aimed to identify potential candidate genes for these traits. Phenotypic information recorded on a population of 2386 three-way crossbreed pigs that were genotyped for 51 468 SNPs was used. We identified three loci of quantitative trait (QTL) regions associated with ADG and three QTL regions associated with ADFI; however, no significant association was found for FCR. A false discovery rate (FDR) ≤ 0.005 was used as the threshold for declaring an association as significant. The QTL regions associated with ADG on Sus scrofa chromosome (SSC) 1 were located between 177.01 and 185.47 Mb, which overlaps with the QTL regions for ADFI on SSC1 (173.26 and 185.47 Mb). The other QTL region for ADG was located on SSC12 (2.87 and 3.22 Mb). The most significant SNPs in these QTL regions explained up to 3.26% of the phenotypic variance of these traits. The non-identification of genomic regions associated with FCR can be explained by the complexity of this trait, which is a ratio between ADG and ADFI. Finally, the genes CDH19, CDH7, RNF152, MC4R, PMAIP1, FEM1B and GAA were the candidate genes found in the 1 Mb window around the QTL regions identified in this study. Among them, the MC4R gene (SSC1) has a well-known function related to ADG and ADFI. In this study, we identified three QTL regions for ADG (SSC1 and SSC12) and three for ADFI (SSC1). These regions were previously described in purebred pig populations; however, to our knowledge, this is the first study to confirm the relevance of these QTL regions in a crossbred pig population. The potential use of the SNPs and genes identified in this study in prediction models that combine genomic selection and marker-assisted selection should be evaluated for increasing the prediction accuracy of these traits in this population.     

QTL mapping of yield and fiber traits based on a four-way cross population in <Emphasis Type="Italic">Gossypium hirsutum</Emphasis> L.

Four-way cross (4WC) involving four different inbred lines frequently appears in the cotton breeding programs. However, linkage analysis and quantitative trait loci (QTL) mapping with molecular markers in cotton has largely been applied to populations derived from a cross between two inbred lines, and few results of QTL dissection were conducted in a 4WC population. In this study, an attempt was made to construct a linkage map and identify QTL for yield and fiber quality traits in 4WC derived from four different inbred lines in Gossypium hirsutum L. A linkage map was constructed with 285 SSR loci and one morphological locus, covering 2113.3 cM, approximately 42% of the total recombination length of the cotton genome. A total of 31 QTL with 5.1–25.8% of the total phenotypic variance explained were detected. Twenty-four common QTL across environments showed high stability, and six QTL were environment-specific. Several genomic segments affecting multiple traits were identified. The advantage of QTL mapping using a 4WC were discussed. This study presents the first example of QTL mapping using a 4WC population in upland cotton. The results presented here will enhance the understanding of the genetic basis of yield and fiber quality traits and enable further marker-assisted selection in cultivar populations in upland cotton.     

Power analysis for quantitative trait locus mapping in populations derived by multiple backcrosses

 Populations derived by multiple backcrosses are potentially useful for quantitative trait locus (QTL) mapping studies. Comparisons of relative power to detect QTL using populations derived by multiple back-crosses are needed to make decisions when mapping projects are initiated. The objective of this study was to theoretically compare the power to detect QTL in populations derived by multiple backcrosses relative to mapping in a recombinant inbred population of equal size. Backcrossing results in a reduction in genetic variance with each generation and also results in an increasing frequency of the recurrent parent marker genotype. The relevant outcome for QTL mapping is a reduction in genetic variance to partition between marker genotype classes and increasing unbalance of the number of individuals contributing to the mean of the marker genotypes. Both of these factors lead to a decrease in the power to detect a QTL as the number of backcross generations increases. Experimental error was held constant with the populations compared. From a theoretical standpoint, backcross-derived populations offer few advantages for QTL detection. If, however, a backcrossing approach is the most efficient method to achieve a desired breeding objective and if QTL detection is an objective of equal or less importance, backcross-derived populations are a reasonable approach to QTL detection. Received: 4 August 1996 / Accepted: 4 April 1997     

Integrating QTL and high-density SNP analyses in mice to identify Insig2 as a susceptibility gene for plasma cholesterol levels

The use of inbred strains of mice to dissect the genetic complexity of common diseases offers a viable alternative to human studies, given the control over experimental parameters that can be exercised. Central to efforts to map susceptibility loci for common diseases in mice is a comprehensive map of DNA variation among the common inbred strains of mice. Here we present one of the most comprehensive high-density, single nucleotide polymorphism (SNP) maps of mice constructed to date. This map consists of 10,350 SNPs genotyped in 62 strains of inbred mice. We demonstrate the utility of these data via a novel integrative genomics approach to mapping susceptibility loci for complex traits. By integrating in silico quantitative trait locus (QTL) mapping with progressive QTL mapping strategies in segregating mouse populations that leverage large-scale mapping of the genetic determinants of gene expression traits, we not only facilitate identification of candidate quantitative trait genes, but also protect against spurious associations that can arise in genetic association studies due to allelic association among unlinked markers. Application of this approach to our high-density SNP map and two previously described F2 crosses between strains C57BL/6J (B6) and DBA/2J and between B6 ApoE(-/-) and C3H/HeJ ApoE(-/-) results in the identification of Insig2 as a strong candidate susceptibility gene for total plasma cholesterol levels.