Genetic Architecture Of Growth Traits Research Articles

Analysis of Growth Trajectories and Verification of Related SNPs in Populus deltoides.

As an important timber genus with high economic and ecological values, Populus is a model for dissecting the genetic architecture of growth traits in perennial forest trees. However, the genetic mechanisms of longitudinal growth traits in poplar remain incompletely understood. In this study, we conducted longitudinal genetic analysis of height and diameter at breast height (DBH) in eleven-year poplar clones using ultra-deep sequencing datasets. We compared four S-shaped growth models, including asymptotic, Gompertz, logistic, and Richard, on eleven-year height and DBH records in terms of five metrics. We constructed the best-fitting growth model (Richard) and determined poplar ontogenetic stages by virtue of growth curve fitting and likelihood ratio testing. This study provides some scientific clues for temporal variation of longitudinal growth traits in Populus species.

Genome-wide association study reveals markers and candidate genes associated with growth in the rice flower carp, an economic fish species of integrated rice-fish culture in China

The rice flower carp (Cyprinus carpio) is an important fish in integrated rice-fishery farming. Here, we performed the first genome-wide association study (GWAS) for seven growth traits (including body mass, total length, body length, body height, body width, caudal-peduncle depth, and eye spacing) in 200 rice flower carp samples using 369,688 high-quality SNPs and 42,225 indels obtained by double-digest genotyping-by-sequencing (ddGBS). The morphometrics of these traits were highly correlated (Pearson’s correlation coefficients = 0.74–0.99, p < 0.001). GWAS detected 15, 5, 4, 26, 7, 16, and 17 loci significant associated (-log10P ≥ 5) with body mass, total length, body length, body width, body height, caudal-peduncle depth, and eye spacing, respectively. Subsequently, within the 50 kb upstream and downstream regions surrounding these significant loci, 38, 19, 18, 20, 52, 27, and 37 candidate genes for the seven growth traits were detected, respectively. Importantly, B6_4352672 and A8_4978825 were significantly associated with more than five growth traits. These results showed loci significantly associated with more than five growth traits will be helpful for future marker-assisted selection (MAS). Interestingly, chromosomes A8 and B25 had many loci significantly associated with growth traits, most of which were shared among multiple growth-related traits. These results indicated that chromosome A8 and B25 may be closely related to growth traits. Our findings not only help understand the genetic architecture of growth traits in fish but facilitate the identification of candidate genes for marker-assisted selection towards breeding faster-growing rice flower carp in the future.

Genome-Wide Association Study of Growth Traits in a Four-Way Crossbred Pig Population.

Growth traits are crucial economic traits in the commercial pig industry and have a substantial impact on pig production. However, the genetic mechanism of growth traits is not very clear. In this study, we performed a genome-wide association study (GWAS) based on the specific-locus amplified fragment sequencing (SLAF-seq) to analyze ten growth traits on 223 four-way intercross pigs. A total of 227,921 highly consistent single nucleotide polymorphisms (SNPs) uniformly dispersed throughout the entire genome were used to conduct GWAS. A total of 53 SNPs were identified for ten growth traits using the mixed linear model (MLM), of which 18 SNPs were located in previously reported quantitative trait loci (QTL) regions. Two novel QTLs on SSC4 and SSC7 were related to average daily gain from 30 to 60 kg (ADG30-60) and body length (BL), respectively. Furthermore, 13 candidate genes (ATP5O, GHRHR, TRIM55, EIF2AK1, PLEKHA1, BRAP, COL11A2, HMGA1, NHLRC1, SGSM1, NFATC2, MAML1, and PSD3) were found to be associated with growth traits in pigs. The GWAS findings will enhance our comprehension of the genetic architecture of growth traits. We suggested that these detected SNPs and corresponding candidate genes might provide a biological foundation for improving the growth and production performance of pigs in swine breeding.

Genome-Wide Association Study and Selective Sweep Analysis Reveal the Genetic Architecture of Body Weights in a Chicken F2 Resource Population.

Rapid growth is one of the most important economic traits in broiler breeding programs. Identifying markers and genes for growth traits may not only benefit marker-assisted selection (MAS)/genomic selection (GS) but also provide important information for understanding the genetic architecture of growth traits in broilers. In the present study, an F2 resource population derived from a cross between the broiler and Baier yellow chicken (a Chinese local breed) was used and body weights from 1 to 12 weeks of age [body weight (BW) 1–BW12)] were measured. A total of 519 F2 birds were genome re-sequenced, and a combination of genome-wide association study (GWAS) and selective sweep analysis was carried out to characterize the genetic architecture affecting chicken body weight comprehensively. As a result, 1,539 SNPs with significant effects on body weights at different weeks of age were identified using a genome-wide efficient mixed-model association (GEMMA) package. These SNPs were distributed on chromosomes 1 and 4. Besides, windows under selection identified for BW1–BW12 varied from 1,581 to 2,265. A total of 42 genes were also identified with significant effects on BW1–BW12 based on both GWAS and selective sweep analysis. Among these genes, diacylglycerol kinase eta (DGKH), deleted in lymphocytic leukemia (DLEU7), forkhead box O17 (FOXO1), karyopherin subunit alpha 3 (KPNA3), calcium binding protein 39 like (CAB39L), potassium voltage-gated channel interacting protein 4 (KCNIP4), and slit guidance ligand 2 (SLIT2) were considered as important genes for broiler growth based on their basic functions. The results of this study may supply important information for understanding the genetic architecture of growth traits in broilers.

Genome-wide association study reveals growth-related markers and candidate genes for selection in Chinese longsnout catfish (Leiocassis longirostris)

Growth-related traits are economically important in fish because they directly affect fish production. The Chinese longsnout catfish (Leiocassis longirostris) is an important aquaculture species and its consumption is growing in China. In this study, using 200 individuals and high-throughput SNP genotyping technology, we performed the first genome-wide association study (GWAS) in L. longirostris to identify putative genomic regions and candidate genes associated with growth traits (including body mass, total length, standard length, head width, snout length, and caudal peduncle depth). The morphometrics of these traits were highly correlated (Pearson's correlation coefficients = 0.88–0.96, p < 0.05). GWAS detected 12, 5, 6, 9, 4, and 8 SNPs loci that were significantly associated (-log10P ≥ 5.21) with body mass, total length, standard length, head width, snout length, and caudal peduncle depth, respectively. Subsequently, within the 100 kb upstream and downstream regions surrounding these significant SNPs, 96, 31, 39, 61, 22, and 36 candidate genes for the 6 growth traits, respectively, were detected. Interestingly, chromosome (Chr) 16 had the most SNPs significantly associated with growth traits, and the majority of those were shared among multiple growth-related traits. In addition, multiple candidate genes surrounding these SNPs have been shown to serve essential functions in biological processes closely-related to growth, such as enox2, ky, and coma1. These results indicated that Chr16 may be central to growth characteristics. Other Chrs had genes involved in bone development, cell proliferation, and fatty acid metabolism, including matn1, sdc3, if2b3, kif3a, h1b, cadh7, rerg, and fabph. Our findings not only help understand the genetic architecture of growth traits in fish, but facilitate the identification of candidate genes for marker-assisted selection towards breeding faster growing L. longirostris in the future.

Genome-wide association studies and haplotype sharing analysis targeting the growth traits in Yandang partridge chickens

The body size of a chicken is an economically important trait as it directly influences the benefits of the poultry industry, but the relevant genetic mechanisms have not yet been elucidated. In this study, we measured eight growth traits for 94 Yandang partridge chickens, then undertook genome-wide association studies (GWAS) for those traits in using a linear mixed model based on 10× whole genomic sequencing data to better understand the knowledge of the genetic architecture of growth traits. Ninety-four individuals and 7647883 SNPs remained after quality control and removal of the sex chromosomes, and these data were used to carry out a GWAS analysis. The result showed that only one significant single-nucleotide polymorphisms (SNP) locates at 14852873 bp on SSC13 surpassed the genome-wide significance level for Keel length (KL). Through linkage disequilibrium analysis and haplotype sharing analysis, we identified one haplotype underlying the SSC13 significantly associated with KL, which could be selected as a potential candidate haplotype that is used in molecular breeding of Yandang partridge chicken. On the other hand, we have learned from a method called bootstrap testing to verify the reliability of GWAS with small experimental samples, which users can access at https://github.com/xuwenwu24/Bootstrap-test.

Discovering of Genomic Variations Associated to Growth Traits by GWAS in Braunvieh Cattle.

A genome-wide association study (GWAS) was performed to elucidate genetic architecture of growth traits in Braunvieh cattle. Methods: The study included 300 genotyped animals by the GeneSeek® Genomic Profiler Bovine LDv.4 panel; after quality control, 22,734 SNP and 276 animals were maintained in the analysis. The examined phenotypic data considered birth (BW), weaning (WW), and yearling weights. The association analysis was performed using the principal components method via the egscore function of the GenABEL version 1.8-0 package in the R environment. The marker rs133262280 located in BTA 22 was associated with BW, and two SNPs were associated with WW, rs43668789 (BTA 11) and rs136155567 (BTA 27). New QTL associated with these liveweight traits and four positional and functional candidate genes potentially involved in variations of the analyzed traits were identified. The most important genes in these genomic regions were MCM2 (minichromosome maintenance complex component 2), TPRA1 (transmembrane protein adipocyte associated 1), GALM (galactose mutarotase), and NRG1 (neuregulin 1), related to embryonic cleavage, bone and tissue growth, cell adhesion, and organic development. This study is the first to present a GWAS conducted in Braunvieh cattle in Mexico providing evidence for genetic architecture of assessed growth traits. Further specific analysis of found associated genes and regions will clarify its contribution to the genetic basis of growth-related traits.

Genome-Wide Association Studies for Growth Curves in Meat Rabbits Through the Single-Step Nonlinear Mixed Model.

Growth is a complex trait with moderate to high heritability in livestock and must be described by the longitudinal data measured over multiple time points. Therefore, the used phenotype in genome-wide association studies (GWAS) of growth traits could be either the measures at the preselected time point or the fitted parameters of whole growth trajectory. A promising alternative approach was recently proposed that combined the fitting of growth curves and estimation of single-nucleotide polymorphism (SNP) effects into single-step nonlinear mixed model (NMM). In this study, we collected the body weights at 35, 42, 49, 56, 63, 70, and 84 days of age for 401 animals in a crossbred population of meat rabbits and compared five fitting models of growth curves (Logistic, Gompertz, Brody, Von Bertalanffy, and Richards). The logistic model was preferably selected and subjected to GWAS using the approach of single-step NMM, which was based on 87,704 genome-wide SNPs. A total of 45 significant SNPs distributed on five chromosomes were found to simultaneously affect the two growth parameters of mature weight (A) and maturity rate (K). However, no SNP was found to be independently associated with either A or K. Seven positional genes, including KCNIP4, GBA3, PPARGC1A, LDB2, SHISA3, GNA13, and FGF10, were suggested to be candidates affecting growth performances in meat rabbits. To the best of our knowledge, this is the first report of GWAS based on single-step NMM for longitudinal traits in rabbits, which also revealed the genetic architecture of growth traits that are helpful in implementing genome selection.

Increased accuracy of genomic predictions for growth under chronic thermal stress in rainbow trout by prioritizing variants from GWAS using imputed sequence data.

Through imputation of genotypes, genome‐wide association study (GWAS) and genomic prediction (GP) using whole‐genome sequencing (WGS) data are cost‐efficient and feasible in aquaculture breeding schemes. The objective was to dissect the genetic architecture of growth traits under chronic heat stress in rainbow trout (Oncorhynchus mykiss) and to assess the accuracy of GP based on imputed WGS and different preselected single nucleotide polymorphism (SNP) arrays. A total of 192 and 764 fish challenged to a heat stress experiment for 62 days were genotyped using a customized 1 K and 26 K SNP panels, respectively, and then, genotype imputation was performed from a low‐density chip to WGS using 102 parents (36 males and 66 females) as the reference population. Imputed WGS data were used to perform GWAS and test GP accuracy under different preselected SNP scenarios. Heritability was estimated for body weight (BW), body length (BL) and average daily gain (ADG). Estimates using imputed WGS data ranged from 0.33 ± 0.05 to 0.55 ± 0.05 for growth traits under chronic heat stress. GWAS revealed that the top five cumulatively SNPs explained a maximum of 0.94%, 0.86% and 0.51% of genetic variance for BW, BL and ADG, respectively. Some important functional candidate genes associated with growth‐related traits were found among the most important SNPs, including signal transducer and activator of transcription 5B and 3 (STAT5B and STAT3, respectively) and cytokine‐inducible SH2‐containing protein (CISH). WGS data resulted in a slight increase in prediction accuracy compared with pedigree‐based method, whereas preselected SNPs based on the top GWAS hits improved prediction accuracies, with values ranging from 1.2 to 13.3%. Our results support the evidence of the polygenic nature of growth traits when measured under heat stress. The accuracies of GP can be improved using preselected variants from GWAS, and the use of WGS marginally increases prediction accuracy.

Weighted Single-Step GWAS Identified Candidate Genes Associated with Growth Traits in a Duroc Pig Population.

Growth traits are important economic traits of pigs that are controlled by several major genes and multiple minor genes. To better understand the genetic architecture of growth traits, we performed a weighted single-step genome-wide association study (wssGWAS) to identify genomic regions and candidate genes that are associated with days to 100 kg (AGE), average daily gain (ADG), backfat thickness (BF) and lean meat percentage (LMP) in a Duroc pig population. In this study, 3945 individuals with phenotypic and genealogical information, of which 2084 pigs were genotyped with a 50 K single-nucleotide polymorphism (SNP) array, were used for association analyses. We found that the most significant regions explained 2.56–3.07% of genetic variance for four traits, and the detected significant regions (>1%) explained 17.07%, 18.59%, 23.87% and 21.94% for four traits. Finally, 21 genes that have been reported to be associated with metabolism, bone growth, and fat deposition were treated as candidate genes for growth traits in pigs. Moreover, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses implied that the identified genes took part in bone formation, the immune system, and digestion. In conclusion, such full use of phenotypic, genotypic, and genealogical information will accelerate the genetic improvement of growth traits in pigs.

An Overview of Genomic Approaches for Characterizing the Genetic Architecture of Growth Traits in Chickens

An Overview of Genomic Approaches for Characterizing the Genetic Architecture of Growth Traits in Chickens

Genome-wide associations and detection of candidate genes for direct and maternal genetic effects influencing growth traits in the Montana Tropical® Composite population

The Montana Tropical® Composite beef cattle have been developed in Brazil to serve as a genetic resource to meet the consumers’ needs for higher-quality meat while animals are raised in extensive production systems under tropical conditions. In order to optimize the selection process for economically important traits in this population, various genetic and genomic studies are still lacking. In this regard, the aim of this study was to assess genetic parameters and identify genomic regions and potential candidate genes associated with various growth traits, using the single-step Genomic Best Linear Unbiased Predictor (ssGBLUP) method. Approximately 400,000 cows, bulls and progeny had measurements for birth weight (BW), weaning weight (WW), yearling weight (YW) and post-weaning weight gain (WG). A total of 1394 animals were genotyped for 27,199 SNPs (after the quality control) to enable implementation of weighted single-step genome-wide association studies. The traits included in this study were shown to be moderately heritable (i.e. heritability estimates ranging from 0.16 ± 0.01 to 0.33 ± 0.04) and the genetic correlations ranged from 0.60 ± 0.067 (between WW and WG) to 0.88 ± 0.08 (between BW and WW). Single-trait weighted genome-wide association studies enabled the identification of 83 genomic regions for direct genetic effects (all traits) and 29 genomic regions associated with maternal genetic effects on BW and WW traits. Furthermore, biological processes and pathways associated with survival to adult age, calf behavior, fatty acid metabolism, muscle development, fertility, and immune system were identified. The findings of this study greatly contribute to a better understanding of the genetic architecture of growth traits in the Montana Tropical® Composite population. Furthermore, the genomic regions identified can be given more importance (weight) when implementing genomic selection for these traits, by using weighted ssGBLUP or Bayesian approaches.

A longitudinal quantitative trait locus mapping of chicken growth traits.

Since the growth traits of chickens are largely related to the production of meat and eggs, it is definitely important to understand genetic basis of growth traits. Although many quantitative trait loci (QTLs) that affect growth traits have recently been reported in chickens, little is known about genetic architecture of growth traits across all growth stages. Therefore, we conducted a longitudinal QTL study of growth traits measured from 0 to 64weeks of age using 134 microsatellite DNA markers on 26 autosomes from 406 F2 females, which resulted from an intercross of Oh-Shamo and White Leghorn chicken breeds. We found 27 and 21 independent main-effect QTLs for body weight and shank length, respectively. Moreover, 15 and 4 pairs of epistatic QTLs were found for body weight and shank length, respectively. Taken together, the present study revealed 48 QTLs for growth traits on 21 different autosomes, and these loci clearly have age-specific effects on phenotypes throughout stages that are important for meat and egg productions. Approximately 60% of Oh-Shamo-derived alleles increased the phenotypic values, corresponding to the fact that Oh-Shamo traits were higher than those of White Leghorn. On the other hand, remaining Oh-Shamo alleles decreased the phenotypic values. Our results clearly indicated that the growth traits of chickens are regulated by several main and epistatic QTLs that are widely distributed in the chicken genome, and that the QTLs have age-dependent manners of controlling the traits. This study implies importance of not only cross-sectional but also longitudinal growth data for further understanding of the complex genetic architecture in animal.

Validation of growth-related quantitative trait loci markers in turbot (Scophthalmus maximus) families as a step toward marker assisted selection

Turbot (Scophthalmus maximus) is a valuable flatfish with current selection programs to increase growth rate in the fifth generation. Disentangling the genetic architecture of growth traits has biological and productive relevance. Growth-related quantitative trait loci (QTL) and associated markers have been previously reported in turbot. Here, we tested the performance of 39 markers associated with three growth-related traits (body weight (BW), body length (BL) and condition factor (FK)) in 18 families for their potential application in marker assisted selection (MAS) in this species. A cost-effective selective genotyping approach was developed by checking the statistical power of small samples at the extreme of the phenotypic distributions. Associations between growth traits and markers were performed using two statistical approaches (contingency chi-square tests and stepwise regression analysis) to identify the most robust set of markers. Twenty markers were detected by both methods, mostly linked to previously reported growth-related QTL and encompassing 11 linkage groups (LGs). The stepwise regression analysis revealed that a subset of seven markers explained nearly 40% and 60% of the phenotypic variation at genotypic and allelic level, respectively, for BW and BL. Seven markers for BW and BL and other two for FK showed association at population level, when all family data were pooled. Validated markers might be useful to go a step forward to improve growth performance through more refined MAS, and as physical anchors for identifying causal genes underpinning growth traits in turbot.

The genetic architecture of growth traits in Salix matsudana under salt stress

Willow (Salix) is one of the most important ornamental tree species in landscape plants. One species, Salix matsudana, is widely used as a shade tree and border tree because of its soft branches and plump crown. Some varieties of S. matsudana were salt tolerant and could grow normally in coastal regions. However, the molecular mechanisms of salt tolerance for S. matsudana have been less clear. Here, we addressed this issue by performing a mapping experiment containing 195 intraspecific F1 progeny of S. matsudana, derived from salt-sensitive ‘yanjiang’ and salt-tolerant ‘9901’, grown by cuttings in a 100 mM NaCl solution. Growth performance of these progeny under salt stress was investigated, displaying marked genotypic variability with the coefficients of variance of 28.64–86.11% in shoot and root growth traits. We further mapped specific QTLs contributing to these differences to the Salix genome. Of the 204 QTLs identified, a few were detected to explain a remarkably larger portion of the phenotypic variation than many others. Many detected QTLs were found to reside in the region of candidate genes of known biological function. The discovery of growth QTLs expressed under salt stress provides important information for marker-assisted breeding of salt tolerant Salix varieties and founds the basis for the application of S. matsudana in coastal afforestation.

Genetic architecture of growth traits in Populus revealed by integrated quantitative trait locus (QTL) analysis and association studies.

Deciphering the genetic architecture underlying polygenic traits in perennial species can inform molecular marker-assisted breeding. Recent advances in high-throughput sequencing have enabled strategies that integrate linkage-linkage disequilibrium (LD) mapping in Populus. We used an integrated method of quantitative trait locus (QTL) dissection with a high-resolution linkage map and multi-gene association mapping to decipher the nature of genetic architecture (additive, dominant, and epistatic effects) of potential QTLs for growth traits in a Populus linkage population (1200 progeny) and a natural population (435 individuals). Seventeen QTLs for tree height, diameter at breast height, and stem volume mapped to 11 linkage groups (logarithm of odds (LOD) ≥ 2.5), and explained 2.7-18.5% of the phenotypic variance. After comparative mapping and transcriptome analysis, 187 expressed genes (10 046 common single nucleotide polymorphisms (SNPs)) were selected from the segmental homology regions (SHRs) of 13 QTLs. Using multi-gene association models, we observed 202 significant SNPs in 63 promising genes from 10 QTLs (P ≤ 0.0001; FDR ≤ 0.10) that exhibited reproducible associations with additive/dominant effects, and further determined 11 top-ranked genes tightly linked to the QTLs. Epistasis analysis uncovered a uniquely interconnected gene-gene network for each trait. This study opens up opportunities to uncover the causal networks of interacting genes in plants using an integrated linkage-LD mapping approach.

Genetic Architecture of Growth Traits Revealed by Global Epistatic Interactions

Epistasis has long been recognized as fundamentally important in understanding the structure, function, and evolutionary dynamics of biological systems. Yet, little is known about how it is distributed underlying specific traits. Based on a global map of epistatic interactions in baker's yeast, Saccharomyces cerevisiae, we show that epistasis is prevalent (∼13% increase from random expectation) and displays modular architecture among genes that underlie the same growth traits. More interestingly, our results indicate that hub genes responsible for the same growth traits tend to link epistatically with each other more frequently than random expectation. Our results provide a genome-wide perspective on the genetic architecture of growth traits in a eukaryotic organism.

A general framework for analyzing the genetic architecture of developmental characteristics.

The genetic architecture of growth traits plays a central role in shaping the growth, development, and evolution of organisms. While a limited number of models have been devised to estimate genetic effects on complex phenotypes, no model has been available to examine how gene actions and interactions alter the ontogenetic development of an organism and transform the altered ontogeny into descendants. In this article, we present a novel statistical model for mapping quantitative trait loci (QTL) determining the developmental process of complex traits. Our model is constructed within the traditional maximum-likelihood framework implemented with the EM algorithm. We employ biologically meaningful growth curve equations to model time-specific expected genetic values and the AR(1) model to structure the residual variance-covariance matrix among different time points. Because of a reduced number of parameters being estimated and the incorporation of biological principles, the new model displays increased statistical power to detect QTL exerting an effect on the shape of ontogenetic growth and development. The model allows for the tests of a number of biological hypotheses regarding the role of epistasis in determining biological growth, form, and shape and for the resolution of developmental problems at the interface with evolution. Using our newly developed model, we have successfully detected significant additive x additive epistatic effects on stem height growth trajectories in a forest tree.