Multi-trait GWAS using imputed high-density genotypes from whole-genome sequencing identifies genes associated with body traits in Nile tilapia

Genetic correlation between smoking behavior and gastroesophageal reflux disease: insights from integrative multi-omics data

BACKGROUND

Observational studies have preliminarily revealed an association between smoking and gastroesophageal reflux disease (GERD). However, little is known about the causal relationship and shared genetic architecture between the two. This study aims to explore their common genetic correlations by leveraging genome-wide association studies (GWAS) of smoking behavior-specifically, smoking initiation (SI), never smoking (NS), ever smoking (ES), cigarettes smoked per day (CPD), age of smoking initiation(ASI) and GERD.

METHODS

Firstly, we conducted global cross-trait genetic correlation analysis and heritability estimation from summary statistics (HESS) to explore the genetic correlation between smoking behavior and GERD. Then, a joint cross-trait meta-analysis was performed to identify shared "pleiotropic SNPs" between smoking behavior and GERD, followed by co-localization analysis. Additionally, multi-marker analyses using annotation (MAGMA) were employed to explore the degree of enrichment of single nucleotide polymorphism (SNP) heritability in specific tissues, and summary data-based Mendelian randomization (SMR) was further utilized to investigate potential functional genes. Finally, Mendelian randomization (MR) analysis was conducted to explore the causal relationship between the smoking behavior and GERD.

RESULTS

Consistent genetic correlations were observed through global and local genetic correlation analyses, wherein SI, ES, and CPD showed significantly positive genetic correlations with GERD, while NS and ASI showed significantly negative correlations. HESS analysis also identified multiple significantly associated loci between them. Furthermore, three novel "pleiotropic SNPs" (rs4382592, rs200968, rs1510719) were identified through cross-trait meta-analysis and co-localization analysis to exist between SI, NS, ES, ASI, and GERD, mapping the genes MED27, HIST1H2BO, MAML3 as new pleiotropic genes between SI, NS, ES, ASI, and GERD. Moreover, both smoking behavior and GERD were found to be co-enriched in multiple brain tissues, with GMPPB, RNF123, and RBM6 identified as potential functional genes co-enriched in Cerebellar Hemisphere, Cerebellum, Cortex/Nucleus accumbens in SI and GERD, and SUOX identified in Caudate nucleus, Cerebellum, Cortex in NS and GERD. Lastly, consistent causal relationships were found through MR analysis, indicating that SI, ES, and CPD increase the risk of GERD, while NS and higher ASI decrease the risk.

CONCLUSION

We identified genetic loci associated with smoking behavior and GERD, as well as brain tissue sites of shared enrichment, prioritizing three new pleiotropic genes and four new functional genes. Finally, the causal relationship between smoking behavior and GERD was demonstrated, providing insights for early prevention strategies for GERD.

Investigating the shared genetic architecture between frailty and insomnia

Background

The epidemiological association between frailty and insomnia is well established, yet the presence of a common genetic etiology is still uncertain. Further exploration is needed to ascertain the causal relationship between frailty and insomnia.

Methods

Utilizing data obtained from genome-wide association studies (GWAS) summaries, we utilized the linkage disequilibrium score regression (LDSC) to determine the genetic correlation existing between frailty and insomnia. The determination of causality was achieved through the application of two-sample Mendelian randomization. We investigated the enrichment of single nucleotide polymorphism (SNP) at various tissue types utilizing stratified LD score regression (S-LDSC) and multimarker analysis of genome annotation (MAGMA). Common risk SNPs were identified using Multi-Trait Analysis of GWAS (MTAG) and Cross-Phenotype Association (CPASSOC). We further investigated the expression profiles of risk genes in tissues using Summary-data-based Mendelian randomization(SMR) based on pooled data, to explore potential functional genes.

Results

Our findings indicated a significant genetic correlation between frailty and insomnia, highlighting SNPs sharing risk (rs34290943, rs10865954), with a pronounced correlation in the localized genomic region 3p21.31. Partitioned genetic analysis revealed 24 functional elements significantly associated with both frailty and insomnia. Furthermore, mendelian randomization revealed a causal connection between frailty and insomnia. The genetic correlation between frailty and insomnia showed enrichment in 11 brain regions (S-LDSC) and 9 brain regions (MAGMA), where four functional genes (RMB6, MST1R, RF123, and FAM212A) were identified.

Conclusion

This study suggests the existence of a genetic correlation and common risk genes between frailty and insomnia, contributing to a deeper comprehension of their pathogenesis and assists in identifying potential therapeutic targets.

Investigating the shared genetic architecture between schizophrenia and body mass index

Evidence for reciprocal comorbidity of schizophrenia (SCZ) and body mass index (BMI) has grown in recent years. However, little is known regarding the shared genetic architecture or causality underlying the phenotypic association between SCZ and BMI. Leveraging summary statistics from the hitherto largest genome-wide association study (GWAS) on each trait, we investigated the genetic overlap and causal associations of SCZ with BMI. Our study demonstrated a genetic correlation between SCZ and BMI, and the correlation was more evident in local genomic regions. The cross-trait meta-analysis identified 27 significant SNPs shared between SCZ and BMI, most of which had the same direction of influence on both diseases. Mendelian randomization analysis showed the causal association of SCZ with BMI, but not vice versa. Combining the gene expression information, we found that the genetic correlation between SCZ and BMI is enriched in six regions of brain, led by the brain frontal cortex. Additionally, 34 functional genes and 18 specific cell types were found to have an impact on both SCZ and BMI within these regions. Taken together, our comprehensive genome-wide cross-trait analysis suggests a shared genetic basis including pleiotropic loci, tissue enrichment, and shared function genes between SCZ and BMI. This work provides novel insights into the intrinsic genetic overlap of SCZ and BMI, and highlights new opportunities and avenues for future investigation.

Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs

BACKGROUND

Genetic correlations between complex traits suggest that pleiotropic variants contribute to trait variation. Genome-wide association studies (GWAS) aim to uncover the genetic underpinnings of traits. Multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS enable detecting variants associated with multiple phenotypes. In this study, we used array-derived genotypes and phenotypes for 24 reproduction, production, and conformation traits to explore differences between the two methods and used imputed sequence variant genotypes to fine-map six quantitative trait loci (QTL).

RESULTS

We considered genotypes at 44,733 SNPs for 5,753 pigs from the Swiss Large White breed that had deregressed breeding values for 24 traits. Single-trait association analyses revealed eleven QTL that affected 15 traits. Multi-trait association testing and the meta-analysis of the single-trait GWAS revealed between 3 and 6 QTL, respectively, in three groups of traits. The multi-trait methods revealed three loci that were not detected in the single-trait GWAS. Four QTL that were identified in the single-trait GWAS, remained undetected in the multi-trait analyses. To pinpoint candidate causal variants for the QTL, we imputed the array-derived genotypes to the sequence level using a sequenced reference panel consisting of 421 pigs. This approach provided genotypes at 16 million imputed sequence variants with a mean accuracy of imputation of 0.94. The fine-mapping of six QTL with imputed sequence variant genotypes revealed four previously proposed causal mutations among the top variants.

CONCLUSIONS

Our findings in a medium-size cohort of pigs suggest that multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS provide very similar results. Although multi-trait association methods provide a useful overview of pleiotropic loci segregating in mapping populations, the investigation of single-trait association studies is still advised, as multi-trait methods may miss QTL that are uncovered in single-trait GWAS.

Whole Genome Sequencing Provides Information on the Genomic Architecture and Diversity of Cultivated Gilthead Seabream (Sparus aurata) Broodstock Nuclei

The gilthead seabream (Sparus aurata) is a species of relevance for the Mediterranean aquaculture industry. Despite the advancement of genetic tools for the species, breeding programs still do not often include genomics. In this study, we designed a genomic strategy to identify signatures of selection and genomic regions of high differentiation among populations of farmed fish stocks. A comparative DNA pooling sequencing approach was applied to identify signatures of selection in gilthead seabream from the same hatchery and from different nuclei that had not been subjected to genetic selection. Identified genomic regions were further investigated to detect SNPs with predicted high impact. The analyses underlined major genomic differences in the proportion of fixed alleles among the investigated nuclei. Some of these differences highlighted genomic regions, including genes involved in general metabolism and development already detected in QTL for growth, size, skeletal deformity, and adaptation to variation of oxygen levels in other teleosts. The obtained results pointed out the need to control the genetic effect of breeding programs in this species to avoid the reduction of genetic variability within populations and the increase in inbreeding level that, in turn, might lead to an increased frequency of alleles with deleterious effects.

Redundancy analysis, genome-wide association studies and the pigmentation of brown trout (Salmo trutta L.)

The association of molecular variants with phenotypic variation is a main issue in biology, often tackled with genome-wide association studies (GWAS). GWAS are challenging, with increasing, but still limited, use in evolutionary biology. We used redundancy analysis (RDA) as a complimentary ordination approach to single- and multitrait GWAS to explore the molecular basis of pigmentation variation in brown trout (Salmo trutta) belonging to wild populations impacted by hatchery fish. Based on 75,684 single nucleotide polymorphic (SNP) markers, RDA, single- and multitrait GWAS allowed the extraction of 337 independent colour patterning loci (CPLs) associated with trout pigmentation traits, such as the number of red and black spots on flanks. Collectively, these CPLs (i) mapped onto 35 out of 40 brown trout linkage groups indicating a polygenic genomic architecture of pigmentation, (ii) were found to be associated with 218 candidate genes, including 197 genes formerly mentioned in the literature associated to skin pigmentation, skin patterning, differentiation or structure notably in a close relative, the rainbow trout (Onchorhynchus mykiss), and (iii) related to functions relevant to pigmentation variation (e.g., calcium- and ion-binding, cell adhesion). Annotated CPLs include genes with well-known pigmentation effects (e.g., PMEL, SLC45A2, SOX10), but also markers associated with genes formerly found expressed in rainbow or brown trout skins. RDA was also shown to be useful to investigate management issues, especially the dynamics of trout pigmentation submitted to several generations of hatchery introgression.

GWAS on Imputed Whole-Genome Sequence Variants Reveal Genes Associated with Resistance to Piscirickettsia salmonis in Rainbow Trout (Oncorhynchus mykiss)

Genome-wide association studies (GWAS) allow the identification of associations between genetic variants and important phenotypes in domestic animals, including disease-resistance traits. Whole Genome Sequencing (WGS) data can help increase the resolution and statistical power of association mapping. Here, we conduced GWAS to asses he facultative intracellular bacterium Piscirickettsia salmonis, which affects farmed rainbow trout, Oncorhynchus mykiss, in Chile using imputed genotypes at the sequence level and searched for candidate genes located in genomic regions associated with the trait. A total of 2130 rainbow trout were intraperitoneally challenged with P. salmonis under controlled conditions and genotyped using a 57K single nucleotide polymorphism (SNP) panel. Genotype imputation was performed in all the genotyped animals using WGS data from 102 individuals. A total of 488,979 imputed WGS variants were available in the 2130 individuals after quality control. GWAS revealed genome-wide significant quantitative trait loci (QTL) in Omy02, Omy03, Omy25, Omy26 and Omy27 for time to death and in Omy26 for binary survival. Twenty-four (24) candidate genes associated with P. salmonis resistance were identified, which were mainly related to phagocytosis, innate immune response, inflammation, oxidative response, lipid metabolism and apoptotic process. Our results provide further knowledge on the genetic variants and genes associated with resistance to intracellular bacterial infection in rainbow trout.

Multiple-trait analyses improved the accuracy of genomic prediction and the power of genome-wide association of productivity and climate change-adaptive traits in lodgepole pine

Background

Genomic prediction (GP) and genome-wide association (GWA) analyses are currently being employed to accelerate breeding cycles and to identify alleles or genomic regions of complex traits in forest trees species. Here, 1490 interior lodgepole pine (Pinus contorta Dougl. ex. Loud. var. latifolia Engelm) trees from four open-pollinated progeny trials were genotyped with 25,099 SNPs, and phenotyped for 15 growth, wood quality, pest resistance, drought tolerance, and defense chemical (monoterpenes) traits. The main objectives of this study were to: (1) identify genetic markers associated with these traits and determine their genetic architecture, and to compare the marker detected by single- (ST) and multiple-trait (MT) GWA models; (2) evaluate and compare the accuracy and control of bias of the genomic predictions for these traits underlying different ST and MT parametric and non-parametric GP methods. GWA, ST and MT analyses were compared using a linear transformation of genomic breeding values from the respective genomic best linear unbiased prediction (GBLUP) model. GP, ST and MT parametric and non-parametric (Reproducing Kernel Hilbert Spaces, RKHS) models were compared in terms of prediction accuracy (PA) and control of bias.

Results

MT-GWA analyses identified more significant associations than ST. Some SNPs showed potential pleiotropic effects. Averaging across traits, PA from the studied ST-GP models did not differ significantly from each other, with generally a slight superiority of the RKHS method. MT-GP models showed significantly higher PA (and lower bias) than the ST models, being generally the PA (bias) of the RKHS approach significantly higher (lower) than the GBLUP.

Conclusions

The power of GWA and the accuracy of GP were improved when MT models were used in this lodgepole pine population. Given the number of GP and GWA models fitted and the traits assessed across four progeny trials, this work has produced the most comprehensive empirical genomic study across any lodgepole pine population to date.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08747-7.

Animal-SNPAtlas: a comprehensive SNP database for multiple animals

Single-nucleotide polymorphisms (SNPs) as the most important type of genetic variation are widely used in describing population characteristics and play vital roles in animal genetics and breeding. Large amounts of population genetic variation resources and tools have been developed in human, which provided solid support for human genetic studies. However, compared with human, the development of animal genetic variation databases was relatively slow, which limits the genetic researches in these animals. To fill this gap, we systematically identified ∼ 499 million high-quality SNPs from 4784 samples of 20 types of animals. On that basis, we annotated the functions of SNPs, constructed high-density reference panels and calculated genome-wide linkage disequilibrium (LD) matrixes. We further developed Animal-SNPAtlas, a user-friendly database (http://gong_lab.hzau.edu.cn/Animal_SNPAtlas/) which includes high-quality SNP datasets and several support tools for multiple animals. In Animal-SNPAtlas, users can search the functional annotation of SNPs, perform online genotype imputation, explore and visualize LD information, browse variant information using the genome browser and download SNP datasets for each species. With the massive SNP datasets and useful tools, Animal-SNPAtlas will be an important fundamental resource for the animal genomics, genetics and breeding community.

Genomic prediction with whole-genome sequence data in intensely selected pig lines

Background

Early simulations indicated that whole-genome sequence data (WGS) could improve the accuracy of genomic predictions within and across breeds. However, empirical results have been ambiguous so far. Large datasets that capture most of the genomic diversity in a population must be assembled so that allele substitution effects are estimated with high accuracy. The objectives of this study were to use a large pig dataset from seven intensely selected lines to assess the benefits of using WGS for genomic prediction compared to using commercial marker arrays and to identify scenarios in which WGS provides the largest advantage.

Methods

We sequenced 6931 individuals from seven commercial pig lines with different numerical sizes. Genotypes of 32.8 million variants were imputed for 396,100 individuals (17,224 to 104,661 per line). We used BayesR to perform genomic prediction for eight complex traits. Genomic predictions were performed using either data from a standard marker array or variants preselected from WGS based on association tests.

Results

The accuracies of genomic predictions based on preselected WGS variants were not robust across traits and lines and the improvements in prediction accuracy that we achieved so far with WGS compared to standard marker arrays were generally small. The most favourable results for WGS were obtained when the largest training sets were available and standard marker arrays were augmented with preselected variants with statistically significant associations to the trait. With this method and training sets of around 80k individuals, the accuracy of within-line genomic predictions was on average improved by 0.025. With multi-line training sets, improvements of 0.04 compared to marker arrays could be expected.

Conclusions

Our results showed that WGS has limited potential to improve the accuracy of genomic predictions compared to marker arrays in intensely selected pig lines. Thus, although we expect that larger improvements in accuracy from the use of WGS are possible with a combination of larger training sets and optimised pipelines for generating and analysing such datasets, the use of WGS in the current implementations of genomic prediction should be carefully evaluated against the cost of large-scale WGS data on a case-by-case basis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00756-0.

Low-density SNP panel for efficient imputation and genomic selection of milk production and technological traits in dairy sheep

The present study aimed to ascertain how different strategies for leveraging genomic information enhance the accuracy of estimated breeding values for milk and cheese-making traits and to evaluate the implementation of a low-density (LowD) SNP chip designed explicitly for that aim. Thus, milk samples from a total of 2,020 dairy ewes from 2 breeds (1,039 Spanish Assaf and 981 Churra) were collected and analyzed to determine 3 milk production and composition traits and 2 traits related to milk coagulation properties and cheese yield. The 2 studied populations were genotyped with a customized 50K Affymetrix SNP chip (Affymetrix Inc.) containing 55,627 SNP markers. The prediction accuracies were obtained using different multitrait methodologies, such as the BLUP model based on pedigree information, the genomic BLUP (GBLUP), and the BLUP at the SNP level (SNP-BLUP), which are based on genotypic data, and the single-step GBLUP (ssGBLUP), which combines both sources of information. All of these methods were analyzed by cross-validation, comparing predictions of the whole population with the test population sets. Additionally, we describe the design of a LowD SNP chip (3K) and its prediction accuracies through the different methods mentioned previously. Furthermore, the results obtained using the LowD SNP chip were compared with those based on the 50K SNP chip data sets. Finally, we conclude that implementing genomic selection through the ssGBLUP model in the current breeding programs would increase the accuracy of the estimated breeding values compared with the BLUP methodology in the Assaf (from 0.19 to 0.39) and Churra (from 0.27 to 0.44) dairy sheep populations. The LowD SNP chip is cost-effective and has proven to be an accurate tool for estimating genomic breeding values for milk and cheese-making traits, microsatellite imputation, and parentage verification. The results presented here suggest that the routine use of this LowD SNP chip could potentially increase the genetic gains of the breeding selection programs of the 2 Spanish dairy sheep breeds considered here.

Fine-mapping and association analysis of candidate genes for papilla number in sea cucumber, Apostichopus japonicus

The papilla number is one of the most economically important traits of sea cucumber in the China marketing trade. However, the genetic basis for papilla number diversity in holothurians is still scarce. In the present study, we conducted genome-wide association studies (GWAS) for the trait papilla number of sea cucumbers utilizing a set of 400,186 high-quality SNPs derived from 200 sea cucumbers. Two significant trait-associated SNPs that passed Bonferroni correction (P < 1.25E-7) were located in the intergenic region near PATS1 and the genic region of EIF4G, which were reported to play a pivotal role in cell growth and proliferation. The fine-mapping regions around the top two lead SNPs provided precise causative loci/genes related to papilla formation and cellular activity, including PPP2R3C, GBP1, and BCAS3. Potential SNPs with P < 1E-4 were acquired for the following GO and KEGG enrichment analysis. Moreover, the two lead SNPs were verified in another population of sea cucumber, and the expressive detection of three potential candidate genes PATS1, PPP2R3C, and EIF4G that near or cover the two lead SNPs was conducted in papilla tissue of TG (Top papilla number group) and BG (Bottom papilla number group) by qRT-PCR. We found the significantly higher expression profile of PATS1 (3.34-fold), PPP2R3C (4.90-fold), and EIF4G (4.23-fold) in TG, implying their potential function in papilla polymorphism. The present results provide valuable information to decipher the phenotype differences of the papilla trait and will provide a scientific basis for selective breeding in sea cucumbers.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42995-022-00139-w.

Comparing Transcriptomes Reveals Key Metabolic Mechanisms in Superior Growth Performance Nile Tilapia (Oreochromis niloticus)

Metabolic capacity is intrinsic to growth performance. To investigate superior growth performance in Nile tilapia, three full-sib families were bred and compared at the biochemical and transcriptome levels to determine metabolic mechanisms involved in significant growth differences between individuals under the same culture environment and feeding regime. Biochemical analysis showed that individuals in the higher growth group had significantly higher total protein, total triglyceride, total cholesterol, and high- and low-density lipoproteins, but significantly lower glucose, as compared with individuals in the lower growth group. Comparative transcriptome analysis showed 536 differentially expressed genes (DEGs) were upregulated, and 622 DEGs were downregulated. These genes were significantly enriched in three key pathways: the tricarboxylic acid cycle (TCA cycle), fatty acid biosynthesis and metabolism, and cholesterol biosynthesis and metabolism. Conjoint analysis of these key pathways and the biochemical parameters suggests that Nile tilapia with superior growth performance have higher ability to consume energy substrates (e.g., glucose), as well as higher ability to biosynthesize fatty acids and cholesterol. Additionally, the fatty acids biosynthesized by the superior growth performance individuals were less active in the catabolic pathway overall, but were more active in the anabolic pathway, and might be used for triglyceride biosynthesis to store excess energy in the form of fat. Furthermore, the tilapia with superior growth performance had lower ability to convert cholesterol into bile acids, but higher ability to convert it into sterols. We discuss the molecular mechanisms of the three key metabolic pathways, map the pathways, and note key factors that may impact the growth of Nile tilapia. The results provide an important guide for the artificial selection and quality enhancement of superior growth performance in tilapia.

Assessing genomic diversity and signatures of selection in Pinan cattle using whole-genome sequencing data

BACKGROUND

Crossbreeding is an important way to improve production beef cattle performance. Pinan cattle is a new hybrid cattle obtained from crossing Piedmontese bulls with Nanyang cows. After more than 30 years of cross-breeding, Pinan cattle show a variety of excellent characteristics, including fast growth, early onset of puberty, and good meat quality. In this study, we analyzed the genetic diversity, population structure, and genomic region under the selection of Pinan cattle based on whole-genome sequencing data of 30 Pinan cattle and 169 published cattle genomic data worldwide.  RESULTS: Estimating ancestry composition analysis showed that the composition proportions for our Pinan cattle were mainly Piedmontese and a small amount of Nanyang cattle. The analyses of nucleotide diversity and linkage disequilibrium decay indicated that the genomic diversity of Pinan cattle was higher than that of European cattle and lower than that of Chinese indigenous cattle. De-correlated composite of multiple selection signals, which combines four different statistics including θπ, CLR, F_ST, and XP-EHH, was computed to detect the signatures of selection in the Pinan cattle genome. A total of 83 genes were identified, affecting many economically important traits. Functional annotation revealed that these selected genes were related to immune (BOLA-DQA2, BOLA-DQB, LSM14A, SEC13, and NAALADL2), growth traits (CYP4A11, RPL26, and MYH10), embryo development (REV3L, NT5E, CDX2, KDM6B, and ADAMTS9), hornless traits (C1H21orf62), and climate adaptation (ANTXR2).

CONCLUSION

In this paper, we elucidated the genomic characteristics, ancestry composition, and selective signals related to important economic traits in Pinan cattle. These results will provide the basis for further genetic improvement of Pinan cattle and reference for other hybrid cattle related studies.

Hybridization alters the shape of the genotypic fitness landscape, increasing access to novel fitness peaks during adaptive radiation

Estimating the complex relationship between fitness and genotype or phenotype (i.e. the adaptive landscape) is one of the central goals of evolutionary biology. However, adaptive walks connecting genotypes to organismal fitness, speciation, and novel ecological niches are still poorly understood and processes for surmounting fitness valleys remain controversial. One outstanding system for addressing these connections is a recent adaptive radiation of ecologically and morphologically novel pupfishes (a generalist, molluscivore, and scale-eater) endemic to San Salvador Island, Bahamas. We leveraged whole-genome sequencing of 139 hybrids from two independent field fitness experiments to identify the genomic basis of fitness, estimate genotypic fitness networks, and measure the accessibility of adaptive walks on the fitness landscape. We identified 132 single nucleotide polymorphisms (SNPs) that were significantly associated with fitness in field enclosures. Six out of the 13 regions most strongly associated with fitness contained differentially expressed genes and fixed SNPs between trophic specialists; one gene (mettl21e) was also misexpressed in lab-reared hybrids, suggesting a potential intrinsic genetic incompatibility. We then constructed genotypic fitness networks from adaptive alleles and show that scale-eating specialists are the most isolated of the three species on these networks. Intriguingly, introgressed and de novo variants reduced fitness landscape ruggedness as compared to standing variation, increasing the accessibility of genotypic fitness paths from generalist to specialists. Our results suggest that adaptive introgression and de novo mutations alter the shape of the fitness landscape, providing key connections in adaptive walks circumventing fitness valleys and triggering the evolution of novelty during adaptive radiation.

Linkage mapping, comparative genome analysis, and QTL detection for growth in a non-model teleost, the meagre Argyrosomus regius, using ddRAD sequencing

Meagre (Argyrosomus regius), is a benthopelagic species rapidly emerging in aquaculture, due to its low food to biomass conversion rate, good fillet yield and ease of production. Tracing a species genomic background along with describing the genetic basis of important traits can greatly influence both conservation strategies and production perspectives. In this study, we employed ddRAD sequencing of 266 fish from six F1 meagre families, to construct a high-density genetic map comprising 4529 polymorphic SNP markers. The QTL mapping analysis provided a genomic appreciation for the weight trait identifying a statistically significant QTL on linkage group 15 (LG15). The comparative genomics analysis with six teleost species revealed an evolutionarily conserved karyotype structure. The synteny observed, verified the already well-known fusion events of the three-spine stickleback genome, reinforced the evidence of reduced evolutionary distance of Sciaenids with the Sparidae family, reflected the evolutionary proximity with Dicentrarchus labrax, traced several putative chromosomal rearrangements and a prominent putative fusion event in meagre’s LG17. This study presents novel elements concerning the genome evolutionary history of a non-model teleost species recently adopted in aquaculture, starts to unravel the genetic basis of the species growth-related traits, and provides a high-density genetic map as a tool that can help to further establish meagre as a valuable resource for research and production.

Genome-Wide Association Study of Body Conformation Traits by Whole Genome Sequencing in Dazu Black Goats

Simple Summary

Body conformation traits are economically important in the goat meat industry. Good growth performance in goats, including an accelerated growth rate, can improve carcass weight and meat yield. The identification of genetic variants associated with these traits provides a basis for the genetic improvement of growth performance. In this study, we measured six body conformation traits, including body height, body length, cannon circumference, chest depth, chest width, and heart girth. By a genome-wide association study of a Chinese meat goat breed, 53 significant single nucleotide polymorphisms and 42 candidate genes associated with these traits were detected. These findings improve our understanding of the genetic basis of body conformation traits in goats.

Abstract

Identifying associations between genetic markers and economic traits has practical benefits for the meat goat industry. To better understand the genomic regions and biological pathways contributing to body conformation traits of meat goats, a genome-wide association study was performed using Dazu black goats (DBGs), a Chinese indigenous goat breed. In particular, 150 DBGs were genotyped by whole-genome sequencing, and six body conformation traits, including body height (BH), body length (BL), cannon circumference (CC), chest depth (CD), chest width (CW), and heart girth (HG), were examined. In total, 53 potential SNPs were associated with these body conformation traits. A bioinformatics analysis was performed to evaluate the genes located close to the significant SNPs. Finally, 42 candidate genes (e.g., PSTPIP2, C7orf57, CCL19, FGF9, SGCG, FIGN, and SIPA1L) were identified as components of the genetic architecture underlying body conformation traits. Our results provide useful biological information for the improvement of growth performance and have practical applications for genomic selection in goats.

Genomic Selection and Genome-Wide Association Analysis for Stress Response, Disease Resistance and Body Weight in European Seabass

Simple Summary

In the present study, the genetic architecture of the stress response, body weight, and disease resistance in European seabass were studied, providing fruitful results for further research. Initially, the above traits were recorded and genotyping on a large scale was performed in those fish. The recorded data and genotypes were combined and analyzed to find genomic areas affecting them as well as to estimate the heritability of those traits. Stress response traits and body weight were medium heritable, while genomic regions affecting them were detected. However, no genomic areas related to disease resistance were revealed. These findings improve our knowledge of the genetic structure of those traits and can be utilized in a breeding program for the genetic improvement of aquaculture broodstocks.

Abstract

The majority of the genetic studies in aquaculture breeding programs focus on commercial traits such as body weight, morphology, and resistance against diseases. However, studying stress response in European seabass may contribute to the understanding of the genetic component of stress and its future use to select broodstock whose offspring may potentially be less affected by handling. A total of 865 European seabass offspring were used to measure body weight and stress response. Moreover, a disease challenge experiment with Vibrio anguillarum was conducted in a subset (332) of the above fish to study disease resistance. Fish were genotyped with a 57k SNP array, and a Genome-Wide Association study (GWAS) was performed. Five SNPs were found to be statistically significant, three of which affect stress indicators and body weight (in a subgroup of the population), and a putative SNP affects growth performance, while no SNP associated with resistance to Vibrio was found. A moderate to high genomic heritability regarding stress indicators and body weight was estimated using the Restricted Maximum Likelihood (REML) process. Finally, the accuracy, along with the correlation between Estimated Breeding Values (EBVs) and Genomic Estimated Breeding Values (GEBVs), were calculated for all the traits.

An association study on imputed whole-genome resequencing from high-throughput sequencing data for body traits in crossbred pigs

Body traits are important economic factors in the pig industry. Genome-wide association studies (GWASs) have been widely applied using high-density genotype data to detect QTL in pigs. The aim of the present study was to detect the genetic variants significantly associated with body traits in crossbred pigs using the Illumina Porcine SNP50 BeadChip and imputed whole-genome sequence data. A set of seven body traits - body length, body height, chest circumference, cannon bone circumference, leg buttock circumference, back fat thickness and loin muscle depth - were measured. Moderate to high heritabilities were obtained for most traits (from 0.14 to 0.46), and significant genetic and phenotypic correlations among them were observed. GWAS identified 714 significantly associated SNPs located at 39 regions on all autosomes for body traits, and a total of seven functionally related candidate genes: PIK3CD, HOXA, PCGF2, CHST11, COL2A1, BMI1 and OSR2. Functional enrichment analysis revealed that candidate genes were enriched in the estrogen signaling pathway, embryonic skeletal system morphogenesis and embryonic skeletal system development. These results aim to uncover the genetic mechanisms underlying body development and marker-assisted selection programs focusing on body traits in pigs.

Genomic Selection and Genome-wide Association Study for Feed-Efficiency Traits in a Farmed Nile Tilapia (Oreochromis niloticus) Population

Nile tilapia is a key aquaculture species with one of the highest production volumes globally. Genetic improvement of feed efficiency via selective breeding is an important goal, and genomic selection may expedite this process. The aims of this study were to 1) dissect the genetic architecture of feed-efficiency traits in a Nile tilapia breeding population, 2) map the genomic regions associated with these traits and identify candidate genes, 3) evaluate the accuracy of breeding value prediction using genomic data, and 4) assess the impact of the genetic marker density on genomic prediction accuracies. Using an experimental video recording trial, feed conversion ratio (FCR), body weight gain (BWG), residual feed intake (RFI) and feed intake (FI) traits were recorded in 40 full-sibling families from the GIFT (Genetically Improved Farmed Tilapia) Nile tilapia breeding population. Fish were genotyped with a ThermoFisher Axiom 65 K Nile tilapia SNP array. Significant heritabilities, ranging from 0.12 to 0.22, were estimated for all the assessed traits using the genomic relationship matrix. A negative but favourable genetic correlation was found between BWG and the feed-efficiency related traits; -0.60 and -0.63 for FCR and RFI, respectively. While the genome-wide association analyses suggested a polygenic genetic architecture for all the measured traits, there were significant QTL identified for BWG and FI on chromosomes seven and five respectively. Candidate genes previously found to be associated with feed-efficiency traits were located in these QTL regions, including ntrk3a, ghrh and eif4e3. The accuracy of breeding value prediction using the genomic data was up to 34% higher than using pedigree records. A SNP density of approximately 5,000 SNPs was sufficient to achieve similar prediction accuracy as the full genotype data set. Our results highlight the potential of genomic selection to improve feed efficiency traits in Nile tilapia breeding programmes.

Mapping of quantitative trait loci underlying a magic trait in ongoing ecological speciation

Background

Telmatochromis temporalis is a cichlid fish endemic to Lake Tanganyika. The normal and dwarf morphs of this fish are a clear example of ongoing ecological speciation, and body size plays an important role in this speciation event as a magic trait. However, the genetic basis underlying this trait has not been studied.

Results

Based on double-digested restriction-site associated DNA (ddRAD) sequencing of a hybrid cross between the morphs that includes F0 male, F0 female, and 206 F2 individuals, we obtained a linkage map consisting of 708 ddRAD markers in 22 linkage groups, which corresponded to the previously reported Oreochromis niloticus chromosomes, and identified one significant and five suggestive quantitative trait loci (QTL) for body size. From the body-size distribution pattern, the significant and three of the five suggestive QTL are possibly associated with genes responsible for the difference in body size between the morphs.

Conclusions

The QTL analysis presented here suggests that multiple genes, rather than a single gene, control morph-specific body size. The present results provide further insights about the genes underlying the morph specific body size and evolution of the magic trait during ecological speciation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07908-4.

A genomic‐based vision on the genetic diversity and key performance traits in selectively bred Arctic charr ( Salvelinus alpinus )

Routine implementation of genomic information for guiding selection decisions is not yet common in the majority of aquaculture species. Reduced representation sequencing approaches offer a cost‐effective solution for obtaining genome‐wide information in species with a limited availability of genomic resources. In the current study, we implemented double‐digest restriction site‐associated DNA sequencing (ddRAD‐seq) on an Arctic charr strain with the longest known history of selection (approximately 40 years) aiming to improve selection decisions. In total, 1730 animals reared at four different farms in Sweden and spanning from year classes 2013–2017 were genotyped using ddRAD‐seq. Approximately 5000 single nucleotide polymorphisms (SNPs) were identified, genetic diversity‐related metrics were estimated, and genome‐wide association studies (GWAS) for body length at different time points and age of sexual maturation were conducted. Low genetic differentiation amongst animals from the different farms was observed based on both the results from pairwise F_st values and principal component analysis (PCA). The existence of associations was investigated between the mean genome‐wide heterozygosity of each full‐sib family (year class 2017) and the corresponding inbreeding coefficient or survival to the eyed stage. A moderate correlation (−0.33) was estimated between the mean observed heterozygosity of each full‐sib family and the corresponding inbreeding coefficient, while no linear association was obtained with the survival to the eyed stage. GWAS did not detect loci with major effect for any of the studied traits. However, genomic regions explaining more than 1% of the additive genetic variance for either studied traits were suggested across 14 different chromosomes. Overall, key insights valuable for future selection decisions of Arctic charr have been obtained, suggesting ddRAD as an attractive genotyping platform for obtaining genome‐wide information in a cost‐effective manner.