Genome-wide association analysis with a 50K transcribed gene SNP-chip identifies QTL affecting muscle yield in rainbow trout

Abstract

29Detection of coding/functional SNPs that change the biological function of a gene may lead to 30 identification of putative causative alleles within QTL regions and discovery of genetic markers 31 with large effects on phenotypes. Two bioinformatics pipelines, GATK and SAMtools, were used 32 to identify ~21K transcribed SNPs with allelic imbalances associated with important aquaculture 33 production traits including body weight, muscle yield, muscle fat content, shear force, and 34 whiteness in addition to resistance/susceptibility to bacterial cold-water disease (BCWD). SNPs 35 were identified from pooled RNA-Seq data collected from ~620 fish, representing 98 families from 36 growth-and 54 families from BCWD-selected lines with divergent phenotypes. In addition, ~29K 37 transcribed SNPs without allelic-imbalances were strategically added to build a 50K Affymetrix 38 SNP-chip. SNPs selected included two SNPs per gene from 14K genes and ~5K non-synonymous 39 SNPs. The SNP-chip was used to genotype 1728 fish. The average SNP calling-rate for samples 40 passing quality control (QC; 1,641 fish) was ≥ 98.5%. Genome-wide association (GWA) study on 41 878 fish (representing 197 families from 2 consecutive generations) with muscle yield phenotypes 42 and genotyped for 35K polymorphic markers (passing QC) identified several QTL regions 43 explaining together up to 28.40% of the additive genetic variance for muscle yield in this rainbow 44 trout population. The most significant QTLs were on chromosomes 14 and 16 with 12.71% and 45 10.49% of the genetic variance, respectively. Many of the annotated genes in the QTL regions 46 were previously reported as important regulators of muscle development and cell signaling. No 47 major QTLs were identified in a previous GWA study using a 57K genomic SNP chip on the same 48 fish population. These results indicate improved detection power of the transcribed gene SNP-chip 49 in the target trait and population, allowing identification of large-effect QTLs for important traits 50 in rainbow trout. 51 52 Introduction 53Aquaculture provides sustainable production of food fish with high protein/low-saturated fat to 54 satisfy increasing U.S. and worldwide demand. To enable increased production by the aquaculture 55 industry and to meet the ever-growing demand for fish, we need fast/efficient growth and high-56 quality fillets. However, a major constraint to increasing production efficiency is the lack of 57 genetically improved strains of fish for aquaculture [1; 2]. Development of tools that will enable 58 genomic selection for improved aquaculture production traits will greatly benefit the aquaculture 59 industry. 61Fast/efficient muscle growth is a major trait affecting profitability of the aquatic muscle food 62 industry. The genetic basis of muscle growth traits is not well studied in fish. Understanding 63 molecular mechanisms of fish muscle growth can facilitate broodstock selection decisions. In 64 addition, fish fillets are nutritionally and economically valuable products. Ske...

Development and Evaluation of a High-Throughput Single-Nucleotide Polymorphism Array for Large Yellow Croaker (Larimichthys crocea)

“…A high polymorphic SNP rate (83.38%) was observed during the evaluation of NingXin-I in the wild and cultured large yellow croaker populations. Compared with the other aquaculture species, about 67.5% polymorphic SNPs were identified during the assessment of the 690K catfish array (Zeng et al, 2017); 64.5 and 86.0% polymorphic SNPs were identified in the 50K (Salem et al, 2018) and 57K (Palti et al, 2015) rainbow trout arrays, respectively, 74.0 and 83.4% polymorphic SNPs were identified in the 58K (Joshi et al, 2018) and 65K (Peñaloza et al, 2020) Nile tilapia arrays, respectively; 70.4% polymorphic SNPs were identified in the 190K Pacific oyster array (Qi et al, 2017); 74.6% polymorphic SNPs were identified in the 38K combined-species SNPs array for Pacific and European oysters (Gutierrez et al, 2017); 67.5% polymorphic SNPs were identified in the 9K Pacific white shrimp array (Jones et al, 2017); 70.6% polymorphic SNPs were identified in the 6K black tiger shrimp array (Baranski et al, 2014); 79.6% polymorphic SNPs were identified in the 200K Atlantic salmon array (Yáñez et al, 2016); 74.06% polymorphic SNPs were identified in the 250K common carp array (Xu et al, 2014); and 74.7% polymorphic SNPs were identified in the 50K Japanese flounder array (Zhou et al, 2020). The polymorphic SNP rate of NingXin-I (83.38%) is one of the highest among aquaculture SNP arrays, although others have achieved also a high polymorphism rate, such as rainbow trout with 86.0%, and Nile tilapia with 83.4%.…”

“…Compared with RAD sequencing, the high-throughput SNP arrays showed higher repeatability and reproducibility, and more straightforward experimental procedures and bioinformatic analyses (Robledo et al, 2018). SNP arrays are efficient and robust tools for genomescale genotyping, which have been developed in many fish species including the 250K common carp array (Xu et al, 2014); the 250K and 690K catfish arrays (Liu et al, 2014;Zeng et al, 2017); the 50K and 58K Nile tilapia arrays (Joshi et al, 2018;Yáñez et al, 2020); the 50K and 57K rainbow trout arrays (Palti et al, 2015;Salem et al, 2018); the 15K, 286K, and 400K Atlantic salmon arrays (Gidskehaug et al, 2010;Houston et al, 2014;Yáñez et al, 2016); the 50K Japanese flounder array (Zhou et al, 2020); the 6K giant tiger shrimp array (Baranski et al, 2014); the 9K Pacific white shrimp array (Jones et al, 2017); and the 38K and 190K oyster arrays (Gutierrez et al, 2017;Qi et al, 2017). The applications of the high-density SNP array in GWAS have identified QTL associated with various traits in multiple species, including growth, disease resistance, heat stress, hypoxia tolerance, morphometric, sex, and body conformation (Abdelrahman et al, 2017;Zenger et al, 2019).…”

Genomic Predictions For Fillet Yield And Firmness In Rainbow Trout Using Reduced-Density SNP Panels

“…The second approach involved reducing the density of the panel based on the percentage of additive genetic variance explained by SNPs for each trait, which were already determined in our previous publications using weighted single-step genomic best linear unbiased prediction (wssGBLUP) analysis [13,19]. Reduced SNP panels with the percentage of additive genetic variance between > 0.05% and > 1.8% were used to evaluate the predictive ability using the fivefold cross-validation strategy.…”

“…In the current study, we used a 50K gene-transcribed SNP chip that has recently been developed for rainbow trout [19]. A total of 1,728 fish were genotyped as previously described [13,19].…”

High-Density Linkage Map and Mapping for Sex and Growth-Related Traits of Largemouth Bass (Micropterus salmoides)

“…Quantitative trait locus localization based on a high-density genetic linkage map can play an important role in promoting marker-assisted selection and breeding of aquaculture varieties (Liu and Cordes, 2004; Wang et al, 2006). In the process of constructing a high-density genetic linkage map (Salem et al, 2018), we gave priority to single-nucleotide polymorphisms (SNPs) because of their stability and abundance as genetic markers. The advent of a new generation of sequencing, including restriction site–related DNA sequencing (RAD-Seq), has revolutionized the genomic approach and allowed the discovery of thousands of SNPs throughout the genome (Davey et al, 2011; Houston et al, 2012).…”