Genome-wide association study and genomic prediction for Streptococcosis resistance in Red Tilapia (Oreochromis spp.)

Name: Sila Sukhavachana

LCSH: Kasetsart University -- Dissertations. (Ph.D. (Aquaculture) 2020

Classification :.LCCS: SH179.T35

LCSH: Kasetsart University. -- Department of Aquaculture -- Dissertations

LCSH: Tilapia -- Diseases

LCSH: Tilapia -- Breeding

LCSH: Streptococcus agalactiae

LCSH: Streptococcus

Abstract: This thesis investigated the potential of genomic selection to accelerate genetic gains in streptococcosis resistance in hybrid red tilapia. In the first experiment, I performed genome-wide association studies (GWAS) using 11,480 single nucleotide polymorphisms (SNPs) in 1,020 fish from 110 families. Thirty fish from each family were challenged by intraperitoneal injection with 0.1 mL Streptococcus agalactiae (SA) solution at the median lethal dose (96 h LD50) of 1 × 109 CFU mL1 and observed for 14 days. Offspring comprised five dead and five survived fish from each family were randomly selected for SNP genotyping using DArTseq genotype by sequencing platform. Resistance to SA was defined as a continuous trait (the number of days to death) and as a binary trait (dead/alive). GWAS results showed that eight and seven markers were associated with both traits. Collectively, these SNPs explained 10% and 1% of genetic variance, with the largest effect on chromosome 11 (~5%) and chromosome 5 (0.43%), respectively. Prediction accuracies of best linear unbiased prediction (BLUP), genomic BLUP (GBLUP), and BayesB models were compared using 10 replicates of five-fold cross-validation. BayesB yielded the highest accuracies (0.31 and 0.20) followed by GBLUP (0.25 and 0.15) and BLUP (0.15 and 0.06) for days to death and binary trait. Due to low selection accuracies from using genome-wide markers, in the second experiment, I re-evaluated genomic prediction by incorporating additional genotypes of 886 challenged fish. A total of 24,582 SNPs were ranked according to the size of their effects in decreasing order. Four genomic prediction models, GBLUP, single-step GBLUP (ssGBLUP), BayesB and BayesC were compared using 19 sets of markers ranging from 500 to 24,582 SNPs. Prediction accuracy of each of the models was improved substantially compared with BLUP (10%) for the top 1,000 SNPs. The GBLUP model (65%) which required less computing time outperformed the remaining models, ssGBLUP (53%), BayesB (47%) and BayesC (42%). Furthermore, the prediction accuracy decreased by 3-14%, depending on the model when using the top 500 SNPs. In the third experiment, I evaluated whether genetic correlation exists between resistance against streptococcosis and aeromonasis in 43 families of 30 days post-hatch Nile tilapia. One hundred-twenty fry from each family were divided into two groups and were subjected to bath challenge in SA or Aeromonas hydrophila (AH) solutions at the median lethal concentration (96 h LC50). Survival was measured as a binary trait (dead/alive) at day 14 post-challenge. Heritability estimates were low for the threshold and the linear animal models for both traits, (0.17 ± 0.04 and 0.18 ± 0.05 for AH; and 0.15 ± 0.03 and 0.15 ± 0.04 for SA). Genetic correlations between resistance to SA and AH were moderately positive (0.41) and favorable for both models.

Kasetsart University. Office of the University Library

Address: Bangkok

Email: tdckulib@ku.ac.th

Name: Supawadee Poompuang

Role: Thesis Advisor

Name: Nichanun Mcmillan

Role: Thesis CO-Advisor

Name: Pumipat Tongyoo

Role: Thesis CO-Advisor

Created: 2020

Modified: 2025-07-25

Issued: 2025-07-25

วิทยานิพนธ์/Thesis

application/pdf

URL: https://www.lib.ku.ac.th/KUthesis/2563/sila-suk-all.pdf

CallNumber: SH179.T35 .S55

eng

DegreeName: Doctor of Philosophy

Level: Doctoral Degraee

Descipline: Aquaculture

Grantor: Kasetsart University

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