AUTHOR=Hasan Md. Mehedi , Thomson Peter C. , Raadsma Herman W. , Khatkar Mehar S. 

TITLE=Genetic analysis of digital image derived morphometric traits of black tiger shrimp (Penaeus monodon) by incorporating G × E investigations

JOURNAL=Frontiers in Genetics

VOLUME=13

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.1007123

DOI=10.3389/fgene.2022.1007123

ISSN=1664-8021

ABSTRACT=<p>The black tiger shrimp, <italic>Penaeus monodon</italic>, is the second most economically important aquaculture shrimp species in the world, and in Australia it is one of the most commonly farmed shrimp species. Despite its economic significance, very few studies have reported the genetic evaluation of economically important morphological size and shape traits of shrimp grown in commercial grow-out environments. In this study we obtained genetic parameter estimates and evaluated genotype-by-environment interaction (GxE) for nine body morphological traits of shrimp derived from images. The data set contained image and body weight (BW) records of 5,308 shrimp, from 64 sires and 54 dams, reared in eight grow-out ponds for an average of 133 days. From the images, landmark based morphological distances were computed from which novel morphological traits associated with size and shape were derived for genetic evaluation. These traits included body weight (BW), body length (BL), body size (BS), head size (HS), Abdominal size (AS), abdominal percentage (AP), tail tip (TT), front by back ratio (FBR), condition factor (CF) and condition factor length (CFL). We also evaluated G×E interaction effects of these traits for shrimp reared in different ponds. The heritability estimates for growth related morphological and body weight traits were moderately high (BW: <italic>h</italic><sup>2</sup> = <italic>0</italic>.32 ± 0.05; BL: <italic>h</italic><sup>2</sup> = <italic>0</italic>.36 ± 0.06; BS: <italic>h</italic><sup>2</sup> = <italic>0</italic>.32 ± 0.05; HS: <italic>h</italic><sup>2</sup> = <italic>0</italic>.31 ± 0.05; AS: <italic>h</italic><sup>2</sup> = <italic>0</italic>.32 ± 0.05; and TT: <italic>h</italic><sup>2</sup> = <italic>0</italic>.28 ± 0.05) and low for abdominal percentage and body shape traits (AP: <italic>h</italic><sup>2</sup> = <italic>0</italic>.09 ± 0.02; FBR: <italic>h</italic><sup>2</sup> = <italic>0</italic>.08 ± 0.02; CF: <italic>h</italic><sup>2</sup> = <italic>0</italic>.06 ± 0.02; and CFL: <italic>h</italic><sup>2</sup> = <italic>0</italic>.003 ± 0.004). G × E interaction were negligible for all traits for shrimp reared in different ponds, suggesting re-ranking is not prevalent for this population. Genetic correlations among growth related morphological traits were high ranging from 0.36 to 0.99, suggesting these traits can be simultaneously improved through indirect genetic selection. However, negative genetic correlations were observed for FBR &amp; CF shape traits with major growth traits (ranged −0.08 to −0.95), suggesting genetic selection for rapid growth will likely result in thick/fatty shrimp over generations. Our study showed image-based landmark data can be successfully employed for genetic evaluation of complex morphological traits of shrimp and is potentially amenable to machine-learning derived parameters in semi-automated high volume phenotyping systems needed under commercial conditions.</p>