Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves

Silva, Fabyano Fonseca e; Zambrano, Maria Fernanda Betancur; Varona, L.; Glória, Leonardo Siqueira; Lopes, Paulo Sávio; Silva, Marcos Vinícius Gualberto Barbosa; Arbex, Wagner; Lázaro, Sirlene Fernandes; Resende, Marcos Deon Vilela de; Guimarães, Simone Eliza Facioni

doi:10.1590/1678-992x-2016-0023

Cited by 6 publications

(7 citation statements)

References 26 publications

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“…The technique of the nonlinear mixed-effects models allows the ranking of dairy cows within each breed and each parity order (ranks not shown). Therefore, one can replace the unstructured G matrix and ascribe a phenotypic value by associating to it genetic merits by the two-step estimation technique with a genomic relationship matrix in the linear mixed model (Silva et al, 2017;Soares et al, 2017). An extension to the generalized linear mixed-effects model is possible (Littell et al, 2006;Vonesh, 2012; Table 5 -Attributes 1 of the lactation records obtained from the two-step estimation based on Wood's equation: specific ascending rate of milk production until peak milk yield (κ α , w −1 ), time at the inflection point (t i , w), specific rate of milk production decline post peak milk yield (κ d , w −1 ), their respective standard errors (SE), and lower (L) and upper (U) 0.99 confidence limits The geometry of the lactation curve based on Wood's equation: a two-step prediction Oliveira et al Stroup, 2013).…”

Section: Discussionmentioning

confidence: 99%

The geometry of the lactation curve based on Wood's equation: a two-step prediction

Oliveira

Sant’Anna

Lourenço

et al. 2020

Revista Brasileira De Zootecnia

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-Lactation records from cows of the southwestern Paraná state, Brazil, form the dataset of this study. We applied the information-theoretic approach to evaluate the ability of the nonlinear Wood, Brody, Dijkstra, and Gamma functions to fit to these data by employing a two-step technique based on nonlinear mixed-effects models and generalized linear mixed-effects models. Wood's equation was fitted with the combination of a first-order autoregressive correlation structure and a variance function to account for heteroscedasticity. This version was the best choice to mimic lactation records. Some geometric attributes of Wood's model were deduced, mainly the ascending specific rate from parturition to peak milk yield and the descending specific rate as a measure of the lactation persistence of the milk yield at peak production. Breed and parity order of the cows were assumed as fixed effects to obtain a reliable model fitting process. Regardless of breed, first-order parity cows had greater persistency than their older counterparts, and the greater the ascending rate of milk yield from the parturition to the peak, the sharper the decrease in milk yield post-peak; therefore, the rates (absolute values) of ascending and descending phases correlated positively. Nonetheless, the actual estimated values of the descending phase rates are negative. Wood's equation was flexible enough to mimic either concave-and convex-shaped lactation profiles. The correlations between both peak milk yield and random estimates for β with total milk yield per lactation were positive. However, peak milk yield might not be the only variable used for ranking cows; the total milk yield integrates all information of the lactation profile through the estimated parameters of Wood's equation.

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Section: Discussionmentioning

confidence: 99%

The geometry of the lactation curve based on Wood's equation: a two-step prediction

Oliveira

Sant’Anna

Lourenço

et al. 2020

Revista Brasileira De Zootecnia

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“…The logistic was selected as best model (see Results section) and therefore used for the GWAS of growth curves through singlestep NMM following Silva et al (2017). This method fitted the two biological meaningful parameters of growth curves (i.e., A and K) through the NMM.…”

Section: Genome-wide Association Studiesmentioning

confidence: 99%

“…According to Silva et al (2017), the SNP effects could be further integrated into the null model through three different ways. First, the SNP effects are assumed to simultaneously affect both A and K parameters, and this full model (M1) was given as follows:…”

Section: Genome-wide Association Studiesmentioning

confidence: 99%

“…Furthermore, the two-step method could be followed by an additional step of multi-trait meta-analysis to indirectly combine the multiple parameters together (Duan et al, 2021). Recently, Silva et al (2017) proposed an alternative modeling framework to integrate the fitting of growth curves and estimation of singlenucleotide polymorphism (SNP) effects simultaneously under nonlinear mixed model (NMM), which was applied to pigs and revealed to have the advantages of higher statistical power and joint modeling of residual effects in comparison with the two-step method. To our best knowledge, this single-step method has not yet been applied to GWAS of growth curves in rabbits.…”

Section: Introductionmentioning

confidence: 99%

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

Liao

Wang

Glória³

et al. 2021

Front. Genet.

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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.

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“…A higher number of significant SNPs for adult weight and maturity rate have been also reported in pigs using nonlinear mixed models (e Silva et al . 2017).…”

Section: Introductionmentioning

confidence: 99%

Identification of new genes and quantitative trait locis associated with growth curve parameters in F2 chicken population using genome‐wide association study

et al. 2021

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Summary The markers which are correlated with the growth curve parameters help in understanding the characteristics of individual growth during the rearing of livestock. This study aimed to identify a set of biomarkers through a GWAS for growth curve parameters in crossbred chickens using the Illumnia 60K chicken SNP Beadchip. Growth data were collected from a total of 301 birds from cross of a broiler line and native chickens. Using the Gompertz–Laird model, two growth curve parameters, the instantaneous growth rate per day (L) and the coefficient of relative growth or maturing index (k), were estimated. The L and k were used to estimate five derived parameters, namely asymptotic (mature) body weight, body weight at inflection point, age at the inflection point, average growth rate and maximum growth rate. These parameters were considered as phenotypic values in the GWAS based on generalized linear models. The results of the GWAS indicated 21 significant markers, which were located near or within 46 genes. A number of these genes, such as GH, RET, GRB14, FTSJ3 and CCK, are important for growth and meat quality in chickens, and some of them are growth related in other species such as sheep and cattle (GPI, XIRP2, GALNTL6, BMS1, THSD4, TRHDE, SHISA9, ACSL6 and DYNC1LI2). The other genes are associated with developmental biological pathways. These genes are particuarly related to body weight, average daily gain and growth QTL. The results of this study can shed light on the genetic mechanism of biological functions of growth factors in broiler chickens, which is useful for developing management practices and accelerating genetic progress in breeding programs.

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Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves

Cited by 6 publications

References 26 publications

The geometry of the lactation curve based on Wood's equation: a two-step prediction

The geometry of the lactation curve based on Wood's equation: a two-step prediction

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

Identification of new genes and quantitative trait locis associated with growth curve parameters in F2 chicken population using genome‐wide association study

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