An F2 Pig Resource Population as a Model for Genetic Studies of Obesity and Obesity-Related Diseases in Humans: Design and Genetic Parameters

Abstract

Obesity is a rising worldwide public health problem. Difficulties to precisely measure various obesity traits and the genetic heterogeneity in human have been major impediments to completely disentangle genetic factors causing obesity. The pig is a relevant model for studying human obesity and obesity-related (OOR) traits. Using founder breeds divergent with respect to obesity traits we have created an F2 pig resource population (454 pigs), which has been intensively phenotyped for 36 OOR traits. The main rationale for our study is to characterize the genetic architecture of OOR traits in the F2 pig design, by estimating heritabilities, genetic, and phenotypic correlations using mixed- and multi-trait BLUP animal models. Our analyses revealed high coefficients of variation (15–42%) and moderate to high heritabilities (0.22–0.81) in fatness traits, showing large phenotypic and genetic variation in the F2 population, respectively. This fulfills the purpose of creating a resource population divergent for OOR traits. Strong genetic correlations were found between weight and lean mass at dual-energy x-ray absorptiometry scanning (0.56–0.97). Weight and conformation also showed strong genetic correlations with slaughter traits (e.g., rg between abdominal circumference and leaf fat at slaughtering: 0.66). Genetic correlations between fat-related traits and the glucose level vary between 0.35 and 0.74 and show a strong correlation between adipose tissue and impaired glucose metabolism. Our power calculations showed a minimum of 80% power for QTL detection for all phenotypes. We revealed genetic correlations at population level, for the first time, for several difficult to measure and novel OOR traits and diseases. The results underpin the potential of the established F2 pig resource population for further genomic, systems genetics, and functional investigations to unravel the genetic background of OOR traits.

Systems genetics of obesity in an F2 pig model by genome-wide association, genetic network, and pathway analyses

“…The phenotypic and genetic (co-)variance components were estimated using a series of bivariate animal models for all combinations between selected traits for the OI, as presented in our previous study (Kogelman et al, 2013); all models were implemented using ASReml (Gilmour et al, 2009). …”

“…The estimated breeding values, representing the animals deviation from the mean of the population, were estimated using the variance component estimation models presented in Kogelman et al (2013) using ASReml (Gilmour et al, 2009). …”

“…Since the OI is derived from estimated genetic (breeding) values calculated after correcting for population structure and all other environmental and fixed effects (see Kogelman et al, 2013) a simple regression of the OI on SNP genotypes was used to perform GWA analysis to avoid double- or over-correction. The basic linear model was:…”

“Sex” and body region effects on bone mineralization in male pigs

“…In agreement with findings in growing humans with higher BMD in girls or young women in comparison to boys or young men (Boot et al, 1997), Kogelmann et al (2013) did not find significant differences for BMD between female and male pigs -also with a slight ad- vantage for female pigs. According to Kranioti et al (2019), human males reach peak bone mass combined with the highest BMD later than females do. Therefore, it seems plausible that young entire boars -as in our study -do not have higher BMD values than the castrated ones (IB or SB).…”

Epigenetic and Transcriptomic Characterization of Pure Adipocyte Fractions From Obese Pigs Identifies Candidate Pathways Controlling Metabolism

“…24 Duroc-Göttingen minipig inter-cross F2 animals were selected from the UNIK resource population (Kogelman et al, 2012) to represent the two extremes of the lean and obese phenotypes, respectively. There were 8 boars and 4 sows in each group, however one lean female was excluded in the upstream data analysis due to incorrect phenotyping, leaving 12 obese and 11 lean animals for the analysis (Table 1, Figure 1).…”