Milk fatty acid (FA) profile is a clear example of complex and multiple correlated traits whose genetic basis is difficult to assess. Although genome-wide association (GWA) studies have been successful in the identification of significant genetic variants for complex traits, when correlated phenotypes are analysed separately, the outcomes are difficult to compare and interpret in a metabolic context. Here, we performed a multivariate factor analysis (MFA) on Italian Simmental and Italian Holstein milk fat profiles to extract latent unobserved factors able to explain correlation structure and common metabolic function among different FAs. Individual factor scores obtained by MFA were used to perform a single-SNP based GWA. In both breeds, MFA was able to extract ten latent factors with specific biological meaning, notably: de novo synthesis, desaturation activity and biohydrogenation. The GWA result confirmed the increased power of joint association analysis on multiple correlated traits and allowed us to identify major candidate genes with well-documented function consistent with the metabolic classification of factors obtained, such as DGAT1, FASN and SCD. © 2020, Institute of Plant Genetics, Polish Academy of Sciences, Poznan.

Palombo, V., Conte, G., Mele, M., Macciotta, N. P. P., Stefanon, B., Marsan, P. A., D'Andrea, M., Use of multivariate factor analysis of detailed milk fatty acid profile to perform a genome-wide association study in Italian Simmental and Italian Holstein, <<JOURNAL OF APPLIED GENETICS>>, 2020; 61 (3): 451-463. [doi:10.1007/s13353-020-00568-2] [http://hdl.handle.net/10807/167139]

Use of multivariate factor analysis of detailed milk fatty acid profile to perform a genome-wide association study in Italian Simmental and Italian Holstein

Marsan, Paolo Ajmone;
2020

Abstract

Milk fatty acid (FA) profile is a clear example of complex and multiple correlated traits whose genetic basis is difficult to assess. Although genome-wide association (GWA) studies have been successful in the identification of significant genetic variants for complex traits, when correlated phenotypes are analysed separately, the outcomes are difficult to compare and interpret in a metabolic context. Here, we performed a multivariate factor analysis (MFA) on Italian Simmental and Italian Holstein milk fat profiles to extract latent unobserved factors able to explain correlation structure and common metabolic function among different FAs. Individual factor scores obtained by MFA were used to perform a single-SNP based GWA. In both breeds, MFA was able to extract ten latent factors with specific biological meaning, notably: de novo synthesis, desaturation activity and biohydrogenation. The GWA result confirmed the increased power of joint association analysis on multiple correlated traits and allowed us to identify major candidate genes with well-documented function consistent with the metabolic classification of factors obtained, such as DGAT1, FASN and SCD. © 2020, Institute of Plant Genetics, Polish Academy of Sciences, Poznan.
Inglese
Palombo, V., Conte, G., Mele, M., Macciotta, N. P. P., Stefanon, B., Marsan, P. A., D'Andrea, M., Use of multivariate factor analysis of detailed milk fatty acid profile to perform a genome-wide association study in Italian Simmental and Italian Holstein, <<JOURNAL OF APPLIED GENETICS>>, 2020; 61 (3): 451-463. [doi:10.1007/s13353-020-00568-2] [http://hdl.handle.net/10807/167139]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/167139
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