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.
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Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Funding
Research was supported by the Italian Ministero dell’Istruzione, dell’Università e della Ricerca—MIUR (Rome, Italy; PRIN—GEN2PHEN project).
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MD, PAM and NPPM conceived and designed the experiment. MD, MM and BS performed the experiment. VP and GC analysed the data. MD and MM interpreted the results. VP and GC wrote the paper. MD, BS, NPPM and PAM edited and reviewed the manuscript. All the authors read and approved the final manuscript. VP and GC contributed equally to this paper.
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All farms adhere to a high standard of veterinary care based on best practice manual under the supervision of the official veterinary service and according to European directives and laws. Sample collection was approved by the Bioethics Committee of the University of Udine.
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Communicated by: Maciej Szydlowski
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ESM 1
Correlation and partial correlation heatmap for the 42 fatty acids used in the multivariate factor analysis in Italian Simmental breed. Upper diagonal elements are the Pearson correlation coefficients; lower diagonal elements are the partial correlation coefficients. Positive correlations are displayed in blue and negative correlations in red color. Color intensity is proportional to the correlation value. (DOCX 89 kb)
ESM 2
Correlation and partial correlation heatmap for the 42 fatty acids used in the multivariate factor analysis in Italian Holstein breed. Upper diagonal elements are the Pearson correlation coefficients; lower diagonal elements are the partial correlation coefficients. Positive correlations are displayed in blue and negative correlations in red color. Color intensity is proportional to the correlation value. (DOCX 89 kb)
ESM 3
Correlation and partial correlation matrix for the 42 fatty acids used in the multivariate factor analysis in Italian Simmental breed. Upper diagonal elements are the Pearson correlation coefficients; lower diagonal elements are the partial correlation coefficients. (DOCX 76 kb)
ESM 4
Correlation and partial correlation matrix for the 42 fatty acids used in the multivariate factor analysis in Italian Holstein breed. Upper diagonal elements are the Pearson correlation coefficients; lower diagonal elements are the partial correlation coefficients. (XLSX 36 kb)
ESM 5
List of genes pinpointed considering boundaries of < 100 kbp (kilo base pairs) on both sides from significant SNPs (Bos taurus ARS-UCD1.2 genome assembly). (XLSX 36 kb)
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Palombo, V., Conte, G., Mele, M. et al. Use of multivariate factor analysis of detailed milk fatty acid profile to perform a genome-wide association study in Italian Simmental and Italian Holstein. J Appl Genetics 61, 451–463 (2020). https://doi.org/10.1007/s13353-020-00568-2
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DOI: https://doi.org/10.1007/s13353-020-00568-2