Fatty Acid Traits Research Articles

Fatty acid traits mediate the effects of uric acid on cancers: a Mendelian randomization study

IntroductionPrevious findings on the association between uric acid (UA) levels and cancer risk are conflicting. Moreover, the mechanisms underlying the interactions between UA levels, fatty acid traits, and cancer outcomes remain complex; it is still unclear whether elevated UA levels influence fatty acid traits and, thereby, contribute to an increased cancer risk. Therefore, we aimed to investigate the association between UA levels and cancer risk, with a specific focus on the potential mediating role of fatty acid traits.MethodsWe employed a Mendelian randomization (MR) analysis utilizing genetic data from large-scale genome-wide association studies to assess the causal relationships among UA levels, fatty acid traits, and cancer risk. The primary method used was the inverse variance-weighted approach alongside Bayesian-weighted Mendelian randomization. Other MR models were also applied for comparison. Sensitivity analyses, based on various statistical assumptions, were also performed to evaluate the robustness of the findings. A two-step MR analysis was conducted to explore the mediating effects of fatty acid traits on the relationship between UA levels and cancer risk.Results and Discussion Elevated UA levels were associated with an increased risk of in situ neoplasms, cervical cancer, and invasive mucinous ovarian cancer, while they were linked to a decreased risk of cancers of the eye and adnexa, small cell lung cancer, bronchus and lung cancer, respiratory system and intrathoracic organ cancers, as well as lung cancer. Mediation analysis revealed that fatty acid traits, particularly the docosahexaenoic acid/trans fatty acid ratio, mediated the relationship between UA levels and lung cancer risk. These findings underscore the potential of fatty acid traits to mediate the association between UA levels and cancer risk, offering new insights for targeted interventions and potentially improving clinical outcomes.

Genome-wide association studies and multi-omics integrative analysis reveal novel loci and their molecular mechanisms for circulating polyunsaturated, monounsaturated, and saturated fatty acids.

Previous genome-wide association studies (GWAS) have identified genetic loci associated with the circulating levels of FAs, but the biological mechanisms of these genetic associations remain largely unexplored. Here, we conducted GWAS to identify additional genetic loci for 19 circulating fatty acid (FA) traits in UK Biobank participants of European ancestry (N = 239,268) and five other ancestries (N = 508 - 4,663). We leveraged the GWAS findings to characterize genetic correlations and colocalized regions among FAs, explore sex differences, examine FA loci influenced by lipoprotein metabolism, and apply statistical fine-mapping to pinpoint putative causal variants. We integrated GWAS signals with multi-omics quantitative trait loci (QTL) to reveal intermediate molecular phenotypes mediating the associations between the genetic loci and FA levels. Altogether, we identified 215 significant loci for polyunsaturated fatty acids (PUFAs)-related traits in European participants, 163 loci for monounsaturated fatty acids (MUFAs)-related traits, and 119 loci for saturated fatty acids (SFAs)-related traits, including 70, 61, and 54 novel loci, respectively. A novel locus for total FAs, the percentage of omega-6 PUFAs in total FAs, and total MUFAs (around genes GSTT1/2/2B ) overlapped with QTL signals for all six molecular phenotypes examined, including gene expression, protein abundance, DNA methylation, splicing, histone modification, and chromatin accessibility. Across 19 FA traits, 65% of GWAS loci overlapped with QTL signals for at least one molecular phenotype. Our study identifies novel genetic loci for circulating FA levels and systematically uncovers their underlying molecular mechanisms.

The constructed high-density genetic map helps to explore the genetic regulation of erucic acid, oleic acid, and linolenic acid contents in Brassica juncea

Rapeseed mustard (Brassica juncea L.) is the third most important oilseed crop in the world but the genetic mechanism underlying its massive phenotypic variation remains largely unexplored. In this study, specific length amplified fragment sequencing (SLAF-Seq) was used to resequence a population comprising 197 F8 recombinant inbred lines (RILs), derived from a cross between vegetable-type Qichi881 and oilseed-type YufengZC in B. juncea. In total, 438,895 high-quality SLAFs were discovered, of which 47,644 were polymorphic, and 3,887 of the polymorphic markers met the requirements for genetic map construction. The final map included 3,887 markers on 18 linkage groups and was 1,830.23 cM in length, with an average distance of 0.47 cM between adjacent markers. Using the newly constructed high-density genetic map, a total of 53 QTLs for erucic acid (EA), oleic acid (OA), and linolenic acid (LNA) were detected and integrated into 8 consensus QTLs with two for each of these traits. For each of these three traits, two candidate genes were cloned and sequence analyzed, indicating colocalization with their respective consensus QTLs. The co-dominant allele-specific markers for Bju.FAD3.A03 and Bju.FAD3.B07 were developed and showed co-localization with their consensus QTL and co-segregation with LNA content, further supporting the results of QTL mapping and bioinformatic analysis. The expression level for the cloned homologous genes was also identified, which was tightly correlated with the EA, OA and LNA contents of different lines. The results would facilitate the improvement of fatty acid traits and molecular breeding of B. juncea. More use of the high-density genetic map created in this study is also discussed.

The Genetic Characteristics of FT-MIRS-Predicted Milk Fatty Acids in Chinese Holstein Cows

Fourier Transform Mid-Infrared Spectroscopy (FT-MIRS) can be used for quantitative detection of milk components. Here, milk samples of 458 Chinese Holstein cows from 11 provinces in China were collected and we established a total of 22 quantitative prediction models in milk fatty acids by FT-MIRS. The coefficient of determination of the validation set ranged from 0.59 (C18:0) to 0.76 (C4:0). The models were adopted to predict the milk fatty acids from 2138 cows and a new high-throughput computing software HiBLUP was employed to construct a multi-trait model to estimate and analyze genetic parameters in dairy cows. Finally, genome-wide association analysis was performed and seven novel SNPs significantly associated with fatty acid content were selected, investigated, and verified with the FarmCPU method, which stands for “Fixed and random model Circulating Probability Unification”. The findings of this study lay a foundation and offer technical support for the study of fatty acid trait breeding and the screening and grouping of characteristic dairy cows in China with rich, high-quality fatty acids. It is hoped that in the future, the method established in this study will be able to screen milk sources rich in high-quality fatty acids.

Identification of genomic regions associated with fatty acid metabolism across blood, liver, backfat and muscle in pigs

BackgroundThe composition and distribution of fatty acids (FA) are important factors determining the quality, flavor, and nutrient value of meat. In addition, FAs synthesized in the body participate in energy metabolism and are involved in different regulatory pathways in the form of signaling molecules or by acting as agonist or antagonist ligands of different nuclear receptors. Finally, synthesis and catabolism of FAs affect adaptive immunity by regulating lymphocyte metabolism. The present study performed genome-wide association studies using FA profiles of blood, liver, backfat and muscle from 432 commercial Duroc pigs.ResultsTwenty-five genomic regions located on 15 Sus scrofa chromosomes (SSC) were detected. Annotation of the quantitative trait locus (QTL) regions identified 49 lipid metabolism-related candidate genes. Among these QTLs, four were identified in more than one tissue. The ratio of C20:4n-6/C20:3n-6 was associated with the region on SSC2 at 7.56–14.26 Mb for backfat, liver, and muscle. Members of the fatty acid desaturase gene cluster (FADS1, FADS2, and FADS3) are the most promising candidate genes in this region. Two QTL regions on SSC14 (103.81–115.64 Mb and 100.91–128.14 Mb) were identified for FA desaturation in backfat and muscle. In addition, two separate regions on SSC9 at 0 – 14.55 Mb and on SSC12 at 0–1.91 Mb were both associated with the same multiple FA traits for backfat, with candidate genes involved in de novo FA synthesis and triacylglycerol (TAG) metabolism, such as DGAT2 and FASN. The ratio C20:0/C18:0 was associated with the region on SSC5 at 64.84–78.32 Mb for backfat. Furthermore, the association of the C16:0 content with the region at 118.92–123.95 Mb on SSC4 was blood specific. Finally, candidate genes involved in de novo lipogenesis regulate T cell differentiation and promote the generation of palmitoleate, an adipokine that alleviates inflammation.ConclusionsSeveral SNPs and candidate genes were associated with lipid metabolism in blood, liver, backfat, and muscle. These results contribute to elucidating the molecular mechanisms implicated in the determination of the FA profile in different pig tissues and can be useful in selection programs that aim to improve health and energy metabolism in pigs.

Association of birth weight with type 2 diabetes mellitus and the mediating role of fatty acids traits: a two-step mendelian randomization study

BackgroundObservational studies have suggested an association between birth weight and type 2 diabetes mellitus, but the causality between them has not been established. We aimed to obtain the causal relationship between birth weight with T2DM and quantify the mediating effects of potential modifiable risk factors.MethodsTwo-step, two-sample Mendelian randomization (MR) techniques were applied using SNPs as genetic instruments for exposure and mediators. Summary data from genome-wide association studies (GWAS) for birth weight, T2DM, and a series of fatty acids traits and their ratios were leveraged. The inverse variance weighted (IVW) method was the main analysis approach. In addition, the heterogeneity test, horizontal pleiotropy test, Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test, and leave-one-out analysis were carried out to assess the robustness.ResultsThe IVW method showed that lower birth weight raised the risk of T2DM (β: −1.113, 95% CI: −1.573 ∼ −0.652). Two-step MR identified 4 of 17 candidate mediators partially mediating the effect of lower birth weight on T2DM, including ratio of polyunsaturated fatty acids to monounsaturated fatty acids (proportion mediated: 7.9%), ratio of polyunsaturated fatty acids to total fatty acids (7.2%), ratio of omega-6 fatty acids to total fatty acids (8.1%) and ratio of linoleic acid to total fatty acids ratio (6.0%).ConclusionsOur findings supported a potentially causal effect of birth weight against T2DM with considerable mediation by modifiable risk factors. Interventions that target these factors have the potential to reduce the burden of T2DM attributable to low birth weight.

Evaluation of genomic prediction considering non-additive genetic effects on fatty acid traits of Japanese Black cattle.

Genomic prediction was conducted using 2494 Japanese Black cattle from Hiroshima Prefecture and both single-nucleotide polymorphism information and phenotype data on monounsaturated fatty acid (MUFA) and oleic acid (C18:1) analyzed with gas chromatography. We compared the prediction accuracy for four models (A, additive genetic effects; AD, as for A with dominance genetic effects; ADR, as for AD with the runs of homozygosity (ROH) effects calculated by ROH-based relationship matrix; and ADF, as for AD with the ROH-based inbreeding coefficient of the linear regression). Bayesian methods were used to estimate variance components. The narrow-sense heritability estimates for MUFA and C18:1 were 0.52-0.53 and 0.57, respectively; the corresponding proportions of dominance genetic variance were 0.04-0.07 and 0.04-0.05, and the proportion of ROH variance was 0.02. The deviance information criterion values showed slight differences among the models, and the models provided similar prediction accuracy.

Identification of Quantitative Trait Nucleotides and Development of Diagnostic Markers for Nine Fatty Acids in the Peanut.

The cultivated peanut (Arachis hypogaea L.) is an important oilseed crop worldwide, and fatty acid composition is a major determinant of peanut oil quality. In the present study, we conducted a genome-wide association study (GWAS) for nine fatty acid traits using the whole genome sequences of 160 representative Chinese peanut landraces and identified 6-1195 significant SNPs for different fatty acid contents. Particularly for oleic acid and linoleic acid, two peak SNP clusters on Arahy.09 and Arahy.19 were found to contain the majority of the significant SNPs associated with these two fatty acids. Additionally, a significant proportion of the candidate genes identified on Arahy.09 overlap with those identified in early studies, among which three candidate genes are of special interest. One possesses a significant missense SNP and encodes a known candidate gene FAD2A. The second gene is the gene closest to the most significant SNP for linoleic acid. It codes for an MYB protein that has been demonstrated to impact fatty acid biosynthesis in Arabidopsis. The third gene harbors a missense SNP and encodes a JmjC domain-containing protein. The significant phenotypic difference in the oleic acid/linoleic acid between the genotypes at the first and third candidate genes was further confirmed with PARMS analysis. In addition, we have also identified different candidate genes (i.e., Arahy.ZV39IJ, Arahy.F9E3EA, Arahy.X9ZZC1, and Arahy.Z0ELT9) for the remaining fatty acids. Our findings can help us gain a better understanding of the genetic foundation of peanut fatty acid contents and may hold great potential for enhancing peanut quality in the future.

Isolation, identification, and pathogenicity of Pseudomonas glycinae causing ginseng bacterial soft rot in China

By tissue separation method, tie-back experiment, and hypersensitive response test in potato, strain XJFL-1 was isolated and identified as the pathogen of ginseng bacterial soft rot in Liaoning Provence, China. The morphological characteristics of XJFL-1 were conformed to the Pseudomonads genus. Microbial fatty acid identification showed the principal cellular fatty acid traits of XLFJ-1 corresponded with Pseudomonas spp. API 50CH test results allowed the differentiation of strain XJFL-1 and MS586T from other closely related Pseudomonas species. The molecular identification, including 16S rRNA analysis and multilocus sequence typing (MLST) analysis, showed that XJFL-1 was in the same branch as P. glycinae MS586T.The genome of XJFL-1 was 6,296,473 bp, with an average guanine/cytosine (G + C) content of 60.72 %. Comparative genomics analysis using ANIb and GGDC algorithms indicated that the maximum value was observed between XJFL-1 and P. glycinae MS586T.The above morphological, cell morphology, and molecular biological identification results supported to identification of XJFL-1 as P. glycinae.This is the first report of P. glycinae as the plant pathogen causing ginseng bacterial root rot in China, which complements the biological significance of the species to a certain extent, enriches the pathogens of ginseng bacterial soft rot, and provides a theoretical basis for further investigation.

Identifying key DNA methylation sites and their cis-methylation quantitative loci for intramuscular fatty acid traits using genome and methylome data in Yorkshire pigs

Single-nucleotide polymorphisms (SNPs) that are located near CpG sites have the potential to influence DNA methylation levels, leading to distinct patterns of DNA methylation. These methylation quantitative trait loci (meQTLs) can influence methylation levels across the entire genome, thereby impacting phenotypic traits. In this study, we utilised reduced representation bisulphite sequencing (RRBS) to analyse DNA methylation in muscle tissue samples collected from 140 Yorkshire pigs. Additionally, we assessed SNPs using the Porcine 50K chip, examined 17 fatty acid traits, and conducted epigenome-wide association studies (EWAS) and genome-wide association studies (GWAS). Our investigation revealed numerous associations between DNA methylation and fatty acid traits, including C16:0, C18:0, C20:3n-3, and C22:6n-3. Notably, a substantial portion of these associations was specifically identified by EWAS and not GWAS. Subsequently, we performed GWAS to identify meQTLs using methylation levels of significant CpGs from the EWAS as traits. The results revealed the predominance of trans-meQTLs; however, we also discovered a cis-meQTL on SSC12. Finally, we reported a crucial CpG site (SSC12:3735523) and its cis-meQTL on SSC12, which are located in close proximity to the FASN gene. Our findings contribute to the identification of CpG sites and their cis-meQTLs associated with fatty acid traits, utilising comprehensive genome and methylome data. Moreover, our study provides novel insights into the molecular mechanisms underlying fatty acid traits in pigs.

Single-step genome-wide association for selected milk fatty acids in Dual-Purpose Belgian Blue cows

The aim of this study was to estimate genetic parameters and identify genomic regions associated with selected individual and groups of milk fatty acids (FA) predicted by milk mid-infrared spectrometry in Dual-Purpose Belgian Blue cows. The used data were 69,349 test-day records of milk yield, fat percentage, and protein percentage along with selected individual and groups FA of milk (g/dL milk) collected from 2007 to 2020 on 7,392 first-parity (40,903 test-day records), and 5,185 second-parity (28,446 test-day records) cows distributed in 104 herds in the Walloon Region of Belgium. Data of 28,466 SNPs, located on 29 Bos taurus autosomes (BTA), of 1,699 animals (639 males and 1,060 females) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic best linear unbiased prediction approach. The proportion of genetic variance explained by each 25-SNP sliding window (with an average size of ∼2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Average daily heritability estimated for the included milk FA traits ranged from 0.01 (C4:0) to 0.48 (C12:0) and 0.01 (C4:0) to 0.42 (C12:0) in the first and second parities, respectively. Genetic correlations found between milk yield and the studied individual milk FA, except for C18:0, C18:1 trans, C18:1 cis-9, were positive. The results showed that fat percentage and protein percentage were positively genetically correlated with all studied individual milk FA. Genome-wide association analyses identified 11 genomic regions distributed over 8 chromosomes [BTA1, BTA4, BTA10, BTA14 (4 regions), BTA19, BTA22, BTA24, and BTA26] associated with the studied FA traits, though those found on BTA14 partly overlapped. The genomic regions identified differed between parities and lactation stages. Although these differences in genomic regions detected may be due to the power of quantitative trait locus detection, it also suggests that candidate genes underlie the phenotypic expression of the studied traits may vary between parities and lactation stages. These findings increase our understanding about the genetic background of milk FA and can be used for the future implementation of genomic evaluation to improve milk FA profile in Dual-Purpose Belgian Blue cows.

Global analysis of the association between pig muscle fatty acid composition and gene expression using RNA-Seq

Fatty acids (FAs) play an essential role as mediators of cell signaling and signal transduction, affecting metabolic homeostasis and determining meat quality in pigs. However, FAs are transformed by the action of several genes, such as those encoding desaturases and elongases of FAs in lipogenic tissues. The aim of the current work was to identify candidate genes, biological processes, and pathways involved in the modulation of intramuscular FA profile from longissimus dorsi muscle. FA profile by gas chromatography of methyl esters and gene expression by RNA-Seq were determined in 129 Iberian × Duroc backcrossed pigs. An association analysis between the muscle transcriptome and its FA profile was performed, followed by a concordance and functional analysis. Overall, a list of well-known (e.g., PLIN1, LEP, ELOVL6, SC5D, NCOA2, ACSL1, MDH1, LPL, LGALS12, TFRC, GOT1, and FBP1) and novel (e.g., TRARG1, TANK, ENSSSCG00000011196, and ENSSSCG00000038429) candidate genes was identified, either in association with specific or several FA traits. Likewise, several of these genes belong to biological processes and pathways linked to energy, lipid, and carbohydrate metabolism, which seem determinants in the modulation of FA compositions. This study can contribute to elucidate the complex relationship between gene expression and FA profile in pig muscle.

Genomic prediction and genetic correlations estimated for milk production and fatty acid traits in Walloon Holstein cattle using random regression models.

The aims of this study were to: (1) estimate genetic correlation for milk production traits (milk, fat and protein yields and fat and protein contents) and fatty acids (FA: C16:0, C18:1 cis-9, LCFA, SFA, and UFA) over days in milk, (2) investigate the performance of genomic predictions using single-step GBLUP (ssGBLUP) based on random regression models (RRM), and (3) identify the optimal scaling and weighting factors to be used in the construction of the H matrix. A total of 302 684 test-day records of 63.875 first lactation Walloon Holstein cows were used. Positive genetic correlations were found between milk yield and fat and protein yield (rg from 0.46 to 0.85) and between fat yield and milk FA (rg from 0.17 to 0.47). On the other hand, negative correlations were estimated between fat and protein contents (rg from -0.22 to -0.59), between milk yield and milk FA (rg from -0.22 to -0.62), and between protein yield and milk FA (rg from -0.11 to -0.19). The selection for high fat content increases milk FA throughout lactation (rg from 0.61 to 0.98). The test-day ssGBLUP approach showed considerably higher prediction reliability than the parent average for all milk production and FA traits, even when no scaling and weighting factors were used in the H matrix. The highest validation reliabilities (r2 from 0.09 to 0.38) and less biased predictions (b1 from 0.76 to 0.92) were obtained using the optimal parameters (i.e., ω = 0.7 and α = 0.6) for the genomic evaluation of milk production traits. For milk FA, the optimal parameters were ω = 0.6 and α = 0.6. However, biased predictions were still observed (b1 from 0.32 to 0.81). The findings suggest that using ssGBLUP based on RRM is feasible for the genomic prediction of daily milk production and FA traits in Walloon Holstein dairy cattle.

A parallel survey on the fatty acid composition in backfat and longissimus lumborum and comparison of their associations with growth and carcass traits in pigs

Compositions of fatty acids are critical parameters of meat nutrition and palatability. This study investigated phenotypes of 21 fatty acid composition traits in both backfat (BF) and longissimus lumborum (LL) tissues in 583 F7 mosaic pigs from an eight-breed crossed heterogeneous population, and their associations with 25 detailed measured growth and carcass traits in the same population. We showed that the LL has significantly higher content of saturated and mono-unsaturated fatty acids, and lower poly-unsaturated fatty acids than BF. Most of fatty acid composition traits in both LL and BF show significant correlations with growth and carcass traits. Notably, the correlations with the growth and carcass traits for fatty acid composition traits in BF are much stronger than that in LL, suggesting closer relationship of the growth and carcass traits with metabolism of the fatty acids in fat than in muscle. The polyunsaturated fatty acid traits including C18:2n6, PUFA, C20:2n6, C20:3n6 were among the fatty acid composition traits showing the strongest correlation with growth and carcass traits in both BF and LL, suggesting an interesting link between the metabolism of polyunsaturated fatty acids in muscle and fat tissues with the growth and carcass phenotypes. The results provide helpful insights that are helpful to the breeding program aiming to simultaneously improve the fatty acid composition, growth and carcass traits in pigs.

Random regression test-day models to describe milk production and fatty acid traits in first lactation Walloon Holstein cows.

We investigated the use of different Legendre polynomial orders to estimate genetic parameters for milk production and fatty acid (FA) traits in the first lactation Walloon Holstein cows. The data set comprised 302,684 test-day records of milk yield, fat and protein contents, and FAs generated by mid-infrared (MIR) spectroscopy, C16:0 (palmitic acid), C18:1 cis-9 (oleic acid), LCFAs (long-chain FAs), SFAs (saturated FAs) and UFAs (unsaturated FAs) were studied. The models included random regression coefficients for herd-year of calving (h), additive genetic (a) and permanent environment (p) effects. The selection of the best random regression model (RRM) was based on the deviance information criterion (DIC), and genetic parameters were estimated via a Bayesian approach. For all analysed random effects, DIC values decreased as the order of the Legendre polynomials increased. Best-fit models had fifth-order (degree 4) for the p effect and ranged from second- to fifth-order (degree 1-4) for the a and h effects (LEGhap: LEG555 for milk yield and protein content; LEG335 for fat content and SFA; LEG545 for C16:0 and UFA; and LEG535 for C18:1 cis-9 and LCFA). Based on the best-fit models, an effect of overcorrection was observed in early lactation (5-35days in milk [DIM]). On the contrary, third-order (LEG333; degree 2) models showed flat residual trajectories throughout lactation. In general, the estimates of genetic variance tended to increase over DIM, for all traits. Heritabilities for milk production traits ranged from 0.11 to 0.58. Milk FA heritabilities ranged from low-to-high magnitude (0.03-0.56). High Spearman correlations (>0.90 for all bulls and >0.97 for top 100) were found among breeding values for 155 and 305 DIM between the best RRM and LEG333 model. Therefore, third-order Legendre polynomials seem to be most parsimonious and sufficient to describe milk production and FA traits in Walloon Holstein cows.

Genetic effects of heat stress on milk fatty acids in Brazilian Holstein cattle

The present study aimed to estimate covariance components of milk fatty acids (FA) and to compare the genomic estimated breeding values under general and heat-stress effects. Data consisted of 38,762 test-day records from 6,344 Holstein cows obtained from May 2012 through January 2018 on 4 dairy herds from Brazil. Single-trait repeatability test-day models with random regressions as a function of temperature-humidity index values were used for genetic analyses. The models included contemporary groups, parity order (1-6), and days in milk classes as fixed effects, and general and thermotolerance additive genetic and permanent environmental as random effects. Notably, differences in heritability estimates between environments (general and heat stress) increased (0.03 to 0.06) for unsaturated FA traits, such as unsaturated, monounsaturated, and polyunsaturated, at higher heat-stress levels. In contrast, heritability estimated between environments for saturated FA traits, including saturated FA, palmitic acid (C16:0), and stearic acid (C18:0) did not observe significant differences between environments. In addition, our study revealed negative genetic correlations between general and heat-stress additive genetic effects (antagonistic effect) for the saturated FA, C16:0, C18:0, and C18:1, which ranged from -0.007 to -0.32. Spearman's ranking correlation between genomic estimated breeding values ranged from -0.27 to 0.99. Results indicated a moderate to strong interaction of genotype by the environment for most FA traits comparing a heat-stress environment with thermoneutral conditions. Our findings point out novel opportunities to explore the use of FA milk profile and heat-stress models.

Genome-wide associations for heat stress response suggest potential candidate genes underlying milk fatty acid composition in dairy cattle

Contents of milk fatty acids (FA) display remarkable alterations along climatic gradients. Detecting candidate genes underlying such alterations might be beneficial for the exploration of climate sensitivity in dairy cattle. Consequently, we aimed on the definition of FA heat stress indicators, considering FA breeding values in response to temperature-humidity index (THI) alterations. Indicators were used in GWAS, in ongoing gene annotations and for the estimation of chromosome-wide variance components. The phenotypic data set consisted of 39,600 test-day milk FA records from 5,757 first-lactation Holstein dairy cows kept in 16 large-scale German cooperator herds. The FA traits were C18:0, polyunsaturated fatty acids (PUFA), saturated fatty acids (SFA), and unsaturated fatty acids (UFA). After genotype quality control, 40,523 SNP markers from 3,266 cows and 930 sires were considered. Meteorological data from the weather station in closest herd distance were used for the calculation of maximum hourly daily THI, which were allocated to 10 different THI classes. The same FA from 3 stages of lactation were considered as different, but genetically correlated traits. Consequently, a 3-trait reaction norm model was used to estimate genetic parameters and breeding values for FA along THI classes, considering either pedigree (A) or genomic (G) relationship matrices. De-regressed proofs and genomic estimated breeding values at the intermediate THI class 5 and at the extreme THI class 10 were used as pseudophenotypes in ongoing genomic analyses for thermoneutral (TNC) and heat stress conditions (HSC), respectively. The differences in de-regressed proofs and in genomic estimated breeding values from both THI classes were pseudophenotypes for heat stress response (HSR). Genetic correlations between the same FA under TNC and HSC were smallest in the first lactation stage and ranged from 0.20 for PUFA to 0.87 for SFA when modeling with the A matrix, and from 0.35 for UFA to 0.86 for SFA when modeling with the G matrix. In the first lactation stage, larger additive genetic variances under HSC compared with TNC indicate climate sensitivity for C18:0, PUFA, and UFA. Climate sensitivity was also reflected by pronounced chromosome-wide genetic variances for HSR of PUFA and UFA in the first stage of lactation. For all FA under TNC, HSC, and HSR, quite large genetic variance proportions were explained by BTA14. In GWAS, 30 SNP (within or close to 38 potential candidate genes) overlapped for HSR of the different FA. One unique potential candidate gene (AMFR) was detected for HSR of PUFA, 15 for HSR of SFA (ADGRB1, DENND3, DUSP16, EFR3A, EMP1, ENSBTAG00000003838, EPS8, MGP, PIK3C2G, STYK1, TMEM71, GSG1, SMARCE1, CCDC57, and FASN) and 3 for HSR of UFA (ENSBTAG00000048091, PAEP, and EPPK1). The identified unique genes play key roles in milk FA synthesis and are associated with disease resistance in dairy cattle. The results suggest consideration of FA in combination with climatic responses when inferring genetic mechanisms of heat stress in dairy cows.

Detection of candidate genes affecting milk production traits in sheep using whole-genome sequencing analysis.

BackgroundArtificial and natural selection for important economic traits and genetic adaptation of the populations to specific environments have led to the changes on the sheep genome. Recent advances in genome sequencing methods have made it possible to use comparative genomics tools to identify genes under selection for traits of economic interest in domestic animals.ObjectivesIn this study, we compared the genomes of Assaf and Awassi sheep breeds with those of the Cambridge, Romanov and British du cher sheep breeds to explore positive selection signatures for milk traits using nucleotide diversity (Pi) and FST statistical methods.MethodsGenome sequences from fourteen sheep with a mean sequence depth of 9.32X per sample were analysed, and a total of 23 million single nucleotide polymorphisms (SNPs) were called and applied for this study. Genomic clustering of breeds was identified using ADMIXTURE software. The FST and Pi values for each SNP were computed between population A (Assaf and Awassi) and population B (Cambridge, British du cher, and Romanov).ResultsThe results of the PCA grouped two classes for these five dairy sheep breeds. The selection signatures analysis displayed 735 and 515 genes from FST and nucleotide diversity (Pi) statistical methods, respectively. Among all these, 12 genes were shared between the two approaches. The most conspicuous genes were related to milk traits, including ST3GAL1 (the synthesis of oligosacáridos), CSN1S1 (milk protein), CSN2 (milk protein), OSBPL8 (fatty acid traits), SLC35A3 (milk fat and protein percentage), VPS13B (total milk production, fat yield, and protein yield), DPY19L1 (peak yield), CCDC152 (lactation persistency and somatic cell count), NT5DC1 (lactation persistency), P4HTM (test day protein), CYTH4 (FAT Production) and METRNL (somatic cell), U1 (milk traits), U6 (milk traits) and 5S_RRNA (milk traits).ConclusionsThe findings provide new insight into the genetic basis of sheep milk properties and can play a role in designing sheep breeding programs incorporating genomic information.

Milk fat as a source of bioactive compounds

Milk fat as a source of bioactive compounds

Association of Cytochrome P450 2A6 (CYP2A6) Gene Polymorphisms with Fatty Acid Traits in Indonesian Native Sheep

A member of the cytochrome P450 superfamily enzyme that play a role in microsomal fatty acids oxidation process encoded by cytochrome P450 2A6 (CYP2A6). This study aimed to examined the CYP2A6 gene polymorphisms and their association on fatty acid traits in Indonesian native sheep. A total of 299 rams aged 10-12 months with body weights between 20-30 kg used to identify the CYP2A6 gene polymorphism are consisted of 36 samples of barbados cross sheep (BCS), 35 samples of compass agrinak sheep (CAS), 20 samples of javanese fat tailed sheep (JFT), 36 javanese thin tailed sheep (JTT), 20 samples of garut sheep (GS), 45 samples of garut composite sheep (GCS), meanwhile for the fatty acid analysis was carried out by using 107 of loin samples from the total sheep. Identification of the CYP2A6 gene polymorphisms were performed using PCR-RFLP (Polymorphism Chain Reaction-Restriction Fragment Length Polymorphism) with the BSmAI restriction enzymes. The amplification product was 286 bp. Polymorphism were found in JFT, JTT, GCS and JS with GG and GT genotypes, while BCS, CAS, and GS were monomorphic with TT genotype. The CYP2A6 BSmAI polymorphism was in Hardy-Weinberg Equilibrium for JFT, JTT and JS, while BCS, CAS, GS, GCS, and combined was deviated based on chi square. A SNP g.49170107 G>T of the CYP2A6 gene polymorphism was significantly associated (P <0.05) with only erucic acid (C22:1n9). The GT genotypes had a higher value than GG genotypes The CYP2A6 gene could be used as a selection marker to improve fatty acid traits in Indonesian native sheep.