Skip to main content

Combined effect of microbially derived cecal SCFA and host genetics on feed efficiency in broiler chickens



Improving feed efficiency is the most important goal for modern animal production. The regulatory mechanisms of controlling feed efficiency traits are extremely complex and include the functions related to host genetics and gut microbiota. Short-chain fatty acids (SCFAs), as significant metabolites of microbiota, could be used to refine the combined effect of host genetics and gut microbiota. However, the association of SCFAs with the gut microbiota and host genetics for regulating feed efficiency is far from understood.


In this study, 464 broilers were housed for RFI measuring and examining the host genome sequence. And 300 broilers were examined for cecal microbial data and SCFA concentration. Genome-wide association studies (GWAS) showed that four out of seven SCFAs had significant associations with genome variants. One locus (chr4: 29414391–29417189), located near or inside the genes MAML3, SETD7, and MGST2, was significantly associated with propionate and had a modest effect on feed efficiency traits and the microbiota. The genetic effect of the top SNP explained 8.43% variance of propionate. Individuals with genotype AA had significantly different propionate concentrations (0.074 vs. 0.131 μg/mg), feed efficiency (FCR: 1.658 vs. 1.685), and relative abundance of 14 taxa compared to those with the GG genotype. Christensenellaceae and Christensenellaceae_R-7_group were associated with feed efficiency, propionate concentration, the top SNP genotypes, and lipid metabolism. Individuals with a higher cecal abundance of these taxa showed better feed efficiency and lower concentrations of caecal SCFAs.


Our study provides strong evidence of the pathway that host genome variants affect the cecal SCFA by influencing caecal microbiota and then regulating feed efficiency. The cecal taxa Christensenellaceae and Christensenellaceae_R-7_group were identified as representative taxa contributing to the combined effect of host genetics and SCFAs on chicken feed efficiency. These findings provided strong evidence of the combined effect of host genetics and gut microbial SCFAs in regulating feed efficiency traits.

Video Abstract


Feed is one of the most expensive components of the farm animal industry costs, accounting for up to 70% of production costs [1]. Strategies to improve production without additional feed supplies are vital to ensuring the profitability and sustainability of the industry. Feed efficiency (FE) depends on the relation between the feed intake (FI) and the growth (or bodyweight gain) of an animal and is described by several indexes, such as feed conversion ratio (FCR) and residual feed intake (RFI). Feed efficiency is influenced by several factors, including the breed of the birds and their sex, age, diet, and management [2]. Energy intake and consumption are the basic daily biological functions of chickens, in theory, the ability to derive more energy from the same amount of feed and reduce all energy consumption apart from that required for daily maintenance would likely reduce feed intake [3, 4]. Thus, feed efficiency could be regulated by energy metabolism and feeding behaviors such as appetite [5, 6]. FCR and RFI are two indicators commonly used to evaluate the feed efficiency of livestock [7, 8]. RFI is preferred over FCR since it reflects the variation in the efficiency of feed utilization by broilers, which is independent of growth traits [9]. The heritability of RFI was reported to be between 0.23 and 0.49, and many genome-wide association studies (GWAS) have indicated that RFI is associated with host genome variation [10,11,12,13,14,15]. Over the last 50 years, the feed efficiency of commercial breeds improved by 50% due to quantitative genetic selection [16]. Additionally, the gut microbiota can markedly affect animal feed efficiency, as symbionts influence host metabolism [17].

The chicken gastrointestinal tract (GIT) includes compartments with varied physiological roles and environments that drive the spatial distribution of microbial populations [18]. Lower species richness in the intestine of chickens is accompanied by greater feeding efficiency, but this difference is not reflected in fecal samples [19, 20]. However, several studies found that bacterial diversity within the intestinal tract is higher in birds with lower FCR or higher feed efficiency [21,22,23,24,25]. Because the cecum is the primary site for food fermentation in monogastric animals, many cecal microbiota studies have been conducted over a wide range of microbiota diversity. Several studies have attempted to identify the intestinal microbes associated with RFI in broiler and layer chickens [15, 22, 26,27,28]. Nevertheless, findings to date have been inconsistent and sometimes contradictory. The low repeatability of microbial trials might be due to the susceptibility of intestinal microbiota communities to differences in diet, environment, management, age, and breed [29]. Many studies calculated the heritability of microbiota, showing a low average of 0.068 [30]. Furthermore, there is a broad range of microbial taxa in the environment [31], which increases the complexity of microbial studies.

A previous study indicated that short-chain fatty acids (SCFAs) present in the caecum were of microbial origin in a germ-free study [32]. SCFAs are well known as energy sources [33]. Hence, identifying a more energy-efficient microbiota is necessary to develop effective strategies to improve feed utilization. There is no previous study on the association between the host genome and SCFA production. As a previous study reported, SCFAs are produced by the gut microbiota, and the interactions between the host genome and the microbiota were reported [15]. SCFAs can work as signaling molecules with the help of G protein-coupled receptors (GPCRs), which are called free fatty acid receptors (FFARs) [33]. GPR43/FFAR2 and GPR41/FFAR3 can interact with the major SCFAs (acetate, propionate, and butyrate), which regulate energy expenditure, preadipocyte differentiation, and appetite control [34, 35]. Feed efficiency traits have been widely investigated in cattle and are affected by feeding behavior and energy metabolism, which could be related to SCFA metabolism due to their effects on appetite and energy homeostasis [5, 36, 37]. Previous RNA-seq results in divergent RFI groups found that differentially expressed genes, such as CAMP, LPL, PCK1, and CCKAR interact with GPCRs in lipid and energy metabolism [38,39,40,41]. Hence, there is some evidence that the gut microbiota could produce SCFAs and possibly regulate host feed efficiency through energy- and appetite-related pathways.

The cecum is the primary fermentation site in poultry and the major site of microbial SCFA production. Changes in the microbiota and SCFA production can affect feed efficiency. Therefore, assessing the relationship between host genetics and the gut microbiota, cecal SCFAs and feed efficiency will improve our understanding of the potential biological variations in feed efficiency and design sustainable approaches to improve feed efficiency in chickens. To achieve this goal, data of host genomics, microbial taxa, and SCFAs in cecum segments were used to clarify the relationships among the microbiota, cecal SCFAs, and host genetic variation. The overall workflow of the present analyses is shown in Fig. 1.

Fig. 1
figure 1

The overall workflow. The workflow works on the pathway “host genome variants-cecal microbiota-cecal SCFA-feed efficiency.” The GWAS study was conducted on gut SCFAs and feed efficiency. The heritability of gut SCFAs was estimated based on genomic data. The SCFA-related SNPs were used to test the loci effect on the gut microbiota and feed efficiency to help screen the vital components of the microbiota. The correlation tests were conducted among gut SCFAs, microbial taxa, and feed efficiency traits

Materials and methods


All chickens were obtained from the fast-growing white-feathered pure line, produced by Xinguang Agricultural and Animal Industrials Co., Ltd. (Mile, China). This line was selected for eight generations for high body weight and feed efficiency traits. RFI testing was conducted on a total of 464 broilers. They were housed in identical individual cages (length × width × height, 30 × 25 × 45 cm) and fed ad libitum. Each day, the amount of fresh feed provided was recorded individually, and residual feed was recorded daily and removed during the period from 28 to 40 days of age. During this period, the animals were fed a corn-soybean meal diet, and detailed information about the diet is described in Additional file 1: Table S1. The bodyweight of each chicken at 28 and 40 days of age was measured using an electronic scale. The RFI calculation method was described by Li et al. [13]. The descriptive statistics of these phenotypes are summarized in Additional file 2: Table S2. The correlation coefficient between RFI and ADFI was 0.61, and significant correlations were found in coefficients between RFI and FCR (0.79), the ratio of the breast (− 0.23), and abdominal fat ratio (0.37) (Additional file 3: Figure S1).

At the age of 41 days, the whole blood was collected from each bird from the wing vein using a vacuum blood tube. Furthermore, broilers were sacrificed 2 h after the last feed to allow time for the feed to be digested in the GIT. Each bird was then euthanized by cervical dislocation. The abdominal fat tissue, whole breast muscle, and thigh on the right side were carefully dissected and weighed promptly with an electronic balance (0.1 g precision). Moreover, the cecal contents (including chyme and mucosa) were collected immediately. All the samples were snap-frozen in liquid nitrogen, transported to the laboratory and stored at − 80 °C for subsequent studies.

Genotyping and quality control

Genomic DNA was extracted from blood samples with the phenol–chloroform method [42]. In total, 464 broilers were resequenced with 150 bp paired-end reads on an Illumina NovaSeq 6000 platform with an average depth of approximately 10 × 1 L coverage conducted by Beijing Compass Biotechnology Co., Ltd. (Beijing, China). Variant calling was performed according to a standardized bioinformatics pipeline for all samples [43, 44]. Specifically, clean sequencing data were aligned to the chicken reference genome (GRCg6a/galGal6: with the Burrows-Wheeler Aligner (BWA)-MEM algorithm [45]. Then, PCR duplicates were removed, and local indel realignment and base quality score recalibration were performed with the Genome Analysis Toolkit (GATK version 3.5) [46]. Variant calling was performed via HaplotypeCaller in GVCF mode with joint genotyping on all samples. Finally, SNPs were filtered with the GATK VariantFiltration protocol. The filtering settings were as follows: variant confidence score (QUAL) < 30.0, QualByDepth (QD) < 2.0, ReadPosRankSum <  − 8.0, total depth of coverage (DP) < 4.0, and FisherStrand (FS) > 60.0. In addition, quality control of the reference panel was conducted with the criteria of MAF ≥ 0.05, only bi-allelic sites, genotyping missing < 0.2, mean depth value between 3 and 30, and site quality value higher than 30. After filtering, 9,540,946 autosome variants remained for the 464 sequenced birds, LD decay was conducted by PopLDdecay [47], and the average LD level in a 5-kb interval was 0.17 (Additional file 4: Figure S2).

16S rRNA gene sequencing and analysis

Three hundred cecal samples were used to conduct 16S rRNA amplicon sequencing. The total DNA of cecal contents was extracted by a QIAamp DNA Stool Mini Kit (QIAGEN, Hilden, Germany). Eight cecal samples were excluded because of DNA extraction failure. Finally, 292 microbial DNA samples were used for 16S rRNA sequencing. Two divergent RFI groups were divided by the rank of the RFI value only. Sixty-nine top-ranked and sixty-nine bottom-ranked RFI samples were assigned to high and low groups. The V4 region of the 16S rRNA gene was amplified using the primer pair 515F/806R (5′-GTGCCAGCMGCCGCGGTAA-3′ and 5′-GGACTACHVGGGTWTCTAAT-3′), and the amplicons were purified and quantified using Agencourt AMPure Beads and the PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA), respectively. After quantification, the barcoded V4 amplicons were pooled and subsequently sequenced using an Illumina MiSeq platform (Illumina, San Diego, CA) to generate 300 bp paired-end reads at Shenzhen BGI Technology Services Co., Ltd. For each sample, there were approximately 50,000 clean reads. Amplicon sequencing bioinformatics was performed with EasyAmplicon v1.0 [48]. Paired-end sequence data were merged, quality-filtered, and dereplicated using VSEARCH v2.15 subcommand –fastq_mergepairs, –fastx_filter, and –derep_fulllength, respectively [49]. Then, the non-redundancy sequences were denoised into amplicon sequence variants (ASVs) with USEARCH v10.0 [50] (via -cluster_otus or unoise3). Chimeras were removed by VSEARCH –uchime_ref against the SILVA database [51]. Feature tables were created by vsearch –usearch_global. The taxonomy of the features was classified by the USEARCH sintax algorithm in SILVA v123. Diversity analysis was carried out using the vegan v2.5–6 package (, and visualized by using the ggplot2 v3.3.2 ( package in R v4.0.2. LEfSe was conducted with the online platform ImageGP ( [52]. Functional profile prediction of microbial communities was conducted by PICRUSt [53], with the Greengenes as the reference database.

SCFA concentration determination

Three hundred cecal samples, the same as those used for amplicon sequencing population, were used for seven SCFA concentration determinations, including acetate, propionate, isobutyrate, butyrate, isovalerate, valerate, and hexanoate. Briefly, samples were thawed on ice, and approximately 50 g of the sample was added to 400 µl of saturated sodium chloride solution, and 50 µl 3 mmol of saturated sodium chloride solution of hydrochloric acid was added. Ultrasonic oscillation at low temperatures was conducted for 20 min. Then, 500-µl ether was added, oscillated sufficiently, and extracted for 10 min. Next, the supernatant was centrifuged for 10 min at 12,000 r/min and 4℃. Then, 50 mg of anhydrous sodium sulfate was added into the supernatant and oscillated for 3 min. Finally, the mixture was centrifuged at 4500 r/min and 4℃ for 5 min, and the supernatant was used for analysis. A total of 2 µl of the solution was analyzed by a TRACE1300-TSQ9000 gas chromatography-mass spectrometry (GC–MS) instrument (Thermo Fisher Scientific, Waltham, MA, USA) at Shenzhen BGI Technology Services Co., Ltd. To determine the absolute SCFA concentration, SCFA standards were prepared at different dilutions with ultrapure water. Then, the protocol described above was conducted to generate standard curves for the seven SCFAs.

Evaluating the effects of host genetics on SCFAs and growth performance

The GWAS for SCFA concentrations were performed for 300 individuals, and growth performance traits were performed for 464 individuals, directly using the univariate linear mixed model (LMM) implemented in GEMMA version 0.98.1 software ( [54]. The SCFA concentrations were log-2 transformed to make them follow a normal distribution (Additional files 5 and 6: Figure S3 and S4). SNP-based heritability analysis was implemented in GCTA (ver 1.93.3) [55]. The GWAS model was described in detail in a previous study [13]. The genotype was dimed as a fixed factor and the additive genetic effect as the random effect. Due to the same generation and sex in this population, no covariate was applied in the LMM model. The statistical model was as follows:

$$y=\alpha +x\beta +u+\epsilon ;u\sim {MVN}_{n}\left(\mathrm{O},{\lambda \tau }^{-1}K\right),\epsilon \sim {MVN}_{n}\left(\mathrm{O},{\tau }^{-1}{I}_{n}\right),$$

where y represents the vector of SCFA values, α represents the vector of the corresponding coefficients including the intercept, x represents the vector of genotypes, β represents the effect size of the marker, u represents the vector of random polygenic effects, ϵ represents the vector of errors, τ−1 represents the variance of the residual errors, λ represents the ratio between the two variance components, K represents the centered relatedness matrix estimated from 9,540,946 variants, and In represents the identity matrix. MVNn represents the n-dimensional multivariate normal distribution. The Wald test was used to select SNPs associated with metabolizable efficiency traits.

The genome-wide significance was assessed using the GEC method [56] to infer effective independent tests. A total of 9,540,946 independent tests overall chromosomal SNPs were obtained, and 8,562,703 SNPs were retained. Then, genome-wide significant and suggestive thresholds were set to 5.84 × 10–9 (0.05/8,562,703) and 1.17 × 10–7 (1/8,562,703), respectively. Manhattan and Q-Q plots were constructed for each trait by the qqman package ( in R (version 4.1.0). SNP positions were updated according to the GRCg6a genome version from NCBI. The closest genes to genome-wide significant and suggestive variants were identified using NCBI annotation of the GRCg6a genome version ( The variance in SCFAs explained by SNPs from GWAS results was calculated by the formula described by Shim et al. [57].

Identification of the specific microbiota association

The associations between qualified taxa, feed efficiency, and SCFA traits were analyzed using a two-part model described by Fu et al. [58]. This model accounts for both binary (present and absent) and quantitative features and is described as follows:


where y is the RFI value or SCFA concentration, b is a binary feature of a specific microorganism and coded as 0 for absent or 1 for present for each sample, and q is the log10-transformed abundance of a specific microorganism. β1 and β2 are the regression coefficients for the binary and quantitative models, respectively, and e is the intercept. The second part of the quantitative analysis was only for the samples in which the specific microorganism was present. The details of the two-part model are illustrated in Additional file 7: Figure S5. P values were obtained from the two-part model association analysis and adjusted by the BH method. If the adjusted P value from the binary model was less than 0.05, the presence or absence of microorganisms was considered to influence feed efficiency. If the adjusted P value from the quantitative model was less than 0.05, feed efficiency was considered associated with the relative abundances of the microorganisms. If the combined P value was less than 0.05, feed efficiency was considered to be associated with both the relative abundances of the microorganisms and the presence or absence of microorganisms.

A Spearman correlation analysis between microbiotas and RFI and FCR was conducted to detect specific microorganisms that significantly influenced feed efficiency. A microorganism was considered to have a significant effect if the adjusted P values from the two-part model association analysis and Spearman correlation were less than 0.05.

Statistical analysis and data visualization

The divergent RFI groups were divided by RFI ranking only, the top 69 individuals were allocated to the HRFI group, and the bottom 69 individuals were allocated to the LRFI group. The divergent PA groups followed similar selection criteria, which were only based on PA value ranking. The top 45 PA individuals were allocated to HPA groups, and the bottom 52 PA individuals were allocated to the LPA group. The details of the data describing the divergent groups are shown in Table 1.

Table 1 Growth performance description of divergent RFI and PA groups

The comparative analysis was conducted between divergent groups, and the t test was conducted by the t.test() function in the R program (version 4.1.0). Welch’s t test was used to determine the differences in the pathways/taxon relative abundances with FDR correction in STAMP v2.1.3 [59]. Some scripts about data format are from Microbiome helper [60]. All the pipeline, training materials, and related scripts were deposited in the EasyAmplicon project on GitHub ( The beta-diversity statistic of divergent groups was determined by Adonis in the amplicon package followed by the EasyAmplicon procedure [61]. Differences were considered statistically significant at P < 0.05. The plots were generated by the ggplot2 package ( in the R program.


Divergent RFI groups had different microbial communities

The RFI distributions of the high (HRFI) and low (LRFI) RFI groups followed the normal distribution, respectively (Additional file 8: Figure S6. A). The richness of the high and low RFI groups was almost higher than 700 (Fig. 2A), which represents a sufficient detection rate (Additional file 8: Figure S6. B). The PCoA analysis was conducted by Bray_Curtis distance, and the plot shows that the β-diversities of the high and low groups were significantly different (Fig. 2B). Seven genera were detected as the major composition of chickens in the divergent RFI groups (Fig. 2C). The detected genera accounted for approximately half of the percentage due to the lower detected ratio, and unassigned taxonomies were allocated to the other part. Twenty-three genera were determined to be significant in terms of relative abundance based on the Wilcoxon rank-sum test with an FDR-adjusted P value less than 0.05 (Fig. 2D).

Fig. 2
figure 2

The microbiota composition between the high RFI and low RFI groups. A The α-diversity index richness compared between the two groups. B The Principal Coordinates Analysis on the ASV level. C The cecal microbiota composition of two groups on the genus level. D Comparisons of genus components between the two groups

Significant correlations were found between SCFA, microbiota, and feed efficiency traits

Comparisons of different SCFAs between the high and low RFI groups were conducted (Fig. 3A). The propionate concentration in the HRFI group (0.090) was significantly higher than that in the LRFI group (0.081). A similar trend was also found for butyrate (0.177 vs. 0.153) between HRFI and LRFI groups. Spearman correlations between SCFAs and growth traits were conducted among individuals (Fig. 3B). Propionate and butyrate were positively correlated with RFI but negatively correlated with BRW and RBR. Propionate was positively correlated with ADFI. The correlations between SCFAs and families were calculated. Christensenellaceae was negatively correlated with propionate, and Christensenellaceae and Bacillaceae were negatively correlated with butyrate (Fig. 3C). Only the relative abundance of Christensenellaceae significantly differed in divergent RFI groups in the above comparison analysis. Moreover, the heatmap of Spearman correlations between SCFAs and genera can be found in Additional file 9: Figure S7. Obvious significant correlations between the microbiota and SCFAs and growth traits were found.

Fig. 3
figure 3

Correlation between SCFAs and feed efficiency and microbial biomarkers of feed efficiency. A Bar plots of the concentration of SCFAs among the high and low RFI groups, “*” means there is a statistical difference between divergent RFI groups. B Spearman correlation between SCFAs and growth performance. C Spearman correlation between SCFAs and taxa at the family level. BW40, body weight at 40 days of age; ADFI, average daily feed intake; THW, thigh weight; BRW, breast weight; ABW, abdominal fat weight; RTH, ratio of thigh weight; RBR, ratio of breast weight; ABR, ratio of abdominal fat

Comparisons of predicted microbial pathways among divergent RFI and propionate groups

Microbial KEGG pathways were predicted through the Greengenes database through PICRUSt. For divergent RFI groups (Fig. 4A), the enriched differential metabolic pathways included amino acid metabolism, lipid metabolism, nucleotide metabolism, glycan biosynthesis and metabolism, and informative pathways included transcription, genetic information process, and cellular processes and signaling. Groups with divergent propionate concentrations were used to conduct KEGG prediction as propionate had the highest correlation with RFI. The top 45 individuals in terms of propionate abundance were divided into the high group (HPA, mean: 0.127), and the bottom 52 individuals were divided into the low propionate abundance group (LPA, mean: 0.046) (Additional file 10: Figure S8. A). Four types of pathways were significantly differentially enriched between the divergent PA groups (Fig. 4B), including transcription, neurodegenerative diseases, amino acid metabolism, and membrane transport. The common enriched pathways in these two groups were found to be transcription and amino acid metabolism. The microbiota between the RFI groups could cause a difference in SCFA concentration.

Fig. 4
figure 4

KEGG pathway prediction by the Greengenes database. A Comparison between divergent RFI groups. B Comparison between divergent propionate groups

SCFA-related genetic variations and their effects on gut microbiota

The results in Fig. 3 show that there were correlations between SCFAs and growth traits and gut microbiota. Propionate and butyrate were positively correlated with RFI but negatively correlated with BRW and RBR. Propionate was positively correlated with ADFI. Christensenellaceae was negatively correlated with propionate, and Christensenellaceae and Bacillaceae were negatively correlated with butyrate. Thus, the association between the host genome and SCFA was examined through GWAS. Only butyrate, propionate, valerate, and isovalerate were significantly associated with host variation. The Manhattan and QQ plots of propionate illustrate the correlation between feed efficiency and the significant signals found on the host genome (Fig. 5A). Furthermore, the Manhattan and QQ plots of the other three SCFAs can be found in Additional file 11: Figure S9. The GWAS results of growth traits, including bodyweight at d40, average daily feed intake, average daily gain, FCR, and RFI were presented in Additional file 12: Figure S10. Only the FCR and RFI had similar genetic regions on chromosome 13, and no common genetic basis was found between SCFAs and feed growth traits. The SNP-based heritability of four SCFAs ranged from 0.183 to 0.401, and the annotation from the GWAS for SCFAs is shown in Table 2. One locus (chr4: 29,414,391–29,417,189) associated with propionate showed significant signals. MAML3, MGST2, and SETD7 were found in a 100-kb upstream and downstream region of the top SNP (Fig. 5B). MAML3 was found to be involved in the Notch signaling pathway, MGST2 participated in glutathione metabolism, metabolism of xenobioticsm drug metabolism and metabolic pathways, and SETD7 played roles in lysine degradation, metabolic pathways, and the FoxO signaling pathway. Moreover, the top SNP found by GWAS of propionate explained approximately 8.43% of the phenotypic variance, and all of the suggestive significant SNPs were located in the intron regions of the genes (Additional file 13: Table S3). The variation in chr4: 29,417,189: G > A resulted from a base transversion. Birds with the major genotypes had lower propionate concentrations than those with the other two genotypes. The average propionate concentrations for the GG, AG, and AA genotypes were 0.074, 0.096, and 0.131 µg/mg, respectively (Fig. 5C). Meanwhile, chickens with the GG genotype had a better feed efficiency, with low RFI (− 0.630 vs. 1.467) and FCR (1.658 vs. 1.685), than those of the AG genotype. However, the feed efficiency of the AA genotype was not significantly different from that of the other two genotypes (Fig. 5D, E). To further investigate the combined effects of the genotypes on phenotypes, microbiota, and SCFAs, the differences were analyzed among the different genotypes using a Wilcoxon rank-sum test. In addition to propionate, acetate, butyrate, and valerate also showed differential concentrations among the different genotypes. The relative abundance of fourteen taxa, including one phylum, one class, one order, three families, and eight genera, differed significantly among different genotypes (Fig. 5F).

Fig. 5
figure 5

GWAS for propionate and associated SNP effects on RFI and the microbiota. A The Manhattan and QQ plots of propionate. B The significant region on chromosome 4 and gene distribution. CE The effect of loci genotyping on propionate, FCR and RFI, ***, **, and ns represent adjusted P values < 0.001, < 0.01, and > 0.05, respectively. F Overview of the effect of the locus on grow performance, SCFAs, and the microbiota

Table 2 Annotation of the GWAS results on SCFAs

Christensenellaceae_R-7_group was identified as the biomarker related to the host genome, feed efficiency and SCFAs

As described above, 14 taxa were associated with the top SNP associated with propionate. Two-part association and Spearman correlation analyses were used to identify the microbial taxa related to feed efficiency and propionate. The results of the two-part association model are presented in Additional file 14: Table S4. RFI and FCR were used to identify the representative taxa for feed efficiency traits, and 21 taxa were found (Additional file 15: Figure S11. A). Eight taxa were identified as associated with propionate using the same method (Additional file 15: Figure S11. B). The biomarkers were selected based on the intersection of the SNP-affected taxa and the taxa related to feed efficiency and propionate concentration. Two taxa, Christensenellaceae and Christensenellaceae_R-7_group, were identified as biomarkers related to host genome, feed efficiency, and propionate concentration (Fig. 6A). These taxa were also detected in the microbial composition detection among divergent RFI and PA groups (Fig. 2 & Additional file 10: Figure S8. D). Significant negative correlations were found with RFI, FCR, and propionate concentration. Slight negative correlations were found with BW40 and ADFI (Fig. 6B). By comparing the relative abundance of these two taxa, it was found that Christensenellaceae_R-7_group was the only genus found in Christensenellaceae family, because of the mean proportions of these two taxa were the same. The relative abundance of Christensenellaceae_R-7_group was approximately 1 ~ 2%, and higher relative abundance was found in the LRFI (Fig. 6C) and LPA group (Fig. 6D).

Fig. 6
figure 6

Biomarker determination from feed efficiency, propionate, and SNP genotyping effects. A The Venn diagram of selecting the biomarkers from SNP (locus genotypes), FE (feed efficiency), and PA (propionate) effects. B Spearman correlation between the biomarkers and different traits. C, D Comparisons of Christensenellaceae/Christensenellaceae_R-7_group among divergent RFI and PA groups, respectively


The effect of RFI-related traits on the microbial community

Feed efficiency is a complex trait influenced by feed intake and body weight. Early feed efficiency studies reported correlations between the gut microbiota and gut microbial community [24, 28]. The lack of difference in α-diversity between the HRFI and LRFI groups here agrees with the results of previous studies in chickens [26], and similar results were also found in pigs [62]. Different groups from one population might show a similar α-diversity, and divergent RFI selection of pig lines indicated different α-diversity [63]. A difference in β-diversity was discovered in the divergent RFI groups, indicating that the specific microbiota affects RFI. However, different breeds and diets can influence the microbial composition, leading to different RFI-related microbiotas with different traits [15, 26, 64, 65]. A fecal microbiota transplant (FMT) trial that identified the fecal microbiota from chickens with high feeding efficiency could improve the feed efficiency in other chickens, and three microbial taxa (Lactobacillus, Dorea, and Ruminococcus) changed in abundance after chickens received this treatment [27]. Our results are consistent with Metzler’s result [27], one member of Ruminococcus (Ruminococcaceae_UCG_014) played an essential role in the low-RFI group, and two members of Ruminococcus (Ruminococcaceae_UCG_008 and Ruminococcaceae_UCG_009) and Lactobacillus were enriched in high-RFI chickens. It was reported that a positive association between improved feed efficiency and the relative abundance of Butyricicoccus and Faecalibacterium is considered beneficial for the health of the animals [66]. However, our results did not agree with some previous studies, even in a white broiler population. In recent reports, Oscillibacter in the cecum and Butyricicoccus in the cloaca were more abundant in low-RFI chickens, and Subdoligranulum variabile in the ileum and two Peptostreptoccaceae members in the ileum and cloaca were negatively correlated with feed efficiency [26]. In our study, cecal Butyricicoccus presented a slightly positive correlation with RFI and nearly no correlation with SCFAs. These results were understandable because different GITs showed different microbial compositions so inconsistent correlations might have been observed. Meanwhile, Subdoligranulum showed a negative correlation with RFI, and a slightly negative correlation with butyrate, propionate, and acetate concentrations. These microbial taxa showed correlations with SCFAs, which could be a primary explanation for why propionate and butyrate concentrations were significantly correlated with RFI.

SCFAs are representative metabolites of gut microbiota function in feed efficiency

Our study found that propionate and butyrate concentrations were significantly different between the HRFI and LRFI groups. Propionic acid is beneficial to the human body as it may play a role in satiety and energy homeostasis via specific mechanisms, including activation of free fatty acid receptors, reducing lipogenesis levels and glucose homeostasis [5]. Butyrate, the anionic part of dissociated butyric acid and its salts, has been implicated in various host physiological functions, including energy homeostasis, obesity, immune system regulation, cancer, and even brain function [67, 68]. Butyrate was reported to induce the relative mRNA expression of Mucin 2 and its secretion in goblet-like cells [69], as well as promote the assembly of occludin through the AMPK pathway [70].

Christensenellaceae and Christensenellaceae_R-7_group were the two taxa found to be biomarkers that showed correlations with propionate concentration, feed efficiency, and locus genotype effects. Christensenellaceae has been widely investigated in the human gut, which suggests that it is highly heritable, regulated by host genetics and inversely related to host body mass index (BMI) [71, 72]. High feed efficiency traits are usually accompanied by leanness and health performance, and it was reported that Christensenellaceae was more enriched in the leaner individuals [73]. Fecal propionate concentration was negatively correlated with feed efficiency [74]. A similar result was reported by Wang et al. [75], who showed that probiotics improved the feed efficiency, with decreased propionate concentration in the rumen. In a previous broiler study, butyrate supplementation improved feed efficiency without affecting the growth rate and decreasing abdominal fat deposition [76]. In this study, butyrate and propionate concentrations showed positive correlations with RFI, which indicated negative correlations with feed efficiency. One hypothesis is that a larger proportion of butyrate was transported into the blood. One literature reported that fermented butyrate and propionate in the caecum can be involved in quick utilization/absorption, and there are relatively high correlations between SCFAs in the cecum and those in the portal and aortic serum [77]. Some literature reported that blood butyrate concentrations could be associated with feed efficiency [78, 79]. An in vivo/vitro assay reported that approximately 90% of SCFAs were absorbed in the hindgut [80]. However, in our study, blood metabolites were not measured. The dynamic balance between the lumen concentration and blood concentration of butyrate could be a focus of future studies.

The association between SCFA and host genome variants

Our study proved that seven cecal SCFAs were moderately to highly heritable and were the first to conduct the GWAS for caecal SCFAs. SNPs associated with propionate concentration were located near MAML3, SETD7, and MGST2. MAML3, a protein of the Mastermind-like proteins family, is a transcriptional coactivator of Notch signaling, and Notch signaling plays a pivotal role in development and homeostasis [81, 82]. MAML3 was also associated with metastatic and WNT signaling activation [83]. Notch and WNT signaling pathways are critical components of the intestinal stem cell signaling network [84]. The weighted SNPs were annotated as SETD7, which is involved in lysine degradation in pigs with low feed efficiency [85]. Lysine intake plays an essential role in intestinal lysine transport and promotes feed intake associated with the piglet gut microbiome [86]. No reports have reported the correlation between feed efficiency and MGST2, but MGST2 was not enriched in a range of metabolic pathways, indicating a potential effect on feed efficiency [87]. Similar regions and genes were found to be associated with valerate and isovalerate. TDRD is associated with spermatogenesis [88]. AFAP1L2 is an adaptor activator of the PI3K-AKT pathway [89], and ABLIM1 is involved in the PI3K/Akt/Rac1 pathway [90]. These results agreed with those of previous studies showing that SCFAs function as signaling molecules in several pathways [91]. In this study, the propionate-associated genomic variants showed a correlation with feed efficiency traits, indicating that the function of MAML3, SETD7, and MGST2, needs further investigation.

The regulatory mechanism for the effect of SCFAs on feed efficiency

SCFAs are the major end products from the fermentation of gut microbiota. Propionate can be biosynthesized from the succinate, acrylate, and propanediol pathways by using succinate, lactate, and deoxyhexose sugar as substrates, respectively [92, 93]. Butyrate can be transformed from butyryl-CoA by phosphotransbutyrylase and butyrate kinase in a direct pathway and by the butyryl-CoA:acetate CoA-transferase route [94, 95]. In our study, the divergent RFI and PA groups had significant differential pathways in transcription and amino acid metabolism. Genes encoding SCFA-producing enzymes were found to be active in a range of microbial strains [96, 97]. Thus, enzyme activity could be a part of explaining the differential transcription levels observed in the present study. Pyruvate is involved in many energy metabolism pathways and is correlated with butyryl-CoA and acetate CoA metabolism, which are substrates for SCFA production. Moreover, pyruvate was reported to be a product of amino acid metabolism [98,99,100]. Thus, the difference in amino acid metabolism found in the RFI and PA divergent groups could be explained by the fact that pyruvate was produced by amino acid metabolism.

The effect of the top SNP (chr4: 29,417,189: G > A) from the propionate GWAS on feed efficiency traits and microbial relative abundance was evaluated in this study. From the phylum to genus level, 14 taxonomies showed significant differences in relative abundance. The different genotypes also resulted in different feed efficiency traits and caecum SCFA concentrations. The previous studies proved that animal dietary supplementation, which can improve growth performance and feed efficiency, was always along with increased SCFA in GIT [101,102,103,104]. However, no literature reported the causal effect between SCFA and feed efficiency. Meanwhile, a previous study reported that poultry caecal SCFAs were originally produced by microbiota [32]. In the selected 14 taxonomies, at the genus level, Christensenellaceae_R-7_group and Pseudobutyrivibrio were significantly negatively correlated with butyrate and propionate concentrations, while Anaerostipes and Eubacterium_coprostanoligenes_group were significantly positively correlated with butyrate or propionate concentrations. All four genera were reported belonging to the propionate or butyrate-produced microbial families [105,106,107]. However, the SCFAs could not be affected by only one of the microbiota variations and must be affected by the microbial composition evolutions in this selection group [106]. Christensenellaceae_R-7_group and Eubacterium_coprostanoligenes_group were both significantly correlated with ADFI, FCR, and RFI, but they had divergent effects. In this study, no supplementation was applied, meaning the maximum SCFA production is at a similar level. No literature reported the correlations between caecal SCFA and feed efficiency in a natural broiler population. Based on our previous hypothesis, a large proportion of SCFAs could be absorbed and unutilized in serum, and SCFAs do have an effect on feed efficiency traits [77]. The relationship between GIT, serum, and target organs needs to be investigated in further study. However, a pathway from host genome variants to feed efficiency can be illustrated. The different genome types could lead to varied microbial compositions, which cause differences in SCFA concentrations in the gut tract. Then, the SCFAs are absorbed into the blood and then utilized in organs, such as the brain, to control feed digestion and appetites, reflecting the changes in feed efficiency traits [5, 36, 37].


Our study provides strong evidence of the pathway that host genome variants influence the cecal SCFA by influencing cecal microbiota and then regulating feed efficiency. Our study concluded that host genetic variation could regulate the caecal microbially derived SCFAs, which play a role in host feed efficiency-related traits. The SNP-based heritability results suggest that the SCFAs had moderate to high heritability (h 2 = 0.183 ~ 0.401). The GWAS showed that four out of seven SCFAs have significant associations with genome variants. SCAF concentrations, microbiota, and feed efficiencies were significantly different among different genotypes for the top SNP. Cecal Christensenellaceae and Christensenellaceae_R-7_group were identified as biomarkers contributing to the combined effect of host genetics and SCFAs on chicken feed efficiency.

Availability of data and materials

The sequencing data generate in this study are available in the Genome Sequence Archive (GSA: The accession number of 16S RNA gene sequencing data is CRA005940. The accession number of resequencing data is CRA006625.



Short-chain fatty acid


Feed efficiency


Feed intake


Feed conversion ratio


Residual feed intake


Genome-wide association study


Gastrointestinal tract


Minor allele frequency


Linkage disequilibrium


Amplicon sequence variants


Gas chromatography-mass spectrometry


Linear mixed model




Fecal microbiota transplant


Body mass index


  1. Zampiga M, Flees J, Meluzzi A, Dridi S, Sirri F. Application of omics technologies for a deeper insight into quali-quantitative production traits in broiler chickens: a review. J An Sci Biotechnolo. 2018;9(1):61.

    Article  Google Scholar 

  2. Sell-Kubiak E, Wimmers K, Reyer H, Szwaczkowski T. Genetic aspects of feed efficiency and reduction of environmental footprint in broilers: a review. J Appl Genet. 2017;58(4):487–98.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Cerrate S, Ekmay R, England JA, Coon C. Predicting nutrient digestibility and energy value for broilers. Poult Sci. 2019;98(9):3994–4007.

    Article  CAS  PubMed  Google Scholar 

  4. Romero LF, Zuidhof MJ, Renema RA, Naeima A, Robinson FE. Effects of maternal energy efficiency on broiler chicken growth, feed conversion, residual feed intake, and residual maintenance metabolizable energy requirements. Poult Sci. 2011;90(12):2904–12.

    Article  CAS  PubMed  Google Scholar 

  5. Byrne CS, Chambers ES, Morrison DJ, Frost G. The role of short chain fatty acids in appetite regulation and energy homeostasis. Int J Obesity (2005). 2015;39(9):1331–8.

    Article  CAS  Google Scholar 

  6. So PW, Yu WS, Kuo YT, Wasserfall C, Goldstone AP, Bell JD, Frost G. Impact of resistant starch on body fat patterning and central appetite regulation. PLoS ONE. 2007;2(12):e1309.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Atchley WR, Gaskins CT, Anderson D. Statistical properties of ratios I empirical results. Syst Biol. 1976;25(2):137–48.

    Google Scholar 

  8. Archer JA, Arthur PF, Herd RM, Parnell PF, Pitchford WS. Optimum postweaning test for measurement of growth rate, feed intake, and feed efficiency in British breed cattle. J Anim Sci. 1997;75(8):2024–32.

    Article  CAS  PubMed  Google Scholar 

  9. Kennedy BW, van der Werf JH, Meuwissen TH. Genetic and statistical properties of residual feed intake. J Anim Sci. 1993;71(12):3239–50.

    Article  CAS  PubMed  Google Scholar 

  10. Pakdel A, van Arendonk JA, Vereijken AL, Bovenhuis H. Genetic parameters of ascites-related traits in broilers: correlations with feed efficiency and carcase traits. Br Poult Sci. 2005;46(1):43–53.

    Article  CAS  PubMed  Google Scholar 

  11. N’Dri AL, Mignon-Grasteau S, Sellier N, Tixier-Boichard M, Beaumont C. Genetic relationships between feed conversion ratio, growth curve and body composition in slow-growing chickens. Br Poult Sci. 2006;47(3):273–80.

    Article  PubMed  Google Scholar 

  12. Aggrey SE, Karnuah AB, Sebastian B, Anthony NB. Genetic properties of feed efficiency parameters in meat-type chickens. Genet Sel Evol. 2010;42(1):25.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Li W, Liu R, Zheng M, et al. New insights into the associations among feed efficiency, metabolizable efficiency traits and related QTL regions in broiler chickens. J Anim Sci Biotechnol. 2020;11:65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Li W, Zheng M, Zhao G, et al. Identification of QTL regions and candidate genes for growth and feed efficiency in broilers. Genet Sel Evol. 2021;53(1):13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Wen C, Yan W, Mai C, Duan Z, Zheng J, Sun C, Yang N. Joint contributions of the gut microbiota and host genetics to feed efficiency in chickens. Microbiome. 2021;9(1):126.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zuidhof MJ, Schneider BL, Carney VL, Korver DR, Robinson FE. Growth, efficiency, and yield of commercial broilers from 1957, 1978, and 2005. Poult Sci. 2014;93(12):2970–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Grice EA, Segre JA. The human microbiome: our second genome. Annu Rev Genomics Hum Genet. 2012;13(1):151–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Maki JJ, Klima CL, Sylte MJ, Looft T. The microbial pecking order: utilization of intestinal microbiota for poultry health. Microorganisms. 2019;7(10):376.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Siegerstetter SC, Schmitz-Esser S, Magowan E, et al. Intestinal microbiota profiles associated with low and high residual feed intake in chickens across two geographical locations. PLoS ONE. 2017;12(11):e0187766.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Bae Y, Koo B, Lee S, Mo J, Oh K, Mo IP. Bacterial diversity and its relationship to growth performance of broilers. Korean J Vet Res. 2017;57(3):159–67.

    Article  Google Scholar 

  21. Stanley D, Geier MS, Denman SE, Haring VR, Crowley TM, Hughes RJ, Moore RJ. Identification of chicken intestinal microbiota correlated with the efficiency of energy extraction from feed. Vet Microbiol. 2013;164(1–2):85–92.

    Article  PubMed  Google Scholar 

  22. Yan W, Sun C, Yuan J, Yang N. Gut metagenomic analysis reveals prominent roles of Lactobacillus and cecal microbiota in chicken feed efficiency. Sci Rep. 2017;7(1):45308.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Torok VA, Hughes RJ, Mikkelsen LL, et al. Identification and characterization of potential performance-related gut microbiotas in broiler chickens across various feeding trials. Appl Environ Microbiol. 2011;77(17):5868–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Stanley D, Denman SE, Hughes RJ, et al. Intestinal microbiota associated with differential feed conversion efficiency in chickens. Appl Microbiol Biotechnol. 2012;96(5):1361–9.

    Article  CAS  PubMed  Google Scholar 

  25. Singh KM, Shah T, Deshpande S, Jakhesara SJ, Koringa PG, Rank DN, Joshi CG. High through put 16S rRNA gene-based pyrosequencing analysis of the fecal microbiota of high FCR and low FCR broiler growers. Mol Biol Rep. 2012;39(12):10595–602.

    Article  CAS  PubMed  Google Scholar 

  26. Liu J, Stewart SN, Robinson K, Yang Q, Lyu W, Whitmore MA, Zhang G. Linkage between the intestinal microbiota and residual feed intake in broiler chickens. Journal of animal science and biotechnology. 2021;12(1):22–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Metzler-Zebeli BU, Siegerstetter SC, Magowan E, Lawlor PG. NE OC, Zebeli Q: Fecal microbiota transplant from highly feed efficient donors affects cecal physiology and microbiota in low- and high-feed efficient chickens. Front Microbiol. 2019;10:1576.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Siegerstetter SC, Petri RM, Magowan E, Lawlor PG, Zebeli Q, O’Connell NE, Metzler-Zebeli BU. Feed restriction modulates the fecal microbiota composition, nutrient retention, and feed efficiency in chickens divergent in residual feed intake. Front Microbiol. 2018;9:2698.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Diaz Carrasco JM, Casanova NA, Fernandez Miyakawa ME. Microbiota, gut health and chicken productivity: what is the connection? Microorganisms. 2019;7(10):374.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Grieneisen L, Dasari M, Gould TJ, et al. Gut microbiome heritability is nearly universal but environmentally contingent. Science. 2021;373(6551):181–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kurilshikov A, Medina-Gomez C, Bacigalupe R, et al. Large-scale association analyses identify host factors influencing human gut microbiome composition. Nat Genet. 2021;53(2):156–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Annison EF, Hill KJ, Kenworthy R. Volatile fatty acids in the digestive tract of the fowl. Br J Nutr. 1968;22(2):207–16.

    Article  CAS  PubMed  Google Scholar 

  33. Koh A, De Vadder F, Kovatcheva-Datchary P, Bäckhed F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell. 2016;165(6):1332–45.

    Article  CAS  PubMed  Google Scholar 

  34. Nøhr MK, Pedersen MH, Gille A, et al. GPR41/FFAR3 and GPR43/FFAR2 as cosensors for short-chain fatty acids in enteroendocrine cells vs FFAR3 in enteric neurons and FFAR2 in enteric leukocytes. Endocrinology. 2013;154(10):3552–64.

    Article  PubMed  Google Scholar 

  35. Nøhr MK, Egerod KL, Christiansen SH, Gille A, Offermanns S, Schwartz TW, Møller M. Expression of the short chain fatty acid receptor GPR41/FFAR3 in autonomic and somatic sensory ganglia. Neuroscience. 2015;290:126–37.

    Article  PubMed  Google Scholar 

  36. Dorji J, MacLeod IM, Chamberlain AJ, et al. Mitochondrial protein gene expression and the oxidative phosphorylation pathway associated with feed efficiency and energy balance in dairy cattle. J Dairy Sci. 2021;104(1):575–87.

    Article  CAS  PubMed  Google Scholar 

  37. Menezes ACB, Valadares Filho SC, Benedeti PDB, Zanetti D, Paulino MF, Silva FF, Caton JS. Feeding behavior, water intake, and energy and protein requirements of young Nellore bulls with different residual feed intakes. J Anim Sci. 2020;98(9):skaa279.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Xu Z, Ji C, Zhang Y, et al. Combination analysis of genome-wide association and transcriptome sequencing of residual feed intake in quality chickens. BMC Genomics. 2016;17:594–594.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Liu R, Liu J, Zhao G, et al. Relevance of the intestinal health-related pathways to broiler residual feed intake revealed by duodenal transcriptome profiling. Poult Sci. 2019;98(3):1102–10.

    Article  CAS  PubMed  Google Scholar 

  40. Xiao C, Deng J, Zeng L, Sun T, Yang Z, Yang X. Transcriptome analysis identifies candidate genes and signaling pathways associated with feed efficiency in Xiayan chicken. Front Genet. 2021;12:607719.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Tan Z, Wang Y, Yang T, et al. Differentially expressed genes in the caecal and colonic mucosa of Landrace finishing pigs with high and low food conversion ratios. Sci Rep. 2017;7(1):14886.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Köchl S, Niederstätter H, Parson W. DNA extraction and quantitation of forensic samples using the phenol-chloroform method and real-time PCR. Methods Mol Biol (Clifton, NJ). 2005;297:13–30.

    Google Scholar 

  43. DePristo MA, Banks E, Poplin R, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011;43(5):491–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Van der Auwera GA, Carneiro MO, Hartl C, et al. From FastQ data to high confidence variant calls: the genome analysis toolkit best practices pipeline. Current Protocols Bioinform. 2013;43(1110):1.

    Google Scholar 

  45. Li H: Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv e-prints 2013:https://arXiv:1303.3997.

  46. McKenna A, Hanna M, Banks E, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Zhang C, Dong S-S, Xu J-Y, He W-M, Yang T-L. PopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files. Bioinformatics (Oxford, England). 2018;35(10):1786–8.

    Google Scholar 

  48. Liu YX, Qin Y, Chen T, Lu M, Qian X, Guo X, Bai Y. A practical guide to amplicon and metagenomic analysis of microbiome data. Protein Cell. 2021;12(5):315–30.

    Article  PubMed  Google Scholar 

  49. Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics (Oxford, England). 2010;26(19):2460–1.

    CAS  PubMed  Google Scholar 

  51. Quast C, Pruesse E, Yilmaz P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41(Database issue):D590-596.

    CAS  PubMed  Google Scholar 

  52. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):R60.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Langille MG, Zaneveld J, Caporaso JG, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013;31(9):814–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet. 2012;44(7):821–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. American J Human Genet. 2011;88(1):76–82.

    Article  CAS  Google Scholar 

  56. Li MX, Yeung JM, Cherny SS, Sham PC. Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets. Hum Genet. 2012;131(5):747–56.

    Article  CAS  PubMed  Google Scholar 

  57. Shim H, Chasman DI, Smith JD, et al. A multivariate genome-wide association analysis of 10 LDL subfractions, and their response to statin treatment, in 1868 Caucasians. PLoS ONE. 2015;10(4):e0120758–e0120758.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Fu J, Bonder MJ, Cenit MC, et al. The gut microbiome contributes to a substantial proportion of the variation in blood lipids. Circ Res. 2015;117(9):817–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Parks DH, Tyson GW, Hugenholtz P, Beiko RG. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics (Oxford, England). 2014;30(21):3123–4.

    CAS  PubMed  Google Scholar 

  60. Comeau AM, Douglas GM, Langille MGI, Eisen J. Microbiome helper: a custom and streamlined workflow for microbiome research. mSystems. 2017;2(1):e00127-00116.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Liu YX, Chen L, Ma T, et al. EasyAmplicon: an easy-to-use, open-source, reproducible, and community-based pipeline for amplicon data analysis in microbiome research. IMeta. 2023;2(1):e83.

    Article  Google Scholar 

  62. Si J, Feng L, Gao J, et al. Evaluating the association between feed efficiency and the fecal microbiota of early-life Duroc pigs using 16S rRNA sequencing. AMB Express. 2020;10(1):115.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Aliakbari A, Zemb O, Billon Y, Barilly C, Ahn I, Riquet J, Gilbert H. Genetic relationships between feed efficiency and gut microbiome in pig lines selected for residual feed intake. J Anim Breed Genet. 2021;138(4):491–507.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Wen C, Yan W, Sun C, et al. The gut microbiota is largely independent of host genetics in regulating fat deposition in chickens. ISME J. 2019;13(6):1422–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Chen C, Fang S, Wei H, et al. Prevotella copri increases fat accumulation in pigs fed with formula diets. Microbiome. 2021;9(1):175–175.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Lingbeek MM, Borewicz K, Febery E, Han Y, Doelman J, van Kuijk SJA. Short-chain fatty acid administration via water acidifier improves feed efficiency and modulates fecal microbiota in weaned piglets. J Anim Sci. 2021;99(11):skab307.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Bourassa MW, Alim I, Bultman SJ, Ratan RR. Butyrate, neuroepigenetics and the gut microbiome: can a high fiber diet improve brain health? Neurosci Lett. 2016;625:56–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Di Sabatino A, Morera R, Ciccocioppo R, et al. Oral butyrate for mildly to moderately active Crohn’s disease. Aliment Pharmacol Ther. 2005;22(9):789–94.

    Article  PubMed  Google Scholar 

  69. Gao X, Chen Y, Chen Z, et al. Identification and antimicrobial activity evaluation of three peptides from laba garlic and the related mechanism. Food Funct. 2019;10(8):4486–96.

    Article  CAS  PubMed  Google Scholar 

  70. Peng L, Li ZR, Green RS, Holzman IR, Lin J. Butyrate enhances the intestinal barrier by facilitating tight junction assembly via activation of AMP-activated protein kinase in Caco-2 cell monolayers. J Nutr. 2009;139(9):1619–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Goodrich JK, Waters JL, Poole AC, et al. Human genetics shape the gut microbiome. Cell. 2014;159(4):789–99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Waters JL, Ley RE. The human gut bacteria Christensenellaceae are widespread, heritable, and associated with health. BMC Biol. 2019;17(1):83–83.

    Article  PubMed  PubMed Central  Google Scholar 

  73. McCormack UM, Curiao T, Buzoianu SG, et al. Exploring a possible link between the intestinal microbiota and feed efficiency in pigs. Appl Environ Microbiol. 2017;83(15):e00380-17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Jiang H, Fang S, Yang H, Chen C. Identification of the relationship between the gut microbiome and feed efficiency in a commercial pig cohort. J Anim Sci. 2021;99(3):skab045.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Wang H, Yu Z, Gao Z et al: Effects of compound probiotics on growth performance, rumen fermentation, blood parameters, and health status of neonatal Holstein calves. J Dairy Sci 2021.

  76. Yang Q, Chen B, Robinson K, et al. Butyrate in combination with forskolin alleviates necrotic enteritis, increases feed efficiency, and improves carcass composition of broilers. J Anim Sci Biotechnol. 2022;13(1):3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Jakobsdottir G, Jädert C, Holm L, Nyman ME. Propionic and butyric acids, formed in the caecum of rats fed highly fermentable dietary fibre, are reflected in portal and aortic serum. Br J Nutr. 2013;110(9):1565–72.

    Article  CAS  PubMed  Google Scholar 

  78. Dechow CD, Baumrucker CR, Bruckmaier RM, Blum JW. Blood plasma traits associated with genetic merit for feed utilization in Holstein cows. J Dairy Sci. 2017;100(10):8232–8.

    Article  CAS  PubMed  Google Scholar 

  79. Leão JM, Coelho SG, Lage CFA, et al. How divergence for feed efficiency traits affects body measurements and metabolites in blood and ruminal parameters on pre-weaning dairy Heifers. Animals (Basel). 2021;11(12):3436.

    Article  PubMed  Google Scholar 

  80. Bai Y, Zhou X, Zhao J, et al. Sources of dietary fiber affect the SCFA production and absorption in the hindgut of growing pigs. Front Nutr. 2022;8:719935–719935.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Saint Just Ribeiro M, Wallberg AE. Transcriptional mechanisms by the coregulator MAML1. Curr Protein Peptide Sci. 2009;10(6):570–6.

    Article  CAS  Google Scholar 

  82. Kitagawa M. Notch signalling in the nucleus: roles of mastermind-like (MAML) transcriptional coactivators. J Biochem. 2016;159(3):287–94.

    CAS  PubMed  Google Scholar 

  83. Alzofon N, Koc K, Panwell K, et al. Mastermind like transcriptional coactivator 3 (MAML3) drives neuroendocrine tumor progression. Mol Cancer Res. 2021;19(9):1476–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Katoh M, Katoh M. WNT antagonist, DKK2, is a Notch signaling target in intestinal stem cells: augmentation of a negative regulation system for canonical WNT signaling pathway by the Notch-DKK2 signaling loop in primates. Int J Mol Med. 2007;19(1):197–201.

    CAS  PubMed  Google Scholar 

  85. Banerjee P, Carmelo VAO, Kadarmideen HN. Genome-wide epistatic interaction networks affecting feed efficiency in duroc and landrace pigs. Front Genet. 2020;11:121.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Yin J, Han H, Li Y, et al. Lysine restriction affects feed intake and amino acid metabolism via gut microbiome in piglets. Cell Physiol Biochem. 2017;44(5):1749–61.

    Article  CAS  PubMed  Google Scholar 

  87. Thulasingam M, Orellana L, Nji E, Ahmad S, Rinaldo-Matthis A, Haeggström JZ. Crystal structures of human MGST2 reveal synchronized conformational changes regulating catalysis. Nat Commun. 2021;12(1):1728.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Honda S, Miki Y, Miyamoto Y, Kawahara Y, Tsukamoto S, Imai H, Minami N. Oocyte-specific gene Oog1 suppresses the expression of spermatogenesis-specific genes in oocytes. J Reprod Dev. 2018;64(4):297–301.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Tanga N, Kuboyama K, Kishimoto A, et al. The PTN-PTPRZ signal activates the AFAP1L2-dependent PI3K-AKT pathway for oligodendrocyte differentiation: Targeted inactivation of PTPRZ activity in mice. Glia. 2019;67(5):967–84.

    Article  PubMed  Google Scholar 

  90. Jin SH, Kim H, Gu DR, Park KH, Lee YR, Choi Y, Lee SH. Actin-binding LIM protein 1 regulates receptor activator of NF-κB ligand-mediated osteoclast differentiation and motility. BMB Rep. 2018;51(7):356–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Gonzalez-Santana A, Diaz Heijtz R. Bacterial peptidoglycans from microbiota in neurodevelopment and behavior. Trends Mol Med. 2020;26(8):729–43.

    Article  CAS  PubMed  Google Scholar 

  92. Scott KP, Martin JC, Campbell G, Mayer CD, Flint HJ. Whole-genome transcription profiling reveals genes up-regulated by growth on fucose in the human gut bacterium “Roseburia inulinivorans.” J Bacteriol. 2006;188(12):4340–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Hetzel M, Brock M, Selmer T, Pierik AJ, Golding BT, Buckel W. Acryloyl-CoA reductase from Clostridium propionicum. An enzyme complex of propionyl-CoA dehydrogenase and electron-transferring flavoprotein. Euro J Biochem. 2003;270(5):902–10.

    Article  CAS  Google Scholar 

  94. Duncan SH, Barcenilla A, Stewart CS, Pryde SE, Flint HJ. Acetate utilization and butyryl coenzyme A (CoA):acetate-CoA transferase in butyrate-producing bacteria from the human large intestine. Appl Environ Microbiol. 2002;68(10):5186–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Louis P, Duncan SH, McCrae SI, Millar J, Jackson MS, Flint HJ. Restricted distribution of the butyrate kinase pathway among butyrate-producing bacteria from the human colon. J Bacteriol. 2004;186(7):2099–106.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Louis P, Young P, Holtrop G, Flint HJ. Diversity of human colonic butyrate-producing bacteria revealed by analysis of the butyryl-CoA:acetate CoA-transferase gene. Environ Microbiol. 2010;12(2):304–14.

    Article  CAS  PubMed  Google Scholar 

  97. Sheridan PO, Louis P, Tsompanidou E, et al. Distribution, organization and expression of genes concerned with anaerobic lactate utilization in human intestinal bacteria. Microbial Gen. 2022;8(1):000739.

    CAS  Google Scholar 

  98. Liu K, Yu H, Sun G, et al. Semi-rational design of L-amino acid deaminase for production of pyruvate and D-alanine by Escherichia coli whole-cell biocatalyst. Amino Acids. 2021;53(9):1361–71.

    Article  CAS  PubMed  Google Scholar 

  99. Weghoff MC, Bertsch J, Müller V. A novel mode of lactate metabolism in strictly anaerobic bacteria. Environ Microbiol. 2015;17(3):670–7.

    Article  CAS  PubMed  Google Scholar 

  100. Huynh TYL, Oscilowska I, Sáiz J, Nizioł M, Baszanowska W, Barbas C, Palka J. Metformin treatment or PRODH/POX-Knock out similarly induces apoptosis by reprograming of amino acid metabolism, TCA, urea cycle and pentose phosphate pathway in MCF-7 breast cancer cells. Biomolecules. 2021;11(12):1888.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Tan FPY, Beltranena E, Zijlstra RT. Resistant starch: Implications of dietary inclusion on gut health and growth in pigs: a review. J An Sci Biotechnol. 2021;12(1):124.

    Article  CAS  Google Scholar 

  102. Xu Y, Yu Y, Shen Y, et al. Effects of Bacillus subtilis and Bacillus licheniformis on growth performance, immunity, short chain fatty acid production, antioxidant capacity, and cecal microflora in broilers. Poult Sci. 2021;100(9):101358.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Chen CY, Chen SW, Wang HT. Effect of supplementation of yeast with bacteriocin and Lactobacillus culture on growth performance, cecal fermentation, microbiota composition, and blood characteristics in broiler chickens. Asian-Australas J Anim Sci. 2017;30(2):211–20.

    Article  CAS  PubMed  Google Scholar 

  104. Tan X, Liu R, Zhao D et al: Large-scale genomic and transcriptomic analyses elucidate the genetic basis of high meat yield in chickens. Journal of advanced research 2023.

  105. Hai NT, Hongsrichan N, Intuyod K, et al. Strongyloides stercoralis infection induces gut dysbiosis in chronic kidney disease patients. PLoS Negl Trop Dis. 2022;16(9):e0010302.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Louis P, Flint HJ. Formation of propionate and butyrate by the human colonic microbiota. Environ Microbiol. 2017;19(1):29–41.

    Article  CAS  PubMed  Google Scholar 

  107. López-Montoya P, Cerqueda-García D, Rodríguez-Flores M, et al. Association of gut microbiota with atherogenic dyslipidemia, and its impact on serum lipid levels after bariatric surgery. Nutrients. 2022;14(17):3545.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


The authors acknowledge the members of Mile Xinguang Agricultural and Animal Industrials Co., Ltd. (Mile, China) for their assistance in the phenotype collection and sampling.


This research was funded by grants from the Key Projects of the National Natural Science Foundation of China (No. 32230101), the China Agriculture Research System of MOF and MARA (CARS-41), the Agricultural Science and Technology Innovation Program (CAAS-ZDRW202005), and Guangdong Provincial Key Research and Development Program (No 2020B02022002).

Author information

Authors and Affiliations



ZH contributed to performing the study, the analysis of data and the writing of the manuscript. RL contributed to the analysis, interpretation of data, and reviewing of the manuscript. MW contributed to the animal trail and data collection. QW and JW contributed to the analysis of data and reviewing of the manuscript. JZ and JD contributed to the sample and data collection. AF and GZ contributed to the design of the study, interpretation of data, and reviewing of the manuscript.

Corresponding authors

Correspondence to Alan G. Fahey or Guiping Zhao.

Ethics declarations

Ethics approval and consent to participate

All experimental procedures with broilers were performed according to the Guidelines for Experimental Animals established by the Ministry of Science and Technology (Beijing, China). Ethical approval on animal survival was given by the animal welfare and ethics committee of the Institute of Animal Sciences (IAS), Chinese Academy of Agricultural Sciences (CAAS, Beijing, China) with the following reference number: IAS2020–83.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

Table S1. Feed ingredients and nutrients composition for chickens during the experiment.

Additional file 2:

Table S2. Descriptive statistics for phenotypes.

Additional file 3:

Figure S1. General description between growth performance.

Additional file 4:

Figure S2. The LD decay of the whole genome.

Additional file 5:

Figure S3. General description between SCFAs with the original data.

Additional file 6:

Figure S4. General description between SCFAs with the log2-transferred data.

Additional file 7:

Figure S5. Two-part association model description from Fu et al.

Additional file 8:

Figure S6. Data description between high RFI and low RFI groups.

Additional file 9:

Figure S7. Heatmaps of growth performance and SCFA with genera.

Additional file 10:

Figure S8. Microbiota composition between high PA and low PA groups.

Additional file 11:

Figure S9. Manhattan and QQ plots for other SCFAs.

Additional file 12:

Figure S10. The GWAS results on growth traits.

Additional file 13:

Table S3. Detailed information on the SNPs associated with the SCFAs.

Additional file 14:

Table S4. Two-part model association on RFI, FCR and Propionate.

Additional file 15:

Figure S11. Significant microbial taxa of divergent feed efficiency and propionate.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, Z., Liu, R., Wang, M. et al. Combined effect of microbially derived cecal SCFA and host genetics on feed efficiency in broiler chickens. Microbiome 11, 198 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: