Análise Genética e Microbioma em Galinhas - Genética I (2024)

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Genetic and microbiome analysis of feed efficiency in laying hensQianqian Zhou,* Fangren Lan,* Shuang Gu,* Guangqi Li,y Guiqin Wu,y Yiyuan Yan,y Xiaochang Li,*Jiaming Jin,* Chaoliang Wen,* Congjiao Sun,* and Ning Yang *,1*National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding andReproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China; andyBeijing Huadu Yukou Poultry Industry Co. Ltd., Beijing, 101206, ChinaABSTRACT Improving feed efficiency is an importanttarget for poultry breeding. Feed efficiency is affected byhost genetics and the gut microbiota, but many of themechanisms remain elusive in laying hens, especially inthe late laying period. In this study, we measured feedintake, body weight, and egg mass of 714 hens from apedigreed line from 69 to 72 wk of age and calculated theresidual feed intake (RFI) and feed conversion ratio(FCR). In addition, fecal samples were also collected for16S ribosomal RNA gene sequencing (V4 region).Genetic analysis was then conducted in DMU packagesby using AI-REML with animal model. Moderate herita-bility estimates for FCR (h2 = 0.31) and RFI (h2 = 0.52)were observed, suggesting that proper selection pro-grams can directly improve feed efficiency. Genetically,RFI was less correlated with body weight and egg massthan that of FCR. The phenotypic variance explainedby gut microbial variance is defined as the microbiability(m2). The microbiability estimates for FCR (m2 = 0.03)� 2022 The Authors. Published by Elsevier Inc. on behalf of PoultryScience Association Inc. This is an open access article under the CCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Received August 3, 2022.Accepted December 5, 2022.1Corresponding author: nyang@cau.edu.cn1and RFI (m2 = 0.16) suggested the gut microbiota wasalso involved in the regulation of feed efficiency. In addi-tion, our results showed that the effect of host geneticson fecal microbiota was minor in three aspects: 1) micro-bial diversity indexes had low heritability estimates, andgenera with heritability estimates more than 0.1accounted for only 1.07% of the tested fecal microbiota;2) the genetic relationship correlations between hostgenetics and different microbial distance were veryweak, ranging from �0.0057 to �0.0003; 3) the micro-bial distance between different kinships showed no sig-nificant difference. Since the RFI has the highestmicrobiability, we further screened out three genera,including Anaerosporobacter, Candidatus Stoquefichus,and Fournierella, which were negatively correlated withRFI and played positive roles in improving the feed effi-ciency. These findings contribute to a great understand-ing of the genetic background and microbial influenceson feed efficiency.Key words: feed efficiency, heritability, microbiability, layer chicken, fecal microbiota2023 Poultry Science 102:102393https://doi.org/10.1016/j.psj.2022.102393INTRODUCTIONFeed costs account for 60 to 70% of the total produc-tion costs in poultry industry. Improving feed efficiencyis one of the important targets in poultry breeding andcontributes to reducing feed costs (Yang et al., 2020).The feed conversion ratio (FCR) and residual feedintake (RFI) are pivotal indicators routinely used toevaluate feed efficiency. The most commonly used mea-sure of feed efficiency is FCR, which is defined as theratio of feed intake (FI) to egg mass in layers(Yuan et al., 2015a). As a sensitive and accurateindicator of feed efficiency, RFI was first proposed byKoch et al. (Koch et al., 1963) and is defined as the feedintake above or below what is predicted for productionand maintenance.The moderate heritability estimated previously(van Kaam et al., 1999; Yuan et al., 2015b; Sell-Kubiak et al., 2017) suggested that host genetics sub-stantially affect feed efficiency, which can be directlyimproved by proper selection programs. Moreover, con-siderable research has focused on the molecular regula-tion mechanisms of feed efficiency and the identificationof reliable molecular markers for feed efficiency in chick-ens (Yuan et al., 2015b).The gastrointestinal tract (GIT) is densely populatedwith microorganisms (Pan and Yu, 2014). The residentintestinal microbiota, specifically its diversity, composi-tion, and function, is likely to influence the feed effi-ciency of chickens (Singh et al., 2014; Stanley et al.,2016; Yan et al., 2017). Elucidating the host effect onhttp://orcid.org/0000-0001-5772-3320https://doi.org/10.1016/j.psj.2022.102393http://creativecommons.org/licenses/by-nc-nd/4.0/http://creativecommons.org/licenses/by-nc-nd/4.0/mailto:nyang@cau.edu.cn2 ZHOU ET AL.the gut microbiome is essential to help design strategiesto modulate its composition to improve production(Siegerstetter et al., 2017; Yan et al., 2017) and health(Fouad and El-Senousey, 2014; Wen et al., 2019); how-ever, the specific contribution of host genetics remainselusive and debated in various studies. Zoetendalet al. (2001) found that human monozygotic twins pos-sess a more similar microbiome than marital partners orunrelated individuals. Several heritable bacterial taxawere identified in a cohort of 416 twin pairs, indicatingthat host genetics influences the composition of thehuman gut microbiome (Goodrich et al., 2014). Similarresults were found in a larger population of 1,126 twinpairs (Goodrich et al., 2016). Combined with whole-genome analysis, researchers have also found associa-tions between host single nucleotide polymorphisms(SNPs) and individual bacterial taxa or pathways(Blekhman et al., 2015; Bonder et al., 2016a; Li et al.,2019). In contrast, the gut microbiome and host geneticsare largely independent in humans with several distinctancestral origins (Rothschild et al., 2018) and similarresults were found in the fat deposition (Wen et al.,2019) and feed efficiency (Wen et al., 2021) of broilerchickens, suggesting a complex relationship betweenhost phenotypes and the gut microbiome. Therefore, theeffect of the interplay between host genetics and micro-biota on phenotypes (such as feed efficiency) needs to beexplored in different species.As a predominantly domesticated animal worldwide,chicken is not only a superior protein source but also apreferable experimental animal model, which can be eas-ily handled to obtain numerous full- and half-sib individ-uals with pedigree information and samples. With theimprovement of laying persistency and stability, extend-ing the laying period is an important goal in breedingcompanies. A decline in feed efficiency has been found inthe later laying periods of chickens (Yuan et al., 2015a).To our knowledge, few studies have focused on the feedefficiency of laying hens, especially in the late layingperiod, resulting in the lack of an accurate and reliabletheoretical foundation for the selection of target traits inbreeding programs to improve feed efficiency. The pur-pose of our study was to 1) estimate genetic parametersfor feed efficiency in the late laying period and investi-gate the contribution of host genetics and fecal micro-biota to feed efficiency, 2) evaluate the contribution ofhost genetics to variation in microbial composition, and3) further identify microorganisms considerably associ-ated with feed efficiency. The findings would help betterimproving feed efficiency in chickens by favoring moreefficient microbiota and selective breeding.MATERIALS AND METHODSAnimals and Samples CollectionThe complete procedure was performed according tothe regulations and guidelines established by the AnimalCare and Use Committee of the China AgriculturalUniversity.A total of 714 hens from a pedigreed line of RhodeIsland Red were used from Beijing Huadu Yukou Poul-try Breeding Co., Ltd., China. Birds were generatedfrom two hatches and housed in individual cages withfree access to feed(Table S1) and water. We then col-lected fecal samples owing to its convenience and non-invasiveness (Yan et al., 2019). Since the adult chickenshave fully developed microbiota and the microbial com-munities are more stable (Videnska et al., 2014;Ngunjiri et al., 2019), fecal samples were collected at 60wk of age once the excreta were discharged and placedin sterile plastic bags on dry ice using forceps. All sam-ples were stored at �80°C immediately after samplecollection.Calculation of Feed EfficiencyFeed consumption, body weight, and egg mass weremeasured from 69 to 72 wk of age. The body weight ofeach bird was measured using an electronic scale at thebeginning and end of the feeding trial. Feed intake wascalculated weekly and the egg weight and egg numberwere recorded every day. An individual metal feedtrough was used to provide mash feed for each hen. Feedwas added daily by hand after weighing the troughs.The remaining feed weight was recorded seven dayslater, and the individual feed intake in this interval wascalculated. This process was repeated for 28 consecutivedays. The sum of feed intake at each interval and thedaily feed intake (DFI) of each hen was calculated. Thedaily egg mass (DEM) was calculated as the product ofthe average egg weight and the total egg number overthe test days. The metabolic body weight (MBW), feedconversion ratio, and residual feed intake were calcu-lated. The FCR was calculated as the ratio of DFI andDEM. The RFI was estimated based on the followingformula first proposed by Luiting and Urff (1991):RFI ¼ FI� b0 þ b1MBWþ b2DEMþ b3BWGð Þwhere b0 is the intercept, and b1 and b2 are partialregression coefficients. All phenotype data lying outside3 SD of the mean were regarded as outliers and excludedfrom the analysis.Genetic Parameters EstimationThe phenotypes were normalized through rank-basedinverse normal transformations using the GenABELpackage in R (Aulchenko et al., 2007). We used AI-REML with an animal model to calculate genetic param-eters (Yuan et al., 2015a) and performed it using theDMU software (Madsen et al., 2018). We constructed aunivariate animal model to obtain estimates of the heri-tability for each trait as follows:y ¼ XBþ Zaþ ey is the vector under observation, X and Z are the inci-dence matrix of fixed effects and random additiveeffects, respectively, b is a vector of fixed effects, a is aGENETICS ANDMICROBIOME OF FEED EFFICIENCY 3vector of random additive effect, and e is the randomresidual effect.DNA Extractions and 16S rRNA GeneSequencing of Fecal MicrobiotaTotal DNA of the fecal microbiota in each bird wasextracted using the QIAamp Stool Mini Kit (QIAGEN,D4015-01) according to the manufacturer’s recommen-dations. PCR amplification of the V4 region (515F-806R) of the bacterial 16S rRNA gene was performed.The PCR reactions were performed in a 30 mL systemcontaining 15 mL of Phusion High-Fidelity PCR MasterMix (New England Biolabs), 0.2 mM forward primer, 0.2mM reverse primer, and 10 ng template DNA. The opti-mum PCR program was as follows: 98°C for 1 min, 30cycles of 98°C for 10 s, 50°C for 30 s, 72°C for 30 s, and afinal extension at 72°C for 5 min.Equal volumes of 1 £ loading buffer (containingSYBR green) were mixed with PCR products and elec-trophoresed on a 2% agarose gel for detection. The PCRproducts were mixed at equidensity ratios. Then, thePCR products were purified using the GeneJETTM GelExtraction Kit (Thermo Scientific). Sequencing librarieswere generated using the Ion Plus Fragment Library Kit48 rxns (Thermo Scientific) following the manufac-turer’s recommendations. Library quality was assessedon a Qubit@ 2.0 Fluorometer (Thermo Scientific). Thelibrary was sequenced on an Ion S5TM XL platform,and 400 bp single-end reads were generated. Single-endreads were assigned to the samples based on their uniquebarcodes and truncated by cutting off the barcode andprimer sequences.Analysis of 16S rRNA Sequencing DataThe 16S rRNA sequencing data were processed usingQuantitative Insights Into Microbial Ecology (QIIME2,version 2019.10) (Bolyen et al., 2019). The preliminaryquality screening was performed for the original high-throughput sequencing data using the QIIME2 pluginDADA2 (Callahan et al., 2016). The chimeric sequenceswere filtered and the remaining sequences were trimmedto a final length of 252 bp. The remaining high-qualitysequences were merged and classified by ampliconsequence variants (ASVs), and the representativesequence of each ASV was used to identify the classifica-tion status and for phylogenetic analysis. Subsequently,ASVs with an average relative abundance <10�6 anddetection rate <1% were removed from the analysisbecause they were generated mainly by sequencingerrors. The identified taxonomies were aligned using theSilva database (Release 132) (Quast et al., 2013). TheASV abundance of each sample and the six-level taxo-nomic classification from phylum to species wereobtained. The alpha and beta diversity were calculatedby using the vegan package (Philip, 2003).Phenotype Prediction Based on HostGenetics and Gut Microbial CommunitiesWe calculated the effect of the microbial communitieson feed efficiency. The phenotypic variance explained bygut microbial variance is defined as microbiability (m2)in animals (Camarinha-Silva et al., 2017; Difford et al.,2018). The construction of the microbial relationshipmatrix (MRM) follows equation described in our previ-ous study (Wen et al., 2019):mij ¼ 1NXNo¼1xio � xoð Þ xjo � xo� �s2owhere mij represents the microbial relationship infeces between birds i and j; xio and xjo represent the rela-tive abundances of ASV o in birds i and j, respectively;xo is the average relative abundance of the ASV o for thewhole population; s2o is the variance of the abundanceof ASV o; and N is the total ASV number.The microbiability was calculated as follows:y ¼ Kcþmþ ewhere y is a vector of the phenotype; c is a vector of fixedcovariates, including batch effect; K is the correspondingmatrix for c; and m is a vector of microbial effects follow-ing the multinomial distribution m » N (0, Ms2m). Mrepresents the MRM. The microbiability was estimatedwith GCTA software (Yang et al., 2011).Contribution of Host Genetics to theVariation of Microbial CompositionTo explore the effects of host genetics on gut micro-biota, the heritabilities of fecal microbes were estimated,and taxa that were detected in less than 30% of the sam-ples were excluded from this analysis. The relative abun-dance of qualified taxa was normalized by using theGenABEL package in R and the following heritabilitycalculation was performed using the DMU package, asmentioned above. In addition, the heritability estimatesof community phenotypes (including Observed ampliconsequence variants (ASVs), Shannon index, Simpsonindex, and Chao l index) were also calculated.In addition to the heritability of microbial taxa andcommunity phenotypes, the correlation between hostgenetics and gut microbial distance were also calculatedfor determining the influence of genetics on microbialcomposition. The host genetic relationship was calcu-lated based on pedigree information using the nadivpackage in R (Wolak, 2012). We then calculated thePearson’s correlation between host genetic relationshipsand microbial distance (Bray−Curtis dissimilarity,unweighted UniFrac distance, and weighted UniFracdistance). We further compared the difference in micro-bial distance among full sibs, half sibs, first cousins, andgenetically unrelated individuals by one-way ANOVA.Table 1. Descriptive statistics for phenotypes of pedigreed hens.Traits N Mean SD CV (%) Maximum MinimumBW69 (g) 714 2111.37 223.52 10.59 2799 1271BW72 (g) 714 2143.09 224.56 10.48 2815 1437MBW (g) 714 2127.23 220.41 10.36 2804 1416MMBW (g) 714 312.91 24.36 7.78 385.33 230.83DEM (g/d) 714 52.90 8.34 15.77 70.72 6.68DFI (g/d) 714119.49 11.40 9.54 160.57 77.13FCR (g:g) 714 2.37 0.94 39.87 16.77 1.60RFI (g/d) 714 �0.01 8.54 � 31.31 �20.68N = Number of birds with phenotypic value.SD = standard deviation.CV = coefficient of variation.BW69 = body weight at 69 wk of age.BW72 = body weight at 72 wk of age.MBW=mean body weight from 69 to 72 wk of age.MMBW=metabolic body weight.DEM = daily egg mass from 69 to 72 wk of age.DFI = daily feed intake from 69 to 72 wk of age.FCR = feed conversion ratio.RFI = residual feed intake.4 ZHOU ET AL.Identification of Feed Efficiency-associatedMicrobiotaSince taxa at lower detection rates are less informativefor difference analysis and association analysis, weretained only genera with a detection rate of more than30%. Pearson’s and Spearman’s correlations were calcu-lated among the microbes and target traits using thepsych package in R.All birds were then sorted by RFI value; the lowest10% (n = 71) and the highest 10% (n = 71) of theranked individuals were considered two distinct groups,and 4 differential methods were used to find specificgenera. First, we used corncob’s differential test func-tion (Martin et al., 2020) to find RFI-associated generabetween two distinct groups. We chose Wald tests(with the default non-bootstrap setting) to perform sig-nificance testing, and we obtained BH FDR-correctedP-values as output. Then, DESeq2 (Love et al., 2014)were performed as follows: 1) estimate size factors; 2)estimate of dispersions from the negative binomial like-lihood for each feature, and subsequent shrinkage ofeach dispersion estimate toward the parametric(default) trendline by empirical Bayes; 3) fitting eachfeature to the specified class groupings with negativebinomial generalized linear models and performinghypothesis testing, for which we chose the default Waldtest. We obtained the resulting BH FDR-corrected P-values for output. Linear discriminant analysis effectsize (LEfSe) was performed to identify bacteriaenriched in two distinct groups (Segata et al., 2011).Relative abundance was converted to percentages forthis analysis. Differences in the features were identifiedin the genus. The LEfSe analysis conditions were as fol-lows: 1) the alpha value for the factorial Kruskal-Wallistest among classes was less than 0.05; 2) the alpha valuefor the pairwise Wilcoxon test among subclasses wasless than 0.05; 3) the threshold on the logarithmic LDAscore for discriminative features was less than 2.0; 4)multiclass analysis was set as all-against-all. Further-more, we calculated Pearson’s correlations between thefeed efficiency-associated microbiota and phenotypes.Subsequently, Pearson’s correlations between feed effi-ciency-associated microbiota and host phenotypes wereperformed using the psych package in R.In addition, the associations between fecal genera andRFI were analyzed using a two-part model (Fu et al.,2015). The binary model is described as: y = b1b + e,where y is the RFI value, b is a binary feature (0 forabsent or 1 for present for each sample), b1 is the regres-sion coefficients for the binary model, and e representsthe residuals. If p value from the binary model was lessthan 0.05, the presence or absence of microorganismscould influence feed efficiency. The quantitative model iswritten as: y = b2q + e, where q is the log10-trans-formed abundance of a microbe, b2 is the regression coef-ficients for the quantitative model, and e represents theresiduals. The analysis was only for the samples in whichthe specific microorganism was present. If p value fromthe quantitative model was less than 0.05, the relativeabundances of microorganisms was significantly associ-ated with feed efficiency.Data AvailabilityThe datasets presented in this study can be foundbelow: NCBI Sequence Read Archive under BioProjectID PRJNA861965.RESULTSDescriptive Statistics of TraitsThe mean, standard deviation (SD), coefficient ofvariation (CV), and minimum and maximum values ofeach trait are summarized in Table 1. The mean valuesof DEM, DFI, FCR, RFI, MBW, and metabolic bodyweight (MMBW) were 52.90 g/d, 119.49 g/d, 2.37 g/g,�0.01 g/d, 2127.23 g, and 312.91 g, respectively. TheCV of the traits in the population had a wide range,from 7.78% to 39.87%. The CVs of DFI and MMBWwere less than 10%, whereas the CVs of FCR and DEMwere greater than 15%, indicating a large phenotypicvariation in these traits. The RFI was approximatelyequal to zero because it represented the residuals of thelinear model.Heritability of FCR, RFI, and Related TraitsAs shown in Table 2, heritability estimates for DEM,MMBW, DFI, FCR, and RFI were 0.30 § 0.09, 0.55 §0.10, 0.38 § 0.10, 0.31 § 0.09, and 0.52 § 0.10, respec-tively. The high heritability of FCR and RFI in ourstudy suggests that host genetics plays a substantial rolein determining the feed efficiency.Correlation of FCR and RFI with Other TraitsWe calculated the genetic and phenotypic correlationcoefficients among RFI, FCR, and other traits. A strongand positive genetic (ra = 0.59) and phenotypicTable 2. Estimates of heritability (h2) for feed efficiency and relevant traits along with estimates of genetic (ra; the upper diagonal) andphenotypic (rp; the lower diagonal ) correlations among traits from 69 to 72 wk.BW69 BW72 MBW MMBW DEM DFI FCR RFIBW69 0.52 (0.10) 1.00 (0.01) 1.00 (0.00) 1.00 (0.00) �0.11 (0.20) 0.46 (0.15) 0.48 (0.17) 0.07 (0.16)BW72 0.94 0.56 (0.10) 1.00 (0.00) 1.00 (0.00) �0.12 (0.20) 0.46 (0.14) 0.49 (0.16) 0.05 (0.16)MBW 0.98 0.99 0.55 (0.10) 1.00 (0.00) �0.11 (0.19) 0.47 (0.14) 0.48 (0.16) 0.07 (0.16)MMBW 0.98 0.99 1.00 0.55 (0.10) �0.11 (0.20) 0.47 (0.14) 0.48 (0.16) 0.07 (0.16)DEM 0.07 0.07 0.07 0.07 0.30 (0.09) 0.31 (0.19) �0.56 (0.15) 0.20 (0.19)DFI 0.31 0.43 0.38 0.38 0.43 0.38 (0.10) 0.67 (0.16) 0.88 (0.05)FCR 0.17 0.26 0.22 0.22 �0.68 0.32 0.31 (0.09) 0.59 (0.14)RFI 0.01 0.01 0.01 0.01 0.12 0.78 0.50 0.52 (0.10)GENETICS ANDMICROBIOME OF FEED EFFICIENCY 5(rp = 0.50) (Table 2) correlation were found betweenRFI and FCR.As expected, the RFI was phenotypically uncorrelatedwith the MMBW and DEM, and the genetic correlationcoefficient between RFI and the two traits were 0.07 and0.20. Compared to the RFI, the genetic and phenotypicassociations between FCR and DEM were closer and allshowed a strong negative correlation (ra = �0.56,rp = �0.68). This meant that selection for low FCR indi-viduals had a greater impact on DEM.The genetic and phenotypic correlation coefficientsbetween RFI and DFI were 0.88 and 0.78, and that twocorrelation coefficients between FCR and DFI were 0.67and 0.32. The genetic and phenotypic correlation coeffi-cients of FCR and DFI were both much less than that ofRFI and DFI. This suggested that choosing RFI wasmore beneficial for individual consumption than FCR.Overall, the FCR was primarily related to growth traitssuch as DEM and BW, whereas the RFI was related toenergy metabolism traits such as DFI.Figure 1. The contribution of the gut microbial community to hostphenotypes.Microbiability of FCR, RFI, and Related TraitsIn addition to host genetics, microbes have importantinfluence on phenotypes, we then estimated the propor-tion of variation of feed efficiency and its relevant traitsexplained by microbiota.We performed 16S rRNA sequencing to characterizethe fecal microbial composition of 714 samples andobtained a total of 31,818,930 quality-filtered sequenceswith an average of 44,564 reads per sample. A total of1,863 ASVs were identified to be clustered with 99%sequence identity and classified into 597 species, 376genera, 159 families, 76 orders, 38 classes, and 22 phyla.At the phylum level, we identified 22 phyla, in whichFirmicutes was the most abundant phylum (73.55%)followed by Fusobacteria (12.19%), Bacteroidetes(10.65%), and Proteobacteria (2.07%) (Fig S1A). Atthe genus level, Lactobacillus, Romboutsia, Fusobacte-rium, Streptococcus, Turicibacter, Bacteroides, Entero-coccus,and Clostridium sensu stricto 1 were identifiedas the dominant genus, and the detailed proportion aredisplayed in pie charts (Figure S1B).Similar to heritability, the microbiability indicates thecontribution of the microbial community to host pheno-type. The microbiability estimated for RFI and FCRwere 0.16 and 0.03 respectively, lower than that of theheritability. Other traits relevant to the feed efficiencyhad moderate microbiability (0.15 for DFI, 0.14 forBW69, 0.12 for BW72, 0.12 for MBW, and 0.12 forMMBW), whereas DEM was close to zero (Figure 1).Among all the feed efficiency-related traits, the microbi-ability of RFI was the highest.Association between Host Genetics and theGut MicrobiotaFecal microorganisms have an effect on host feed effi-ciency, and elucidate the host effect on the gut micro-biome is essential to help design microbial strategies toimprove production. We first estimated the heritabilityof alpha-diversity indexes and taxa at phylum, class,order, family, genus, and species levels. The a-diversityparameters including the Chao 1, Shannon index, Simp-son index, and Observed ASVs were used as phenotypesto estimate the pedigree-based heritability (Table S2).Host genetics minimally determined the microbiotadiversities in the feces.A total of 343 taxa (binary:177; quantitative:166)were used for heritability estimation (Table S3). Theaverage heritability estimate was 0.02 for all fecal taxa.However, as shown in Figure 2A, species such as Lacto-bacillus vagin*lis (h2 = 0.28), Lactobacillus agilis(h2 = 0.22), Lactobacillus aviaries (h2 = 0.18), andFigure 2. The heritability of each microorganism. (A) Heritability estimates for the taxa with a heritability of more than 0.1. * indicates taxathat are unclassified at the level, and p, c, o, f, g, and s represent phylum, class, order, family, genus, and species level, respectively. (B) Count distri-bution histogram of detection rates of identified microbial genera. (C) The sum of the relative abundances of microbial genera with different detec-tion rates. (D) The heritability of each genus. Only genera with a heritability of more than 0.1 are exhibited with microbial names.6 ZHOU ET AL.Lactobacillus reuteri (h2 = 0.13), which belong to thegenus Lactobacillus, were more heritable.We then focused on the heritability of fecal genera tolearn more about the role of host genetics played in fecalmicrobial community. Among 376 identified genera, 51microbes possessed a detection rate between 30% and60%, 50 had a detection rate greater than 60%, and thedetection rate of the remaining genera was close to zero(Figure 2B). Notably, although most genera weredetected with a low detection rate, genera with >30%detection rate accounted for 98.43% of the total commu-nity sequences (Figure 2C), indicating that these generacould represent the fecal community. Among 101 micro-bial genera, Escherichia-Shigella (h2 = 0.17), Faecalita-lea (h2 = 0.14), Candidatus Stoquefichus (h2 = 0.12),Anaerosporobacter (h2 = 0.10), and [Clostridium]innocuum group (h2 = 0.12) had a heritability estimatemore than 0.1 (Figure 2D). The relative abundance ofEscherichia-Shigella, Faecalitalea, Candidatus Stoquefi-chus, Anaerosporobacter, and [Clostridium] innocuumgroup was 0.31%, 0.14%, 0.23%, 0.38%, and 0.01%,respectively. These genera belonged to the phyla Firmi-cutes and Proteobacteria, accounting for 1.07% of thetested fecal microbiota (Table S4). The abovementionedresults indicated that although several taxa with higherheritability and greater genetic influence, the effect ofhost genetics on the entire microorganisms is limited.To further verify the limited influence of host geneticson microorganisms, we calculated the correlation of hostgenetics and microbial distance and difference of micro-bial b-diversity among different host genetic kinshippairs of individuals.Most pairs of chickens showed no or a low degree ofgenetic relatedness, and the correlations between hostgenetics and different microbial distance were very weakranging from �0.0057 to �0.0003 (Figure 3A). Since thegenetic relationship was generally low, we further com-pared the difference in the beta diversity among fullsibs, half sibs, first cousins, and genetically unrelatedbirds. Based on the pedigree, we obtained 494 full-sibpairs, 2,959 half-sib pairs, 2,964 first cousins and theremaining pairs were considered as unrelated pairs. Sim-ilar results were also observed in Figure 3B. Whetherfull-sib, half-sib pairs, first cousins, or unrelated birds,the microbial distance between different kinshipsshowed no significant difference.Correlation Analysis of the ScreenedMicrobes and Host PhenotypesTo investigate the correlations between fecalmicrobes and host phenotypes, Pearson’s and Spear-man’s correlations were performed. It is obvious thatmost taxa were not significantly associated with phe-notypes, as shown in Figure 4A and B. A total of 416ASVs were significantly correlated with the pheno-types (Pearson: 156; Spearsman: 260) and correlationcoefficients ranged from -0.12 to 0.14 (Table S5). Simi-lar to the results of microbiability, more taxa wererelated to body weight and RFI. The ASVs that wereassociated with host phenotypes in both methodsbelonged to the phyla Firmicutes (74.4%), Bacteroi-detes (18.4%), Proteobacteria (4.3%), and Actinobac-teria (2.8%; Figure 4C).Identification of Genera Associated withFeed EfficiencyIdentification of bacteria associated with host pheno-types in animals may offer a direct approach to the iden-tification of probiotic bacteria for use in animalproduction. Since RFI had a relatively high microbiabil-ity, two-tailed tests (corncob, DESeq2, and LEfSe) anda two-part model association analysis were performedfor the divergent RFI groups to detect the RFI-associ-ated genera. We first selected individuals based on the10% highest (H; n = 71) and 10% lowest (L; n = 71)RFI. As shown in Table S6, RFI significantly differedbetween the high-(16.80) and low-RFI (�13.31) groups.Figure 3. Effect of genetic kinship on fecal microbiota. (A) Scatter plot of the host genetic kinship of pairs of individuals (x axis) and theirmicrobial distance (bray−Curtis dissimilarity, unweighted unifrac distance, and weighted unifrac distance) (y axis). The correlation coefficient andp value between host genetic relationships and microbial distance are exhibited. (B) Difference of microbial distance among full sibs, half sibs, firstcousins, and genetically unrelated individuals. The central red point indicates the mean value in the corresponding group.GENETICS ANDMICROBIOME OF FEED EFFICIENCY 7The FCR and DFI were significantly different in the Hand L groups, with a difference of 0.83 for FCR and31.07 g for DFI, which were consistent with the resultsof the correlation analysis above.We then screened the microorganisms associated withRFI by different methods between high-and low-RFIbirds and found 8, 6, and 4 significantly different generaby corncob, DESeq2, and LEfSe, respectively. TheFigure 4. The association between fecal genera with host phenotypesadjusted p values. The plot indicates correlation coefficient (y-axis) plottedtions of significant microbes on phyla and traits.genera Anaerosporobacter, Candidatus Stoquefichus,Fournierella, and Faecalitalea were simultaneouslyidentified by the three methods (Figure 5A; Figure S2;Table S7). Four associations were detected by quantita-tive analysis, and fifteen associations were identified bybinary analysis (Figure 5B; Table S8). Notably, Anaero-sporobacter was found both in the binary and quantita-tive models, suggesting that both the presence/absenceby Pearson’s (A) and Spearman’s (B) correlations. Scatter plot of theagainst taxonomic microbes (x-axis, detection rate >30%) (C) Distribu-Figure 5. Identification of feed efficiency-associated microbiota (A) Number of genera associated with RFI detected by differentmethods andtheir overlaps. (B) Two-part model for association analysis between RFI and gut microbiota at genus level. Only genera that overlap with the two-tailed differential bacteria are exhibited with microbial names.8 ZHOU ET AL.and abundances of the genus can affect RFI. Amongthese genera, Anaerosporobacter, Candidatus Stoquefi-chus, and Fournierella were observed in both the associ-ation analysis and 2-tailed tests (Figure 5B).The detection rate of RFI-associated microorganismsin 714 hens ranged from 67.09% to 78.01%(Figure 6A). The average abundance of Anaerosporo-bacter, Candidatus Stoquefichus, and Fournierella was0.38%, 0.23%, and 0.59%, respectively. The detectionrate and average abundance of RFI-associated generawere 2-3 times higher in the L group than that in the Hgroup (Table S9), indicating their positive roles inimproving feed efficiency. Pearson’s correlation coeffi-cients were calculated for all birds to visualize the rela-tionship between phenotypes and the associatedmicrobes. As shown in Figure 6B, Anaerosporobacter,Candidatus Stoquefichus, and Fournierella were signifi-cantly correlated with RFI. These fecal genera werepositively and moderately correlated with each other(Figure S3). Meanwhile, Anaerosporobacter was alsocorrelated with FCR (Table S10). Moreover, based onFigure 6. Details of feed efficiency-associated microorganisms. (A) Relason correlation between RFI-associated microorganisms and host phenotypeinsignificant correlations.the relatively high heritability estimates, Anaerosporo-bacter and Candidatus Stoquefichus were more suscep-tible to host genetics.DISCUSSIONFeed efficiency is an important economic trait. Withthe extension of the laying cycle, feed utilization tendsto decline (Yuan et al., 2015a). Therefore, improvingfeed efficiency in the late laying period of chicken is akey problem that breeders need to solve; further contin-ued improvement will be aided by better understandingof the factors influencing feed efficiency.Genetic and breeding strategies are effective toenhance feed efficiency in laying hens, feed utilizationhas been improved through artificial selection of feedefficiency traits (Thiruvenkadan et al., 2010). Under-standing the genetic background of feed efficiency isessential and would contribute to chicken breeding andfurther genomic studies.tive abundance and detection rate of RFI-associated genera. (B) Pear-s. Yellow lines represent significant correlations and grey lines representGENETICS ANDMICROBIOME OF FEED EFFICIENCY 9The heritability of a16 to 42 wk of age finnish Leghornpopulation was reflected by RFI values of 0.46(Schulman et al., 1994). Another study in a brown egglayer line found that the heritability estimate of the RFIwas moderate (0.47) (Wolc et al., 2013). As for FCR,the heritability estimates ranged from 0.208 to 0.452 forFCR from day-1 to 16th wk of age in a selected line ofRhode Island Red chicken (Das et al., 2015). A recentstudy in heat-challenged commercial white egg-layinghens found that the heritability of FCR was 0.23 from24 wk of age to 28 wk of age (Rowland et al., 2019).Moreover, in 2 laying periods of chickens from a WhiteLeghorn and Dongxiang reciprocal cross, estimates forheritability of RFI and FCR were 0.21 and 0.19, repec-tively, from 37 and 40 wk, and 0.29 and 0.13,respec-tively, from 57 and 60 wk (Yuan et al., 2015a). Ingeneral, different breeds and ages of chickens showed dif-ferent heritabilities of the FCR and RFI, and the moder-ately estimated heritability in our study was consistentwith these previous studies. The moderate estimatedheritability indicated the presence of sufficient geneticvariability for the traits in the population. The selectionfor either of these 2 indices (FCR and RFI) can undoubt-edly increase the feed efficiency.FCR was negatively correlated with DEM but posi-tively correlated with DFI phenotypically and geneti-cally, suggesting the selection for FCR may lead tosimilar ratios but different DFI and outputs (Schulmanet al., 1994; Difford et al., 2016; Camarinha-Silva et al.,2017). The positive and high correlation between RFIand FI in our study agreed with a previous study on twolaying periods of chickens (Yuan et al., 2015a). Thegenetic and phenotypic correlation between RFI andDEM is weak (Schulman et al., 1994), suggesting thatselection for RFI can improve the feed efficiency by lessFI and supplying the same amount of egg mass, and RFIis a more desirable trait for characterizing feed effi-ciency. These results are important for the continueddevelopment of strategies to improve feed efficiency inchicken breeding and production.In addition to host genetics, the gut microbiota is alsoimportant for feed efficiency (Yan et al., 2017). Diffordet al. (2016) first proposed the proportion of microbialvariance to phenotypic variance as “microbiability” indairy cattle. In pigs, Camarinha-Silva et al. (2017) andWeishaar et al. (2020) reported the presence of a mediumto high microbiability of daily gain, feed intake, and feedconversion rate. Our previous study demonstrated thatthe cecal microbiota accounted for 28% of the RFI vari-ance in broiler chickens (Wen et al., 2021). Medium-to-high microbiabilities for feed-related traits have also beenidentified in Japanese quails (Vollmar et al., 2020). Themicrobiability estimate for RFI was medium in our popu-lation, confirming that the effect of the gut microbiomeon feed efficiency in pigs and poultry was medium to high(Khanal et al., 2021).Moreover, the lower estimate of microbiability com-pared with heritability in our study and a previous study(Wen et al., 2021) implied that host genetics is a moreimportant determinant for the feed efficiency in chicken.Similarly, a lower estimate of microbiability than that ofheritability for RFI, FCR, and DFI was found in pigs(Khanal et al., 2021). In addition to feed efficiency-related traits, the lower estimate of microbiability com-pared with heritability was also found in fat depositiontraits in chicken (Wen et al., 2019) and pigs(Tang et al., 2020). However, higher estimates of micro-biability for feed efficiency than their corresponding her-itabilities were also found in pigs (Camarinha-Silvaet al., 2017). Interestingly, a study in swine found thatthat the proportion of variance captured by the micro-biome varied over time (Khanal et al., 2021). The differ-ences of the comparison between microbiability andheritability may account for host age, population struc-ture, genetic relatedness, different traits and environ-mental factors.Multiple factors, including diet (Fouad and El-Senou-sey, 2014), medication (Weersma et al., 2020), andgenetics (Bonder et al., 2016b) influence the gut micro-biota composition. The role of host genetics in shapingintestinal flora has been investigated in humans(Zoetendal et al., 2001) and livestock (Xiao et al., 2015;Pandit et al., 2018; Bergamaschi et al., 2020). In ourstudy, no significant difference was observed betweengenetics kinships, which is consistent with the findingthat microbiome composition is not significantly associ-ated with genetic ancestry in humans (Rothschild et al.,2018). Similar to previous studies, genera with high heri-tability identified in this study belonged to Firmicutes(Xiao et al., 2015) and Proteobacteria (Bergamaschiet al., 2020), and accounted for a low proportion of thetested fecal microbiota (Difford et al., 2018). Our previ-ous study also showed that no significant relationshipbetween host genetic kinship and gut community havebeen found at five different gut microbial sites in broilers(Wen et al., 2021). However, several studies in differentspecies have indicated that host genetics influence gutmicrobial composition (Org et al., 2015; Goodrich et al.,2016; Bonder et al., 2016a; Li et al., 2019; Aliakbariet al., 2021; Grieneisen et al., 2021). Whether hostgenetic variation plays arole in determining microbialcomposition is debatable and could be attributed to dif-ferent populations and sampling sites. A disadvantageof this study is that it only collected fecal samples.Although fecal sampling is non-invasive and convenient,it does not fully represent the entire intestine (Yanet al., 2019), and we should take multiple intestinal seg-ments into account in the following research. Overall,our observations implied that host genetics plays aminor role in determining microbiome composition, andmost of the variation in the gut microbial community isdue to factors other than host genetics.Considering the difference in finding significant taxaby using corncob and DESeq2 (Nearing et al., 2022),two-tailed tests including corncob, LEfSe, and DESeq2were used for intersection of significant differential taxa.Moreover, a two-part model of all birds was also usedto in our study for pinpointing more reliably microor-ganisms. We identified that Anaerosporobacter, Candi-datus Stoquefichus, and Fournierella were more10 ZHOU ET AL.abundant in feed-efficient birds. Candidatus Stoquefichusbelonging to the family Erysipelotrichaceae, has beenreported to be negatively correlated with serum inflam-matory cytokines in mice (Yang et al., 2021). Moreover,strong evidence supports an association between Erysi-pelotrichaceae and host lipid metabolism and inflamma-tion (Kaakoush., 2015). Therefore, we speculate thatCandidatus Stoquefichus facilitates the establishment ofthe intestinal barrier by inhibiting the production ofinflammatory cytokines, which is beneficial for hostenergy absorption.Fournierella and Anaerosporobacter have beenreported to be significantly associated with intrahepaticfat accumulation (Yaskolka Meir et al., 2021). Fournier-ella might be involved in bile secretion and tryptophanmetabolism in rabbits with diarrhea (Wang et al., 2022).Anaerosporobacter may cause vascular damage andworsen renal function in murine models (Li et al., 2020)and was reported to be associated with an increasein trimethylamine oxide (TMAO) content in vivo(Wang et al., 2016). In addition, non-alcoholic fatty liverdisease patients have a lower fecal abundance of Anaero-sporobacter (Wong et al., 2013). However, there havebeen no studies on the relationship between fecal Four-nierella or Anaerosporobacter and feed efficiency. Theprecise mechanism by which these two bacterial taxaaffect feed efficiency warrants further study. Interest-ingly, the heritability estimates of Anaerosporobacterand Candidatus Stoquefichus were relatively high, indi-cating that genetic selection can be used to regulate theabundance of flora and improve the host feed efficiencyfor specific microbiota.In conclusion, our study described feed efficiency andits relevant traits in the late laying period of a RhodeIsland Red pure line chickens. The moderate heritabilityestimates for both RFI and FCR suggested that feed effi-ciency can be directly improved by proper selection pro-grams. We found that host genetics play a moreimportant role in shaping feed efficiency than fecalmicrobiota and the effect of host relatedness kinship onfecal microbial distance was weak. Several genera,including Anaerosporobacter, Candidatus Stoquefichus,and Fournierella were significantly associated withresidual feed intake. Our findings provide a promisingstrategy to improve feed efficiency from the perspectiveof the host genetic background and microorganisms.ACKNOWLEDGMENTSThis research was funded by the National NaturalScience Foundation of China (No 31930105), ChinaAgriculture Research Systems (CARS-40) and theNational Key Research and Development Program ofChina (2022YFF1000204).DISCLOSURESThe authors declare that they have no conflict ofinterest.SUPPLEMENTARY MATERIALSSupplementary material associated with this articlecan be found in the online version at doi:10.1016/j.psj.2022.102393.REFERENCESAliakbari, A., O. Zemb, Y. Billon, C. Barilly, I. Ahn, J. Riquet, andH. Gilbert. 2021. Genetic relationships between feed efficiency andgut microbiome in pig lines selected for residual feed intake. J.Anim. Breed Genet. 138:491–507.Aulchenko, Y. S., S. Ripke, A. Isaacs, and C. M. van Duijn. 2007.GenABEL: an R library for genome-wide association analysis. Bio-informatics 23:1294–1296.Bergamaschi, M., F. 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and microbiome analysis of feed efficiency in laying hensINTRODUCTIONMATERIALS AND METHODSAnimals and Samples CollectionCalculation of Feed EfficiencyGenetic Parameters EstimationDNA Extractions and 16S rRNA Gene Sequencing of Fecal MicrobiotaAnalysis of 16S rRNA Sequencing DataPhenotype Prediction Based on Host Genetics and Gut Microbial CommunitiesContribution of Host Genetics to the Variation of Microbial CompositionIdentification of Feed Efficiency-associated MicrobiotaData AvailabilityRESULTSDescriptive Statistics of TraitsHeritability of FCR, RFI, and Related TraitsCorrelation of FCR and RFI with Other TraitsMicrobiability of FCR, RFI, and Related TraitsAssociation between Host Genetics and the Gut MicrobiotaCorrelation Analysis of the Screened Microbes and Host PhenotypesIdentification of Genera Associated with Feed EfficiencyDISCUSSIONDisclosuresSupplementary materialsReferences