Feed consumption contributes a large percentage for total production costs in the poultry industry. Detecting genes associated with feeding traits will be of benefit to improve our understanding of the molecular determinants for feed efficiency. The objective of this study was to identify candidate genes associated with feed conversion ratio (FCR) via genome-wide association study (GWAS) using sequence data imputed from single nucleotide polymorphism (SNP) panel in a Chinese indigenous chicken population.A total of 435 Chinese indigenous chickens were phenotyped for FCR and were genotyped using a 600K SNP genotyping array. Twenty-four birds were selected for sequencing, and the 600K SNP panel data were imputed to whole sequence data with the 24 birds as the reference. The GWAS were performed with GEMMA software.After quality control, 8,626,020 SNPs were used for sequence based GWAS, in which ten significant genomic regions were detected to be associated with FCR. Ten candidate genes, ubiquitin specific peptidase 44, leukotriene A4 hydrolase, ETS transcription factor, R-spondin 2, inhibitor of apoptosis protein 3, sosondowah ankyrin repeat domain family member D, calmodulin regulated spectrin associated protein family member 2, zinc finger and BTB domain containing 41, potassium sodium-activated channel subfamily T member 2, and member of RAS oncogene family were annotated. Several of them were within or near the reported FCR quantitative trait loci, and others were newly reported.Results from this study provide valuable prior information on chicken genomic breeding programs, and potentially improve our understanding of the molecular mechanism for feeding traits. Increasing the productivity of livestock species and minimizing their environmental impact are the major goals in the livestock husbandry . ImproviCompared with previous studies, genome-wide association studies (GWAS) can accurately identify genes involved in economically important traits of cattle , pigs 77, and chThe objective of this study was to identify candidate genes associated with FCR via GWAS in a Chinese indigenous chicken population using sequence data imputed from a SNP panel.The study population was obtained from a Chinese indigenous chicken breed that has been maintained for 25 generations by the Wens Nanfang Poultry Breeding Co. Ltd . We obtained 435 male birds from the 25th generation, which were generated by 30 males and 360 females from the 24th generation and were reared with the same recommended nutritional and environmental conditions. The FCR was calculated according to recorded data during the feeding trial. For more details about this population, please refer to Zhang et al .Gallus gallus 4.0 genome using the Burrows-Wheeler Alignment tool [http://sourceforge.net/projects/picard/files/picard20tools/1.119/). GATK software [All of 435 male chickens from the 25th generation and 15 sires of the 24th generation were selected for genotyping. The genomic DNA from the 450 birds was extracted from blood samples using the NRBC Blood DNA Kit  according to the manufacturer’s instructions, and DNA samples were analyzed for genome DNA concentration and integrity. DNA samples of the appropriate quality were genotyped using 600K Affymetrix Axiom HD chicken genotyping array, which contains 580,961 SNPs distributed on 28 autosomes, two linkage groups (LGE64 and LGE22C19W28_E50C23), and two sex chromosomes. Then, twenty-four key individuals were selected for sequencing from the 435 birds and their 15 sires based on a strategy of maximizing the expected genetic relationship, while maximizing the proportion of unique genomes sequenced in the population. In this process, we used G matrix to calculate the genetic relationship between key individuals and the remaining population, details of the selection of key individuals in our study were described by Ye et al ,12. Finaent tool . Duplicasoftware  was usedsoftware  with theMixed linear model was used for the GWAS, and the analysis was performed with GEMMA software (Zhou and Stephens ). The stY is the vector of phenotypic values for all individuals, b is a vector of fixed effects including the batch effect, which has three levels, a is the substitution effect of the SNP under consideration, u is the random additive genetic effect; X and Z are the design matrices for b and u, respectively; S is the design vector for a; e is the vector of random residuals. In this model, u and e were assumed to have the structure G is the additive genomic relationship matrix which was derived from the sequence data, X is the n×p matrix of genotypes of n individuals’ p markers, Xi as its ith column representing genotypes of ith SNP X̄i, as the sample mean and In as a n×1 vector of 1’s, where software  was usedTo visualize the result from the GWAS, Manhattan plots and quantile-quantile plots were drawn by the qqman package  in R forhttp://www.ensembl.org/index.html) and NCBI (https://www.ncbi.nlm.nih.gov/) annotation of the Gallus gallus 4.0 genome version.Candidate genes which were located within or nearby the significant SNPs (within 0.3 Mb) were identified by Ensembl . Based on the physical location, QTLs close to the significant SNPs were extracted, with a maximum distance of 5 Mb between the QTL and the SNP to be compared.SNPs with p-value <1.0×10Descriptive statistics for trait FCR are presented in −8  for a genome-wide significance level, and to 1.58×10−6  for a genome-wide suggestive significance level.The effective number of independent tests was 631,181 as calculated by the simple method. Hence, the threshold p-value was adjusted to 7.92×10ZBTB41) was found to be associated with FCR, and 9 SNPs reached the suggestive significance level (USP44), leukotriene A4 hydrolase (LTA4H), and ETS transcription factor (ELK3), respectively. One SNP was located on chromosome 2 and 60 Kb distance away from its nearest R-spondin 2 (RSPO2) gene. Two SNPs were located on a narrow region of chromosome 4 (from 15.59 Mb to 16.04 Mb), and the nearest genes were inhibitor of apoptosis protein 3 (IAP3) and sosondowah ankyrin repeat domain family member D (SOWAHD). Another two SNPs were located on the positions of 1.39 Mb and 2.71 Mb of chromosome 8 respectively, with the first SNP locating on the 14th intron of calmodulin regulated spectrin associated protein family member 2 (CAMSAP2) and the other locating on the 4th intron of potassium sodium-activated channel subfamily T member 2 (KCNT2). The remaining 1 SNP was located on the position of 3.20 Mb of chromosome 26 and member of RAS oncogene family (RAP1A) was its nearest gene. In the case of using chip data, only one SNP reach a suggestive significance level, and the SNP was also detected by using imputed whole sequence . The hereditability estimates for FCR were higher than the estimate of Aggrey et al . Based oTo our expectation, the imputed sequence data including more SNPs could capture more genetic variation and detect more signals than a SNP array. With the same statistical model and significance level for both datasets, the SNPs detected from imputed whole sequence data were more than that from the chip data. On one hand, this demonstrated the power of imputed whole sequence data. On another hand, the stronger associations with imputed genotypes were partly due to some imputation artefacts driven by allele frequencies in the imputed loci.SOWAHD is a protein coding gene linked to Iroquois genes, suggesting that regulatory constraints underlie the maintenance of the Iroquois-Sowah syntenic block [ZBTB41 is a protein gene, which may be involved in transcriptional regulation [KCNT2 is a human gene that encodes the KNa protein potassium channel activated by internal raises in sodium or chloride ions [In this study, the ten candidate genes detected for FCR are partly functionally related to feeding traits. Functional study showed that USP44 prevents the premature activation of the anaphase-promoting complex and regulating centrosome separation, positioning, and mitotic spindle geometry. LTA4H is an enzyme that would generate leukotriene B4 (LTB4) . LTB4 isic block . CAMSAP2ic block ,29. ZBTBgulation . KCNT2 iide ions . RAP1A eide ions .QTLs that reported to be associated with chicken FCR are distributed across the whole genome. Part of them were reported several times in previous studies and overlapped with significant SNPs detected in this study, which suggested that the candidate regions detected in this study were reliable. Other SNPs that reached the suggestive significant levels were not located in QTL region may be the new candidate loci or statistically false positive signals.The selection of significant level was seriously considered in this study. Though Bonferroni correction is widely used, it is rigorous in the genotype data with high linkage disequilibrium. Especially for such a high density imputed sequence data, the false negative results are not neglectable . Hence, In this study, we conducted a genome wide association study for FCR using both imputed whole sequence data and SNP array in a Chinese indigenous chicken population. A list of significant SNPs and ten candidate genes were identified, and several of these regions were overlapped with QTLs reported in previous studies. Results from this study provided valuable prior information for chicken genomic breeding program and would potentially improve our understanding of the molecular mechanism for feeding traits.


Bemisia tabaci, is a species complex of more than 40 cryptic species and a major agricultural pest. It causes extensive damage to plants mainly by transmitting plant viruses. There is still a lack of genomic data available for the different whitefly species found in Brazil and their bacterial endosymbionts. Understanding the genetic and transcriptomic composition of these insect pests, the viruses they transmit and the microbiota is crucial to sustainable solutions for farmers to control whiteflies. Illumina RNA-Seq was used to obtain the transcriptome of individual whiteflies from 10 different populations from Brazil including Middle East-Asia Minor 1 (MEAM1), Mediterranean (MED) and New World 2 (NW2). Raw reads were assembled using CLC Genomics Workbench and subsequently mapped to reference genomes. We obtained whitefly complete mitochondrial genomes and draft genomes from the facultative bacterial endosymbiont Hamiltonella for further phylogenetic analyses. In addition, nucleotide sequences of the GroEL chaperonin gene from Hamiltonella from different populations were obtained and analysed. There was concordance in the species clustering using the whitefly complete mitogenome and the mtCOI gene tree. On the other hand, the phylogenetic analysis using the 12 ORF’s of Hamiltonella clustered the native species NW2 apart from the exotics MEAM1 and MED. In addition, the amino acid analysis of GroEL chaperonin revealed a deletion only in Hamiltonella infecting NW2 among whiteflies populations analysed which was further confirmed by PCR and Sanger sequencing. The genomic data obtained in this study will aid understanding the functions that Hamiltonella may have in whitefly biology and serve as a reference for further studies regarding whiteflies in Brazil.The whitefly,  Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is an insect pest of significant economic importance of a wide variety of agricultural crops such as tomatoes, beans, cassava, cotton and ornamentals [B. tabaci causes damage directly through feeding and indirectly through the transmission of plant pathogenic viruses, belonging to the genera Begomovirus, Carlavirus, Crinivirus, Ipomovirus and Torradovirus [B. tabaci is classified as a cryptic species complex composed of at least 40 morphologically indistinguishable species on the basis of the mtCOI gene [B. tabaci species have been reported to date, the natives species from the Americas, New World 1 (NW1) and New World 2 (NW2) and the exotic species Middle East-Asia Minor 1 (MEAM1) and Mediterranean (MED) [B. tabaci differ in several aspects including the range of host plants utilized, the capacity to cause plant disorders, attraction of natural enemies, response to pesticides and plant virus transmission capabilities [B. tabaci [The whitefly amentals . B. tabaadovirus . CurrentCOI gene . In Brazan (MED) . The exoan (MED) ,8. Specibilities . Brazil bilities  has high. tabaci . Current. tabaci –14.B. tabaci might also depend on the inherited bacterial endosymbionts whose functions are not fully understood [Portiera aleyrodidarum [B. tabaci, different facultative symbionts have been described, including Arsenophonus, Hamiltonella, Wolbachia, Cardinium, Fritschea and Rickettsia [Hamiltonella defensa is a maternally transmitted gamma-proteobacterium found sporadically in sap-feeding insects, including aphids, psyllids, and whiteflies [B. tabaci, such as MEAM1, MED and NW2 harbor Hamiltonella [Hamiltonella is highly distributed throughout the Brazilian territory and was detected in 89,5% of the MEAM1 specimens and approximately 50% of the MED specimens analyzed [Hamiltonella in populations of whiteflies in Brazil may have serious implications to virus transmission. The GroEL proteins encoded by Hamiltonella has been found in Israeli B. tabaci (MEAM1) populations interacting with the coat protein of begomovirus and therefore facilitating virus transmission [B. tabaci (MEAM1 and MED) did not interact with the virus and therefore were not involved in virus transmission [Hamiltonella can play different roles depending on the vector species. In pea aphids, Acyrthosiphon pisum (Harris), Hamiltonella can block larval development of the solitary endoparasitoid wasps Aphidius ervi and Aphidius eadyi, rescuing the aphid host [The complexity of derstood . The oblderstood ,17. In wodidarum . In addiodidarum ,19, faciodidarum ,21, confodidarum  and resiodidarum . These bodidarum ,24. In Bckettsia ,20. Amoniteflies –27. Prevltonella ,28. Moreanalyzed . The highid host .B. tabaci complex followed by phylogenetic analysis. In addition, the diversity of the facultative endosymbiont Hamiltonella was inferred analyzing 12 different ORF’s. The GroEL amino acid sequences of Hamiltonella from different B. tabaci species were also analyzed. Our goal was to add further details concerning phylogenetic diversity of mitochondrial genome and Hamiltonella among Brazilian populations of B. tabaci. In addition, a GroEL protein analysis was carried out that may give further insights about the functions that Hamiltonella may have in whitefly biology.In this study, we sequenced ten transcriptomes of single whitefly specimens from different Brazilian populations and characterized the full mitochondrial genome belonging to three different species  of the B. tabaci populations  were analysed, including two exotics species: Middle East-Asia Minor (MEAM1) and Mediterranean (MED); and a native species: New World 2 (NW2). Populations from colonies were previously identified by sequencing and analysis of the mtCOI gene using the primers C1-J-2195 and TL2-N-3014 [Samples were obtained from pure colonies and straight from the field. Four different 2-N-3014 .The RNA extraction of a single individual whitefly was carried out using the ARCTURUS PicoPure kit with modifications . ExtractTotal RNA from each individual whitefly sample was used for cDNA library preparation using the Illumina TruSeq Stranded Total RNA Preparation kit as described by the manufacturer . Later on, sequencing of 10 samples was carried out using the HiSeq2000 on a rapid run mode generating 2x50 bp paired end reads. Base calling, quality assessment and image analysis were conducted using the HiSeq control software v1.4.8 and Real Time Analysis v1.18.61 at the Macrogen Korea.de novo sequence assembly tool in CLCGW. The assembly parameters consisted of mismatch cost (2), insertion cost (3), deletion cost (3), length fraction (0.5), similarity fraction (0.9) and minimum contig length of either 500 bp or 1000 bp.The raw transcriptome data was trimmed using the software CLC Genomics Workbench v8.5.1 (CLCGW) with quality scores limit set to 0.01, ambiguous limit set to 2. Trimmed reads were then assembled into contigs using B. tabaci species of the complex, a different reference mitogenome was used: KU877168 for MEAM1, JQ906700 for MED and AY521259 for NW2. When the mapped contigs did not covering the full length of the mitochondrial genome of the reference sequence, we resorted to mapping trimmed reads to the reference sequence and thus the whole length of the reference was covered. Mapping was performed with the following setting in Geneious software; minimum overlap 10%, minimum overlap identity 80%, allow gaps 10%, fine tuning set to iterate up to 10 times at custom sensitivity. A consensus between the mapped trimmed reads and the reference was used to form new mitochondrial genome. Improvements on the draft mitochondrial genomes were carried out using the software Pilon, a tool for genome assembly improvement [The whiteflies complete mitochondrial genome was obtained by mapping of the assembled contigs to reference genomes from GenBank using the software Geneious v9.1.3 . For eacrovement . Subsequrovement . Other wrovement ,15,36–38rovement  followedHamiltonella reference genome (CP016303) to obtain the facultative endosymbiont draft genome. Mapping was performed with the following setting in Geneious software; minimum overlap 10%, minimum overlap identity 80%, allow gaps 10%, fine tuning set to iterate up to 10 times at custom sensitivity. Afterward, draft genomes were aligned to the reference using the whole genome alignment tool LASTZ version 1.02.00 [Assembled contigs as well as trimmed reads from each sample were mapped to a  1.02.00  within G 1.02.00 .Hamiltonella of B. tabaci (AF130421). As the mapped contigs did not cover the full length of the coding region of the reference sequence AF130421, we resorted to mapping trimmed reads to the reference sequence to get the full coverage of the whole length from the reference. A consensus between the mapped trimmed reads and the reference was used to form new chaperonin GroEL sequences, open reading frames (ORF) were predicted in Geneious. In addition, other chaperonin GroEL sequence from A. pisum was downloaded from Genbank (CP001277) and added to the analysis. Sequences obtained were aligned using MAFFT v7.309 [CCTC TGCG TCAG ATTG TGGT and GroEL 1,663 Rev–TCAT ACCA TTCA TTCC GCCC A) for a PCR reaction  followed by nucleotide sequencing to confirm the results obtained by NGS.Trimmed reads were mapped onto the reference sequence for chaperonin GroEL gene from T v7.309  followedAll the phylogenetic analyses were run on 384 nodes on the Magnus supercomputer . Mitochondrial genome phylogenetic analysis were performed by MrBayes 3.2.2. . AnalyseHamiltonella phylogenetic analysis was performed on DNA sequences of 12 protein-coding genes for a dataset with 9 taxa using ExaBayes version 1.4.1; [In addition, the n 1.4.1; . BayesiaThe sequenced number of reads from the 10 samples ranged from 29,840,288 to 64,080,188 among the ten samples and the number of contigs assembled ranged from 7,730 to 41,165 .Hamiltonella in B. tabaci populations from Brazil was carried out analysing 12 ORF’s from eight single whitefly transcriptomes totalizing an alignment of 6,378bp length. The analysed ORF’s were: DNA transformation protein tfoX, porin OmpA, acyl carrier, 50S ribosomal protein L3, 50S ribosomal protein L23, 30S ribosomal protein S17, 50S ribosomal protein L24, 50S ribosomal protein L5, rpsN, nucleotide exchange factor GrpE, porin and DNA-binding protein. The phylogenetic analysis from the sequenced accessions separate into two deeply divergent clades representing the native species from the Americas (NW2) and the exotics (MEAM1 and MED) (Hamiltonella were obtained (The diversity of and MED) . Furtherobtained .B. tabaci single whitefly transcriptomes of Brazil. In addition to the data obtained in this study, the analysis also included Hamiltonella GroEL sequences downloaded from GenBank from the pea aphid A. pisum (CP001277) and from B. tabaci MEAM1 (CP016303). Analysis of the translated proteins revealed a three amino acids deletion present only in the B. tabaci NW2 species and in the pea aphid A. pisum  gene was obtained from six A. pisum . The delB. tabaci were obtained . Phylogenetic analysis of the complete mitochondrial genomes separated different species of the complex in distinct clades . Furtherobtained .B. tabaci and its association with the facultative endosymbiont Hamiltonella. We show the evolutionary differences between native populations of B. tabaci species of the Americas and invasive B. tabaci species. From our data the genetic differences are not confined to the mtCOI gene, but extend to the rest of the mitogenome and to the facultative endosymbiont Hamiltonella. These findings will contribute to the understanding about the functions that Hamiltonella may have in B. tabaci biology and will aid the correct identification of B. tabaci specimens.In this study we present the phylogenetic relationship of B. tabaci: MEAM1, MED and NW2. Complete mitochondrial genomes comparisons are essential for better understanding the evolutionary genetic relationships between members of the B. tabaci species complex [B. tabaci to the global database will contribute for more accurate identification and will serve as references for further full mitochondrial genome phylogenies studies. The phylogenetic analysis of the full mitogenome  coat protein (CP) and GroEL present in the haemolymph of B. tabaci [Buchnera GroEL leads to the degradation of the virus and to a markedly decrease in transmission efficiency of the virus [It’s known that facultative bacterial endosymbionts are associated with viral transmission ,21 and dciencies . The facoviruses . Hamiltoemolymph . Several. tabaci ,45. Disthe virus ,45,46.Hamiltonella [H. defensa found in the current study and biological features of field populations of B. tabaci. The existence of biological and behavioral differences between the native species New World and the exotic MEAM1 have been reported already [B. tabaci are found more often colonizing weeds and wild plants [Hamiltonella, have found a different transmission efficiency of Brazilian whitefly-transmitted viruses between the species [Euphorbia yellow mosaic virus (EuYMV) is transmitted more efficiently by NW2 compared to MEAM1 and that the crinivirus, Tomato chlorosis virus (ToCV) and the carlavirus, Cowpea mild mottle virus (CpMMV) are transmitted more efficiently by MEAM1 than NW2 [B. tabaci transmitted viruses.There is still a lack of knowledge regarding the interactions among vector, symbionts and whitefly-transmitted viruses within Brazilian populations. Previous surveys conducted in Brazil have found that field populations of MEAM1, MED and NW2 analyzed frequently harbor ltonella , 41. The already ,47. The d plants . In addithan NW2 ,49, whicHamiltonella from NW2 and MEAM1 added to the deletion of three amino acids present in the homologue GroEL Chaperonin protein might aid to explain the difference in the transmission efficiencies among native and exotic species present in Brazil if further studies were carried out.The reasons for a difference in transmission efficiencies by NW2 and MEAM1 are still unknown but might be related to several factors related to the host, the viruses or other facultative endosymbiont found in the vector. However, the data found in this study of phylogenetic differences between insect symbiotic Hamiltonella and the complete mitochondrial of Brazilian B. tabaci populations is unprecedented and essential to serve as a reference for further studies regarding whiteflies in Brazil. The phylogenetic and amino acid analysis revealed genetic diversity between Hamiltonella from native and exotic populations that will aid for better understanding about the functions that Hamiltonella may have in whitefly biology.The genomic data obtained in this study from the facultative endosymbionts, S1 TableThe identity percentage was obtained on Geneious software v9.1.8.(DOCX)Click here for additional data file.S2 TableThe identity percentage was obtained on Geneious v9.1.8.(DOCX)Click here for additional data file.


The main goal of selection is to achieve genetic gain for a population by choosing the best breeders among a set of selection candidates. Since 2013, the use of a high density genotyping chip (600K Affymetrix® Axiom® HD genotyping array) for chicken has enabled the implementation of genomic selection in layer and broiler breeding, but the genotyping costs remain high for a routine use on a large number of selection candidates. It has thus been deemed interesting to develop a low density genotyping chip that would induce lower costs. In this perspective, various simulation studies have been conducted to find the best way to select a set of SNPs for low density genotyping of two laying hen lines.To design low density SNP chips, two methodologies, based on equidistance (EQ) or on linkage disequilibrium (LD) were compared. Imputation accuracy was assessed as the mean correlation between true and imputed genotypes. The results showed correlations more sensitive to false imputation of SNPs having low Minor Allele Frequency (MAF) when the EQ methodology was used. An increase in imputation accuracy was obtained when SNP density was increased, either through an increase in the number of selected windows on a chromosome or through the rise of the LD threshold. Moreover, the results varied depending on the type of chromosome (macro or micro-chromosome). The LD methodology enabled to optimize the number of SNPs, by reducing the SNP density on macro-chromosomes and by increasing it on micro-chromosomes. Imputation accuracy also increased when the size of the reference population was increased. Conversely, imputation accuracy decreased when the degree of kinship between reference and candidate populations was reduced. Finally, adding selection candidates’ dams in the reference population, in addition to their sire, enabled to get better imputation results.Whichever the SNP chip, the methodology, and the scenario studied, highly accurate imputations were obtained, with mean correlations higher than 0.83. The key point to achieve good imputation results is to take into account chicken lines’ LD when designing a low density SNP chip, and to include the candidates’ direct parents in the reference population. In 2001, Meuwissen et al. [However, the genotyping costs induced by the HD SNP chip remain high for a routine use on a large number of selection candidates. It has therefore been deemed interesting to develop a low cost genotyping approach, which can be achieved through the development of a low density genotyping chip. In that perspective, a set of SNP markers has to be selected to enable the imputation (prediction) of missing genotypes on a high density SNP chip. Imputation involves predicting the high density genotyping of selection candidates from their low density genotyping and from the high density genotyping of the reference population . This apTo date, many studies on genotype imputation have been led in the bovine, porcine, ovine and poultry sectors. These studies have been conducted using different software such as FImpute , Beagle Gallus gallus genome [Several factors influencing imputation accuracy have been studied in the literature. These factors need to be taken into account when designing a low density SNP chip, in order to get accurate imputation. The SNP density of low density SNP chips , the effs genome , especias genome –18 have In this study, several factors affecting imputation accuracy were therefore investigated. These factors are: SNP density, LD threshold, MAF of imputed SNPs, chromosome size, the methodology used to design the low density SNP chip , the composition of the reference population in terms of size and degree of kinship, as well as the effect of using female genotypes. Various in silico analysis were conducted in order to choose the best strategy to achieve low density genotyping of two different laying hen lines.The populations studied were comprised of two different commercial pure lines of Rhode Island (RI) and Leghorn (L) laying hens. Each line was created and selected by Novogen . The RI line was comprised of 2370 chickens split in four generations. The L line was comprised of 1483 chickens split in two generations. For both lines, each generation was divided in three batches and a new batch was produced every 6 months from 2010 to 2015 . From generation G3, born in November 2014, genomic selection was routinely implemented, by genotyping only the breeders. This enabled to reduce the generation interval for sires from 90 to 30 weeks. This accounts for the lower number of sires genotyped in November 2014 and May 2015.For the L line and for each batch, all male breeders were genotyped. Female breeders were genotyped starting from the last batch of the first generation.Blood was taken from the brachial veins of the animals. DNA was extracted and hybridized using the 600K Affymetrix® Axiom® HD genotyping array . GenotypGallus gallus genome [Each individual was genotyped for 580,961 SNPs. According to the fifth annotation release of s genome , these SP < 10− 4). Subsequently, the SNPs with unknown location or located on sexual chromosome W were removed, as well as the animals showing pedigree incompatibilities. Most of the SNPs had to be removed because they showed zero MAF. This was to be expected, since the HD SNP chip was designed for both layers and broilers and for a ratio of 1:2.Genotypes were filtered through six successive steps with classical thresholds Table , includiFor the RI line and for the L line respectively, 300,351 SNPs and 2362 individuals and 245,669 SNPs and 1474 individuals remained available for the analyses.From the remaining SNPs of the RI and L lines, several in silico low density SNP chips were designed by selecting a subset of SNPs Table . Both liMany studies, mainly in non-avian sectors , 11, 22,However, considering the heterogeneous structure of chicken linkage disequilibrium (LD) , it woulEight population scenarios were set up and differed depending on the reference and candidate populations Table . The ind1) and (D2) correspond to cases where the individuals of the candidate population are directly related to the reference population, because of the presence of their sires and/or dams in the reference population. In scenarios (C), (E) and (F), the individuals of the candidate population are also directly related to the reference population, but this time the size of the reference population was increased by adding individuals from previous generations. Finally, scenarios (G), (H) and (I) correspond to cases where the reference population does not include the sires of the candidate population, as a generation gap was introduced between the reference population and the candidate population.Scenarios (A), (B), (DThe scenario (A) concerned RI and L lines, and the others concerned only the RI line.Based on the low density SNP chips designed and on the population scenarios simulated, seven different parameters were studied, in order to investigate their influence on imputation accuracy.The first parameter studied was the effect of SNP density on the low density SNP chips. This study was conducted with the 11 low density SNP chips designed with the EQ methodology as well as with the 9 low density SNP chips designed with the LD methodology.Secondly, the effect of the LD threshold used to design the 9 low density SNP chips was investigated.Thirdly, the effect of minor allele frequencies of imputed SNP on imputation accuracy was studied. This study was done using the low density SNP chips of 3K and 10K SNPs, designed according to the two methodologies, i.e. EQ and LD.Fourth, the effect of the type of chromosome  was studied for both the equi and the LD chips, with a density of 3K and 10K SNPs.The first four parameters were studied on scenario (A), for both lines, and concerned the low density SNP chips used. The lines were studied independently of each other.5)The effect of the size of reference population on imputation accuracy was studied by comparing scenarios (B) - (C) and scenarios (D) - (E) - (F), adding individuals from previous generations in the reference population in some of the scenarios.6)The study of the effect of the degree of kinship between reference population and candidate population was conducted by comparing scenarios (B) - (C) - (G) and scenarios (D) - (F) - (H) - (I).7)Finally, the effect of the presence or absence of the dams in the reference population was investigated on scenario (D), by taking into account or not taking into account the dams in the reference population.The remaining last three parameters were studied at an equivalent SNP density of 10K SNPs, i.e. 10Kequi and LD0.5 low density SNP chips, and of 3K SNPs, i.e. 3Kequi and LD0.05 low density SNP chips. This was meant to focus on the effects of population structure and was done for the RI line only. The number of generations for the L line was insufficient and so did not enable to study the effects of population structure.FImpute V2.2  was used“turnoff_fam” activated. This option enabled the software to turn off family imputation and to use the whole range of haplotypes of the reference population to achieve imputation. The information brought by the sire was not used. There was however one exception, which concerned scenario D2, where both sires and dams were present in G2. In that case, the analysis was carried out without activating the “turnoff_fam” option.Moreover, because only the sires were present in the reference population, FImpute was used with the option Following the suggestion of Hickey et al.  and CaluThe influence of the parameters, i.e. marker density, LD threshold, MAF of imputed SNPs, chromosome type, and composition of the reference population through the cumulative use of generations in the reference population, degree of kinship between reference population and candidate population and the effect of using dams’ genotypes in the reference population, was investigated.The evolution of mean correlations between true and imputed genotypes according to the number of SNPs on the low density SNP chips was studied on scenario (A), for both lines and both methodologies Fig. .Fig. 2EvFor both lines and for both methodologies, an increase in mean correlations was observed when the number of SNPs on the low density SNP chips was increased. Regarding the RI line and the EQ methodology, the mean correlation was 0.875 for 2992 SNPs and 0.973 for 19,910 SNPs. Regarding the same line and the LD methodology, the mean correlation was 0.893 for 3205 SNPs and 0.977 for 16,615 SNPs. An inflexion point was also noticed between 5000 SNPs and 10,000 SNPs.− 5 for 20,000 SNPs  for both lines Fig. . When thThe influence of minor allele frequencies of imputed SNPs was studied on scenario (A), using both methodologies. The 10Kequi and the LD0.5 SNP chips were used for the RI line and the 10Kequi and the LD0.6 SNP chips were used for the L line. The influence of minor allele frequencies was also studied for a density of 3K SNPs, using the 3Kequi and LD0.05 SNP chips for the RI line (LD0.1 for the L line). The results for the density of 10K SNPs are the only ones shown, since they are similar to those obtained with the density of 3K SNPs. The same results were obtained for both lines and the results for the RI line are illustrated in Fig. With the 10Kequi SNP chip, an increase in imputation accuracy was noticed when the MAF of imputed SNPs was increased Fig. a. ComparFinally, by looking the MAF distribution of the SNPs of the different low density SNP chips, the SNPs of the 10Kequi SNP chip had mostly high MAF Fig. b, whereaThe influence of the type of chromosome was studied on scenario (A), for both lines, with the two low density 10K SNPs, i.e. 10Kequi and LD0.5 chips for the RI line (LD0.6 for the L line). The influence of the type of chromosome was also studied at a density of 3K SNPs, using the 3Kequi and LD0.05 chips, for the RI line (LD0.1 for the L line). The results for the density of 10K SNPs are the only ones shown, since they are similar to those obtained with the density of 3K SNPs. Chromosomes were split into four different groups: macro-chromosomes (1 to 5), intermediate chromosomes (6 to 10), micro-chromosomes (11 to 28 and 33), and sexual chromosome Z .Regarding the RI line Fig. a in conj− 1), was studied for each type of chromosome. For the RI line  and the LD0.6 SNP chip (for the L line), the ratio was 8.22 ± 0.76 SNP.Mb1), (E) and (F)).The influence of the size of the reference population on imputation accuracy was studied by adding individuals from previous generations in the reference population. The study was conducted for the RI line, using both methodologies. Two different cases were considered, with the imputation of G2 and G3 generations respectively as candidate populations Table . The dif1), to (E) and (F), the increase in imputation accuracy was significant and went from 0.868 to 0.896 to 0.912 for the 3Kequi SNP chip, from 0.953 to 0.965 to 0.974 for the 10Kequi SNP chip, from 0.892 to 0.914 to 0.929 for the LD0.05 SNP chip and from 0.973 to 0.978 to 0.983 for the LD0.5 SNP chip.From scenario (B) to scenario (C), at an equivalent SNP density of 3K SNPs, the mean correlations increased from 0.884 to 0.914 for the 3Kequi SNP chip, and from 0.899 to 0.921 for the LD0.05 SNP chip. At an equivalent SNP density of 10K SNPs, the mean correlations increased from 0.961 to 0.973 for the 10Kequi SNP chip, and from 0.975 to 0.981 for the LD0.5 SNP chip. Similarly, from scenario (D1), with 73 male breeders of G2 as the reference population, to scenario (H), with 120 male breeders of G1 as the reference population, and scenario (I), with 132 male breeders of G0 as the reference population. A gap of one generation was created between the reference population and the candidate population in scenario (H). A gap of two generations was created in scenario (I).The influence of the degree of kinship on imputation accuracy was studied on the RI line, for both methodologies and in two different cases corresponding to the imputation of G2 and G3 Table . The difRegarding G2 imputation, which goes from scenario (B) to scenario (G), mean correlations decreased from 0.884 to 0.841 for the 3Kequi SNP chip, and from 0.961 to 0.952 for the 10Kequi SNP chip. For the LD0.05 SNP chip, imputation accuracy decreased from 0.899 to 0.872, whereas for the LD0.5 SNP chip, it decreased from 0.975 to 0.973. The differences in mean correlations were therefore significant.1) to scenario (H), imputation accuracy decreased from 0.868 to 0.811 for the 3Kequi chip and from 0.953 to 0.936 for the 10Kequi SNP chip. For the LD0.05 and LD0.5 SNP chips, imputation accuracy respectively decreased from 0.892 to 0.852 and from 0.973 to 0.965. The differences in mean correlations were significant. However, by further increasing the gap to two generations (scenario (I)), imputation accuracy became a little bit higher, compared to the scenarios with a gap of one generation (H). The differences in mean correlations were significant, with imputation accuracy values of 0.821 for the 3Kequi SNP chip, 0.940 for the 10Kequi SNP chip, 0.856 for the LD0.05 SNP chip, and 0.967 for the LD0.5 SNP chip.Regarding G3 imputation, which goes from scenario (D1) and scenario (D2) (Table 1). For scenario (D2), the 73 male breeders and 662 female breeders of G2 were taken into account. In addition, in scenario (D2), imputation was done without activating the “turnoff_fam” option, because of the presence of sires and dams in the reference population. This enabled FImpute to use both pedigree and haplotype diversity to achieve imputations.The influence of the information regarding dams on imputation accuracy was studied on the RI line, using both methodologies and by comparing scenario (D2) Table . The dif1), in which dams’ genotype was not taken into consideration, imputation accuracy was 0.868 for the 3Kequi SNP chip, 0.953 for the 10Kequi SNP chip, 0.892 for the LD0.05 SNP chip and 0.973 for the LD0.5 SNP chip. When adding dams’ genotype (D2), imputation accuracy increased to 0.946 for the 3Kequi SNP chip, 0.983 for the 10Kequi SNP chip, 0.953 for the LD0.05 SNP chip and 0.989 for the LD0.5 SNP chip. The differences in mean correlations were significant.Regarding scenario  for similar correlation. An inflexion point was also noticed between 5000 SNPs and 10,000 SNPs.These results are in line with those found in the literature , 27, wheFor both lines, an increase in mean correlation was observed when the LD threshold increased. The rise in mean correlations, linked to the increase in the LD threshold, can be explained by the way the selection of SNPs was done, that is by clustering the whole set of high density SNPs according to their pairwise LD. When the LD threshold is high (0.8 for instance), the number of clusters of SNPs is also high, because few pairs of SNPs are in very strong LD with each other. A great number of SNPs are therefore present on the low density SNP chips Fig. . ConversFinally, when observing the evolution of imputation accuracy as a function of LD threshold, the inflexion point was much less distinct than it was when observing the evolution of imputation accuracy as a function of the number of SNPs on the low density SNP chips.Regarding the EQ methodology, the increase in imputation accuracy related to MAF was expected , 26. CorThe results showed that better imputation accuracy was obtained with the LD0.5 SNP chip than with the 10Kequi SNP chip, except for the macro-chromosomes (no significant differences) and for sexual chromosome Z, where the ranking of the SNP chips was reversed. However, the results for sexual chromosome Z proved better with the EQ methodology than with the LD methodology.− 1). This enabled to understand the performances of each low density SNP chip, and in turn to understand the evolution of mean correlations on each low density SNP chip.For each chromosome, and consequently for each type of chromosome, the results needed to be related to the ratio of selected SNPs on the low density SNP chips, per chromosome size (SNP.Mb− 1) was stable and the number of SNPs on the SNP chip was proportional to the size of the chromosomes. However, in the specific case of sexual chromosome Z, the significant differences with the expected ratio of 10 SNP.Mb− 1 are due to the number of SNPs kept on chromosome Z after quality control. This number was 38% for the RI line and only 18% for the L line. In addition, the distribution of the SNPs kept on chromosome Z was non-homogeneous (particularly on the L line), which resulted in large intervals devoid of SNPs  and the LD0.6 SNP chip (for the L line), the ratio varied depending on the type of chromosome, and the variation was higher than the variation observed with the equidistance based approach.With the LD methodology, the specific structure of the LD in layer chickens was taken into account and the number of SNPs on each chromosome was not proportional to the size of the chromosomes. According to Robert et al. , for a fFinally, for both lines, better imputation accuracies were obtained with the LD methodology than with the EQ methodology. In addition, higher imputation accuracies were obtained for the L line, compared to the RI line. This was expectable because of the number of SNPs retained and distributed over the genome after quality control in both lines. The number of SNPs was greater for the RI line  than for the L line . The L line has therefore less polymorphic markers and can be better imputed, compared to the RI line .The increase in the size of the reference population, achieved by adding individuals from previous generations, resulted in an increase in mean correlations. Indeed, increasing the size of the reference population led to an increase in the size of the library of reference haplotypes. The probability of finding haplotype fragments of the candidate in the library of reference haplotypes was therefore increased. These results are consistent with those found in the literature , 11, 13.Finally, the ranking of the methodologies remained unchanged despite the increase in the size of the reference population: the LD0.05 and the LD0.5 SNP chips respectively achieved better results than the 3Kequi and the 10Kequi SNP chips.The gap of one generation introduced in scenarios (G) and (H), as well as the gap of two generations introduced in scenario (I), led to a decrease in the degree of kinship between the reference population and the candidate population. The downward trend in imputation accuracy in scenarios including a gap of one generation (G2 and G3 imputation) was due to a decrease in the degree of kinship between reference population and candidate population. This in turn led to a decrease in the size of haplotype fragments that are common to the reference population and the candidate population. The decrease in the size of haplotype fragments can be explained by the recombination process that occurs over the generations. Selection candidates have therefore smaller haplotype fragments in common with the reference population. The probability of mistakenly identifying a haplotype fragment common to reference population and candidate population is consequently increased, which results in a lower number of good imputations. Moreover, the decrease in imputation accuracy was higher at a density of 3K SNPs than it was at a density of 10K SNPs. Indeed, in the case of G2 imputation, there was a decrease of respectively 0.043 and 0.027 for the 3Kequi and the LD0.05 SNP chips, and a decrease of respectively 0.009 and 0.002 for the 10Kequi and the LD0.5 SNP chips. Likewise, in the case of G3 imputation, there was a decrease of respectively 0.057 and 0.040 for the 3Kequi and the LD0.05 SNP chips and a decrease of respectively 0.017 and 0.008 for the 10Kequi and the LD0.5 SNP chips. The influence of the generation gap between the reference population and the candidate population was more important at a density of 3K SNPs than it was at a density of 10K SNPs. The greater decrease in imputation accuracy at a density of 3K SNPs can be explained by the fact that the SNP density was not sufficient to compensate for the loss of imputation accuracy caused by the generation gap.1) to scenario (H), one can notice that the increase in the size of the reference population did not enable to get better imputation, in spite of the generation gap. For scenario (D1) the reference population was comprised of only 73 sires. In scenario (H), it was comprised of 120 sires from G1. Therefore, the loss of the information brought by the direct sires was not counterbalanced by the increase in the size of the reference population.Moreover, in the case of G3 imputation, which goes from scenario (D1). With 132 sires from G0 (scenario (I)) and 120 sires from G1 (scenario (H)), the increase in the size of the reference population did not counterbalance the loss of information brought by the direct sires. Consequently, a key point to get good imputation accuracy is to include the direct parents, or at least the direct sires, of the candidate population.This observation still held true when the generation gap was further increased to two generations (scenario (I)). In that case, for both methodologies and for each low density SNP chip, a significant increase in mean correlations was noticed, compared to scenario (H). This improvement in imputation accuracy was due to the increase in the size of the reference population. However, these results were still lower than the mean correlations obtained with scenario . Therefore, when the SNP density is very low, a better alternative would be to genotype both dams and sires to achieve good imputation accuracy, rather than genotyping individuals from previous generations.Finally, once again, the ranking of the methodologies remained unchanged when the dams were included in the reference population: the LD0.05 and the LD0.5 SNP chips still respectively achieved better results than the 3Kequi and the 10Kequi SNP chips.The above studies showed that, whatever the SNP chip used, the methodology and the scenario studied, highly accurate imputations were obtained, with mean correlations higher than 0.83. These studies also highlighted two key points allowing for good imputation results. The first one, related to SNP chip factors, is the necessity to take into consideration the particular structure of the LD of chicken species. Indeed, each time the two methodologies were compared, better results were obtained with the LD methodology. In particular, when studying the type of chromosome, except for sexual Z chromosome (for both lines), better imputation accuracies were obtained with the LD methodology. More precisely, this methodology enabled to optimize the number of SNPs on macro-chromosomes and to densify the number of SNPs on intermediate and micro-chromosomes. The second key point, related to the influence of population structure, is to include the direct parents, or at least the direct sires, of the candidate population in the reference population. Indeed, it was shown that the contribution of the direct parents (or sires) was more important than the contribution of the size of the reference population. For an equivalent quantity of information, the 10K SNPs chips achieved better results than the 3K SNPs chips. However, the results proved that the loss of imputation accuracy noticed in the case of 3K SNPs (compared to the results obtained with 10K SNPs) could be largely compensated by genotyping both the dams and the sires of the candidate population. Consequently, the choice of a very low density SNP chip will have to be considered, if new technologies are implemented with a reduction of the cost of this type of SNP chip.Finally, the objective of genetic selection is to choose the most suitable individuals for the traits studied. The results of the genomic evaluations from all the different imputations strategies will be studied, in order to determine and finalize the best strategy to implement for low density genotyping of laying hen lines.


Genetic regions in the rice genome are identified that affect the proportion of grains that remain intact after milling (the head rice yield) and the degree of chalkiness in the grain. indica rice with integrated single- and multi-locus genome-wide association studies using 2.26 million single-nucleotide polymorphisms. We identified novel haplotypes that underly higher HRY on chromosomes 3, 6, 8, and 11, and that lower grain chalkiness in a fine-mapped region on chromosome 5. Whole-genome sequencing of 92 IRRI breeding lines was performed to identify the genetic variants of HRY and chalkiness. Rare and novel haplotypes were found for lowering chalkiness, but missing alleles hindered progress towards enhancing HRY in breeding material. The novel haplotypes that we identified have potential use in breeding programs aimed at improving these important traits in the rice crop.Higher head rice yield (HRY), which represents the proportion of intact grains that survive milling, and lower grain chalkiness (opacity) are key quality traits. We investigated the genetic basis of HRY and chalkiness in 320 diverse resequenced accessions of  Oryza sativa) grown in paddy systems amounts to 730 mt, resulting in 484 mt of polished grains after milling to remove the husk and the bran layers. As a staple food, rice provides at least 20% of the daily caloric requirement for ~3.5 billion people worldwide. To meet the future demands of a growing global population, it is estimated that annual milled rice production needs to increase by additional 105 mt by 2035. Despite the development of newer, high-yielding varieties, many farmers continue to plant the popular ‘mega-varieties’ produced between 1966–1985  is the proportion of ‘intact’ grains that remain after milling, and comprises grains that are at least 75% of the original length   in combination with targeted-gene association studies (TGAS) have emerged as a two-pronged strategy that can assist in narrowing down the significant trait-specific gene(s) underlying QTLs and in revealing allelic variants that regulate complex grain-quality traits (Oryza sativa subsp. indica accessions (262 landraces and 58 improved lines) were selected from 3000 resequenced rice genomes (The 3000 JXB online). The accessions were grown in field plots of 12.5×50 m in three replicated randomized blocks during the 2014 wet season and during the 2015 dry season at the Experimental Station of the International Rice Research Institute (IRRI), Laguna, Philippines . Standard crop management procedures were applied. Upon reaching seed maturity, the individual plots were harvested manually and hand-threshed at an optimum seed moisture content (MC) of 22–24%. For the controlled drying treatment, grains were then subjected to flat-bed drying to achieve 12–14% MC and subsequently placed in a seed storage room maintained at 18 °C before being milled.A diversity panel of 320 To further test the effect of moisture stress on HRY, dried harvested grains (~200 g samples) with 12–14% MC were soaked in reverse osmosis water for 60 min at ambient temperature until the MC of duplicate samples reached the optimum level of 22–24%. The samples were then placed inside a pre-heated oven at 45 °C until they returned to 12–14% MC. The samples were then placed at room temperature inside an air-tight plastic ziplock bag for 1 h, after which HRY was determined.Subsamples of grains  from the control and moisture stress treatments were dehulled using a Satake dehuller  with the settings optimized for the different grain shapes. Each sample was then milled using Grainman mill  for 45 s. Head rice was separated from broken kernels using a post-harvest drum type-sizing machine (Satake rice machine type TRG with three different layers) and HRY (%) was calculated as the mass proportion of milled rice retained as head rice after complete milling. The stability value was calculated by dividing the HRY value under stress by the HRY value under controlled condition. The HRY evaluation was conducted according to the ISO 17025 guidelines.indica lines from the diversity panel with SNP-based genotyping data of 700K  of each cultivar were used to determine percentage chalkiness. Chalkiness was estimated using a SeedCount SC5000 Image Analyser  as percentage grain chalkiness (PGC) using optimized methods. Grains were scattered randomly onto the sample tray of the instrument, and after scanning PGC was estimated using the in-built software after calibration according to the manufacturer’s instructions.We evaluated a total of 106 breeding lines developed by IRRI between 1966–2015 for HRY and chalkiness. Briefly, the lines were grown in field plots during wet (2015) and dry (2016) seasons according to standard agronomic practice (Genomic DNA extracted from leaf samples of 92 breeding lines were subjected to whole-genome sequencing using the HiSeq X10 platform (Illumina) and 150-bp pair-end reads were generated at ~30× coverage. After completion of the sequencing process, the raw reads were filtered to remove adaptor sequences and low-quality reads.IRRI conducted in-depth market surveys during 2015–2016 involving 886 rice farmers from Bangladesh, the eastern part of India, Cambodia, and the Philippines . An inteindica accessions was used for calling of genome-wide SNPs against reference genomes of japonica (cv. Nipponbare) and indica (MH63 and ZS97) (https://pypi.org/project/WarpedLMM/) to adjust the phenotype distribution. Genotype files were filtered prior to running the association analysis. Individual genotypes and SNPs were retained with a missing rate of not more than 5% and further filtered for a minor allele frequency of at least 5%. This plink2-based filtering step (https://www.cog-genomics.org/plink/2.0/) resulted in a final set of 320 indica lines that accounted for 2 260 030 SNPs against the Nipponbare genome, 802 117 SNPs against the MH63 genome, and 450 604 SNPs against the ZS97 genome. For a diversity panel of 300 lines, we used the previously published 700K SNP data from A resequenced diversity panel consisting of 324 indica resequenced lines. SNPs were tagged based on linkage disequilibrium (LD) from the set of SNPs with –log10P>5 for detailed analysis of HRY and PGC. The complete parameters used for clumping (in plink2) were adapted from −7.6. Haploview  GWAS methods, namely FASTmrEMMA  and indica (ZS97). The longest proteins were selected in cases where splice variants were present. Collinearity was then established using MCScanX , percent brown rice (PBR), weight of brown rice (WBR), percent milled rice (PMR), weight of milled rice (WMR), and PGC. The diversity panel showed a high degree of variation for HRY (6.22–63.8%) . While HRY was moderately correlated with PMR and WMR (r2=0.54–0.55) across the 320 lines, a high level of correlation was observed between HRY and WHR (r2≥0.99). HRY was found to be poorly correlated with PBR and WBR (r2=0.09–0.10) (r2=–0.25), although with low significance. Thus, strategies employed towards lowering chalkiness alone might not contribute significantly to increase HRY, indicating that it is important to independently examine the underlying genetic variations that contribute to increasing HRY in the pre-breeding pool.As we did not observe much genetic variation for HRY in existing breeding lines, we examined a diversity panel of 320 resequenced 2–63.8%)  with a b09–0.10) , and alsH2=0.84. Grain length showed negative correlations with HRY and WHR in the moisture stress treatment (r2=–0.30 for both), with a stronger relationship compared to control conditions (r2=–0.13)  . These r2=–0.13) , c, suggjaponica reference genome, the GWAS was run for all milling quality traits phenotyped in the 320 indica lines under controlled drying and moisture stressed conditions from 2015 dry season. We identified association peaks exceeding the threshold (–log10P>5) for HRY and WHR on chromosome 3 (30.09 Mb), chromosome 6 (27.57–27.62 Mb), chromosome 8 (25.05–25.20 Mb), and chromosome 11 (25.78–25.79) under controlled drying conditions, with a narrow-sense heritability h2=0.76 (10P>5), we utilized a multi-locus GWAS analysis and further confirmed that the significant loci for HRY traits on chromosomes 3, 6, 8, and 11 co-located with the single-locus GWAS results  through single-locus GWAS with a relatively high level of significance (–log10P>7.0) (Chromosome 6 (27.57–27.62 Mb), including the candidate gene LOC_Os06g45580 that encodes the ubiquitin E3 RING protein with protein degradation activity, possessed a high number of significant SNPs that showed association with HRY . The tar10P>7.0) .indica reference genomes  along with covariate of HRY values obtained from controlled drying conditions. The HRY stable regions were mapped to a 30.4-kb significant genomic region on chromosome 8 (5.7Mb from the HRY control peak) . The sig genomes . The hot genomes . Using T genomes , Table 2 genomes .GW5/qSW5 gene  that is known to regulate grain width and weight in rice  that was highly associated with PBR . Another in rice .japonica (Nipponbare) and indica (ZS97) reference genomes, and identified collinearity and break-points in the respective genomes  fine-mapped genomic region on chromosome 5 mapped between 5.12–5.43 Mb, which further confirmed with the multi-locus GWAS peaks (n=16), showed higher beta values that explained the lower median PGC value of 6.6  for LOC_Os05g09520  that covered significant tagged SNPs in the regulatory, exonic, and intronic regions  explained the significant phenotypic variance (PV) of 21%. The lines possessing this haplotype had a low-chalkiness phenotype, with a median PGC of 7% (GW5 gene (LOC_Os05g09520) in the same LD  were present in a separate LD-block (GW5/qSW5 (LOC_Os05g09520) that regulates grain width is in another LD-block  (Using genotyping data derived from the ZS97 reference genome . The SNPLD-block  while GW protein . Similarprotein) .GW5 region) exerted a negative effect on chalkiness with a lower beta value. This might be attributable to the presence of few SNPs in the target region . Moreover, this region was found to be unique as it did not match any of the cloned chalk genes/mutants that have previously been reported  that resulted in high HRY in the selected entified , b. Inteast Asia . Group 3ast Asia .chalk5.1 also identified five groups  of chromosome 5 (5.12–5.43 Mb) that influenced PGC classified the breeding lines into four groups . A phyloe groups , d. Notae groups , d. Benee groups , d. Groung lines , f. Intrper se combined with lack of progress in other important economic traits related to HRY have made it difficult to replace the widely adopted rice mega-varieties (High head rice yield (HRY) and low chalkiness are key traits that determine the milling and appearance qualities of rice, and hence they can significantly affect economic returns to farmer and millers. Current trends of limited genetic gains in yield arieties . In Eastarieties  due to sarieties , but hasindica lines combined with ultra-dense SNP genotyping allowed us to identify fine-mapped genetic regions from chromosome 6 and 8 with relatively high heritability  from the previously cloned gene chalk5 V-PPase that encodes for inorganic pyrophosphate (PPi) H+-translocation activity  has been reported to contribute to higher grain width in japonica cultivars  compared to chalk5 V-PPase (16.7%), which can be attributed to the lower frequency of representation of chalk5.1 alleles in the breeding material. Together, the two chalkiness genes identified on chromosome 5 explain 26.12% of the PV, indicating their likely potential benefits in future breeding initiatives to reduce chalkiness.TGAS in combination with GWAS allowed us to narrow down the hotspot region to a locus encompassing three candidate genes , which im of GW5 . The low5.1 gene , suggestchalk 5.1 gene in the rice breeding programs to improve milling and appearance quality attributes for long-slender indica varieties.In summary, the fine-mapped haplotypes of chromosome 5 identified for reducing chalk have been successfully explored in the present IRRI breeding programs. In contrast, the novel haplotypes identified for enhanced HRY from chromosome 8 in the diversity panel were not found in the 92 IRRI breeding lines. To ensure future economic benefits for farmers, superior alleles contributing for high HRY from chromosome 8 need to be recombined with the JXB online.Supplementary data are available at Table S1. List of accessions used in the study together with the haplotypes for HRY that were identified in the hotspot regions of chromosomes 8 and 11.Table S2. List of haplotypes identified for HRY stability within the candidate region on chromosome 8 in the accessions used in this study.Table S3. Single-locus GWAS for SNPs detected against the Nipponbare reference genome with corresponding orthologous IDs in ZS97 and MH63.Table S4. List of haplotypes identified for lowering chalkiness in hotspot region on chromosome 5 in the accessions used in this study.Table S5. Single-locus GWAS for SNPs detected against the ZS97 reference genome with corresponding orthologous IDs in MH63 and Nipponbare.Table S6. Single-locus GWAS for SNPs detected against the MH63 reference genome with corresponding orthologous IDs in ZS97 and Nipponbare.Table S7. Genes/QTLs previously characterized in mutants as influencing grain chalkiness.indica lines.Fig. S1. Phenotypic variation for milling quality traits and chalkiness in 320 resequenced indica lines.Fig. S2. Principal component analysis for the 320 resequenced indica germplasm.Fig. S3. Manhattan plots generated by GWAS for milling quality traits within the Fig. S4. Single-locus GWAS for HRY showing the association of the genomic regions of chromosomes 6 and 11.Fig. S5. Variation in the phenotypic values of HRY and chalkiness observed for different haplotypes across dry and wet seasons.japonica cultivar Nipponbare and the indica cultivar ZS97.Fig. S6. Comprehensive representation of structural variation and collinearity between the indica reference genomes ZS97 and MH63.Fig. S7. Single-locus GWAS for grain chalkiness showing the main candidate loci on chromosomes 5 using the Fig. S8. Single-locus GWAS for PGC showing the significant association in the chromosome 5 hotspot region using ZS97 as the reference genome.Fig. S9. Nucleotide sequence alignment in the main chromosome 5 hotspot region within cultivars with extreme chalkiness phenotypes.Fig. S10. SNPs in the genomic sequence of the chromosome 5 hotspot region that regulate PGC in the three reference genomes, Nipponbare, ZS97, and MH63.Fig. S11. Allelic variation in the chromosome 5 hotspot region and allele distribution in IRRI breeding lines and selected core-collection cultivars.erz256_suppl_Supplementary_Figures_S1-S11Click here for additional data file.erz256_suppl_Supplementary_Tables_S1-S7Click here for additional data file.GM and RA conducted the GWAS, TGAS, and haplotype analyses; RA supervised the planting, harvesting, and drying of the rice diversity panel and IRRI varieties; MD conducted and interpreted the farmer survey; VB and LM optimized the methods for chalkiness and HRY assays, and SB analysed the data; NS, AG, and MKM conceptualized the work and NS supervised the conduct of all experiments and bioinformatics analyses; NS wrote the manuscript with contributions from VB and SB; all authors read and contributed to the revision of manuscript.


Crossbreeding is widely used aimed at improving crossbred performance for poultry and livestock. Alleles that are specific to different purebreds will yield a large number of heterozygous single-nucleotide polymorphisms (SNPs) in crossbred individuals, which are supposed to have the power to alter gene function or regulate gene expression. For pork production, a classic three-way crossbreeding system of Duroc × (Landrace × Yorkshire) (DLY) is generally used to produce terminal crossbred pigs with stable and prominent performance. Nonetheless, little is known about the breed-of-origin effects from purebreds on DLY pigs. In this study, we first estimated the distribution of heterozygous SNPs in three kinds of three-way crossbred pigs via whole genome sequencing data originated from three purebreds. The result suggested that DLY is a more effective strategy for three-way crossbreeding as it could yield more stably inherited heterozygous SNPs. We then sequenced a DLY pig family and identified 95, 79, 132 and 42 allele-specific expression (ASE) genes in adipose, heart, liver and skeletal muscle, respectively. Principal component analysis and unrestricted clustering analyses revealed the tissue-specific pattern of ASE genes, indicating the potential roles of ASE genes for development of DLY pigs. In summary, our findings provided a lot of candidate SNP markers and ASE genes for DLY three-way crossbreeding system, which may be valuable for pig breeding and production in the future. Crossbreeding strategy for animals  is a classic and effective method aimed at improving crossbred performance . Using cSus scrofa) were domesticated at least ~9,000 years ago and have been used as a major source of animal proteins in the human diet  genes to investigate the breed-of-origin effects from purebreds on crossbred individuals for bacterial resistance , peripheAll experimental procedures and sample collection methods in this study were approved by the Institutional Animal Care and Use Committee .In this study, we first downloaded genome sequencing data of 30 pig individuals  with mean genome coverage of ~15.47× for each individual . We thenThe genome sequencing data of pigs in this study were first mapped to reference pig genome  using BWA (v.0.7.8) with default options . We usedTo calculate the probability of heterozygous SNP (PHS) of simulated offspring generated by different crossbreeding systems, we first calculated the frequency of three genotypes  for each SNP locus based on the ~12.56M population-scale SNPs for Duroc , Landrace  and Yorkshire  respectively. We then simulated crossbreeding between each two pig breeds and recalculated the frequency of the same three genotypes for simulated offspring. Based on this method, we could obtain PHS for each SNP locus of the simulated offspring generated by different crossbreeding systems.P Value Cutoff = 0.01, Min Enrichment = 1.5’  based on the ~12.56M population-scale SNPs. Function enrichment analyses were performed using the online toolkit ‘Metascape’ with parameters ‘Min Overlap = 3, t = 1.5’ .P-values were adjusted by bonferrioni method , NA (not significant for ASE), no heterozygous SNP  enrichment of the mRNA to remove ribosomal RNA (rRNA). All RNA-Seq libraries were sequenced using an Illumina HiSeq4000 platform, generating a total of ~111.52 Gb transcriptome data . After qi method . Each gegous SNP . A high-gous SNP .http://hgdownload.soe.ucsc.edu/goldenPath/susScr11/bigZips/susScr11.fa.gz; Picard, http://picard.sourceforge.net; GCTA, http://www.gcta-ga.org.Reference pig genome, r = −0.99, P < 0.01). This was most likely contributed to the closer genetic relationship between L and Y . Taking together, we suggest that DLY is a more effective strategy for three-way crossbreeding system among Duroc, Landrace, and Yorkshire breeds as it could yield more stably inherited heterozygous SNPs.To estimate the distribution of heterozygous SNPs in three-way crossbred pigs, we calculated the PHS for simulated offspring generated by three kinds of three-way crossbreeding systems, namely, DLY, Landrace × (Yorkshire × Duroc) (LYD) and Yorkshire × (Duroc × Landrace) (YDL) –1F. This L and Y . As a re L and Y , LY signr = 0.98, P < 0.01), but significantly different with the other four pigs  , two parents  and six offspring  with mean genome coverage of 38.51× for each individual . A total < 0.01) . This reS. scrofa ENSEMBL gene annotation (v.92) revealed that more than half (~59.68%) of the loci were enriched in intergenic regions  were identified to harbor a nonsynonymous SNP locus  using ANNOVAR software  and S. s regions , suggest regions . The maj regions . We founNP locus , indicatAHI1 is required for both cerebellar and cortical development and has been previously shown to be associated with fat development and obesity via regulating insulin signaling , ‘acylglycerol metabolic process’ (P = 6.31 × 10−8) and ‘regulation of cholesterol metabolic process’ (P = 8.13 × 10−7)  , suggestcontents . VIM encbody fat . ACSL1 eg breeds .P = 2.88 × 10−8), ‘steroid metabolic process’ (P = 1.82 × 10−7), ‘monocarboxylic acid metabolic process’ (P = 2.95 × 10−7), ‘small molecule catabolic process’ (P = 2.82 × 10−6), ‘Valine, leucine and isoleucine degradation’ (P = 2.14 × 10−5)  .P = 3.09 × 10−9), ‘muscle system process’ (P = 8.13 × 10−6), ‘muscle structure development’ (P = 6.76 × 10−4), ‘response to calcium ion’ (P = 4.07 × 10−3) and ‘Tight junction’ (P = 5.89 × 10−3)   and S7.Taken together, we speculate that ASE genes may play important roles for the development of DLY pigs, indicating the potential influence of breed-of-origin effects from purebreds on gene expression of crossbred individuals.In this study, we simulated three kinds of three-way crossbreeding system among Duroc, Landrace and Yorkshire breeds, and estimated the distribution of heterozygous SNPs in terminal crossbred pigs via whole genome sequencing data from three purebreds. We found DLY three-way crossbreeding system could yield more stably inherited heterozygous SNPs in the terminal crossbred offspring, suggesting that DLY is a more effective strategy for three-way crossbreeding. We also sequenced a DLY pig family and identified a lot of ASE genes in adipose, heart, liver and skeletal muscle. These ASE genes exhibited a tissue-specific pattern and were involved in many important functional pathways, indicating the potential influence of breed-of-origin effects from purebreds on gene expression of DLY crossbred pigs. These discoveries may provide a valuable resource for pig breeding and production in the future and set an excellent model of crossbreeding analysis for other agricultural animals.10.7717/peerj.8009/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.8009/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.8009/supp-3Supplemental Information 3Click here for additional data file.


Formosa have an expanded set of laminarin hydrolases and transporters that belonged to the most abundant proteins in the environmental samples. In vitro experiments with cultured isolates allowed us to determine the functions of in situ expressed key enzymes and to confirm their role in laminarin utilization. It is shown that laminarin consumption of Formosa spp. is paralleled by enhanced uptake of diatom-derived peptides. This study reveals that genome reduction, enzyme fusions, transporters, and enzyme expansion as well as a tight coupling of carbon and nitrogen metabolism provide the tools, which make Formosa spp. so competitive during microalgal blooms.Polysaccharide degradation by heterotrophic microbes is a key process within Earth’s carbon cycle. Here, we use environmental proteomics and metagenomics in combination with cultivation experiments and biochemical characterizations to investigate the molecular details of in situ polysaccharide degradation mechanisms during microalgal blooms. For this, we use laminarin as a model polysaccharide. Laminarin is a ubiquitous marine storage polymer of marine microalgae and is particularly abundant during phytoplankton blooms. In this study, we show that highly specialized bacterial strains of the Bacteroidetes phylum repeatedly reached high abundances during North Sea algal blooms and dominated laminarin turnover. These genomically streamlined bacteria of the genus  Phytoplankton blooms produce large quantities of beta-glucans, such as laminarin, a soluble β-1,3-glucan with β-1,6 side chains. The breakdown of these polysaccharides by heterotrophic microbes is a central part of the marine carbon cycle. Diatoms alone are estimated to produce ~ 5–15 Gt of laminarin per year as their storage compound, making it a major food resource for heterotrophic marine organisms , 3. ThisFor complete degradation of one polysaccharide, microbes must have an adapted glycolytic pathway that contains multiple enzymes, which individually address each of the different glycosidic linkages and structural compositions present in the macromolecule. The genes of glycan-degrading pathways cluster in operons named polysaccharide utilization loci (PULs). Recent works suggest that each polysaccharide requires a corresponding PUL . HorizoFormosa during diatom-dominated spring blooms off the North Sea island Helgoland [Formosa spp. are prominent candidates for the recycling of laminarin during spring microalgae blooms.In previous studies, we reported the high abundance  of the flavobacterial genus elgoland , 18. Furelgoland . TogetheFormosa during microalgal blooms in general and for laminarin utilization in particular. We examined two strains, Formosa Hel3_A1_48 (referred to as strain A) and Formosa Hel1_33_131 (strain B), both of which were isolated from the same sampling location [Formosa B) allowed us to show that a specialized enzyme repertoire represents one of the adaptive mechanisms that provide a competitive advantage in substrate exploitation. Using laminarin as a model substrate, we demonstrate how a microalgal glycan resource can promote the enrichment of individual dominating taxa from an initially diverse microbial community with similar metabolic functions. Our data indicate that Formosa B tightly couples glycan utilization with the uptake of nitrogen compounds. This suggests that a balanced carbon and nitrogen diet is required for competitive laminarin utilization during phytoplankton blooms.In this study, we explored molecular strategies, which provide competitive advantages to the genus location , and whilocation . The comFormosa sp. Hel1_33_131 (Formosa strain B) and Formosa sp. Hel3_A1_48 (Formosa strain A) were isolated by dilution cultivation during a spring and a summer phytoplankton bloom, respectively, from surface water near the North Sea island Helgoland in the German Bight [−1 peptone, 0.1 g L−1 casamino acids, 0.1 g L−1 yeast extract, 200 μM NH4Cl, and 16 μM KH2PO4) with defined carbon sources as substrates at 12 °C during gentle shaking at 55 rpm. For the proteome analyses, described below, d-glucose and laminarin  were used as carbon sources (concentrations: 2 g L−1). In addition, the utilization of chitin  was tested in this medium and these cultures were used as a control condition for the in vitro proteome analyses with glucose and laminarin. All growth experiments were carried out in triplicates. Cells were harvested by centrifugation , and the resulting pellets and supernatants were stored at − 80 °C until use.The investigated strains an Bight . Growth an Bight  (with 0.Formosa A (Hel3_A1_48) and B (Hel1_33_131) DNA was extracted according to the protocol of Zhou et al. [Formosa A) and 2,735,158 bp for Hel1_33_131 (Formosa B). Gene prediction and annotation (including the phylogeny-guided carbohydrate-active enzyme (CAZyme) annotations provided in Supplementary Table Formosa sp. Hel3_A1_48 (Formosa strain A) and CP017260.1 for Formosa sp. Hel1_33_131 (Formosa strain B).For genome sequencing of the strains u et al. . SequencFormosa strain B. Details of the protein extraction and subproteome enrichment can be found in The soluble intracellular proteome, the enriched membrane-associated proteome, and the soluble extracellular proteome was characterized from exponentially growing cells of Formosa B (Hel1_33_131) and of common laboratory contaminants.Peptides were subjected to a reversed phase C18 column chromatography on a nano ACQUITY-UPLC  and separated as described by Otto et al. . Mass spt test (permutation-based FDR 0.05).Protein searches were performed using MaxQuant with the integrated Andromeda engine  with a phttp://proteomecentral.proteomexchange.org) via the PRIDE partner repository [The mass spectrometry proteomics data are available through the ProteomeXchange Consortium  was hydrolyzed over the course of 60 min at 37 °C with 100 nM purified enzyme  in 50 mM MOPS buffer at pH 7. The preparation and purification of debranched laminarin as well as the determination of kinetic parameters of the three enzymes FbGH30, FbGH17A, and FbGH17B acting on native and debranched laminarin is explained in The cloning of the FbGH17A gene (locus tag FORMB_24720) and the FaGH17B gene (locus tag FORMB_24740) is described in −1 mixed 1:1 with a “well solution” , 9% PEG8K) supplemented with 15% ethylene glycol. The crystals were cryoprotected prior to freezing in the “well solution” supplemented with ethylene glycol to a final concentration of 30%. Crystals were frozen by flash freezing in liquid nitrogen in nylon loops. X-ray diffraction data were collected at the DESY P11 beamline. The structure was solved by molecular replacement using PHASER in the phenix suite [Crystals of FbGH17A were obtained by hanging drop vapor diffusion of the protein with 12.3 mg mLix suite  using thix suite . The modix suite  and Cootix suite , and valFormosa strains A and B. Both have a single chromosome with a GC content of 36.4 and 36.6%, respectively. With 2,016,454 bp (strain A) and 2,735,158 bp (strain B) they possess small genomes compared with other marine polysaccharide-degrading Flavobacteriia [We sequenced and annotated the genomes of the acteriia , 35, 36.Formosa strains A and B are representatives of two previously uncultured clades of the genus Formosa. These occur not only in the North Sea, but also in surface waters from coastal and open ocean sites throughout the world   laminarin revealed the ability of ion Fig. . AdditioFormosa B on laminarin and to verify whether laminarin specifically controls the expression of the genomically detectable PULs we performed cultivation experiments with this bacterium with purified laminarin as growth substrate. Growth curves of Formosa B in HaHa medium with laminarin, glucose and only protein extracts, respectively, are shown in Fig. Formosa B   than on laminarin itself   , FaGH17B (locus tag: FORMB_24740), and FbGH30 (locus tag: FORMB_24730). As all three proteins are encoded in a single gene cluster, we hypothesized that these enzymes might work together in spatial proximity. To test this hypothesis, we conducted a series of biochemical experiments, which revealed that the FbGH30 enzyme hydrolyzed the β-1,6-linked glucose side chains of laminarin (−1) Fig. , whereas−1) Fig. . Prefere−1) Fig.  and was http://www.sbg.bio.ic.ac.uk/phyre2/html/page.cgi?id=index)), with the last C-terminal helix and the attached GH17 domain . Other laminarin-degrading marine Bacteroidetes like Gramella forsetii KT0803T (79 peptidases), Polaribacter sp. Hel1_85 (84 peptidases), Jejuia pallidilutea (58 peptidases), and Flaviramulus ichthyoenteri (63 peptidases) show a comparable number of peptidase genes, although their genomes are around twice as large as that of Formosa B. Our proteome analysis of Formosa B revealed that 41 peptidases are expressed in the presence of laminarin  combines a glycoside hydrolase (GH17) with a membrane spanning transport protein and may represent an adaptive mechanism for laminarin utilization. The integration of the transport and hydrolysis processes into a single protein could facilitate improved consumption of the sugars by increasing the activity of the fused GH17. Increased activity would also reduce the necessary enzyme copy number, and thereby resource consumption for synthesis of this protein. To our knowledge, such a transporter-CAZyme-fusion has not been described for other bacteria as yet, but its conservation in nature suggests that this could provide a significant benefit.The exceptionally strong accumulation of a putative porin in glucose- and laminarin-controlled cultures and the co-induction of the 12 surrounding genes of this porin-encoding genomic cluster, all of which play a role in nitrogen metabolism, could indicate a function of this transporter protein in the uptake of peptides as nitrogen and amino-acid source. Endo-acting proteases might degrade proteins released by lysed microalgae into peptides, which are then imported through the porin into the periplasm. An efficient capture of these peptides with a highly abundant porin system might be especially useful in the highly diffusive marine environment. With laminarin and glucose as easily metabolizable carbon sources, such a strategy could be crucial for a balanced carbon and nitrogen diet.Bacteroidetes are primary degraders of microalgal polysaccharides during phytoplankton blooms, and are therefore key players in marine carbon cycling. However, underlying enzymatic mechanisms and adaptations that drive the specialization of these highly competitive bacteria remain obscure. We reveal and prove in this study the specific activity and ecological niche of two abundant marine Bacteroidetes strains in complex microbial communities during diatom-driven phytoplankton blooms. Our results show an extraordinary degree of specialization for the Formosa strains A and B, which enable these marine Bacteroidetes to successfully compete for laminarin against a multitude of other laminarin-degrading microbes in bloom situations [Formosa B during growth on laminarin suggests that these bacteria pursue a complex uptake strategy, which encompasses both sugars and nitrogen compounds. This may make marine Flavobacteriia so successful in their diffusion-open environment.Members of the phylum tuations , 44, 45.Supporting InformationSI Figure S6SI Table S1SI Table S2ASI Table S2BSI Table S2CSI Table S3SI Table S4SI Table S5SI Table S6SI Table S7SI Table S8AB