Sorghum bicolor L. [Moench]), the genomic basis of adaptation to the semiarid Sahelian zone versus the subhumid Soudanian zone is largely unknown. To address this issue, we characterized a large panel of 421 georeferenced sorghum landrace accessions from Senegal and adjacent locations at 213,916 single‐nucleotide polymorphisms (SNPs) using genotyping‐by‐sequencing. Seven subpopulations distributed along the north‐south precipitation gradient were identified. Redundancy analysis found that climate variables explained up to 8% of SNP variation, with climate collinear with space explaining most of this variation (6%). Genome scans of nucleotide diversity suggest positive selection on chromosome 2, 4, 5, 7, and 10 in durra sorghums, with successive adaptation during diffusion along the Sahel. Putative selective sweeps were identified, several of which colocalize with stay‐green drought tolerance (Stg) loci, and a priori candidate genes for photoperiodic flowering and inflorescence morphology. Genome‐wide association studies of photoperiod sensitivity and panicle compactness identified 35 and 13 associations that colocalize with a priori candidate genes, respectively. Climate‐associated SNPs colocalize with Stg3a, Stg1, Stg2, and Ma6 and have allelic distribution consistent with adaptation across Sahelian and Soudanian zones. Taken together, the findings suggest an oligogenic basis of adaptation to Sahelian versus Soudanian climates, underpinned by variation in conserved floral regulatory pathways and other systems that are less understood in cereals.Uncovering the genomic basis of climate adaptation in traditional crop varieties can provide insight into plant evolution and facilitate breeding for climate resilience. In the African cereal sorghum ( The modest genome size (~800 Mbp) of sorghum relative to other grass species , CCT‐domain regulators , and florigens   confer stay‐green  postflowering drought tolerance in lines derived from Ethiopian durra , and GEA have provided an understanding of worldwide sorghum genetic structure across diverse agroclimatic regions  were collected from various agro‐ecological zones of Senegal in 1976 , which is surrounded by Senegal, and Mauritania (n = 15), which shares border along the Senegal River Valley, were also included in our panel. Six improved varieties  from the sorghum breeding program based at the Centre National de Recherche Agricole (CNRA) and two sorghum conversion lines, SC 1,067  and SC 417 , were included. Information about the SSG including botanical race, geographic origin, local name, and ethno‐linguistic group from which the landrace was collected are presented in Data The Senegalese sorghum germplasm (SSG) used in the present study were obtained from the U.S. Department of Agriculture (USDA) Germplasm Resources Information Network (GRIN). These accessions (2.2ApeKI for DNA digestion and 384‐plex barcode ligation (4 × 96‐plex) following the GBS protocol , placed into 96‐well plates, and dried in a lyophilizer for two days. Genomic DNA of SSG accessions was extracted from ~50 mg dried leaf tissue using the BioSprint robot with DNeasy Mini Kit (Qiagen) according to the manufacturer's instructions. DNA was quantified with PicoGreen and normalized to 10 ng/μl DNA for each sample. The GBS library was constructed using the restriction enzyme 2.3The SNP calling was done based on 1,208 samples including the accessions from the SSG panel and accessions from the GDP. Single‐end sequence reads obtained from Illumina sequencing and raw sequencing data from the GDP were processed with the TASSEL 5 GBS v2 pipeline  of SNP variation was performed using the snpgdsPCA function of the R package SNPRelate  among SNPs was used to estimate LD using TASSEL 5  function  among subgroups defined based on eco‐geography was estimated using the Weir and Cockerham method in VCFtools. FST values among subgroups obtained at K = 7 from the TESS3 program were calculated using the R package HierFstat , and across putative prebottleneck and postbottleneck events . Selective sweeps were detected using the CLR method in SweeD program  and panicle compactness . For GEA, both MLM and general linear models (GLM) were used. Nineteen WorldClim‐derived bioclimatic variables  in GAPIT in R  from   was 0.00054 in durra and 0.00060 in guinea accessions. The average pairwise LD (r2) decreased from its initial value (~0.5) to 0.2 at 220 kb, 150 kb, and 81 kb in durra, guinea, and whole SSG, respectively  were identified  found at K = 7 from TESS3 results was determined using the FST analysis , which were mostly formed by guinea accessions where guineas in the center were differentiated from guineas in the south. FST of 0.185 and 0.052 were estimated between guinea and durra accessions in the SSG, and between SSG durra and GDP durra, respectively.To further characterize genetic structure and gene flow among groups, we used Bayesian model‐based clustering. ADMIXTURE revealed a hierarchical genetic structure and high amount of gene flow among subpopulations File . Cross vs Figure . We inved Figure a. The TEs Figure b. The dup > 0.001)  between guinea (Soudanian) and durra (Sahelian) accessions within the SSG. Since guinea sorghums are generally more genetically diverse than durra sorghums, we used πguinea in the numerator and πdurra in the denominator to identify low‐diversity genomic regions in the durra genome. Nucleotide polymorphism was reduced in durra compared to guinea across most of the genome, with notably low π on pericentromeric regions of chromosome 2, 5, 7, and 10  since the divergence of durra from its common ancestor with guinea types, (b) from Ethiopian durra (center of durra origin) to West African durra , (c) and from West African durra to Senegalese durra Figure c. We als3.4n = 64) implicated in stay‐green, flowering time, or inflorescence morphology colocalized with CLR outliers. Given that the candidate genes were identified a priori from the literature, a liberal cutoff of 1 Mb was used to define colocalization between CLR outlier regions and candidate genes. Sixteen out of 47 CLR outliers colocalized with candidate genes  in guinea  in durra  were identified for photoperiod sensitivity for the whole SSG and SSG without durra, respectively  in durra (800 kb) and guinea (500 kb). Among the associated SNPs, 35 and 26 colocalized with photoperiodic flowering candidate genes for the whole SSG and SSG excluding durra, respectively. For panicle compactness, 48 and 124 significantly associated SNPs were found for the whole SSG and SSG excluding durra, respectively  associations were not near any a priori candidate genes. For panicle compactness, significantly associated SNPs colocalized with SP1, CRCK3, TCP24, DFL2, Sb‐ra2, vt2, and rel2. The SNP S1_55302939 (within the SP1 gene) was significant in both GWAS approaches, while S1_55305415 (1 kb away from SP1) was only significant when using the whole SSG panel. Two of the highly significant  associations were not near a priori candidate genes.Photoperiod sensitivity‐associated SNPs were found near floral regulators N15 Data . Most of3.6p‐value > 0.05) with environment variables including precipitation of the driest quarter  colocalized with SNPs associated with mean temperature of the driest and warmest quarters, precipitation of the driest, warmest and wettest quarters, and longitude .We performed GEA to identify SNPs associated with climate variables Data . Based or Figure a, mean tr Figure a, and prr Figure b. Associr Figure c becauser Figure . Associaude Data . The SNPStg3a and Ma6 and variation within these loci, we generated the LDheatmap of the 50 kb region surrounding each SNP  was found between S2_60708848 and other SNPs in the Stg3a locus. The SNP S6_691400 was in LD with two SNPs in Ma6. The genotypes carrying the minor allele at S2_60708848 were distributed in the southern subhumid environments  and S3_57321183  were mostly found in durra landraces and few guinea landraces distributed in the dry areas of Senegal , and the spatial and hierarchical genetic structure observed within guinea group Figure  is consiFST (0.052) suggest that durra sorghums moved with Fulani people from northeast Africa  was present in durra genotypes distributed in the drier areas of the Sahelian zone characterized by short growing seasons and low rainfall (<400 mm per year) , PhyB (Ma3), PhyA, and Ma1 (SbPRR37), colocalized with phenotype‐associated SNPs  found near photoperiod‐associated SNPs are photoperiod‐regulated activators of floral induction in U.S. sorghum . The minor alleles in/near SP1  observed in durra accessions and some guinea accessions  representing high‐quality markers useful for the genomic dissection of adaptive and complex traits. High rates of SNPs with low frequency minor alleles (about 60% of the data had MAF < 0.05) were detected. One possible explanation may be related to the fact that these accessions are mostly landraces grown in their center of origin; thus high number of rare polymorphisms might be segregating at intermediate frequency in the germplasm. In the USDA‐NPGS Ethiopian sorghum collection, similar patterns of MAF were found where 60% of detected SNPs had MAF < 0.05 (Cuevas, Rosa‐Valentin, Hayes, Rooney, & Hoffmann, The moderate decay of LD observed within the germplasm Figure b is consThe stay‐green loci may be useful to improve for drought adaptation in the Sahel via marker‐assisted selection. Circadian clock‐related genes influence crop yield under abiotic stress (Bendix, Marshall, & Harmon, None declared.G.M., D.F., and N.C. conceived and managed the study. F.M., J.F, and Z.H. generated the SNP data set. J.F. analyzed the data. J.F. and G.M. wrote the manuscript. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Populus nigra trees from a breeding population were selected and genotyped with a 12K custom Infinium Bead-Chip. Forty-three of those individuals corresponding to nodal trees in the pedigree were fully sequenced (reference), while the remaining majority (target) was imputed from 8K to 1.4 million SNPs using FImpute. Each SNP and individual was evaluated for imputation errors by leave-one-out cross validation in the training sample of 43 sequenced trees. Some summary statistics such as Hardy-Weinberg Equilibrium exact test p-value, quality of sequencing, depth of sequencing per site and per individual, minor allele frequency, marker density ratio or SNP information redundancy were calculated. Principal component and Boruta analyses were used on all these parameters to rank the factors affecting the quality of imputation. Additionally, we characterize the impact of the relatedness between reference population and target population.Genomic selection accuracy increases with the use of high SNP (single nucleotide polymorphism) coverage. However, such gains in coverage come at high costs, preventing their prompt operational implementation by breeders. Low density panels imputed to higher densities offer a cheaper alternative during the first stages of genomic resources development. Our study is the first to explore the imputation in a tree species: black poplar. About 1000 pure-breed During the imputation process, we used 7540 SNPs from the chip to impute 1,438,827 SNPs from sequences. At the individual level, imputation accuracy was high with a proportion of SNPs correctly imputed between 0.84 and 0.99. The variation in accuracies was mostly due to differences in relatedness between individuals. At a SNP level, the imputation quality depended on genotyped SNP density and on the original minor allele frequency. The imputation did not appear to result in an increase of linkage disequilibrium. The genotype densification not only brought a better distribution of markers all along the genome, but also we did not detect any substantial bias in annotation categories.This study shows that it is possible to impute low-density marker panels to whole genome sequence with good accuracy under certain conditions that could be common to many breeding populations.The online version of this article (10.1186/s12864-019-5660-y) contains supplementary material, which is available to authorized users. In genome-wide analyses, the accuracy of genomic associations and predictions tends to increase with the density of marker coverage , 2. AlthPopulus nigra were made available from the French breeding population. This sample was structured into 35 families resulting from 23 parents. Available families resulted from two mating sets. As shown in the Table For this study, 1039 μg of DNA per accession, using Illumina TruSeq®DNA PCR-Free Sample Preparation kit. Briefly, indexed library preparation was performed with DNA fragmentation by AFA (Adaptive Focused Acoustics TM) technology on Covaris focused-ultrasonicator, all enzymatic steps and clean up were realized according to manufacturer’s instructions. Single or dual indexes were used. Final libraries were quantified by using qPCR using KAPA Library Quantification Kit and Life Technologies QuantStudio TM Real-Time PCR system. Fragment size distribution of libraries was assessed by High Sensitivity DNA assay either on Agilent 2100 Bioanalyzer or on Caliper LabChip®GX nucleic acid analyser. Equimolar pools of multiplexed samples, up to 11, were engaged in sequencing using 4 lanes. After clusters generation on CBot, paired-end sequencing 2 × 150 sequencing by synthesis (SBS) cycles was performed either on a Illumina HiSeq®2000/2500 running in high output mode (one lane) or on Illumina HiSeq®4000 (three lanes). Reads were trimmed with Trimmomatic (v. 0.32) [P. trichocarpa version 3.1 genome [Populus species reference genome, so it was possible to have two alternative alleles and no reference allele in the aligned sequences. We finally kept only SNPs and Indels that were detected by both callers and consistent with Mendelian segregation. To simplify, SNPs and Indels were both called SNPs hereafter. All individuals were genotyped using the Populus nigra 12K custom Infinium Bead-Chip  [We used the sequences of 6 parents previously sequenced by Genome Analyzer IIx from Illumina . For thev. 0.32) , and map1 genome  using BW1 genome . Picard 1 genome  were use1 genome , 40. The1 genome , and (2)1 genome  to filtePropi), and by positions   and between true and imputed positions . In our case, this latter correlation (Cors) was not always available for computation. The reason was that some SNPs had such a low allelic frequency that monomorphic outcomes happened after imputation, leading to zero variances. In order to account for this frequency-dependent outcome, alternatively, we used the option proposed by Badke et al. (2014) [cProps: corrected SNP proportion). FImpute offers an imputation mode based on allelic frequency (option “random_fill”), which gives us a lower bound for imputation accuracy by individual  and by SNP (lbProps: lower bound SNP proportion).We used the FImpute software (v 2.2) , as many. (2014)  to correMEAN_DEPTH); and the level of relatedness defined according to the following categories : parent of factorial , parent of multiple pair mating design (MultiplePair_parents), progeny of factorial , progeny of multiple pair mating design (MultiplePair_progenies) and French wild population (Unrelated). At SNP level, the following factors were considered: sequencing depth (DEPTH) across individuals; per-site SNP quality from the SNP calling step ; minor allele frequency (FreqOri); the ratio between SNPchip density and SNPseq density in non-overlapping 500kb windows (RatioDensity); the p-value of an exact Hardy-Weinberg Equilibrium test (hweOri) for each site as defined by Wigginton et al. (2005) [Weight) obtained by the LDAK5 software [Props, lbProps and cProps) were analysed according to the different factors by a principal component analysis. The factor’s relevance to describe the imputation quality variables were quantified with a Boruta algorithm which is a wrapper built around the random forest classification algorithm implemented in the R (R Core Team 2015) package Borut [We considered different factors describing the heterogeneity between individuals and between markers imputations, and we checked to what extent these factors affected imputation. The first factors were at the individual level: the sequence depth (. (2005)  and the software . The varge Borut . This alPlink software , 51 was P. trichocarpa v3.1 gene set.We were interested in assessing to what extent imputation could change the annotation profile of covered SNPs, notably given the fact that the process involved a substantial change in density. Changes in annotation profiles from enriched to non-enriched but denser genotypes could be of relevance when using the resulting genotypes to fit prediction models for a large spectrum of traits. To get an annotation profile, a gene annotation analysis was performed. The tool Annovar (v. 2017Jul16)  was usedP. trichocarpa reference genome v.3.1. In average, 91.7% of reads were mapped, 76.5% were paired and only 2.2% were singletons. The genome coverage was calculated by individual, and it varied between 4X and 52X, with a mean coverage of 13X , SNPs and Indels in the study were both denoted under the same acronym of “SNPs” hereafter. To have the best quality in genotype calling, we kept the positions where the genotype calling was at least 95% similar between the two callers for all individuals. Mendelian segregation was checked on available trios, and 142,974 positions were removed for which the progeny were inconsistent with parents. For the chip, after applying quality filters, 7540 SNPs were recovered for the population under study and were used to impute 1,466,586 SNPs from sequences along the 19 Chromosomes. In other words, we imputed 99% of the data.Sequence datasets for every individual were mapped on the Cori) was strongly correlated with the individual proportion of SNPs correctly imputed (Propi) per chromosome  than those in the Multiple pair mating design progenies (from 0.93 to 0.96 and from 0.80 to 0.88). Progenies from either of the two schemes had higher Propi and Cori values than those in the parental groups (from 0.87 to 0.90 and from 0.57 to 0.65). The parents of the factorial mating design resulted in the most variable ranges for Propi and Cori with respectively from 0.88 to 0.99 and 0.6 to 0.96, respectively, although that class had on average higher values than those found in parents in the multiple pair mating scheme. Finally, the unrelated individuals are in the lowest part of Propi and Cori variation (with respectively from 0.89 to 0.90 and from 0.62 to 0.63). There was no separate group within individual’s categories  was moderately correlated to Propi  for the multiple pair-mating progenies, factorial progenies and factorial parents than for the multiple pair mating parents and unrelated individuals. In Fig. MEAN_DEPTH) and Propi was found at individual level whereas a poorly significant correlation seems to be present between depth (MEAN_DEPTH) and lbPropi and Cori  suggests that similar information was relayed by these two variables despite the frequency-based correction. The Figure S3 [see Additional file Props, lbProps, cProps), as a function of different classes of minor allele frequency (FreqOri). While for low FreqOri, Props and lbProps distributions remained similar, with increasing frequencies their respective distributions tended to separate from each other. The frequency dependent correction applied to cProps was strongest at low frequencies, making cProps much lower on average than the other two counterparts. With increasing frequency, that correction was weaker with cProps getting closer to both Props and lbProps. This suggests that, while the problem of sensibility to frequencies can be easily overcome, cProps shows imputation qualities that can be far lower than what is actually observed. The first 5 axes of the principal component analysis (PCA) considering the three estimates of imputation quality and six factors that potentially affect this quality, explained 90% of the variance , the SNP quality (QUAL), cProps, the ratio between SNPchip density and SNPseq density (RatioDensity) and, to lesser extent, to the level of unique information contributed by each SNP (Weight). Props was negatively correlated to the FreqOri and positively correlated to the p-value of an exact Hardy-Weinberg Equilibrium test (hweOri) and to lbProps. In Fig. FreqOri (-0.94), and positively correlated to hweOri (0.78), lbProps (0.92) and Props (0.87). Sequencing quality parameter QUAL and DEPTH are highly correlated to the second dimension (respectively 0.68 and 0.8). RatioDensity and cProps were correlated to the third and fifth dimensions whereas the Weight variable was only strongly correlated to the fourth dimension. The Boruta analysis ranked the importance of the different factors considered to explain the variation in Props, cProps and lbProps variables , while their level of polymorphism was not forced towards low allele frequencies (cProps > 0.80). The SNPs distribution along the genome after imputation was more homogeneous than what was initially available with the SNPchip  calculated in SNPchip and SNPseq sets is represented in Fig. A total of 93.4% of SNPchip and 99.79% of SNPseq were annotated  densification in marker coverage is possible in up to 1000 individuals through imputation from a few sequenced nodal individuals (43). Simultaneously, we have achieved imputation qualities higher than 0.84, which is sufficient for a heterozygous species like poplar but may be insufficient when working with species involving inbred lines. This imputation quality is similar to the one obtained on horses  with ImpThe use of a “leave-one-out” cross validation scheme allowed us to ascertain the actual quality of the imputation, both by individuals and by SNP positions. The proportion of alleles correctly imputed by SNP gave the actual value of the imputation quality, although with the drawback of an allele frequency bias. Indeed, a selection based on that proportion by SNP alone could potentially favor positions with low MAF over the rest, as imputation is easier when one of the alternative alleles is rare. The correction we used based on the work of Badke et al.  compensaMany factors can affect imputation quality like LD, density ratio, minor allele frequency or relatedness between target and reference populations , 57. OurImputation requires some degree of LD in existing genomes to reconstruct missing positions . WheneveFrom an operational point of view, our results showed that imputation can represent a good strategy to reduce genotyping costs. By using a few well-chosen sequenced individuals in the population, very good imputation results could be obtained and considerably increase the number of SNPs available. It is therefore possible to create a low-density chip to impute at high density via sequenced individuals. This could minimize differences in imputation quality along the genome and avoid any over-representation of certain chromosome regions. This type of strategy can be used in a breeding improvement program on several generations. Yet, it would be required to add high density genotyping or sequences every generation  in orderIn conclusion, we have demonstrated in this study that high imputation quality is possible even from low density marker sets. The relatedness had an important impact on the imputation quality at the individual level, but it is possible to impute unrelated individuals with a good performance. All factors studied here had an impact on the imputation quality at the SNP level, but there is no obvious way to use their effects as criteria for a pre-imputation filter. The genotype densification towards sequences induced a decrease of linkage disequilibrium, due to the spectra of low allelic frequencies. The densification allowed to correct bias in variant annotation profile of the SNPchip marker set, with a better distribution in all genomic region categories.Additional file 1Sequencing, pedigree and reference information’s of each reference individuals. (CSV 4.33 kb)Additional file 2Relationship between the proportion of alleles correctly imputed by each leave-one-out individual (Propi) and the lower bound individual proportion of SNP correctly imputed lbPropi).The different colors correspond to the different individual classes in the mating regimes, and each point represents the values for one chromosome and one individual. (PDF 11.1 kb)Additional file 3Relationship between the sequencing depth and imputation quality variables at individual level. On the top of the diagonal: Pearson’s correlations. The distribution of each variable is shown on the diagonal. On the bottom of the diagonal: the bivariate scatter plots. (PDF 58.3 kb)Additional file 4Props (Green), lbProps (Purple), cProps (Orange)), as a function of different classes of minor allele frequency (FreqOri). (PDF 42.3 kb)Variation of the three different estimates of imputation quality at the SNP level (


FST, to remove regions of low variation across pigs. An extraordinary large sweep between 52 and 61 Mb on chromosome X was separately analyzed based on SNP-array data of F2 individuals from a cross of Goettingen Minipigs and large pigs.Domestication has led to substantial phenotypic and genetic variation in domestic animals. In pigs, the size of so called minipigs differs by one order of magnitude compared to breeds of large body size. We used biallelic SNPs identified from re-sequencing data to compare various publicly available wild and domestic populations against two minipig breeds to gain better understanding of the genetic background of the extensive body size variation. We combined two complementary measures, expected heterozygosity and the composite likelihood ratio test implemented in “SweepFinder”, to identify signatures of selection in Minipigs. We intersected these sweep regions with a measure of differentiation, namely 2 cross-breds.Selective sweep analysis identified putative sweep regions for growth and subsequent gene annotation provided a comprehensive set of putative candidate genes. A long swept haplotype on chromosome X, descending from the Goettingen Minipig founders was associated with a reduction of adult body length by 3% in FAR, previously known to influence pig performance and carcass traits, is the most obvious potential candidate gene within this region.The resulting set of genes in putative sweep regions implies that the genetic background of body size variation in pigs is polygenic rather than mono- or oligogenic. Identified genes suggest alterations in metabolic functions and a possible insulin resistance to contribute to miniaturization. A size QTL located within the sweep on chromosome X, with an estimated effect of 3% on body length, is comparable to the largest known in pigs or other species. The androgen receptor The online version of this article (10.1186/s12864-018-5009-y) contains supplementary material, which is available to authorized users. Sus scrofa), the process of domestication of the wild boar led to animals that span from large fattening pigs to the so called ‘miniature pigs’ or simplified ‘minipigs’. Their sizes differ by up to one order of magnitude. Among the minipigs the Goettingen Minipig (GMP) is one of the smallest breeds under a stringent breeding scheme [IGF1).The livestock species of today display vast phenotypic variation. Domestication and breed formation processes have shaped these species by increasing the variation in traits related to, performance, fitness, morphology and appearance, thereby changing the - phenotypically rather uniform - wild ancestors to the illustrious collection of our modern breeds. Focusing on body size, Haldane  discusseg scheme , 4. The g scheme . This pig scheme  suggesteSOCS2 and GRB10 with IGF1 could exist that plays an important role in the dwarfism of the GMP. Due to the limited marker density of the SNP array it was not possible to reveal causal mutations.The availability of porcine SNP chips offers the possibility to screen the genome for regions carrying genetic variants associated with the reduced size of minipigs. Gaerke et al.  conducteThe current reference genome is based on the sequence of a Duroc pig with the first studies, using this reference to provide insight into the porcine demography and evolution  and into6 biallelic SNPs genome-wide, of which 29 × 106 were polymorphic or fixed for the alternative allele in the minipigs. After filtering, 35 × 106 loci were polymorphic in all samples and 19.8 × 106 only in minipigs. For European domestics, European wild boars, Asian domestics and Asian wild boars, these numbers were 19 × 106, 9.4 × 106, 19.5 × 106 and 19.2 × 106, respectively. Subsequent in-silico pooling left 27.6 × 106 loci with sufficient information to compare minipigs against large pigs.Biallelic SNPs are the most common class of variants used in genetic studies of animal genomes. Since SNP calling from WGS data is not limited to prior knowledge on variant positions, the number of SNPs is an indicator of variability in the analysed dataset and of strictness of the variant discovery and filtering. SNP calling from the DNA sequencing data revealed 46 × 10FST also showed that the minipigs were closer to the Asian breeds than to the European breeds , while both minipig breeds were marginally closer to the domestic groups of both continents than to the respective wild boars. This effect is smaller for the GMP , whereas there is clear distinction for the BMP, which is much closer to both domestic groups than to the wild boars . The FST value between both minipig groups is 0.09. The highest overall differentiation has been estimated between European and Asian wild boars , 8 (4.6 Mb), 13 (5.2 and 2.9 Mb), 14 (3.6 Mb) , which represent functional categories, linked to every detected gene were checked for over-representation of certain GOs within sweeps compared to the unselected background, to identify functional categories rather than single candidates. The Ensembl porcine gene set 79 annotation within sweep regions on the autosomes revealed 524 genes  known from the studies of Rubin et al.  and Ai eThe phylogenetic tree of all sequenced animals based on all markers inside the first region Fig. , shows tSince the haplotype carried in the region chrX:52–61 Mb appears to be typical for minipigs, we used genotyping data from two former studies  to deter1 and F2 generation. Using the aforementioned SNP loci, we determined which haplotypes were inherited. As shown in Table 1 females should be heterozygous and all males should be hemizygous for the large pig haplotype. In the F2, half of the females are expected to be homozygous for the large pig haplotype, the other half heterozygous. The F2 males should be hemizygous, one half for the minipig haplotype, the other half for the large pig haplotype. The observed haplotypes match the expected Mendelian proportions.Under the assumption of no recombination within the selective sweep region on X and the cross-breeding scheme of Pant et al. , we expeThe distribution of phenotypic values of the analyzed traits of height and length at the ages of scanning and slaughtering are displayed in Table p-value was 0.0718 and the subsequent conservative LSD test showed significant differences between the haplotypes. Only the sex and the age were important for length at age of scanning. The breed of the mother in the P0 did not influence the size traits of young animals at age of scanning. Figure Table For the two traits, where the haplotype effect was significant, males carrying an X-chromosome copy inherited from the minipig ancestor were significantly smaller than the ones carrying the large pig copy, while there was no significant size difference between homozygous females of large pig origin and the heterozygous females.The respective violin plots of the linear predictors, which can be interpreted as corrected phenotypes for the four haplotype*sex classes, show a clear distinction of the males by haplotype origin.AR, which has several functions in physiological processes related to growth, body conformation and reproduction. Besides its crucial role in spermatogenesis and male fertility [AR suffer a late onset of obesity while being normally sensitive to insulin [AR is activated by the growth factors IGF, KGF and EGF in tumors [We found 18 known genes lying within the first sweep region on chromosome X  against the Duroc reference. When the de novo assembled GMP genome with a length of 2.44 Gb [One of the often discussed issues for the quality of WGS studies is the quality of the alignment and the depth at which samples have been sequenced. The proportion of aligned reads to the current reference genome of a Duroc  was roug 2.44 Gb  was mappThe number of discovered SNPs in a genome depends on the sequence identity between the reference genome and the examined samples, which is in turn dependent on the phylogenetic distance, the variation inside the breeds and the number of individuals. Additionally, the reliability of calling SNPs and determining genotypes from WGS data is also dependent on the sequencing depth, where for example, reliable calling of a homozygous (heterozygous) SNP requires 15X (30X) coverage . From thST of the sequenced animals showed a clear differentiation between European and Asian pig breeds. This result is in agreement with the current scientific consensus, that domestication occurred independently in Europe and Asia around 9000 years ago [Analysis of the genetic distance and Fears ago . In contears ago , the hisears ago . Later, ears ago . Our resears ago .In the phylogenetic tree, the Goettingen and the Mini-LEWE are located between the Asian and the European cluster. Looking at the breed histories, both breeds are synthetic crosses of the Vietnamese Potbellied Pig with European breeds. In the case of the Mini-LEWE, the crossing partner was the Saddleback pig and “Deutsches veredeltes Landschwein” (comparable to Large White) . The GMPCOMT variant increases the risk of having children with reduced birth weight [TPST2 or PATZ1 leads to growth retardation in mice [This study compares two contrasting groups in order to reveal the genetic background of the reduced body size: various large pig breeds from all over the world versus a group of two minipig breeds. Such a study design has been proven efficient before in detecting regions of differentiating selection before in chicken  and pigsh weight , knock o in mice , 35.SOCS2, GRB10 and IGF1 as potential cause of small body size in minipigs. This finding supported the hypothesis of Simianer and Köhn [IGF1. This hypotheses seems natural, since the effect of IGF1 on growth in, for example, Pygmies [IGF gene, a signature of selection would be expected around the respective gene as it was found in dogs, where small breeds carry a unique coding sequence of IGF1 [IGF genes or receptor loci. This coincides with findings of Zenobi et al. [IGF1 or IGF2, nor to a missing response to or reduced secretion of growth hormones. Reduced transcription, manifested in low transcription levels of the IGF genes or other growth hormones, could be ruled out and alterations in the underlying genes seem unlikely. But still the insulin growth factor signaling cascade is a widely considered key mechanism for growth. Our results suggest an alternative function: A possible mechanism behind the dwarf phenotype could be a resistance of the target tissues to insulin. Symptoms of this, i.e. a hampered blood glucose clearance after insulin stimulation, which could be facilitated by a disordered lipid metabolism [PPARG is an outstanding candidate, having direct effects on insulin resistance [A former study by Gaerke et al.  on the sand Köhn , that th Pygmies  is known of IGF1 . Howeveri et al.  who conctabolism  or an intabolism  have beetabolism . Focusintabolism . Even aftabolism , suspectsistance  and muscsistance . Furthersistance , 45.GH) deficiency [GH is also secreted in the pituitary gland and it was recently communicated that a knock-out of the growth hormone receptor gene GHR using genome editing technology led to further miniaturization of a bama minipig at 15 kg maturity weight [GH or its receptor genes, we find only the CLR test to show increased evidence of selection about 1 Mb away from GHR, but no sign of differentiation between the large and the minipig group. Therefore our results do not support the hypothesis that selection on one of the GH genes is underlying the minipig dwarfism.Another kind of proportional dwarfism is caused by growth hormone  that controls cell proliferation and differentiation. Klingseisen and Jackson  report t signals  and has  signals .The porcine X-chromosome carries a selective sweep of outstanding extent . Using tThe subsequent evaluation of an F2 generation from a GMP x Yorkshire and a GMP x Duroc cross for four body size traits showed that males inheriting the GMP haplotype were significantly smaller than a male carrying the European haplotype for three of the four traits, while there was no significant effect on the fourth trait (Length at scanning). These results confirm that the analyzed region carries an allele influencing body size. Due to the cross-breeding scheme no females carrying only the minipig haplotypes on both chromosomes were available. The lack of a significant differentiation between females carrying the large pig haplotype on both copies of the chromosome X and heterozygous females indicates that the large pig haplotype could carry an allele that is dominant over the allele of the minipig haplotype covering the effect of the GMP allele, even though another study  found thPLAG1, CHCHD7 and MOS was found with an allele substitution effect of 2 cm height at withers [LCORL and PLAG1, together explained a difference of 5.3 cm in body length in domestic pigs. Since we could not find signals of selection neither at LCORL nor PLAG1 in our study, it is noticeable that the effect size of the chrX locus described herein has a similar effect size. Among the genes located in this region, the androgen receptor appears to be the most prominent candidate for a gene underlying the growth differences between pigs carrying opposite haplotypes. AR is influenced by several growth factors [AR [AR. It could be shown that the MiniLEWE allele led to higher expression of the AR in several tissues including the uterus, and had effect on several performance and carcass traits. The haplotype that contains AR carried by all studied GMP was most likely identical to the aforementioned MiniLEWE allele and introduced by the Vietnamese Potbellied Pig during breed foundation. This pig breed was originally not only chosen for its small size, but also for the much higher litter size compared to the Minnesota Minipig [r = 0.2 in pigs, the current breeding scheme for low body weight and high fertility might have favored the Asian haplotype and AR seems to be one of the underlying causal genes for the miniature body size.The differences of 3.5% (3 cm) in body length at the age of slaughtering and 4.4% (3 cm) in height at age of slaughtering are QTL effects of considerable magnitude. Reviewing other QTL studies on size, height and growth shows that the underlying QTL architecture can be highly different dependent on trait or organism. Whereas, in humans, height is a highly heritable trait, influenced by at minimum 180 genetic loci  and SNP  withers . Rubin e factors , has kno factors , 17 and  factors , 66. Anotors [AR  on perfo Minipig , which F Minipig  estimateComparison of WGS data of minipigs against data of various large pig breeds is a logical approach in order to reveal the genetic background of body size in pigs. Signature of selection analysis with multiple complementary methods provided a comprehensive set of putative sweep regions, spanning approximately 2% of the autosomal genome. The set of associated genes and the consecutive GO term overrepresentation analysis suggest that energy metabolism, alterations in central elements of the MAPK pathway, and a possible insulin resistance might play a role in body size inheritance of miniature pigs. Additionally, the density of resequencing data proved to be especially useful to analyze a large sweep region on chromosome X, since the SNP chip available so far holds just few SNPs of limited information in that region. We identified three SNPs on the genotyping chip, serving as perfect markers to determine the respective haplotypic state of an individual in future studies. The effect size of the QTL of 3 cm in body length and height underlying this selective sweep is comparable to the largest QTL for body size traits known from other studies in mammals.We extracted DNA from 10 representative contemporary GMP sows from the experimental herd of the University of Goettingen. DNA from 2 Mini-LEWE (BMP) sows, a miniature breed developed in Berlin, Germany and a DNA-pool of 10 female BMPs from the University of Veterinary Medicine Hannover was also prepared. Whole genome re-sequencing was performed at the Science for Life laboratory at Uppsala University, Sweden on an Illumina HighSeq2000 as paired end sequencing with an aimed average sequencing depth of 12X. The raw sequencing data is deposited in ENA under project accession PRJEB27654.We added Publicly available re-sequencing data underlying the studies of Rubin et al. , Fang etWe aligned all sequence reads to the reference genome susScrofa3  using tIn order to obtain a reliable dataset for the selective sweep analysis, we applied a stringent filtering process on the variant call set, by first removing InDels and multi-allelic SNPs and filtering with GATK for a comprehensive set of quality criteria described in the methods section. The filters for chromosome X were adjusted separately taking into account the reduced depth of this chromosome in males. In addition, to keep a sample record a minimum genotype quality (GQ) of 20 was required for sequenced individuals and a minimum depth of coverage of 4 was required for pools.in-silico pool (MPP) comprised the Goettingen Minipigs, the Mini-LEWE and the Mini-LEWE-pool. For this, we calculated the reference allele frequency per breed for each locus. For each called SNP, the reference allele frequency in each in-silico pool was then calculated as the unweighted average of the respective breed reference allele frequencies. SNP loci for which less than 50% of the breeds in one of the two groups had a record were excluded.For further analyses we constructed two contrasting in-silico pools: the large pig virtual pool (LPP) made up of Duroc, Hampshire, Jiangquhai, Large White, Landrace, Meishan, Pietrain, the European wild boars and the Asian wild boars. The minipig FST with custom R scripts [For the detection of genomic regions with influence on the small size of the minipigs, we calculated heterozygosity and  scripts  and comb scripts .Hexp = 2p(1 − p) where p is the reference allele frequency in the MPP and afterwards averaged it in sliding windows of 100 kb with 80% overlap. We then normalized the Hexpvalues of individual windows into Z-scores by adjusting the value using the mean and standard deviation derived from all 100 kb windows along autosomes and the X-chromosome independently. We defined candidate selective sweeps using an outlier approach whereby a window that fell below a value of − 2.34 (lowest 1%) was required to initially call a sweep, such sweeps were then extended to each side until values exceeded − 1.64 (lowest 5%).We calculated expected heterozygosity per locus as The CLR test , implemepi is the frequency of the reference allele in group i, ni is the number of samples within a group i, and The differentiation between the LPP and the MPP was determined by the fixation indexed after , where pST signals. The heterozygosity signature method found 5.3% of the genome to be under selection, ~ 30% thereof (1.6% of the genome) intersecting with extreme FST signals. 0.3% of the whole genome was shared between CLR and heterozygosity signature. Finally, we used the union of CLR and expected Heterozygosity signals intersecting with FST for further analysis.A selective sweep was assumed, when regions showed decreased expected heterozygosity in the minipig or when the composite likelihood ratio test overlapped with signals of high differentiation between the two groups. We required a minimum width of 200 kb and extended the final regions by 0.5 Mb to each side. Figure 1 - similarity between samples, where similarity was the proportion of a genome of an individual being identical by state (IBS) with another animal’s genome. Based on this, we constructed the neighbor joining tree using PHYLIP [FST values from all autosomal SNP loci with 90% or more animals with genotypes that passed filters. This was done in each contrasting combination of the individual data of European breeds, Asian breeds and minipigs and for the subgroups European domestic breeds, European wild boars, Asian domestics, Asian wild boars, Goettingen Minipigs and Mini-LEWE, respectively , Sj is the sex, Aijk is the animal’s age at measurement in days, Hk is the haplotype, either 1 for homozygous females and hemizygous males carrying the large pig haplotype, 2 for heterozygous females and 3 for hemizygous males carrying the minipig haplotype. Each bl depicts the linear regression coefficient of the age or the respective interaction of a factor with age. eijk is the residual error. The full model was reduced by stepwise backward selection of factors with the highest p-values until only significant factors remained.Effects of the minipig haplotype on the four phenotypical traits “shoulder height at slaughter”, “body length at slaughter”, “shoulder height at age of scanning” and “body length at age of scanning” were estimated using proc. “mixed” from SAS 9.4 . The fulWe employed the R package “rehh”  to estimAdditional file 1:Figure S1. Multi-Dimensional-Scaling of the distance matrix underlying the phylogenetic tree, based on chromosomes 1, 8 and 13. (DOCX 95 kb)Additional file 2:Table S1. Genome-wide estimated FST values between different contrasts of breed groups for all loci with call-rate ≥ 90%, over diagonal, standard errors below diagonal. (XLSX 10 kb)Additional file 3:Table S2. Genome-wide estimated FST values between different contrasts of breed groups for all loci with call-rate ≥ 90%, over diagonal, standard errors below diagonal. (XLSX 9 kb)Additional file 4:Table S3. Putative selective sweeps with genes contained. (XLSX 12 kb)Additional file 5:Table S4. Potentially overrepresented GO-Terms in selective sweeps. (XLSX 14 kb)Additional file 6:Figure S2. Neighbor-joining tree based on all SNPs in the second part of the selective sweep region on chromosome X between 64 to 96 Mb. (DOCX 61 kb)Additional file 7:Table S5. Overview of SNPs in the region from 52 to 61 Mb on chromosome X, including results of filtering. (XLSX 10 kb)Additional file 8:Table S6. Occurring haplotypes in region 52 to 61 Mb on chromosome X and numbers of carrier animals. (XLSX 10 kb)Additional file 9:Table S7. Genes located in first region of large sweep on chromosome X. (XLSX 9 kb)Additional file 10:Table S8. Overview of sampled breeds and descriptive statistics of the re-sequenced samples. (XLSX 10 kb)


Capsicum annuum) germplasm that control the number of potato virus Y (PVY) particles entering the plant (i.e. effective population size at inoculation) and the PVY accumulation at the systemic level (i.e. census population size). Using genotyping‐by‐sequencing (GBS) in a core collection of 256 pepper accessions, we obtained 10 307 single nucleotide polymorphisms (SNPs) covering the whole genome. Genome‐wide association studies (GWAS) detected seven SNPs significantly associated with the virus population size at inoculation and/or systemic level on chromosomes 4, 6, 9 and 12. Two SNPs on chromosome 4 associated with both PVY population sizes map closely to the major resistance gene pvr2 encoding the eukaryotic initiation factor 4E. No obvious candidates for resistance were identified in the confidence intervals for the other chromosomes. SNPs detected on chromosomes 6 and 12 colocalized with resistance quantitative trait loci (QTLs) previously identified with a biparental population. These results show the efficiency of GBS and GWAS in C. annuum, indicate highly consistent results between GWAS and classical QTL mapping, and suggest that resistance QTLs identified with a biparental population are representative of a much larger collection of pepper accessions. Moreover, the resistance alleles at these different loci were more frequently combined than expected by chance in the core collection, indicating widespread pyramiding of resistance QTLs and widespread combination of resistance QTLs and major effect genes. Such pyramiding may increase resistance efficiency and/or durability.In this study, we looked for genetic factors in the pepper ( They have been widely used because they provide an almost complete resistance against pathogens, are environmentally friendly and can be easily introgressed by backcrossing. The major limit of this type of genetic control relies on the evolutionary potential of the pathogen population  – pepper (Capsicum annuum) system, the greater durability of the polygenic resistances is mostly due to the additional level of resistance conferred by the genetic background, which reduces viral accumulation  in Arabidopsis thaliana , which is constant (0.59). The observed heterozygosity remains unchanged and low (0.035), a result explained by the multiplication through selfing of the accessions and the preferential autogamy of C. annuum. Previously, the structure of the whole Capsicum spp. collection has revealed six distinct clusters , with some degree of admixture between the clusters  was composed of the wild subspecies C. annuum var. glabriusculum only  included all the cultivated subspecies nly Fig. . The gennly Fig. . The firet al., The pairwise kinship matrix among the 256 individuals was assessed using the VanRaden equation  as impleP < 0.001, Kruskal–Wallis test), attesting to the large diversity of quantitative resistance in the pepper germplasm . They also both showed a high broad‐sense heritability, with h2 = 0.98 for IF and h2 = 0.80 for VA. The four genetic clusters previously identified were significantly contrasted for IF and VA  induced by a green fluorescent protein (GFP)‐tagged PVY clone on the cotyledons, and the relative virus accumulation (variable VA) of a variant of PVY isolate SON41p in the apical leaves. Both traits were highly variable and showed significant differences between the accessions  Table . Four SNis Table .et al., pvr6 gene, and one QTL on chromosome 6 (VA‐6) , 7 (PVY‐7) and 12 (PVY‐12) , 6 (position 234 143 013 bp), 9 (position 58 056 303 bp) and 12 (position 235 513 719 bp). Among the core collection, none of the accessions carried a resistance allele at the SNPs localized on the four different chromosomes at the same time, 11 accessions carried a resistance allele at the SNPs localized on three different chromosomes, and 30 accessions carried at least a resistance allele at two SNPs localized on two different chromosomes Table .2 = 28.58, P = 3.7 × 10−7 for chromosome 4 position 1 151 249 bp and chromosome 6 position 234 143 013 bp detected with CMLM; χ2 = 6.44, P = 0.02 for chromosome 4 position 340 333 bp and chromosome 6 position 234 143 013 bp detected with MLMM). The same result has been found for SNPs on chromosomes 4 and 9 , for SNPs on chromosomes 4 and 12 , for SNPs on chromosomes 6 and 9  and for SNPs on chromosomes 6 and 12 .We then analysed the distribution of resistance and susceptibility alleles for pairs of SNPs belonging to different chromosomes Fig. . The resr2 = 0.02 for the SNPs detected with the MLMM and 0.11 for the SNPs detected with the CMLM , while VA is a proxy of the census population size. Ne can be defined as the number of viral particles that pass their genes to the next generation  on inoculated pepper cotyledons, and VA, the PVY accumulation  at the systemic level. IF is a proxy of the effective population size . Taranto et al. (r2 = 0.2) in a collection of 222 C. annuum accessions. Multiple factors can cause changes in LD, such as bottlenecks, selective breeding or mating system  and pvr2 was included in the confidence interval of candidate genes .The association mapping has revealed seven different SNPs in association with IF and/or VA Table . On chrones Fig. . To confes Table . The assNe at inoculation) and VA  of the gene, which encodes a truncated, non‐functional protein  isolates , and therefore provides resistance against at least two viruses belonging to different families.Finally, the QTLs have been detected using two variants of the PVY‐SON41p isolate and we have not tested yet if their spectrum of action could be extended to other PVY strains. The PVY‐SON41p isolate belongs to the clade C1, which includes most of the PVY isolates infecting C. annuum core collection has been analysed , 6 (nine to ten accessions) and 9 (nine accessions). None of these accessions carry a resistance allele at the QTL on chromosome 12, but few resistance alleles were present at this QTL in the core collection, the minor allele frequency of the SNP being low (2%). Even if the number of wild accessions in the core collection is small, our data tend to show that the resistance QTLs were already in association in the wild populations of C. annuum. As we have already seen, our results also demonstrate that the resistance QTLs are in coupling in the cultivated accessions. We can therefore hypothesize that these associations between resistance QTLs were advantageous for the plants, and that they have been partly maintained in pepper accessions through the process of crop domestication. Since the locus detected on chromosome 4 is most likely the major resistance gene pvr2, the domestication process could have selected for the pyramiding of multiple resistance QTLs, but also for the pyramiding of both resistance QTLs and major resistance genes. In past decades, multiple breeding strategies have been proposed to control plant disease  maintained in the Institut National de la Recherche Agronomique (INRA) in Avignon is composed of 1352 non‐redundant accessions, including 11 cultivated and wild species, which originated from 89 countries and five continents  that can be divided into the cultivated subspecies C. annuum var. annuum, which represents more than 90% of the C. annuum accessions, and the wild subspecies C. annuum var. glabriusculum. All the accessions have been previously genotyped with a set of 28 simple sequence repeat (SSR) markers  and the observed heterozygosity (Ho) of the core collection were calculated using GenAlEx v. 6.5 software  and the relative PVY accumulation (VA). Two viruses deriving from a cDNA clone of PVY isolate SON41p and belonging to the pathotype PVY‐0,1,3 were used. Both clones carried a substitution in the VPg, the 115K or the 119N, which allows them to overcome the Nicotiana tabacum 'Xanthi' plants. Then, the two cotyledons of each plant were mechanically inoculated 28 days after sowing, for both PVY clones. For IF, the number of foci showing green fluorescence was quantified 5 days post‐inoculation under specific light wavelength (450–490 nm) for 20 cotyledons per accession. For VA, 1 g from three uninoculated apical leaves was sampled from each inoculated plant 1 month post‐inoculation. Samples from each plant were separately ground in a phosphate buffer . To reduce the number of samples and make the VA estimation achievable, the ten samples of ground leaves per accession were pooled into two groups of four samples and one group of two samples, reducing the experiment from ten measures of VA per accession to three. A control experiment had been performed previously on 30 plants to confirm that the estimation of VA was accurate when using pooled samples . Next‐generation sequencing was performed in a 150‐bp single‐read mode using three lanes on a HiSeq3000 platform  at Genotoul .The core collection was sown a third time in greenhouse conditions. One plant per accession was used for DNA extraction. DNA was isolated from 80 mg of frozen young leaves with the DNeasy Plant Mini Kit  and the extracted DNA was quantified using a Qubit fluorometer . DNA of high quality was obtained for 276 accessions, which were used to perform GBS. The genome complexity was reduced by ddRADseq (Peterson https://github.com/timflutre/quantgen/blob/master/demultiplex.py). Adapters were then removed in the 3ʹ ends and the sequence reads were filtered for quality  using cutadapt , ranging from 1 to 10. The optimal K value was inferred from the log probability of the data (LnP(D)) and delta K  estimated from pairwise comparisons between SNPs.Population structure was estimated using the Bayesian model‐based clustering method implemented in STRUCTURE v. 2.3.4 software . For the two phenotypic traits (IF and VA), broad‐sense heritability was estimated using the formula h2 = σ2G /(σ2G + σ2E/n), where σ2G corresponds to the genotypic variance, σ2E to the environment variance and n to the number of replicates . A log(x + 1) transformation was applied to the number of PVY primary IF and a cube‐root transformation was applied to the VA to approximate a normal distribution. Associations between susceptibility and resistance alleles for pairwise SNPs among the core collection were analysed using chi‐squared tests and Monte Carlo simulations (10 000 000 replicates).Statistical analyses were performed using R software  metaprogramme of INRA and by the Région Provence‐Alpes‐Côte d’Azur. The experimental work was supported by the SMaCH metaprogramme and the ANR PRC project ArchiV (2019‐2022).The authors declare that there are no conflicts of interest.Fig. S1 Allelic richness (score) capture for different core collection sizes. A random (red) and a maximization (black) sampling strategies are represented.Click here for additional data file.Fig. S2 Polymorphisms distribution in the 12 pepper chromosomes for (a) all SNPs, (b) insertion polymorphisms and (c) deletion polymorphisms. The number of SNP, insertion or deletion is indicated in brackets for each chromosome.Click here for additional data file.Fig. S3 Linkage disequilibrium decay (r2) against the genetic distance (bp) of each chromosome throughout the CM334 pepper reference genome.Click here for additional data file.Fig. S4 Delta K values calculated by Evanno’s method.Click here for additional data file.Fig. S5 Morphological and developmental phenotypes of the accessions in the Capsicum annuum core collection. For each plot, the accessions are distributed among the four clusters determined by the structure analysis. The whole INRA pepper germplasm (Capsicum spp.) has been previously characterized for several plant and fruit traits . The traits are (a) the fruit length (cm), (b) the fruit width (cm), (c) the fruit weight (gram), (d) the pericarp thickness (mm), (e) the flowering earliness, which represents the number of days between sowing and first anthesis and which is expressed relatively to a control genotype  and (f) the number of leaves on the primary axis. The letters a and b indicate the different groups obtained after pairwise comparisons using the Nemenyi test (P < 0.05).Click here for additional data file.Fig. S6 Distribution of pairwise kinship estimates across the core collection.Click here for additional data file.Fig. S7 Distribution of the pepper accessions according to the alleles they carry for six pairs of SNPs belonging to different chromosomes. The two bars split the accessions according to the allele at the first SNP, and the black and grey colours split the accessions according to the allele at the second SNP. Chi‐squared test with Monte Carlo simulation (100 000 000 replicates) was used to compare accession distribution among the different categories .Click here for additional data file.Fig. S8 Distribution of interchromosomal linkage disequilibrium (LD) between (a) chromosomes 4 and 6, (b) chromosomes 4 and 9, (c) chromosomes 4 and 12, (d) chromosomes 6 and 9, and (e) chromosomes 6 and 12. LD between pairs of SNPs detected in genome‐wide association studies are indicated by dotted lines.Click here for additional data file.Fig. S9 Control experiment for the accuracy of the virus accumulation measured with pooled samples. Two pepper accessions with 15 plants per accession were inoculated with a cDNA clone of PVY isolate SON41p. Apical leaves of each plant were ground in a phosphate buffer  and the virus accumulation was assessed with a quantitative DAS‐ELISA for each plant. Samples of ground leaves were then pooled. For each accession, 1.8 mL of ground leaves belonging to two different plants and 0.9 mL of ground leaves belonging to four different plants were pooled together. Once again, a DAS‐ELISA was performed to quantify the virus accumulation of the pooled samples. The relative virus accumulation obtained for the individual plants and the pool of (a) two and (b) four plants was compared for the two pepper accessions (illustrated by circles and triangles). Significant positive correlations were found for both types of pools.Click here for additional data file.Table S1 List of the 256 Capsicumannuum accessions of the core collection and clustering of these accessions based on population structure. For 50 accessions, the alleles at the locus pvr2 are given.Click here for additional data file.Table S2 SNPs identified with genome‐wide association studies and associated with the number of primary infection foci induced by PVY‐GFP (IF) and PVY accumulation (VA) in pepper when excluding the wild subspecies C. annuum var. glabriusculum from the core collection. NS: not significant.Click here for additional data file.Table S3 Number of accessions carrying a resistance (R) or susceptibility (S) allele at the most significant SNPs identified on chromosomes 4 (positions 1 151 249 and 340 333 bp), 6 (position 234 143 013 bp), 9 (position 58 056 303 bp) and 12 (position 235 513 719 bp). For the two SNPs analysed at chromosome 4, when at least one of them was carrying a resistance allele, the accession was considered to carry a resistance allele at this chromosome.Click here for additional data file.Table S4 Candidate genes associated with the SNPs detected for the number of primary infection foci and the virus accumulation.Click here for additional data file.Text S1 Candidate genes in QTL confidence intervals that could be linked to PVY resistance.Click here for additional data file.


Gracilariopsis lemaneiformis is an important commercial macroalga. Whole-genome resequencing was conducted to identify single nucleotide polymorphisms (SNPs) in parental gametophytes and 60 F1 gametophytes of Gp. lemaneiformis in this study, and 9,989 SNPs located in nonrepetitive sequences were obtained. Among these SNPs, 92.02% of loci were identified as having a heterozygous genotype in at least one gametophyte, and 48.07% of loci had identical heterozygous genotypes in the 62 gametophytes. For each gametophyte, the proportions of homozygous and heterozygous loci ranged between 13.74 and 21.61% (mean of 17.04%) and between 66.36 and 83.59% (mean of 77.12%), respectively. The remainder were missing loci, representing an average 5.84%. Sources of heterozygous SNPs were free of exogenous DNA contamination, cross contamination among individuals, plastid and mitochondrial sequences, chimeras of different thallus parts or different cells, and repetitive sequences. Genotypes of heterozygous SNPs were verified by Sanger sequencing of PCR products and monoclones. Duplications of chromosomal rearrangements in the genome of Gp. lemaneiformis might explain the presence of heterozygous SNPs in haploid gametophytes. Gracilariopsis lemaneiformis (Bory de Saint-Vincent) E. Y. Dawson, Acleto & Foldvik is a commercially important macroalga that is mainly utilized for agar extraction and abalone aquaculture , allowing the selection of a trait by analyzing molecular markers that are closely linked to the target trait’s loci. It is an effective way to accelerate the breeding process  is heterozygous at one SNP locus, F1 gametophytes (n) should be homozygous and the segregation ratios of the two genotypes should be 1:1 at this locus. However, we identified abundant heterozygous SNP loci in gametophytes, and we intend to investigate where the heterozygous SNP loci originated. This study promotes investigating the genomic characteristics of Gp. lemaneiformis, which will be of great importance in guiding studies on the genomic evolution of marine algae.Because of the industrial importance of the species and the usefulness of SNP markers in species breeding, SNPs were exploited in the parental gametophytes and 60 FGp. lemaneiformis includes a gametophyte phase, which is equal to the haploid population, and it can be used for genetic linkage mapping. In this study, the female gametophyte ♀6 and male gametophyte ♂9 were produced from Cultivar Lulong No. 1, which is tetrasporophyte strain. F1 tetrasporophytes were produced from a cross between ♀6 and ♂9. Then, mature F1 tetrasporophytes gave rise to tetraspores, which subsequently developed into F1 gametophytes. Whole-genome resequencing was carried out for the two parental gametophytes and 60 F1 gametophytes.The life history of Genomic DNA was extracted using a Plant Genomic DNA Kit  following the manufacturer’s instructions, and at least 1 μg qualified genomic DNA was used for constructing a whole-genome resequencing library. Genomic DNA was sheared randomly into fragments, and 300–500-bp fragments were purified using gel electrophoresis. The sequencing library was prepared using a TruSeq™ DNA Sample Prep Kit and a TruSeq PE Cluster Kit. The prepared library was sequenced with paired end reads (2 × 150 bp) on an Illumina HiSeq 4000 sequencing platform. The whole-genome resequencing data of 62 gametophytes have been deposited in Short Read Archive (SRA) database with Project number PRJNA574029, under the accession number SRP223151.https://github.com/jstjohn/SeqPrep) with the following parameters: “'-q 20 -L 75 -B AGATCGGAAGAGCGTCGTGT -A AGATCGGAAGAGCACACGTC.” Sickle (https://github.com/najoshi/sickle) was applied to perform read data trimming with default parameters to produce clean data  in this study. The high quality sequencing reads were aligned to the reference genome sequence using BWA (http://bio-bwa.sourceforge.net/) software in “bwa aln” mode. The genome survey sequencing of Gp. lemaneiformis had been conducted previously (http://samtools.sourceforge.net/) software, the sequencing depth and coverage were calculated based on alignments performed by custom PERL scripts.Raw sequence reads were processed using custom PERL scripts to separate them into individual fastq format files based on the barcode sequence of each sample. Contamination reads, like those containing adaptors or primers, were identified using SeqPrep . Then, variant call format (VCF) files were generated by quality filtering (VariantFiltration with parameters: QD < 2.0 || FS > 60.0 || MQ <40.0 || SOR > 10.0). Furthermore, VCF files were filtered using VCFtools .The valid BAM file was used to detect SNPs using the GATK “UnifiedGenotyper” function  in one parent but heterozygous in the other, were suspected to result from cytoplasmic inheritance. Sequences of 400 bp containing the target SNP were aligned to the complete plastid and mitochondrial sequences.2+, 0.2 mm dNTPs, 0.5 μm of each primer, 0.5 U Taq DNA polymerase  and autoclaved distilled water to a total volume of 20 μl. The PCR program performed was as follows: initial denaturation at 94°C for 5 min; 35 cycles of denaturation for 1 min at 94°C, annealing for 1 min at 60°C and extension for 1 min at 72°C; a final extension step for 5 min at 72°C. Sanger sequencing of PCR products and monoclones were completed by TsingKe Biological Technology SNP primers were designed using Primer 6.0 and synthesized by Sangon Biotech . The reaction solution for PCR amplification contained 20 ng genomic DNA, 1× PCR buffer, 1.5 mm MgGp. lemaneiformis was cut from the base to the top, obtaining 22 1 cm segments. The segments included nine representative parts: base, middle part, and tip of primary/secondary branch; junctions of primary and secondary branches; junctions of secondary and tertiary branches; tertiary branches. The genomic DNA of every segment was extracted and amplified using primers for SNP locus scaffold1837_730, scaffold7339_502 and scaffold7339_533, and then, the PCR products were analyzed by Sanger sequencing.To determine whether heterozygous SNPs resulted from chimerism, a single gametophyte of A gametophyte thallus was used to produce a cell suspension by grinding in liquid nitrogen and filtering. Then, single cells were isolated in a glass capillary tube under a microscope. Genomic DNA of each cell was extracted and amplified using a Single Cell Whole Genome Amplification Kit  following the manufacturer’s instructions. Then, heterozygous SNP loci were identified using PCR amplification and Sanger sequencing.1 gametophytes were 1.21, 1.36 and 1.62 Gb, respectively. The GC content range per individual was between 45.03 and 52.53%, with a mean of 48.92%, and all three values were in the normal range. The mean Q20 value was greater than 95%, and the mean Q30 value was greater than 91%, revealing the high quality of sequencing. The depth of the female, male gametophyte parents, and the mean of the F1 gametophytes, were 12×, 14× and 16×, respectively. Each gametophyte was sequenced to an average 80.15% coverage of the genome. The base distribution and base quality distribution of whole-genome resequencing data were shown in The statistical data of the whole-genome resequencing are listed in 1 gametophytes was between 30.20 and 83.75%, with an average of 57.33%. The differences in the mapping rates among individuals might be caused by exogenous DNA contamination or unprovided reference genome sequences of Gp. lemaneiformis.The high quality sequencing reads of each individual were aligned to the reference genome sequences, and the mapping results are displayed in As a result, the miss rate of SNP genotypes was less than 15% in 62 gametophytes, and 46,499 SNPs were identified, representing 1.83% homozygous loci (no homozygous genotypes occurred in the 62 gametophytes) and 98.17% heterozygous loci . Further statistical analyses revealed that 22,640 SNP loci had identical heterozygous genotypes in the 62 gametophytes, accounting for 48.69% of the total SNPs.1 gametophytes. Genotyping results of representative SNP loci in two parents and partial gametophytes are displayed in The annotation of repetitive sequences had been conducted previously . SNP locFor each gametophyte, the proportions of homozygous and heterozygous loci ranged between 13.74 and 21.61% (mean of 17.04%) and between 66.36 and 83.59% (mean of 77.12%), respectively. The remainder were missing loci, representing an average 5.84%  genotypes in one parent, but heterozygous genotypes in the other parent, were aligned to the complete plastid and mitochondrial sequences of 1 gametophyte are displayed in Information regarding primers that were used to amplify SNP loci are listed in All 22 segments that were cut from one gametophyte were amplified using primers for SNP locus scaffold1837_730. The Sanger sequencing results of each segment were completely coincident, showing two peaks, A and G, and even the height differences between peaks A and G were identical  of a cross between safflower Carthamus tinctorius L. and its wild progenitor C. palaestinus Eig  identified by whole-genome resequencing in 97.02 Mb , its comThe reference genome sequences selected in this study were from the first genome version, which consisted of 125,685 scaffolds with an N50 length of 20 kb  and not 1 gametophytes of Gp. lemaneiformis using whole-genome resequencing technology, laying a foundation for construction of genetic linkage map and localization of quantitative trait locus. We further identified 2,386 SNPs which were located in 818 genes. For example, SNPs  were located in the gene region of LEM_GLEAN_10003235 whose function was annotated as carotenoid isomerase, which is an important enzyme in synthesis pathway of carotenoids. Moreover, LEM_GLEAN_0001204, the gene encoding phosphoglucose isomerase in metabolic pathways of agar, contains one SNP locus (scaffold1981_ 16188). These SNPs could be potentially used as genetic markers for the selection and provided the theoretical basis of molecular marker assisted breedingSNP markers were first developed in FGp. lemaneiformis. There are several kinds of chromosomal rearrangement, including the duplication or deletion of a chromosomal segment, the inversion of a segment and the translocation of segments between non-homologous chromosomes , or high-throughput chromosome conformation capture. SMRT sequencing can produce considerably longer and highly accurate reads from single unamplified molecules. The average read length of PacBio RS II platform is about 15 kb. The long SMRT sequencing reads will span complex repeats and missing bases, which will substantially improve genome complete assembly. A complete genome has the potential to facilitate genome-assisted breeding and will be useful to investigate genomic patterns in the evolution of marine algae.A chromosome level of assembly is required to verify the source of the heterozygous SNPs. This study promoted the assembly of a The datasets generated for this study can be found in the Short Read Archive (SRA) database with Project number PRJNA574029, under the accession number SRP223151 (SRR10177194 – SRR10177255).ZS and YH contributed conception and design of the study. YH, WZho and JW constructed the mapping population. YH performed the experiments. YH and ZS analyzed the data. MJ, HC, JZ, WZha, XF, YL, BX and PG contributed reagents/materials/analysis tools. YH wrote the manuscript and ZS made revision. All authors read and approved the submitted version.This work was supported by the China Agriculture Research System (CARS-50), the National Natural Science Foundation of China (No. 31372529) and the Fundamental Research Funds for the Central Universities (No. 201762016).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Cannibalism is an important welfare problem in the layer industry. Cannibalism is a social behavior where individual survival is affected by direct genetic effects (DGE) and indirect genetic effects (IGE). Previous studies analysed repeated binomial survival, instead of survival time, which improved accuracies of breeding value predictions. Our study aimed at identifying SNPs associated with DGE and IGE for survival time, and comparing results from models that analyse survival time and repeated binomial survival.Survival data of three layer crosses  were used. Each individual had one survival time record and 13 monthly survival (0/1) records. Approximately 30,000 single nucleotide polymorphisms (SNPs) were included in the genome-wide association study (GWAS), using a linear mixed model for survival time, a linear mixed model and a generalized linear mixed model for repeated binomial survival (0/1). Backwards elimination was used to determine phenotypic and genetic variance explained by SNPs.The same quantitative trait loci were identified with all models. A SNP associated with DGE was found in cross W1 * WA, with an allele substitution effect of 22 days. This SNP explained 3% of the phenotypic variance, and 36% of the total genetic variance. Four SNPs associated with DGE were found in cross W1 * WB, with effects ranging from 16 to 35 days. These SNPs explained 1 to 6% of the phenotypic variance and 9 to 44% of the total genetic variance. Our results suggest a link of DGE and IGE for survival time in layers with the gamma-aminobutyric acid (GABA) system, since a SNP located near a gene for a GABA receptor was associated with DGE and with IGE (not significant).This is one of the first large studies investigating the genetic architecture of a socially-affected trait. The power to detect SNP associations was relatively low and thus we expect that many effects on DGE and IGE remained undetected. Yet, GWAS results revealed SNPs with large DGE and a link of DGE and IGE for survival time in layers with the GABAergic system, which supports existing evidence for the involvement of GABA in the development of abnormal behaviors.The online version of this article (10.1186/s12711-018-0409-7) contains supplementary material, which is available to authorized users. Mortality due to cannibalism has important welfare and economic implications in the commercial laying hen industry. Brinker et al.  reportedArabidopsis. To increase the power of IGE detection, they did not consider loci that did not have DGE in their IGE analyses.The availability of genomic information has increased our understanding of complex traits, but studies have mainly focussed on DGE. Results from genome-wide association studies (GWAS) on DGE show that most quantitative traits in livestock are highly polygenic and that variants tend to be associated with more than one trait . HoweverFrom a statistical point of view, survival time is a difficult trait because many laying hens are still alive at the end of the recording period. For these hens, true survival time cannot be observed but is known to exceed the length of the recording period (censored). Several statistical techniques have been proposed to deal with survival data and IGE, including survival time analysis , 9 and tThis study had two aims: (1) to identify SNPs associated with direct and indirect effects for survival time in laying hens that show cannibalism; and (2) to compare GWAS results from analysis of survival time versus repeated binomial survival (0/1).Data were collected under the control of Hendrix Genetics. Hendrix Genetics complies with the Dutch law on animal welfare. Hendrix Genetics provided data on three crossbred White Leghorn layer lines. Crossbreds descended from one sire line W1) and three dam lines , and were coded W1 * WA, W1 * WB, and W1 * WC. In contrast to Brinker et al. [ and threA total of 159 sires and 3218 dams were used, with 48 to 57 sires per cross. For each cross, matings between sires and dams were randomly assigned, which resulted in approximately two female offspring per dam. The sire pedigree was recorded for all offspring. The dam’s pedigree was initially unknown but a reconstructed pedigree, based on genomic information, was provided by Hendrix Genetics.Chickens of the three crosses hatched simultaneously in the Netherlands. One-day old chickens were vaccinated, wing-banded, and transported to Canada. Chickens had intact beaks. Chickens were transported to a laying house at approximately 17 weeks of age. The laying house consisted of two wings. In each wing, rows were grouped into six double rows that each contained two levels, with a corridor between each double row to allow access to cages. Each cage contained five individuals of the same sire. Thus, cage mates were either paternal half-sibs or full-sibs. A standard commercial layer diet and water were provided ad libitum. A feeding trough was in front of the cages and each cage had its own drinking nipples. The light intensity was stronger at the top level compared to the bottom level, due to closer proximity to light sources. Further details are in Brinker et al. [Dead hens were removed daily but the cause of death was not determined. The wing band number and cage number were recorded after death. The study was terminated when hens were approximately 75 weeks old. Survival at the end of the study was 78, 75, and 63%, for crosses W1 * WA, W1 * WB, and W1 * WC, respectively.e.g. with hens descending from multiple sires) were removed from the dataset. Cages with hens descending from multiple sires were identified based on genomic information.Cages with initially less than five hens and cages with mistakes in their composition , the laying period was divided into 13 months. For each month, survival was coded 1 if the laying hen was alive at the end of that month and as 0 if not. Thus, a survival record (0/1) was available for each month. In total, 24,960, 24,375, and 21,060 monthly records were available for crosses W1 * WA, W1 * WB, and W1 * WC respectively  in the parental population were removed from the full dataset. SNPs contributing more than one Mendelian error and individuals contributing more than five Mendelian errors were also removed from the dataset. Remaining Mendelian errors were set to missing.Birds were genotyped based on DNA extracted from blood, using a custom made Illumina 60 K chicken SNP BeadChip, which included 52,232 SNPs across chromosomes 1 through 28, Z, W, and two unmapped linkage groups, along with some unassigned SNPs. PLINK , 12 was i.e. for genotypes associated with Mendelian errors.The number of genotyped crossbreds and the number of SNPs available after quality control for each cross are in Table Data were analyzed separately for each cross. Three statistical models were compared: a linear mixed model for survival time (STM), a linear mixed model for repeated binomial survival , and a generalized linear mixed model for repeated binomial survival . All models were implemented using ASReml .i.e. family structure in the population). This animal model incorporated the genetic covariance structure across individuals, to avoid spurious SNP associations due to relatedness.First, genetic parameters were estimated without SNP effects in the model (see models below). Five generations of (reconstructed) pedigree information on sires and dams were included in all genetic analyses. In our data, cages consisted of paternal half-sibs, with an occasional full-sib. Therefore, direct and indirect polygenic effects were strongly confounded , 14. WitSecond, SNP effects were estimated one by one, including both the direct SNP effect of the individual and the summed indirect SNP effects of its cage mates in the model. For all models, variance components were fixed to the estimated values from the corresponding model without the SNP effect. Direct and indirect SNP effects were fitted simultaneously, because a GWAS with direct SNP effects only would also capture part of the indirect effect of the SNP, as the related group mates have an above-average probability to carry the same alleles.fixed represents the fixed effect of the combination of wing-row-level; Survival time records were analysed using the following linear mixed model:fixed represents the interaction effect of wing-row-level with time, which was a sixth-order polynomial of time, fitted as a fixed effect, which was used to model the survival curve across time and to allow this curve to depend on location ; Repeated binomial records on survival (0/1) were analysed using a repeated measures model that included random regressions on time (hence RMM.t). Following , the modd traits . Finallyl are in .To account for the binomial distribution of survival (0/1), we used a generalized linear mixed model with a logit link function. ASReml uses approximate likelihood techniques, of which the limitations are discussed in the Results and discussion section . The modThe GLMM includes only a genetic intercept and no regression on time because the non-linear link function takes the change in variance over time into account. Consequently, at the beginning of the recording period, the variance of the survival probabilities can be (near) zero even when l are in .10p values of SNP effects and the size of the inflation factor for p values . Pearson correlations between − log10p values were calculated to quantify the agreement between the three models.The three models, STM, RMM.t, and GLMM were compared by reviewing -logp values compared to their expected distribution under the null hypothesis that the SNP has no association with the trait. The extent of deviation of the observed distribution from the expected distribution was expressed as λ, where a λ equal to 1 means no deviation [p values [A quantile–quantile plot (Q–Q plot) was used to investigate the distribution of the observed eviation . In casep values .q-value package [p values. The q-value package calculates an FDR based on the distribution of p values, which represents the minimum FDR when the SNP effect is called significant, which was set to 0.3. This is a liberal threshold that also reveals suggestive SNP associations with survival time and was chosen because little is known about the background of DGE and IGE for survival time, this study being one of the first large ones.Multiple testing was accounted for by controlling the false discovery rate (FDR) using the  package  in R [18q < 0.3) in the model to account for possible linkage disequilibrium (LD) between them, testing their model fit, and dropping the least significant SNP effect. This was repeated until all SNPs reached the FDR threshold. Then, for each remaining SNP, we calculated the genetic variance explained by the SNP following Falconer and Mackay [A backwards elimination method was used to obtain an estimate for phenotypic and genetic variance explained by SNPs. Backwards elimination involved including all direct and indirect SNP effects below the genome-wide FDR threshold  or strong(-er) associations in all models. This is in line with findings of Rönnegård et al. [10p values between results from a model fitting average phenotypes and a model fitting repeated measures was higher than 0.9 [There were no evident differences between GWAS results from STM, RMM.t, and GLMM Tables , 4 and , 2 and 3d et al. , who invthan 0.9 . Rönnegåthan 0.9  concludep < 0.001 between the three models after genomic control  [see Additional file Table p values from the GLMM to those of the other two models, STM and RMM.t, we found that the Q–Q plots from the GLMM behaved well and were similar to those for STM and RMM.t [see Additional files 10p values from the GLMM were highly correlated with those from STM and RMM.t  for GWAS, which was fitted with ASReml . ASReml .t Table , and ManBased on these results, we concluded that there was no evidence that RMM.t and GLMM outperformed STM. Thus, in the remainder of this paper, we will only show results from STM.q < 0.3 in cross W1 * WA and W1 * WB, but none in cross W1 * WC. In none of the crosses, SNPs were associated with indirect effects for survival time at q < 0.3.Figures q < 0.3. Of these, one SNP was on chromosome 4 at 42 Mb. The remaining 16 SNPs were on chromosome 2, with one SNP at 46 Mb, and 15 SNPs in the region between 87 and 89 Mb. The latter 15 SNPs were in high LD with most pairwise r2 higher than 0.9. After backwards elimination, one SNP (rs317294317) remained in the model , chromosome 5 (two SNPs at 54 Mb), chromosome 7 (at 6 Mb), chromosome 9 (at 17 Mb), and chromosome 20 (at 20 Mb). After backwards elimination, four SNPs remained in the model , which plays an important role in the regulation of neurotransmitters in the brain. GABA is an inhibitor of neuronal activity and plays an important role in physiological and behavioral stress response in many species [Mus musculus.The  species –25. Zhan species  found thHTR2C gene, which is involved in the serotonergic system, that was associated with indirect genetic effects. Another study on aggression in chickens revealed a role of the dopaminergic system [In addition to GABA, serotonin and dopamine are also important neurotransmitters and are known to be associated with several behavioral disorders in a variety of species. Moreover, several studies have reported a link between the serotonergic, dopaminergic, and GABAergic pathways –28. Biscc system . Moreovec system  investigc system  concludePTPRN2 In cross W1 * WB, SNP rs313098101 remained in the model after backwards selection. This SNP is an intron variant on chromosome 2. The associated gene is PTPRN2  was insufficient for detection of IGE associated SNPs, i.e., with lower mortality, fewer individuals will die due to cannibalism and less indirect genetic variance will be available for SNP detection. For cross W1 * WC, theoretical power for GWAS was lower than for W1 * WA and W1 * WB and more individuals died before blood sampling. These hens were given an average genotype while having an extreme phenotype, which may explain why no direct and indirect SNP associations were found in cross W1 * WC.Theoretical power to detect indirect SNP effects was higher than power to detect direct SNP effects. This is due to the multiplication by the number of group mates for indirect SNP effects. It is, therefore, possible that SNP with a lower MAF can be detected at n of DGE , 3, 35. n of DGE , and somGABBR2. This supports existing evidence of the involvement of GABA in the development of abnormal behaviors.This is one of the first large studies that investigates the genetic architecture of a trait by considering both direct and indirect genetic effects. Our results indicate that the same QTL were identified using either a linear mixed model of survival time or models of repeated binomial survival (0/1). Although the power was relatively low, and many SNP associations may have not been detected, our results revealed a link of the GABAergic system with direct and indirect genetic effects for survival time in crossbred layers. The associated gene was Additional file 1. QQ-plots of direct SNP effects for STM, RMM.t, and GLMM.Additional file 2. QQ-plots of indirect SNP effects for STM, RMM.t, and GLMM.Additional file 3. Number of direct and indirect SNP effects with p < 0.001 for all crosses and models after genomic control.Additional file 4. Power of direct and indirect SNP effects.