SIVgor) causes persistent infection in critically endangered western lowland gorillas (Gorilla gorilla gorilla) from west central Africa. SIVgor is closely related to chimpanzee and human immunodeficiency viruses . We established a noninvasive method that does not interfere with gorillas' natural behaviour to provide wildlife pathogen surveillance and health monitoring for conservation. A total of 1,665 geo‐referenced fecal samples were collected at regular intervals from February 2006 to December 2014 (123 sampling days) in the Campo‐Ma'an National Park (southwest Cameroon). Host genotyping was performed using microsatellite markers, SIVgor infection was identified by serology and genetic amplification was attempted on seropositive individuals. We identified at least 125 distinct gorillas, 50 were resampled (observed 3.5 times in average) and 38 were SIVgor+ . Six groups of gorillas were identified based on the overlapping occurrence of individuals with apparent high rates of gene flow. We obtained SIVgor genetic sequences from 25 of 38 seropositive genotyped gorillas and showed that the virus follows exponential growth dynamics under a strict molecular clock. Different groups shared SIVgor lineages demonstrating intergroup viral spread and recapture of positive individuals illustrated intra‐host viral evolution. Relatedness and relationship genetic analysis of gorillas together with Bayesian phylogenetic inference of SIVgor provided evidence suggestive of vertical transmission. In conclusion, we provided insights into gorilla social dynamics and SIVgor evolution and emphasized the utility of noninvasive sampling to study wildlife health populations. These findings contribute to prospective planning for better monitoring and conservation.Simian immunodeficiency virus ( Several independent RNA extractions were performed for each specimen with reactive antibodies, followed by multiple RT‐PCR attempts to increase the chance of SIVgor genetic amplification. Total nucleic acids were extracted with the NucliSens system , and RT‐PCR reactions were performed using SIVgor and SIVgor/SIVcpz/HIV‐1 consensus primers that targeted the env  and pol (245 or 330 bp of the integrase) regions as previously described  were obtained from at least one nucleic acids extract of each gorilla to improve the estimation of relatedness and relationship among the different individuals. Genetic diversity was quantified by estimating observed and expected heterozygosis. Test for Hardy–Weinberg equilibrium (HWE) for each locus and test for linkage disequilibrium between loci were performed using the package adegenet Jombart, .rd) was calculated using the Wang estimator  of microsatellite haplotypes was constructed using the Prevosti . The clustering of gorillas was performed following a stepwise procedure coded in 2.5gp41 and pol, respectively). The datasets were analyzed using a HKY+4Γ  nucleotide substitution model plus a demographic coalescent tree prior model   except for loci vWF, D7s817, D8s1106, D10s1432, and D2S1333 .In addition to the initial visit in 2004 where we identified the first SIVgor infected gorilla CPg‐ID001), a total of 24 visits to the Campo‐Ma'an National Park were conducted between February 2006 and December 2014. There were on average three visits per year  that lasted around five consecutive days  for a total of 123 sampling days. A total of 1,665 fecal samples were collected in this period with complete geo‐referenced data . Both groups kept their original identity when this sample was removed in sensitivity analyses. Sample CR6687 was found directly on track rather than a nesting site so its occurrence next to samples from another group may reflect a chance event rather than the existence of a unique larger group. The ranges of Groups A and E also overlapped once when using a range ≥200 m due to the proximity of individuals in a single sampling day during February 2007. However, both groups kept their distinctiveness when this observation was removed in sensitivity analysis. It is worth mentioning that western lowland gorillas occasionally from “nesting super‐groups” where two groups nest at distances of 30–50 m Bermejo, .Most groups were polygynous. For instance, the most representative samples  of groups A, B, C, and F had between one or three males and the number of females ranged between four and 11 Figures –S4. FemaThe percentage of individuals with SIVgor infection in the most representative samples ranged between 10% and 60%; in average, 30%of individuals were SIVgor positive at any given point Figure –S5.3.3A MSN of microsatellites using Prevosti's distance is shown in Figure We used three different metrics to assess interindividual's relatedness and we retained the pairs that had the highest estimates. In the Wang's metric, we kept the pairs with an estimated value over 0.6. In the ML calculations, we kept the pairs that were discriminated as parent–offspring pairs and where the log likelihood of the individuals being unrelated was at least seven times less than the log likelihood of the individuals being parent–offspring. In the Bayesian calculations, a cutoff value of 0.05 was chosen for the posterior probability of a pair being false given frequencies of shared alleles. Under these constrains, there were 23 different pairs of gorillas that had a close genetic relationship by at least one metric and that were relatives in the MSN. Eight pairs were proposed by two methods and four pairs were proposed by all three methods . The individuals in each of the 23 pairs belonged to the same inferred group in 56% of the cases. Three pairs have both individuals SIVgor positive , and 10 pairs have one individual SIV positive. Among noninfected SIVgor pairs there were six, three and one female/female, female/male, and male/male pairs, respectively. The same proportions were observed among the ten pairs with one individual SIVgor positive.Supporting Information Figure We further dissected the genetic relatedness of individuals over time in order to elucidate any family history. In the case of group A  and seven individuals were potentially seroconverters  or later samples (2012–2013).gp41 gene sequences from 25 genotyped gorillas and partial pol gene sequences for 16 of them. The models with the higher log marginal likelihood using either stepping stone sampling or path sampling were a strict molecular clock model and an exponential demographic model (Table e Bayes factors [BF] >10) support for strict clocks over relaxed ones  over the remaining parametric models and “Strong” (2 loge 6.7) over the nonparametric Skyride; weak support was observed for pol when comparing the exponential model to the remaining parametric ones (2 loge BF ~ 1.7) and “Positive” (2 loge BF 3.1) when compared to the Skyride. For pol, the inferred time to the most common recent ancestor (TMRCA) was around 1970 , the evolutionary rate was 1.27 × 10−3  and the exponential growth rate was 0.039 per year. For env, the inferred TMRCA was around 1964 , the evolutionary rate was 5.02 × 10−3  and the exponential growth rate was 0.033 per year.We obtained partial el Table . In all Figure env tree. This pattern could result from either a mixed infection or an exceptionally high viral substitution rate. However, viral samples from individual CPg‐004 collected during 2011–2013 were quite similar, and comparable to the degree of similarity among viral samples collected from other individuals over longer periods of time, that is, CPg‐001 (2004–2008), CPg‐006 (2011–2014), CPg‐010 (2007–2014). Furthermore, we conducted additional analysis constraining the samples from the same individual to be monophyletic and the model selection using path sampling/stepping‐stone sampling preferred the unconstrained model . These observations favor the mixed infection scenario but we warrant a warning regarding the validity of this conclusion given the uncertainty  in the corresponding internal branches.Sequences from the same individual collected in consecutive days were not identical suggesting a certain degree of intra‐host diversity. Furthermore, we observed that sequences from individuals CPg‐004 and CPg‐005 (who frequently were observed together within group A) sampled in 2007 were distinct from those collected during 2011–2013 in the Concerning the phylogenetic relationships of viral samples from seroconverters Individuals: CPg‐041, CPg‐066, and CPg‐128 grouped with samples from the corresponding same group; CPg‐032 grouped with CPg‐031 whose group identity was uncertain and their closest relative was the clade containing CPg‐001, which belonged to the former's same group; and CPg‐036 closest relative belonged to another group. For the two remaining seroconverters (CPg‐094 and CPg‐115), there were no viral sequence data available.3.5In order to assess SIVgor transmission we opted for less stringent constraints—in the interindividual's relatedness analysis—to include more pairs where both individuals were SIVgor positive and had SIVgor genetic data. These constraints were as follows: the pairs that had a Wang estimate over the full siblings threshold (0.43 was the data‐inferred relatedness‐threshold for full siblings), the pairs that were discriminated as parent–offspring in the ML analysis and where the log likelihood of the individuals being unrelated was at least three times less than the log likelihood of the parent–offspring, and the pairs with a posterior probability below 0.5 in the Bayesian calculations.Figure Env gene tree along samples CPg‐001, CPg‐009, CPg‐031, and CPg‐033. Moreover, pairs CPg‐001/CPg‐032 and CPg‐001/CPg‐033 had high relatedness values in least one of the metrics and were relatives in the MSN. This group also appeared in the pol gene tree. Therefore, it may be the case that at least two individuals among the set CPg‐001/CPg‐031/CPg‐032/CPg‐033 represent a true parent–offspring pair that led to vertical transmission of SIVgor. Similarly, the set CPg‐047/CPg‐093/CPg‐152 scored the highest in the metric of Wang, they were relatives in the MSN and their SIVgor strains of CPg‐047/CPg‐093 were closely related within the env gene tree and thus denote another example. Individuals CPg‐032, CPg‐033 CPg‐093, and CPg‐152 were identified later than the remaining individuals in their corresponding set and they were also documented once. Altogether, these observations underscore two instances for which vertical transmission could not be excluded. By contrast, the pairs CPg‐037/CPg‐074, CPg‐037/CPg‐041, CPg‐032/CPg‐041 scored high in all relatedness metrics and some of them were relatives in the MSN but their respective SIVgor strains fell apart in both gene trees. Therefore, in these instances the mots likely scenario is the horizontal transmission of SIVgor.Individuals from the pair CPg‐010/CPg‐032 scored high in all relatedness's metrics and fell in a well‐supported clade within the 4The present study provides new insights into the ecology of unhabituated gorillas from the Campo‐Ma'an National Park and into SIVgor evolution. Our findings expanded those of et al. Etienne et al. : firstlyWestern lowland gorillas live in groups  that typically include one adult silverback, multiple females, and their offspring : the boundaries of the genetic groups coincided with courses of major rivers suggesting that major landforms drive the patterns of genetic differentiation.Gorillas are among the relatively few mammal species for which both sexes usually disperse from their natal groups  is argued to be less frequent than horizontal transmission . The junction of our genetic and phylogenetic findings provides evidence of SIVgor transmission among close relatives in two different sets of gorillas. Closely related viruses have being observed in a presumed parent–offspring pair of chimpanzees suggesting vertical transmission (Keele et al., Modern‐day SIV diversity was generated by extensive recombination and cross‐species transmission (Bell & Bedford, Our study has limitations. Our sampling relied on the opportunistic collection of samples and therefore we cannot exclude that some regions of the Campo‐Ma'an National Park and some groups were missed. Although individuals from both sexes frequently disperse when reaching sexual maturity (Stoinski et al., Despite all the aforementioned issues, our results validate a noninvasive approach to the recovery of otherwise unobtainable information directly related to the ecology of gorillas and of infectious diseases in the wilderness. In the same vein, this approach could allow to survey the microbiome and the virome using metagenomics. For example, D'arc et al. provided evidence of association of specific mammalian viral families and SIVgor in a dysbiosis context (D'Arc et al., In conclusion, given the critically endangered status of western lowland gorillas, a noninvasive sampling represents a viable approach to study wild population and disease and the findings derived from it contribute to the prospective planning for better monitoring and conservation and also contribute to a better understanding of SIVgor evolution and transmission.None declared.CJVA designed and performed data analysis, interpreted the data and wrote the manuscript; AA, AE, MD performed laboratory and data analysis; EMN coordinated field studies; MP designed analysis, interpreted the data, and wrote the manuscript; all authors discussed the results and implications.New sequences were deposited in GenBank under accessions MH646552‐MH646620. Microsatellite genotypes are available as Supporting Information Table  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. Click here for additional data file. Click here for additional data file.


For dairy cattle, sexed semen of genomically proven young bulls could substantially improve profitability thus increase prospects of small holder farmers buying-in into genomic breeding programs.Genomic selection (GS) has resulted in rapid rates of genetic gains especially in dairy cattle in developed countries resulting in a higher proportion of genomically proven young bulls being used in breeding. This success has been undergirded by well-established conventional genetic evaluation systems. Here, the status of GS in terms of the structure of the reference and validation populations, response variables, genomic prediction models, validation methods, and imputation efficiency in breeding programs of developing countries, where smallholder systems predominate and the basic components for conventional breeding are mostly lacking is examined. Also, the application of genomic tools and identification of genome-wide signatures of selection is reviewed. The studies on genomic prediction in developing countries are mostly in dairy and beef cattle usually with small reference populations  and are mostly cows. The input variables tended to be pre-corrected phenotypic records and the small reference populations has made implementation of various Bayesian methods feasible in addition to GBLUP. Multi-trait single-step has been used to incorporate genomic information from foreign bulls, thus GS in developing countries would benefit from collaborations with developed countries, as many dairy sires used are from developed countries where they may have been genotyped and phenotyped. Cross validation approaches have been implemented in most studies resulting in accuracies of 0.20–0.60. Genotyping animals with a mixture of HD and LD chips, followed by imputation to the HD have been implemented with imputation accuracies of 0.74–0.99 reported. This increases the prospects of reducing genotyping costs and hence the cost-effectiveness of GS. Next-generation sequencing and associated technologies have allowed the determination of breed composition, parent verification, genome diversity, and genome-wide selection sweeps. This information can be incorporated into breeding programs aiming to utilize GS. Cost-effective GS in beef cattle in developing countries may involve usage of reproductive technologies (AI and  In addition, these companies provide an efficient system for delivering superior genetics from the genomics pipeline.Genomic selection (GS) has resulted in rapid rates of genetic gains especially in dairy cattle in developed countries with the consequence that a higher number of currently artificial insemination (AI) active sires are genomically proven young bulls in the USA . Consequently, the number of genotyped animals tend to be limited; are mostly females, and this has major influence on both the size and structure of the reference and validation populations.In developing countries especially in Africa and Asia, most of the production occurs in small holder systems which are characterized by small herd sizes, lack of performance, and pedigree recording and therefore, the non-existence of conventional genetic evaluation systems  are assumed to be at linkage disequilibrium (LD) with QTLs in the genome. Therefore, the use of SNPs as markers enables all QTLs in the genome to be indirectly identified through the mapping of chromosome segments defined by adjacent SNPs. The implementation of GS usually involves estimating the SNP effects in a reference population which consists of individuals with phenotypic records and genotypes. This is then followed by prediction of genomic estimated breeding values (GEBV) for selection candidates  with no phenotypes of their own . This implies the running of conventional evaluations to compute EBVs, which are subsequently de-regressed (dEBV) and used as input variables for SNP-BLUP or GBLUP genomic predictions . Recently, some developed countries have implemented single-step genomic evaluations, ssGBLUP, mostly in beef cattle .Generally, the accuracy of genomic prediction is usually based mostly on correlations between the direct genomic breeding and the dEBV or adjusted phenotypes in the validation data set. When adjusted phenotype is used, the correlation coefficient is divided by the square root of the heritability of the trait to measure the correlation between predicted and true breeding values −1], where Np is the number of individuals with phenotypes and genotypes in the reference population, h2 is the heritability of the trait or reliability of breeding values in the reference population, and Me is the number of independent chromosome segments. Me can be computed as Me = 2NeL, with Ne equals the effective population size and L, the genome length in morgans. Using typical values of 100 and 30 for Ne and L, respectively,  of GEBV, have showed great variation Table . In someA major issue with the implementation of GS is the cost of genotyping and it constitutes one of the obstacles to GS in developing countries. Several studies have therefore examined the use of cheaper low-density Chips or investigated the use of low numbers of SNPs accompanied by imputation on the accuracy of genomic prediction.Boison et al.  examinedAliloo et al.  investigIn addition to examining the efficiency of imputation from different commercial chips, the study of Aliloo et al.  also exaThe influence of foreign genotypes on imputation accuracy when imputing from 6, 9, 50, and 77 K chips to 45 K markers used in the USA genomic evaluations in 2014, was examined by García-Ruiz et al. . The SNP chip called the INDUSCHIP, consisting of 45,700 SNPs, has been developed from HD genotypes of mostly four indicus breeds  and their taurine crosses mostly with Holstein and Jersey, in India and has been employed for the determination of breed composition and genomic prediction for milk yield.A purpose-built LD SNP chip for the purpose of GS in cross bred populations . The non-existence of AI companies to drive genetic improvement programs, implies that genetic and genomic evaluations would inevitably be linked to either National Artificial Insemination Centers or breed societies to help deliver the superior genetics. This is the current approach being exploited by ADGG while encouraging public-private partnership in the space. The beef breeds in South Africa are in the process of implementing GS but current activities are still limited to defining the reference population and understanding the population structure.The Africa Dairy Genetic Gains (ADGG) project in Tanzania and Ethiopia are currently establishing a pipeline for routine genetic evaluation using the genomic relationship matrix, in addition to screening and selecting young bulls using the genomic predictions  and genome-wide signatures of selection, introgression, and/or admixture. The studies have led to the identification of many genes and some incorporated into selection schemes. In developed countries, whole genome sequence analysis and GS are being applied in breeding schemes of major food animals . In developing countries, genomic technologies are applied to assessing genetic diversity and admixture and signatures of selection to identify genomic regions and variants contributing to variation.*African taurine genome composition as well as European taurine ancestry  and two African Zebu  cattle, whose origin traces back to about 130 years ago  has resulted in a large number of studies investigating genome-wide signatures of selection in indigenous cattle in developing countries and especially in African cattle. For instance, 18 candidate regions under selection and intersecting genes and QTLs associated with production and reproduction performance and adaptation to environmental stress  were identified in East Africa cattle from the analysis of SNP genotype datasets  as a one stop shop which centralizes functional access to modern breeding technologies and marker service laboratory, data management and analysis for crops. A similar initiative for livestock, or incorporating livestock requirement to such a center, will boost genomic activities and increase cost efficiency. The rapid developments in marker technologies has led to high-throughput platforms for SNP genotyping and hence reduced costs. However, in the absence of such centers as described above, good outsourced cost-effective genotyping services which are easily accessible are now available. This provides opportunities to increase the efficiency of implementing advanced genomics in developing countries.The major factor limiting the application of GS in developing countries is poor breeding infrastructure that is fundamental to conventional breeding, lack of routine recording of reliable phenotypes and good analytical tools to synthesize the data, providing timely feedback to help improve farmer management and husbandry techniques. The ADGG has sort to address some of these major bottlenecks in East Africa by employing recent developments in information and communication technology (ICT). In addition, as Ribaut et al.  indicateThe provision of bundled services beyond just GS will accelerate the adoption and use of molecular tools including GS. Programs for genetic improvement utilizing genomics approaches should include the development of tools for parentage verification, breed composition determination, mating tools that exploit genomic information, traceability, breed characterization, and tools for computing genomic inbreeding readily and addressing issues relating to sustainable utilization. Such approach maximizes the benefits of genotyping and increases cost-efficiency.in vitro fertilization (IVF)  was investigated, the annual increase in genetic gain was 79% relative to the base situation. Carvalheiro (Generally, in the beef industry, GS is expected to generate a more modest increase in genetic gain for regular traits compared to dairy cattle partly due to the breeding structure and relatively limited use of AI. Strategies for optimizing cost-benefits for the application of GS in beef cattle in developing countries are still being investigated. Carvalheiro  comparedvalheiro  concludevalheiro . Carvalhvalheiro  further Another development in reproductive technologies that is more likely to have a profound effect in the dairy cattle industry is the use of sexed semen. In the small holder system, the cost of purchasing a replacement heifer constitute a major capital investment not easily affordable to most of the farmers. Also, the milking of the dairy cow constitutes the main source of income in the dairy farmer in India, given the sacred status of cattle. The use of sexed semen of genomically proven young bulls with a very high probability of a female calf, could substantially improve productivity and profitability of small holder farmers and therefore offers prospect for farmers buying-in into genomic breeding programs. Thus, continuous improvement in semen sorting technologies and methods to enhance conception rates with use of sexed semen opens up future prospects for the application of GS.Collaboration between developing and developed countries will be important in implementing genomic breeding technologies in the former, especial in dairy cattle, where there has been a large importation of bulls. It is likely that most of these bulls have been genotyped in the developed countries and willingness to share genotypes and some other relevant performance data will help in enlarging the reference population and hence the accuracy of genomic predictions in developing countries. Some of the possible impacts have been demonstrated by Li et al. .The ability of Governments to put in place enabling policies, statutory and regulatory frameworks that encourage private-public partnerships will be crucial in the long term in sustaining breeding programs based on conventional or genomic approaches. Also, the limited genomic data in each country calls for pooling of data across multiple countries or geographic regions to maximize the benefits of GS. Initial possible increases in accuracies, the result of pooling data across two countries have been demonstrated (Mrode et al., RM and JM undertook most of the work in this manuscript and contributed equally. JO and AO contributed as part of the team that generated data for the initial work on genomic prediction in East Africa and part of the current ADGG project that is stimulating GS in East Africa.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.


P < 0.05) reduced prediction accuracy when all lines were analyzed together. However, when inbreds and hybrid trait values were both scored relative to the mean trait values for the respective populations, the inclusion of inbred phenotypic datasets moderately improved genomic predictions of the hybrid genomic estimated breeding values. Here we show that modern approaches to genotyping by sequencing can enable genomic selection in pearl millet. While historical pearl millet breeding records include a wealth of phenotypic data from inbred lines, we demonstrate that the naive incorporation of this data into a hybrid breeding program can reduce prediction accuracy, while controlling for the effects of heterosis per se allowed inbred genotype and trait data to improve the accuracy of genomic estimated breeding values for pearl millet hybrids.Pearl millet is a non-model grain and fodder crop adapted to extremely hot and dry environments globally. In India, a great deal of public and private sectors’ investment has focused on developing pearl millet single cross hybrids based on the cytoplasmic-genetic male sterility (CMS) system, while in Africa most pearl millet production relies on open pollinated varieties. Pearl millet lines were phenotyped for both the inbred parents and hybrids stage. Many breeding efforts focus on phenotypic selection of inbred parents to generate improved parental lines and hybrids. This study evaluated two genotyping techniques and four genomic selection schemes in pearl millet. Despite the fact that 6× more sequencing data were generated per sample for RAD-seq than for tGBS, tGBS yielded more than 2× as many informative SNPs (defined as those having MAF > 0.05) than RAD-seq. A genomic prediction scheme utilizing only data from hybrids generated prediction accuracies (median) ranging from 0.73-0.74 (1000-grain weight), 0.87-0.89 (days to flowering time), 0.48-0.51 (grain yield) and 0.72-0.73 (plant height). For traits with little to no heterosis, hybrid only and hybrid/inbred prediction schemes performed almost equivalently. For traits with significant mid-parent heterosis, the direct inclusion of phenotypic data from inbred lines significantly ( Cenchrus americanus (L.) Morrone; Syn. Pennisetum glaucum (L.) R. Br.] is able to grow on infertile and marginal soils under limiting soil moisture conditions and high soil temperatures. It is a climate resilient species, and is one of the most widely grown millets globally Pearl millet [Plant breeding for hybrid crops requires generating and testing large numbers of hybrids under different field conditions. Performing crosses to generate F1 hybrids is a labor intensive process. Top-crossing between B- and R-lines can reduce the amount of labor required per cross, but only in crossing schemes where many female lines are being crossed to one or a few male lines. Evaluating each new hybrid across field trials with several environments also requires significant time and resources. As a result, methods for selecting parental inbred lines and determining which crosses are likely to yield the best hybrids is a critical part of crop improvement. In pearl millet, a widespread approach has been used to evaluate the phenotypes of new potential inbred parents as a first pass screen of their potential in hybrid breeding programs.Traditionally, mid-parental values have been a common way to predict performance of hybrids on the basis of inbred values, combined with estimates of General Combining Ability (GCA) in cases where phenotypes cannot be scored in parental lines or individuals directly Approximately 90-100 pearl millet hybrids are currently cultivated on about 5 million hectares in India The use of GP/GS to obtain estimated breeding values have been widely evaluated and employed in inbreeding crops such as wheat Here we evaluated two potential genotyping strategies – RAD-seq https://figshare.com/articles/pearl_millet_genomic_selection_field_layout/5969230).Field trials were conducted at four locations in India, spanning two agro-ecological zones  of pearl millet cultivation. The Hisar and Jamnagar sites fall within the A zone of pearl millet cultivation in northwest India, while Dhule and Patancheru are located in the B zone of pearl millet cultivation in southern (peninsular) India Phenotypic traits scored include days to 50% flowering (days), plant height (centimeters), grain yield (kilograms/hectare), and 1000-grain weight (grams). The criteria used to measure each of these four traits were as follows. 1) Plant height (centimeters): Plant height for a given plant was measured from where the main stem meets to soil to the tip of the panicle of the primary tiller at the time of harvest. For each plot, five random plants were randomly selected for height measurements and the mean value of these five measurements was reported; 2) Days to 50% flowering (days): Days to flowering was measured as the time between the planting date and the date at which at least 50% of plants within a given plot exhibited the initiation of stigma emergence on the panicle of their primary tiller; 3) 1000 seed weight (grams): 200 seeds were counted out from the pooled grain collected from a given research plot, weighed, and multiplied by a factor of 5 to determine 1000 seed weight (grams); 4) Grain yield (kilograms/hectare): For each entry, all panicles within a given a plot were harvested at physiological maturity, and these panicles were sun dried for 10 to 15 days and then threshed for grain yield. Planting density and plot size varied across locations, but for each location the total yield was multiplied by the number of plots per hectare to estimate the final yield per hectare.Thirty to thirty five seeds from each inbred line were sown in a four inch pot in a darkroom at ICRISAT°s Patancheru. The pots were maintained at a temperature between 18° and 25°. Etiolated leaf tissues were harvested eight days after planting. Pooled leaf tissue from 20 to 25 seedlings per line was collected for DNA extraction. DNA was extracted using a modified DNA extraction method described by Raw sequence data obtained from both genotyping strategies was analyzed using the same analytical pipeline to enable accurate comparisons between the two. Raw reads were aligned to the pearl millet reference genome . Genetic markers with low MAFs (Minor Allele Frequency) are frequently removed prior to quantitative genetic analysis The initial set of SNPs was filtered to exclude any SNP site more than two alleles were identified, sites where only one genotype call was present, sites where more than 10% of samples with genotype calls heterozygous, sites where the minor allele was not identified in at least 5 samples, and sites where <20% of individuals had a genotype call for the site. These sets of filtered SNPs were used to calculate missing data rate , heterozygosity ) and minor allele frequency  + # of samples with heterozygous genotype calls) / 2 × total sample without missing sample). Then they were imputed using Beagle (Version: 16-06-2016). The filtered but unimputed and imputed SNP sets used in this paper have been uploaded to FigShare /2 on a scale of 0 to 2, where 0 is a genotype call of homozygous reference allele, and 2 is a genotype call of homozygous non-reference allele.A linear mixed model was used to estimate the best linear unbiased prediction (BLUP) for the phenotypic traits of inbred and hybrid lines. In the model, genotype (G), location (L), genotype by location interaction (G×L), replication (R) and block (B) were treated as random effects.In addition, the calculated variance of factors were used to estimate broad-sense heritability https://jyanglab.github.io/g3tools/) was used to perform the above analyses.In the analysis presented in The mid-parent heterosis for each trait was calculated from the BLUP values using the following formula:y is the matrix of BLUPs of all individuals, μ is the overall mean, k is the total number of SNPs, ith SNP genotype, ith SNP and e is the residual.BLUP values for lines with genotypic information – either direct genotyping for inbred lines or projected genotypes for hybrids – were used as training data for genomic prediction. For each approach described in results, predictions were conducted using the implementation of RR-BLUP (Ridge Regression Best Linear Unbiased Prediction) in the R-package rrBLUP 6) to 16,384 (214) plus total projected hybrid SNPs were tested. For each subsampled set of SNPs, the individuals were divided into 5 groups, and five separate genomic prediction analyses were conducted, using four of the five groups as training data and the remaining group as testing data. The mean correlation coefficient across these five sub-predictions was treated as a single estimate of the accuracy of the prediction model for estimates of accuracy and standard deviation.For each trait, randomly selected subsets of SNPs ranging from 64 . Finally, all hybrids with genotype and genotype data were used to train a model that then produced genomic estimated breeding values for all 36,864 possible hybrid .https://doi.org/10.6084/m9.figshare.5969230; doi: https://doi.org/10.6084/m9.figshare.5566843.The authors affirm that all data necessary for confirming the conclusions of this article are represented fully within the article and its tables and figures. Supplemental material available at Figshare: doi: i.e., the proportion of total variance in trait values explained by genetic factors – for grain yield, plant height, flowering time and 1000-grain weight were estimated to be 0.60, 0.86, 0.88 and 0.74 respectively for pearl millet hybrids and 0.72, 0.92, 0.88 and 0.74 for inbreds. However, caution should be used in interpreting differences between the inbred and hybrid heritability values, given the large difference in the number of individuals between the two populations. BLUP values were calculated for these four traits (see methods). Each trait exhibited an approximately normal distribution (Phenotypic variance was partitioned into four components: genotype (G), environment (E), interaction between genotype and environment (G×E), residual (R). For each trait, the pattern of relative contribution of each of these factors was roughly similar between inbred and hybrid pearl millet populations . Plant hribution . Heterosribution . Note thA total of 4,550 million barcoded RAD-seq reads were generated on an Illumina HiSeq 2000, for an average of 12.2 million reads per sample. tGBS libraries were sequenced using seven Ion Proton runs, generating a total of 810 million raw reads included 584 million of barcoded reads, for an average of 2.1 million reads per sample.After aligning to the pearl millet reference genome and quality filtering (See Methods), 649,067 polymorphic SNPs were identified using RAD-seq data and 73,291 SNPs were identified using tGBS data. As expected given the different subsets of the genome targeted by these two technologies t-test).The ability of genomic prediction using projected hybrid genotypes from both genotyping methods was then assessed for each phenotype using cross validation. For each tested set of SNPs, 20 random rounds of fivefold cross validation were performed. The median correlation coefficient of 1000-grain weight, days to flowering time, grain yield and plant height using all available SNPs was 0.73, 0.89, 0.51 and 0.72 for RAD-seq and 0.74, 0.87, 0.48 and 0.73 for tGBS . The differences in prediction accuracies observed for the two methods, either utilizing random sub-sampling of equal numbers of SNPs for each dataset or all SNPs obtained using each genotyping method were not statistically significant (student’s P < 0.05) in accuracy relative to the scheme 1 (M1) for all four phenotypes, although, consistent with its high residual values when fitting the original BLUP, the accuracy of prediction for yield was the lowest of the four phenotypes (P = 0.17) or significant decreases in prediction accuracy  compared to a purely hybrid phenotype data training set. Scheme 3B (M3B), which, instead of employing absolute trait values for inbreds and hybrids, summarized phenotypes as the differences between the predicted trait value for a given inbred or hybrid line and the mean trait value for either all hybrids or all inbreds (P = 6.00e-4), and grain yield (P = 5.03e-9).Four different approaches (see Methods) to genomic prediction were evaluated to test whether inbred phenotypic data can add value to genomic prediction as part of a hybrid breeding program. Scheme M1, which used trait trait from inbreds to predict genomic estimated breeding values of hybrids performed the worst of all four approaches for all four phenotypic traits tested. Notably, the rank of traits by mid-parent heterosis had a perfect negative correlation with the rank of the traits by phenotypic prediction accuracy using inbred parent training data. Scheme 2 (M2) produced a significant increase  random SNPs provided significant (P < 0.05) predictive ability for all traits and all schemes tested. The relatively small set of inbred parents used to create the set of hybrids tested as part of this analysis may have resulted in inflated apparent prediction accuracies for each trait. When using a hold-two-parents out approach to segregating hybrids with common parentage between the training and testing datasets (see Methods), accuracy was lower and standard deviations of prediction accuracy were higher , indicating that our estimates of prediction accuracy are likely to be optimistic relative to the ability to predictions for hybrids where one or both parents have not previously served as parents for tested hybrids.Increases in prediction accuracy coming from increasing numbers of markers tended to plateau at smaller total marker numbers for scheme 1 (M1 which is inbred only) than for scheme 2 (M2 which is hybrid only), with scheme 3 (M3 which is inbreds plus hybrids) was intermediate between the two. However, even 64 .per se by calculating BLUPs separated for inbred and hybrid lines eliminated this negative impact on prediction accuracy and could in fact increase prediction accuracy for some traits relative to predicting using data from hybrid lines alone. In addition we found prediction accuracy was equivalent for SNPs generated through either RAD-seq or tGBS. Given the greater number of high MAF (>0.05) SNPs generated per million reads with tGBS, this methodology is likely to be more cost effective in the context of many genomic selection based breeding programs. In addition, the more rapid turn-around time enabled by Ion Proton sequencing . Here we found that when using a conventional additive genomic prediction model (RR-BLUP), traits with higher median heterosis (grain yield and plant height) experienced a decrease in prediction accuracy when inbred data were naively incorporated into the training dataset (M3A). Segregating BLUP means for inbreds and hybrids (M3B) statistically significantly moderately enhanced prediction accuracy for grain yield (P = 5.03e-9) and flowering time (P = 6.00e-4), compared to a scheme which excluded phenotypic data from inbred parents (M2). While statistically significant, the absolute values of the increases in prediction accuracy are moderate: flowering time  and grain yield . Yield was both the most difficult trait to predict, and the trait where the inclusion of inbred trait data provided the largest increase in prediction accuracy. In this study the number of inbred lines for which genotypic and phenotypic data were available was quite small. Even in scheme 3B, inbred lines made up less than 15% of the training dataset. Given that extensive inbred trait datasets exist for pearl millet, it may be that the incorporation of genotypic and phenotypic data from larger numbers of inbred lines would produce a larger absolute increase in prediction accuracy. As inbred lines must be grown prior to the production of hybrid seed, the collection of trait data from these lines comes at relatively low cost, and may have additional value when integrated into training datasets which also include genotypic and phenotypic data from a sample of hybrid lines.Consistent with earlier studies in maize, we found that inbred trait values alone were poor predictors of hybrid performance, particularly for yield e Gama and Hallauer (1977); Even small numbers of selected SNPs can achieve relatively high prediction accuracy in this pearl millet population. The implementation of a hybrid GS/GP guided pearl millet breeding program has the potential to significantly improve the efficiency of breeding efforts . However


The increase in accuracy of prediction by using genomic information has been well-documented. However, benefits of the use of genomic information and methodology for genetic evaluations are missing when genotype-by-environment interactions (G × E) exist between bio-secure breeding (B) environments and commercial production (C) environments. In this study, we explored (1) G × E interactions for broiler body weight (BW) at weeks 5 and 6, and (2) the benefits of using genomic information for prediction of BW traits when selection candidates were raised and tested in a B environment and close relatives were tested in a C environment.A pedigree-based best linear unbiased prediction (BLUP) multivariate model was used to estimate variance components and predict breeding values (EBV) of BW traits at weeks 5 and 6 measured in B and C environments. A single-step genomic BLUP (ssGBLUP) model that combined pedigree and genomic information was used to predict EBV. Cross-validations were based on correlation, mean difference and regression slope statistics for EBV that were estimated from full and reduced datasets. These statistics are indicators of population accuracy, bias and dispersion of prediction for EBV of traits measured in B and C environments. Validation animals were genotyped and non-genotyped birds in the B environment only.Several indications of G × E interactions due to environmental differences were found for BW traits including significant re-ranking, heterogeneous variances and different heritabilities for BW measured in environments B and C. The genetic correlations between BW traits measured in environments B and C ranged from 0.48 to 0.54. The use of combined pedigree and genomic information increased population accuracy of EBV, and reduced bias of EBV prediction for genotyped birds compared to the use of pedigree information only. A slight increase in accuracy of EBV was also observed for non-genotyped birds, but the bias of EBV prediction increased for non-genotyped birds.The G × E interaction was strong for BW traits of broilers measured in environments B and C. The use of combined pedigree and genomic information increased population accuracy of EBV substantially for genotyped birds in the B environment compared to the use of pedigree information only. The difference in production conditions between highly bio-secure breeding (B) and commercial production environments (C) can lead to genotype-by-environment interaction (G × E) in broiler chicken . IndicatThe ultimate goal of a breeding program for broilers is to increase the genetic gain of the birds’ performance in the C environment only. To improve genetic gain, sib-testing of purebred birds in environments B and C is an option . Due to There has been growing interest for genomic selection in poultry breeding programs because of the higher accuracy of prediction compared to pedigree-based evaluation. The increase in accuracy of prediction from using dense genotypes is due to improved measurement of the relationships between animals and better prediction of the Mendelian sampling terms . ImproveIn addition, a typical breeding program that uses genomic selection in broiler chicken often applies a selective genotyping strategy. In broilers, important traits such as BW and feed efficiency can be recorded before sexual maturity. In situations of limited resources for genotyping, only a proportion of the birds that are potential parents with best performances will be genotyped. The selective genotyping strategy can increase accuracy of selection and genetic gain compared to random genotyping strategies . HoweverTwo main objectives of our study were: (1) to explore genotype-by-environment interactions for BW in broilers raised in breeding bio-secure (B) and commercial production (C) environments, and (2) to use genomic information to increase the accuracy of predicted breeding values of birds in the B environment for BW traits measured in the C environment.Data obtained from the poultry breeding company, Cobb-Vantress, included (BW) performances of purebred broiler chicken that were tested in breeding bio-secure (B) and standard commercial production (C) environments. These two environments represent the standards for biosecurity used in commercial breeding programs and the C environment represents normal commercial production conditions in Western countries. In both systems, birds were reared in large flocks under floor conditions. The data included BW records from 16 time-steps (TS) of selection that covered roughly 2.5 generations. A time-step involves selection and testing activities in the breeding programs that are carried out within a relatively short period of time. Birds that hatched at each TS were transferred to either the B or C environment for phenotype testing. Each full sib group was split between the B and C environments such that each bird will have full and half-sibs in both environments. Parents were selected from birds tested in the B environment only. In other words, parents did not have any phenotypic records in the C environment. In each TS, all the offspring of birds in the C environment were hatched at the same time while the offspring of birds in the B environment were hatched at several successive time points. Sires and dams of the offspring in each TS were from several previous TS.BW of the broilers tested in the B environment was recorded once, at 6 weeks of age (BW6.B), for TS 1–10 and recorded once or twice, at 5 and 6 weeks of age (BW5.B and BW6.B), for TS 11–16. All birds in the B environment at TS 11–16 had BW5 records, but only 33% of those birds had BW6 records. The BW of the broilers tested in the C environment were recorded at 5 and 6 weeks of age (BW5.C and BW6.C) for TS 5–10 and only at 5 weeks of age (BW5.C) for TS 11–16. Data editing was performed as in Chapter 4 of Chu  by removA medium-density SNP chip with 55,792 SNPs was used for genotyping . For quality control, the missing rate for SNPs was set at < 0.05 and the call rate for birds at > 0.95. Also, SNPs with a minor allele frequency lower than 0.01 were removed. After quality control, 39,767 genotyped birds with 50,562 SNPs remained for constructing the genomic relationship matrix. All parents had genotype information. Although all the birds in the C environment were genotyped, after quality control, genotyping information of 41 birds in C was not used to construct the genomic relationship matrix. Only part of the birds in the B environment were genotyped.y is a vector of male or female phenotypic records of BW at the original scale; b is a vector of fixed factors of hatch TS of the bird, TS of the parents, and dam age in classes of 1 week; X, Z, and W are incidence matrices; a, c and e are the vectors of the direct additive genetic effects, permanent environmental maternal effects and residuals, respectively. These random effects were assumed to be normally distributed: A is the pedigree relationship matrix; Id is the identity matrix for dams; I is the identity matrix for individual birds; A preliminary study showed that male and female BW had different variances, but were highly correlated, which reflects scaling effects . ModelliStandardization was applied, so that each phenotypic record was divided by the corresponding phenotypic standard deviation estimated from model (1). A multi-trait PBLUP model was used to estimate variance components and predict EBV from the standardized phenotypic records of BW in the B and C environments. The PBLUP model (2) includes eight traits but after transformation, the variances of genetic and maternal effects were equalized and correlations between sexes were equal to 1, so rank reduction was used for these effects whereas individual variances and covariances were estimated for the residual. The model was as follows:ed ranks  for maleed ranks  for male−5 [Variance components were estimated using the PBLUP multivariate model (2) for two reasons: (1) the selective genotyping applied to birds in the B environment might lead to overestimation and bias of variance components estimates with a ssGBLUP model ; and (2)−5 . In addiω = 0.01 [The standardized variance components of the PBLUP model estimated from the REML module were used to calculate EBV for ssGBLUP multivariate models using the DMU5 procedure in the DMU package . Becauseω = 0.01 , 29 on tVanRaden .Cross-validation was carried out to evaluate accuracy, bias and dispersion of EBV for genotyped and non-genotyped validation birds in the B environment. Validation was based on EBV estimated from the full and reduced datasets. The full dataset contained all the phenotypic records from TS 1–16, whereas the reduced dataset contained only phenotypic records from TS 1–12. The reduced dataset was a subset of the full dataset, from which records of 14,187 birds in the B environment and 5988 birds in the C environment at TS 13–16 were removed. The validation birds were individuals in the B environment at TS 13–16. Such a design was used to avoid having two or more generations of validation birds. The validation individuals can be genotyped birds or non-genotyped birds. Cross-validation measures were EBV statistics for validation birds that were estimated from the full and reduced datasets , i.e. thectively . Expectaaccraccf , where \ectively .The two validation models were the PBLUP and ssGBLUP models. Standardized covariance components that were estimated from the PBLUP model using the full dataset were used to predict EBV in the PBLUP and ssGBLUP models using the full or reduced datasets. The vectors Parameters that were estimated from the PBLUP model (2) were on the standardized scale for all BW traits. The estimates were re-scaled back to the original scale for male and female BW traits. Rescaling of (co)variance matrices used formula  to 5):3:35):3\doMatrix r et al. .The number of records, means, and standard deviations of BW records for broiler chicken raised in the B and C environments are in Table The standard deviation of BW records increased from 5 to 6 weeks of age and the mean BW increased by 500 to 600 g between weeks 5 and 6. As a result, the change in the coefficients of variation was relatively small. For example, the coefficient of variation for BW records of male birds in B environment was equal to 0.098 at both weeks 5 and 6 and those of male broilers in the C environment were equal to 0.174 and 0.163 at weeks 5 and 6, respectively.Mean BW was greater in males than in females at the same age. The standard deviation of BW was also higher in males than in females, and thus the difference in coefficients of variation between sexes was relatively small. The relative difference in means of BW between males and females was larger for records in the B than the C environment. The relative difference in standard deviations between males and females was smaller for records in the B than the C environment.Table Additive genetic variances, heritabilities and permanent environmental maternal effects of traits measured in the B and C environments for male and female BW5-6 were estimated from the PBLUP model 2) and are in Table  and are Estimates of the heritability of traits measured in the C environment tended to be higher than those measured in the B environment. Heritabilities of BW5-6 ranged from 0.27 to 0.30 and from 0.31 to 0.37 for the same traits measured in the B and C environments, respectively. With age increasing from 5 to 6 weeks, heritability of the traits measured in C decreased, whereas that of the traits measured in B tended to increase. The ratio of the permanent environmental maternal variance to the total phenotypic variance for the traits measured in C decreased from week 5 to 6, whereas that for the traits measured in B tended to increase.The genetic variance for BW was considerably larger in males than in females when considering the traits in the same environment and at the same week of age. The absolute and relative differences in variances between male and female traits were larger for traits measured in C than in B. However, the differences in heritabilities and permanent environmental maternal effects were mostly negligible between male and female traits measured in the same environment and at the same week of age.Estimates of variances, heritabilities and permanent environmental maternal effects differed between male and female traits because the PBLUP model uses heterogeneous residual variances and different scaling for sexes. However, genetic correlations and permanent environmental maternal correlations between sexes were assumed to be 1 for the same traits. Therefore, a single correlation estimate was obtained for the different sexes combined. Table The genetic correlations between BW measured in B and C ranged from 0.48 to 0.54. The genetic correlation of BW between the B and C environments tended to decrease as week of age increased from 5 to 6. Genetic correlations of 0.54 between BW5.B and BW5.C and 0.48 between male BW6.B and BW6.C were obtained. Genetic variances in each environment were larger for BW6 than BW5. The correlation between BW5 and BW6 tended to be higher when measured in C (0.99) than when measured in B (0.96).The permanent environmental maternal correlations between traits measured in B and C ranged from 0.59 to 0.72. The permanent environmental maternal correlations between BW at 5 and 6 weeks and BW measured in B and C followed similar trends as the genetic correlations. For example, the environmental correlations between environments B and C were lower for BW6 than for BW5. Permanent environmental maternal correlations between BW5 and BW6 in the same environment were very high but still a bit higher for traits measured in C than in B.The ts Table . These cWith the PBLUP model, With the ssGBLUP model, Regardless of the model used, Genetic parameters for male and female BW at 5 and 6 weeks of age raised in bio-secure breeding (B) and commercial production (C) environments were estimated using a PBLUP multivariate model. A multivariate ssGBLUP model was used to predict EBV of BW measured in B and C environments. Cross-validations were carried out to assess population accuracy, bias and dispersion of EBV predictions of traits measured in C for genotyped and non-genotyped birds in the B environment when the PBLUP and ssGBLUP models were used.The difference between the B and C environments is mainly determined by hygiene conditions. The strict bio-secure conditions of the B environment are regulated to prevent worldwide spread of diseases to production farms and to avoid the risk of losing the purebred lines . In contIn our study, estimates of the genetic correlation between BW measured in B and C ranged from 0.479 to 0.535. Statistically, re-ranking refers to genetic correlations different from 1, but in practice, re-ranking is commonly considered important in breeding programs when the correlation is lower than 0.8 . The genWith the more challenging C environment, we did expect larger variances for traits measured in C than for those measured in B, but we also obtained higher heritabilities for traits measured in C than for the same ones measured in B. Kapell et al.  found tht-7, to t-4 to t days.The production environment also influenced the maternal environmental effects. Permanent environmental maternal correlations between BW measured in B and C were significantly lower than 1. The permanent environmental maternal effect of traits measured in C became smaller as age increased from 5 to 6 weeks. In contrast, the permanent environmental maternal effect of traits measured in B tended to increase as age increased from 5 to 6 weeks. In previous studies in poultry, a reduction in the permanent environment maternal effect on BW with increasing age was found –40. HoweThe effects of the production environment on age-by-genotype interactions were not clear, but the genetic correlation between BW5 and BW6 tended to be lower for traits measured in B than in C. The effects of the production environment on sex-by-genotype interactions were mainly related to the relative difference in variances between male and female BW traits because the genetic correlation between male and female BW within each environment was assumed to be 1 in the multivariate model (2). This assumption was based on our preliminary results that the sex-by-genotype interaction only led to the scaling effect between male and female BW.Based on the strong G × E interaction found in our study, selection for performance under commercial conditions will greatly increase response to selection for BW in broiler breeding programs when birds are phenotype-tested in both B and C environments , 43. In An increase in population accuracy of EBV for genotyped birds has been shown in many studies , 44–46, The ectively . The \dobroilers . The prebroilers . The Menbroilers , 49, andMost studies , 44–46 r ssGBLUP , 20–22,  ssGBLUP , 20, 21.Validation groups of genotyped and non-genotyped birds had different population accuracies of EBV even with the PBLUP model, which might be related to a non-random distribution of validation birds into the groups. In our dataset, the mean of BW measured in B was higher for genotyped birds than for non-genotyped birds, but the standard deviation of the records in B was lower for genotyped birds than for non-genotyped birds. All parents had genotyping information. This indicated that a selective genotyping strategy was applied for birds raised in B. Within the validation birds, the genotyped birds that are from the top of the distribution have higher relationships between individuals than the non-genotyped birds. From the numerator relationship matrix that was calculated from all animals in the pedigree, the average additive genetic relationship was 0.024 between validation genotyped birds and 0.019 between validation non-genotyped birds. The higher average relationship between genotyped birds results in less variation in EBV estimated from PBLUP, and thus lower population accuracy  for genoWhen genomic information was used and evaluated in a G × E sib-testing breeding program for broilers, our study was faced with several challenges. The estimation of variance components was challenging with such a large number of genotyped birds, computation-demanding multi-trait models, and selective genotyping. In addition, it was not clear which cross-validation strategies to use in the situation where selection candidates reside in B and do not have performance records in C. Cross-validation is commonly based on the correlation between phenotypes that are corrected for fixed effects from the PBLUP model using the full dataset and EBV that are estimated from PBLUP or ssGBLUP model using the reduced dataset . HoweverStrictly, the accuracy . It descz et al.  reportedelection . HoweverReverter . Using dThe accuracy . Individaccuracy . Populataccuracy . For exaaccuracy .Sex-by-genotype interaction has been investigated for different traits in broiler chicken , 52, 53,−6 to 10−5 [The data on BW5-6 of the birds measured in C used in the current study is the same as in Chapter 4 of Chu , but the to 10−5  for the Genetic parameters were estimated for male and female body weight (BW) at 5 and 6 weeks of chicken that were raised in breeding bio-secure (B) and production commercial environments (C). Several indications of interaction between genotype and testing environment (B and C) were found including different average performances, correlations significantly lower than 1, heterogeneous variances and different heritabilities for traits measured in B and C. In addition, genomic information on birds raised in both B and C was used for prediction of EBV of birds in B for BW traits measured in the B and C environments. To evaluate the prediction of EBV, cross-validation statistics of EBV were estimated, namely population accuracy, bias and dispersion of EBV from reduced and full datasets. We found that the use of combined pedigree and genomic information in ssGBLUP substantially increased population accuracy of EBV for genotyped birds compared to the use of only pedigree data in PBLUP. Accuracy of EBV also increased for non-genotyped birds, but bias of EBV prediction increased for non-genotyped birds. In summary, the differences between the B and C environments result in a strong G × E interaction with genetic correlations ranging from 0.479 to 0.535 between BW traits of broilers measured in the B and C environments. In order to ensure maximum genetic gain under commercial conditions, breeding programs should establish recording systems under commercial conditions to provide their customers with genotypes that are well adapted to the commercial environment. Compared to the use of pedigree data only, the use of combined pedigree and genomic information increases population accuracy of EBV substantially for genotyped birds that are raised in the B environment.


The impact of the parental origin of associated alleles in GWAS has been largely ignored. Yet sequence variants could affect traits differently depending on whether they are inherited from the mother or the father, as in imprinted regions, where identical inherited DNA sequences can have different effects based on the parental origin. To explore parent-of-origin effects (POEs), we studied 21 quantitative phenotypes in a large Hutterite pedigree to identify variants with single parent  effects, and then variants with opposite parental effects. Here we show that POEs, which can be opposite in direction, are relatively common in humans, have potentially important clinical effects, and will be missed in traditional GWAS. We identified POEs with 11 phenotypes, most of which are risk factors for cardiovascular disease. Many of the loci identified are characteristic of imprinted regions and are associated with the expression of nearby genes. Sahar Mozaffari et al. conduct a parent of origin genome-wide association study in a large Hutterite pedigree to determine parent-of-origin effects on 21 phenotypes. They identify effects on 11 phenotypes , most of which are associated with cardiovascular disease. SNRPN gene from the father results in Prader-Willi Syndrome but inheriting a loss of function mutation for the UBE3A gene from the mother results in Angelman Syndrome2. Long noncoding RNA genes at this and other imprinted regions act to silence  genes in cis. Imprinted genes are often part of imprinted gene networks, suggesting regulatory links between these genes5. More than 150 imprinted genes have been described in humans6, but there are likely many other, as yet undiscovered, imprinted loci.Genome-wide association studies (GWAS) typically treat alleles inherited from the mother and the father as equivalent, although variants can affect traits differently depending on whether they are maternal or paternal in origin. In particular, parent-of-origin effects (POEs) can result from imprinting, where epigenetic modifications allows for differential gene expression on homologous chromosomes that is determined by the parental origin of the chromosome. Mutations in imprinted genes or regions can result in diseases. For example, two very different diseases, Prader-Willi Syndrome and Angelman Syndrome are due to loss of function alleles in genes within an imprinted region on chromosome 15q11-13. Inheriting a loss of function mutation for the 10. Kong et al.7 discovered one locus associated with breast cancer risk only when the allele is inherited from the father and another locus associated with type 2 diabetes risk only when the allele is inherited from the mother. Garg et al.8 reported parent-of-origin cis-eQTLs with known or putative imprinted genes affecting gene expression. Two additional studies by Zoledziewska et al.11 and Benonisdottir et al.6 identified opposite POEs on adult height at known imprinted loci. Both studies reported associations with variants at the KCNQ1 gene, and one showed additional opposite POEs with height at two known imprinted loci (IGF2-H19 and DLK1-MEG3)6. These studies provide proof-of-principle that alleles at imprinted loci can show POEs, some with opposite effects, with common phenotypes.Previous studies have utilized pedigrees to test maternal and paternal alleles separately for association with phenotypes or with gene expression to uncover new imprinted loci16. Similar to Kong et al.7, our method does not require data on the parent and only uses the parent-of-origin informative alleles, which were assigned and phased using PRIMAL17. In contrast to Kong et al.7 which used binary traits, our method tests for parent-of-origin effects on quantitative traits, similar to Benonisdottir et al.6, which tested for parent-of-origin effects on height.Many existing studies and methods identify parent-of-origin effects use case/parent trios or case/mother duos17. We identify variants with maternally inherited or paternally inherited effects only and variants with opposite POEs. Some of the identified regions have characteristics similar to known imprinted genes. Overall, we show that this model can identify putative imprinted regions with POEs for a broad range of clinically relevant quantitative phenotypes.No previous study has included a broad range of human quantitative phenotypes or has studied genome-wide variants with effects in different directions depending on the parent-of-origin. To address this possibility, we develop a statistical model that directly compares the effects of the maternal and paternal alleles to identify effects that are different, including those that are opposite. We apply this model in a study of 21 common quantitative traits that were measured in the Hutterites, a founder population of European descent for which we have phased genotype datap < 5 × 10–8) with each of five of the 21 traits: low-density lipoprotein level (LDL)-cholesterol, triglycerides, carotid artery intima media thickness (CIMT), left ventricular mass index (LVMI), and monocyte count. The results of all 21 GWAS are summarized in Supplementary Table We first performed standard GWAS of 21 traits in the Hutterites . This model is especially powerful to identify variants with parental effects in opposite directions.Maternal and paternal GWAS: Using the same phenotypes, genotypes, pedigree, and criteria for significance as in the standard GWAS, we tested for maternal and paternal effects on each trait by testing each parentally inherited allele with the trait of interest, similar to previous studies11. Variants were considered to have POEs if they had a p-value less than 5 × 10–8 in only one parent and were not significant in the standard GWAS  were associated with maternally inherited alleles only.Overall, seven phenotypes had genome-wide significant parent-of-origin associations: four in the maternal only GWAS and three in the paternal only GWAS. Three cardiovascular disease (CVD)-associated phenotypes  and one lung function phenotype (forced expiratory volume in 1 second , equivalent to Xp+ XM. n × 1 vector of genotypes with possible values . 40. Parent-of-origin allele frequency of significant SNPs are in Supplementary Table 36. For this study, sequencing reads were reprocessed as follows. Reads were trimmed for adaptors using Cutadapt (with reads < 5 bp discarded) then remapped to hg19 using STAR indexed with gencode version 19 gene annotations42. To remove mapping bias, reads were processed using WASP mapping pipeline43. Gene counts were collected using HTSeq-count44. VerifyBamID was used to identify sample swaps to include individuals that were previously excluded45. Genes mapping to the X and Y chromosome were removed; genes with a Counts Per Million (CPM) value of 1 (expressed with less than 20 counts in the sample with lowest sequencing depth) were also removed. Limma was used to normalize and convert counts to log transformed CPM values46. Technical covariates that showed a significant association at p < 0.05 with any of the top 10 principal components were regressed out (RNA Integrity Number and RNA concentration).RNA-seq data from LCLs were available for 430 Hutterites included in a previous study LCL RNA-seq data was used to test the single parent model for the most significant SNP from the maternal or paternal only GWAS for each phenotype. We selected all genes detected as expressed in the LCLs and residing within 1 Mb of each most significant associated SNP. Summary of the SNPs and genes tested are in Supplementary Table LCL RNA-seq data was used to test the opposite effect model for the most significant SNP in each region that was associated with a phenotype in the parent-of-origin opposite effects GWAS. We selected all genes detected as expressed in the LCLs and residing within 1 Mb of each associated SNP. Summary of the SNPs and genes tested are in Supplementary Table 47. The parent-of-origin genotypes were evaluated with a custom script applied to the 1308 complete trios present in the cohort. Trait measures were adjusted by age and age squared; residuals were inverse-normalized by quantile transformation. Single parent effects were tested separately for maternal and paternal allele using epacts (test q.emmax)48. Opposite effects were tested with the same methods as in the Hutterites, described above.The SardiNIA study is a longitudinal population-based cohort study started in 2001 to study quantitative traits of biomedical relevance with a special emphasis on those influencing aging. In a first survey, the project recruited 6148 individuals from four towns in the Lanusei Valley  and assessed 98 quantitative traits in over 62% of the eligible population living in the region (age 14–102 years), and at least 96% of the initial cohort have all grandparents born in the same province. Recently, the study recruited 773 additional individuals, involving a total of 6921 subjects. The longitudinal study, now in its 14th year and in its fourth phase, collected the longitudinal information on more than 1000 quantitative traits, including inflammatory markers and immune related traits, that can be scored on a continuous scale. Written informed consent was obtained from all participants. Genotypes were obtained after imputation with a reference panel generated from low pass sequencing of 3514 Sardinians as described in Sidore et al.https://github.com/smozaffari/PO_GWASCode for PO-GWAS: Description of Additional Supplementary FilesSupplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3


Many cultural traits are not transmitted independently, but together as a package. This can happen because, for example, media may store information together making it more likely to be transmitted together, or through cognitive mechanisms such as causal reasoning. Evolutionary biology suggests that physical linkage of genes (being on the same chromosome) allows neutral and maladaptive genes to spread by hitchhiking on adaptive genes, while the pairwise difference between neutral genes is unaffected. Whether packaging may lead to similar dynamics in cultural evolution is unclear. To understand the effect of cultural packages on cultural evolutionary dynamics, we built an agent-based simulation that allows links to form and break between cultural traits. During transmission, one trait and others that are directly or indirectly connected to it are transmitted together in a package. We compare variation in cultural traits between different rates of link formation and breakage and find that an intermediate frequency of links can lower cultural diversity, which can be misinterpreted as a signature of payoff bias or conformity. Further, cultural hitchhiking can occur when links are common. Many definitions of complexity rely on some quantification of an artefact's component parts and the extent to which those parts are integrated e.g. ,2). Simi. Simi2])In this paper, we focus on the last question and present a model where cultural traits can be linked and transmitted as a package. Linkage between individual evolving components is not a new concept and the analogous genetic mechanism, genetic linkage, is well studied. When chromosomes are inherited, genes on the same chromosome are transmitted together unless recombination occurs, in which case a segment of the chromosome is replaced with the corresponding segment from another chromosome. Genetic linkage has many profound evolutionary consequences, such as allowing neutral or deleterious alleles to spread by hitchhiking (e.g. ), hinderMany have tried to infer underlying cultural transmission processes from population-level patterns –12, inclExisting models of cultural evolution often treat traits as completely packaged together, or independent units that can be studied in isolation. As interest increases in models that involve multiple traits such as cultural niche construction, cumulative culture, cultural phylogeny and correlated evolution, there is an increasing need for models that incorporate an appropriate theory of cultural linkage. A complete theory of cultural linkage should include (i) a solid psychological theory of link formation and maintenance, (ii) a description of the transmission of linked traits and the links between them, and (iii) a model of how innovation interacts with linked traits and how novel traits arrive in cultural packages.In the following, for each of these three essential components, we make the simplest possible assumptions. However, a deeper understanding of link formation mechanisms will be crucial to understanding the role of linkage in any real cultural system and should be the focus of urgent future work. Initially, we assume traits have no synergistic effects  nor are any incompatible. The links form randomly at a fixed rate between any two traits in an individual's cultural repertoire. This is consistent with a mechanism of link formation where, for example, a role model demonstrates actions in sequence. An individual learning those actions might learn both the actions and the sequence—in other words, the actions and the temporal links between them. Of course, a huge diversity of mechanisms is likely to exist. For example, traits may be transmitted together owing to causal reasoning , prestigThe aim of this paper is to understand whether links between cultural traits alter the cultural composition of the population compared to independent trait transmission. To this end, we model the evolution of cultural traits under various cultural transmission processes and investigate how the population-level signature of such processes, summarized by the level of cultural diversity, changes if we allow links to exist between cultural traits. This analysis provides us with first results on how the existence of links between cultural traits can interfere with signatures of cultural evolution and our ability to detect cultural transmission processes from cultural frequency data. We discuss the circumstances under which we expect linked cultural transmission to be important—where we need to include a realistic theory of cultural linkage in order to replicate the dynamic of cultural change—and the circumstances under which current theory is sufficient.2.Below we provide model details and all information required by the Overview, Design concepts, and Details protocol for simulation studies of this type . We deveN individuals, possessing h cultural traits of which each can take k possible variants. As described below, each individual can form a link between any two traits, which influences the transmission dynamic. We do not assume any demographic processes: individuals are not lost or added to the population.We consider a population of (a)k the lowest. The pay-off of variant j of trait i isis ∈  describes the maximum pay-off difference between variants at trait i, thus a neutral trait always has is = 0. The pay-off of individual r isj(r)∈{1, 2, … ,k} denotes the variant adopted by individual r for trait i.Each individual has a pay-off value which is the product of the pay-off effects of all variants adopted. Examples of pay-off may be forage success, monetary income or gifts received. If all variants of a trait provide the same pay-off, we say the trait is neutral. By contrast, if the variants of the trait provide different pay-offs, we label the trait ‘functional’ (following the terminology of ). We assk in functional traits as a ‘detrimental’ variant in the sense that, other traits being the same, an individual carrying variant k always has a lower pay-off than those carrying another variant. We make no assumption about whether the variant causes direct harm to the individual.For convenience, we refer to variant (b)rp according to either unbiased, pay-off-biased, or conformist-biased transmission. In the unbiased case, each individual, r, is chosen as an interaction partner with probability rp = 1/(N − 1). In the pay-off-biased case, the probability is proportional to the pay-off, r,f so that rq is the frequency of the variant carried by r and κs is the strength of conformity )(c)b). Additionally, we have shown that through the process of hitchhiking, a neutral associated trait can possess the signature of a functional trait during a cultural sweep and for some time after the sweep (figures 4c and 5a–c).Many studies are interested in inferring the underlying processes of cultural transmission from the available data, often population-level frequencies of cultural variants. Recent studies have clearly shown that there exist theoretical limits to inferring processes of cultural transmission from population-level patterns: one should not expect a one-to-one mapping between population-level statistics and the underlying transmission process as different scenarios can lead to comparable population-level patterns ,27,35,36figure 4These results make the identification of links and packages in real-world situations an important task. While there are established procedures to collect the type of sequence data needed to identify linkage in a genetic system, it is difficult to collect such detailed data in a cultural context.(d)There is a dearth of empirical studies aiming to identify the presence of links between cultural traits, but there is no reason to assume that links are rare, and evidence for hitchhiking does exist (e.g. ). We urgSuch a complete theory of linked cultural transmission may prove crucial to our understanding of some basic metrics of cultural evolution in a variety of domains. As mentioned above, complex human technology represents an extreme of large, relatively stable cultural packages. However, the story of the evolution of technologies from chimpanzee nut cracking to complex human artefacts like axes and arrows is the story of the formation and maintenance of links between once-distinct cultural objects. Thus, to understand cultural complexity in its many forms, we must continue to develop a solid theory of cultural linkage.