Seriola lalandi). The principal aim of this study was to address this knowledge gap and to assess predictive ability of genomic Best Linear Unbiased Prediction (gBLUP) for traits of commercial importance in a yellowtail kingfish population comprising 752 individuals that had DNA sequence and phenotypic records for growth traits . The gBLUP method was used due to its computational efficiency and it showed similar predictive performance to other approaches, especially for traits whose variation is of polygenic nature, such as body traits analysed in this study. The accuracy or predictive ability of the gBLUP model was estimated for three growth traits: body weight, folk length and condition index.Genomic prediction using Diversity Arrays Technology (DArT) genotype by sequencing platform has not been reported in yellowtail kingfish (2) from linear regression analysis ranging from 0.49 to 0.71.The prediction accuracy was moderate to high (0.44 to 0.69) for growth-related traits. The predictive ability for body weight increased by 17.0% (from 0.69 to 0.83) when missing genotype was imputed. Within population prediction using five-fold across validation approach showed that the gBLUP model performed well for growth traits , with the coefficient of determination  contains supplementary material, which is available to authorized users. Seriola lalandi has emerged as an important marine finfish for aquaculture, not only in Australia but also in many other parts of the world including Japan, Latin America , the United States and New Zealand [2 = 0.15 to 0.41) were estimated for body and carcass traits [Yellowtail kingfish (YTK)  Zealand . In Aust Zealand . To date Zealand . Genetic Zealand . Perform Zealand –9. In ths traits .S. lalandi. Further, the cost of whole genome sequencing is still high, especially for non-model species. Genotyping by sequencing (GBS) has been used as an alternative to whole genome sequencing approach due to its low cost of genotyping per animal, large amount of high quality genetic markers and the suitability of the markers for genomic prediction/selection. Among the Restricted-site Associated DNA sequencing (RAD-seq) methods, DArTseq™ sequencing technology represents a combination of a DArT complexity reduction methods and next generation sequencing platforms [S. lalandi).Along with the conventional selective breeding approach based on phenotype and pedigree, we are interested in exploring potential use of molecular and genomic information to accelerate genetic gain in this line of YTK, especially improving traits that are difficult or expensive to measure or lowly heritable. To date, high density SNP array-based chips are not commercially available for yellowtail kingfish latforms –13. Similatforms , 15 or glatforms . To dateSeriola lalandi), using the DArT sequencing technology. It is the most common method used to perform genomic prediction for complex traits whose variation is of polygenic nature. Specifically, we report the gBLUP prediction for growth traits , using five-fold across validation conducted in the same population. The effects of the number of markers on the predictive ability of gBLUP model were also estimated.Therefore, the principal aim of this study was to investigate the predictive ability of genomic Best Linear Unbiased Prediction (gBLUP) method for commercial traits of economic importance in yellowtail kingfish  and coded as 1 and zero, respectively. The incidence of skin fluke and deformity recorded under field condition in this population was low  and hence, results from genomic prediction for these traits were not tabulated in this paper.In this study, a total of 752 DNA samples of yellowtail kingfish (YTK) were sequenced using DArT-seq technology . Fish biometrics, fluke abundancy and deformities were recorded before they were released back to the cage.A detailed description of the population is given in our earlier studies , 3, 17.S. lalandi transcriptome sequences  [Fin tissue samples of a total of 1568 fish were used to extract total genomic DNA (gDNA) using DNeasy Blood and Tissue kits  and NucleoMag® 96 Tissue kit . A panel of eight microsatellite markers that consisted of five newly developed candidates from  YTK019)  and thre YTK019) , 19 were YTK019) .DNA amplification was achieved using Qiagen Multiplex PCR PLUS Kits  in 13.5 μL reactions, each containing 1.25 μL of 10× primer mix, 6.25 μL of Multiplex PCR Master Mix, 2.75 μL of RNase free water, 1.25 μL of Q-Solution and 2.0 μL of approximately 20 ng template gDNA. Amplification was performed using an Eppendorf Mastercycler nexus  with cycling conditions as follows: initial denaturation at 95 °C for 15 min, followed by 35 cycles of 95 °C for 30 s, 57 °C for 90 s, and 72 °C for 30 s; with a final extension at 68 °C for 30 min.PIC) was computed in CERVUS v3.0. Parentage assignment was completed using COLONY software [PCR products were separated by capillary electrophoresis on an AB 3500 Genetic Analyser (Applied Biosystems) at the University of the Sunshine Coast. Fragment sizes were determined relative to an internal lane standard  using GENEMARKER v1.95 software  and double-checked manually. Individuals with low or missing peaks were amplified/genotyped a second time and checked for evidence of large allele dropout, extreme stuttering and null alleles, based on 1000 bootstraps and a 95% confidence interval. Tests for HWE at each locus and genotypic linkage equilibrium among pairs of loci, numbers of alleles and the observed and expected heterozygosities of each locus were determined using GENALEX v6.5, while polymorphic information content  and the PstI-SphI method was selected. DNA samples were processed in digestion/ligation reactions principally as per Kilian et al. [Four methods of DArTseq™ complexity reduction were tested in n et al.  but repln et al. . ReverseOnly “mixed fragments” (PstI-SphI) were effectively amplified in 30 rounds of PCR using the following reaction conditions: 94 °C for 1 min, then 30 cycles of 94 °C for 20 s, 58 °C for 30 s, 72 °C for 45 s and 72 °C for 7 min.After PCR equimolar amounts of amplification products from each sample of the 96-well microtiter plate were bulked and applied to c-Bot (Illumina) bridge PCR followed by sequencing on Illumina Hiseq2500. The sequencing (single read) was run for 77 cycles.Sequences generated from each lane were processed using proprietary DArT analytical pipelines. In the primary pipeline the fastq files were first processed to filter away poor quality sequences, applying more stringent selection criteria to the barcode region compared to the rest of the sequence (minimum Phred pass score = 30 and minimum pass percentage = 75). In that way the assignments of the sequences to specific samples carried in the “barcode split” step were very reliable. Approximately 2,500,000 sequences per barcode/sample were identified and used in marker calling. Finally, identical sequences were collapsed into “fastqcoll files”. The fastqcoll files were “groomed” using DArT PL’s proprietary algorithm which corrects low quality base from singleton tag into a correct base using collapsed tags with multiple members as a template. The “groomed” fastqcoll files were used in the secondary pipeline for DArT PL’s proprietary SNP and SilicoDArT (presence/absence of restriction fragments in representation) calling algorithms (DArTsoft14).https://www.ncbi.nlm.nih.gov/sra/SRP130211 (Release date: 2019-02-28) Release date: 2019-02-28.For SNP calling all tags from all libraries included in theDArTsoft14 analysis are clustered using DArT PL’s C++ algorithm at the threshold distance of 3, followed by parsing of the clusters into separate SNP loci using a range of technical parameters, especially the balance of read counts for the allelic pairs. Additional selection criteria were added to the algorithm based on analysis of approximately 1000 controlled cross populations. Testing for Mendelian distribution of alleles in these populations facilitated selection of technical parameters discriminating well true allelic variants from paralogous sequences. In addition, multiple samples were processed from DNA to allelic calls as technical replicates and scoring consistency was used as the main selection criteria for high quality/low error rate markers. Calling quality was assured by high average read depth per locus . Regarding whole read quality, the minimum Phred pass score was set at 10 and the minimum pass percentage was 50. The average SNP calling rate was 92% and the fish sample calling rate was 95%. The raw sequence data (accession number SRP130211) is available at Sample and markers statistics is given in Additional file P > 0.05). This has been a result of early communal rearing of all families soon after birth.Restricted maximum likelihood (REML) method applied to a linear  mixed model was used to estimate heritability for traits studied. The model included the fixed effect of stock origin (wild and F1 fish) and the additive genetic random effect of individual fish in the pedigree. Our preliminary analysis using logarithmic likelihood ratio test (LRT) indicated that the common full-sib effect was not significant (2) for the traits studied was estimated as h2 = σ2A/σ2P where σ2A is the additive genetic variance, σ2P the phenotypic variance (σ2P = σ2A + σ2E) and σ2E is the residual variance. Pedigree based analysis of heritability were carried out using the ASREML software package [Heritability (h package .Genomic best linear unbiased prediction (gBLUP) method was used to assess the predictive ability of the model for traits studied, using SVS Suite . This method (gBLUP) generally shows similar predictive capacity to other non-linear approaches, especially for growth traits as used in this study. Theoretical statistics of the gBLUP method was discussed in earlier reports . In brieG) derived from DNA marker information to calculate genomic breeding values for each individual in the pedigree Clark et al., [y is the vector of phenotypic observations , m = overall mean, X is the incidence matrix containing fixed effects (i.e. stock origin in this study) in b. The matrix Z relates records to genomic values, a is the SNP effect and e is the residual error. In this model, gBLUP assumes equal variance σ2z for all loci/SNPs:σ2g is the genetic variance and ϕ is the normalisation factor.The (gBLUP) method uses genomic relationship matrix , and in each round (e.g. k-1), one sub-set was selected as a test  and the model fitted with four folds to predict the validation set.2-value were used to predict phenotypes with the following model:X is the fixed effects matrix, M is the genotype matrix, and The gBLUP five-fold cross validation model used was the same as described above. The allele substitution effects (ASE) and fixed effect coefficients obtained from iterations and k-folds that gave the largest R2) from the regression analysis . We compared the actual and predicted phenotypes by association test and linear regression analysis. The coefficient of determination  was als2 = 0.11 to 0.42).A total of 752 fish sampled from a population of 1568 individuals having phenotype data was sequenced using DArT marker technology. The number of observations, mean and standard deviation) for three traits studied are given in Table The pseudo-heritability  was moderate to high (0.44 to 0.69) Table .Table 2A2) from the linear regression analysis of the actual on predicted phenotypes ranged from 0.49 to 0.71 across the growth traits. The 5-fold cross validations using gBLUP for body weight and length are given in Fig. In addition, within population prediction using five-fold cross validation approach showed that the correlation between the predicted and actual phenotypes for all traits especially for weight and length were very high, mentclass2pt{minimThe proportion of missing genotype was only about 14% in this population; however, imputations were made to have a complete genotype . The imputation slightly increased predictive ability of genomic prediction for growth related traits Fig. . For bodFigure The central theme of this study was to determine the predictive ability of genomic prediction for traits of economic importance to enable the application of genomic selection in yellowtail kingfish (YTK). Our gBLUP analysis shows that the predictive ability obtained for growth-related traits  was moderate to high (0.44 to 0.69). A similar predictive ability was observed for body traits when numerator relationship matrix obtained from the pedigree was included in analyses. A linear regression analysis of the actual on predicted phenotypes also indicated a high predictive capacity of our models for the observed phenotype. The findings are particularly significant because one of the promising features of genomic selection is to reduce efforts of phenotyping in selection populations, and the performance of unphenotyped animals can be predicted from the training and validation populations from which they are derived. Our prediction within the same population using five-fold across validation approach also confirmed that there was a high correlation between the training and validation datasets for body weight, Overall our results are in line with other studies reported in the literature that based on computer simulations or using empirical data from practical genetic enhancement breeding programs in farmed animals  or aquacIn the present study, we also found that the predictive ability of the gBLUP model is affected by the number of markers used in our analysis. A random sample of 20, 40, 60 and 80% of the total number of markers  was analysed and the predictive ability of gBLUP model decreased by 13 to 18% across traits, in comparison with those obtained when the complete set of markers was used. In addition, we performed gBLUP analysis on a sub-set of 7988 markers after quality control. The marker filtering was based on two main criteria: i) < 10% missing values and 2) a minor allele frequency > 0.05. Linkage disequilibrium (LD) pruning was also applied to remove highly correlated markers. However, the predictive ability of the five-fold cross validation model for body traits was somewhat higher than those obtained from the full marker set , e.g. Our five-fold across validation within the same population indicated very high predictive ability of the gBLUP model used especially for body weight. In this study, the full data that comprised 752 fish originating from the generation in 2014 were split into training and validation subsets. It is however necessary, in a future study, to sequence offspring of this population produced in the latest generation and re-train the model to ensure that capacity of gBLUP prediction is maintained before genomic selection can be considered as an option for future genetic improvement in this YTK population. The capacity of genomic prediction is likely to be reduced in subsequent generations, mainly as a result of recombination that causes a breakdown of the linkage disequilibrium (LD) between markers and quantitative trait loci (QTL). The prediction ability also depends on the number of distant ancestral generations included in the training set; the further generations apart the lower accuracy may be observed .Collectively, the results achieved from this study showed the potential for genomic selection in this population of YTK. However, to enable the application of genomic selection in different populations , genotyping a larger sample of animals would be needed for further prediction and validation. Genomic prediction equations developed for one population may not be applicable to another due to LD decays across generations as reported in farmed animals . A largeCaligus rogercresseyi [Piscirickettsia [Flavobacterium psychrophilum [× with 10,000 markers and this can provide comparable genomic prediction accuracy as SNP arrays. Improved accuracy with imputation from a low- (256 SNPs) to high-density panel (250 K SNPs) is also shown in Atlantic salmon [This study is the first reporting genomic prediction in marine yellowtail kingfish. Earlier studies involved computer simulation and showed potential benefits of genomic selection for aquatic animal species . Nielsencresseyi , Piscirickettsia  or Flavorophilum . A synthrophilum  demonstrc salmon . Return S. lalandi.Main findings from our first genotyping by sequencing study in yellowtail kingfish include: i) a large number of markers were derived from DArTseq™ sequencing technology and they can be used for genomic prediction and selection, ii) the predictive ability of gBLUP model for growth-related traits  was high to enable the application of genome-based selection in this population, and iii) the markers subsets (e.g. 80% of the full set used in this study) can provide a comparable predictive capacity for growth characters. However, genomic prediction for traits with low heritability  using genotyping by sequencing technology and imputation deserves further study in this population of yellowtail kingfish. A large scale routine data recording from multiple generations in in-depth pedigree populations is also needed to improve accuracy of genomic breeding value estimation for other traits of commercial importance in this species Additional file 1:Basic statistics of the sequence data used. Descriptive population genetic estimates and statistics of Single Nucleotide Polymorphisms (SNPs). (DOCX 27 kb)


In addition, numerous new candidate genes were also identified. For example, GRMZM2G154278 in HZS, which belongs to the cell cycle control family, was closely linked to a QTN of the ear height/plant height (EH/PH) trait; GRMZM2G051943 in 207, which encodes an endochitinase precursor (EP) chitinase, was closely linked to a QTN for kernel density; and GRMZM2G170586 in Mo17 was closely linked to a QTN for ear diameter. Complex correlations among these genes were also found. Many IBD segments and genes were included in the formation of FP lines, and complex regulatory networks exist among them. These results provide new insights on the genetic basis of complex traits and provide new candidate IBD regions or genes for the improvement of special traits in maize production.Maize foundation parents (FPs) play no-alternative roles in hybrid breeding because they were widely used in the development of new lines and hybrids. The combination of different identity-by-descent (IBD) segments and genes could account for the formation patterns of different FPs, and knowledge of these IBD regions would provide an extensive foundation for the development of new candidate FP lines in future maize breeding. In this paper, a panel of 304 elite lines derived from FPs, i.e., B73, 207, Mo17, and Huangzaosi (HZS), was collected and analyzed using 43,252 single nucleotide polymorphism (SNP) markers. Most IBD segments specific to particular FP groups were identified, including 116 IBD segments in B73, 105 in Mo17, 111 in 207, and 190 in HZS. In these regions, 423 quantitative trait nucleotides (QTNs) associated with 15 agronomic traits and 804 candidate genes were identified. Some known adaptation-related genes, e.g.,  Zea mays L.) hybrids are important for providing sufficient food, feed and energy for the increasing needs of modern society. Maize hybrid breeding has undergone three stages: (1) double-cross hybrids using inbred lines derived from open-pollinated varieties, including landraces in the 1930s; (2) more productive single-cross hybrids in the late 1950s after the appearance of some heterotic groups, such as Iowa Stiff Stalk Synthetic (SS), Non-Stiff Stalk (NSS) and Iodent (IDT); and (3) superior commercial hybrids for breeding, which became increasingly privatized after the pre-1980s [Excellent maize  markers. We performed a comprehensive analysis of IBD segments transmitted from these FPs to their descendants. Further, 180 representative lines of the 304 lines were selected to evaluate phenotypes based on the results of genotyping analysis. In total, 15 agronomic traits were investigated across multiple environments in three years, and a GWAS was conducted. The consistency between the GWAS results and quantitative trait loci (QTLs) mapping results was investigated to deduce the putative functions for each IBD segment. In addition, 9 representative lines with high membership (more than 0.75) and known pedigrees in each FP group were selected to extract RNA-seq data. Some candidate genes located within IBD segments were identified through genomic, transcriptomic and phenotypic variation analysis. The objectives of this study were to 1) identify specific phenotypes and IBD segments from a given FP and its descendants, 2) uncover the functions of IBD segments, 3) identify major genes involved in the formation of maize FP lines, 4) explore genomic patterns of maize FP formation, and 5) provide new insight for dissecting the genetic basis of complex traits.The four FP lines B73, Mo17, 207, and HZS and their relevant descendants were collected, including 304 lines with co-ancestor membership of greater than 0.5. These lines were divided into four FP groups according to the results of population structure analysis and pedigree information . In totahttp://support.illumina.com/array/array_kits/maizesnp50_dna_analysis_kit.html). When the maize seedlings were one month old, the leaves of five plants were sampled in bulk to extract genomic DNA according to the modified CTAB procedure [http://qteller.com/qteller3/rna_data_sources.php). In addition, 36 inbred lines from a particular group of B73, Mo17, 207 and HZS were sampled according to the top ancestor memberships and known pedigree information, and the transcriptomic expression data of the genes in kernels at 15 days after pollination were extracted from the datasets published previously [The newly collected 180 inbred lines were genotyped using the Maize SNP50 bead chip including 56,110 SNPs .According to the results of the population genetic structure analysis, one group containing a classical FP line and its descendants was denoted as the relevant FP group, which was named the representative FP line. For each group, genetic diversity (GD) was evaluated using 43,252 SNPs in PowerMarker V3.25  and was By combining the results of principal component analysis (PCA), a total of 180 lines covering more than 99.72% of the genetic diversity of the original panel were sampled . This paANOVA was performed using the PROC GLM model. Pearson correlation analysis of each trait across different environments was calculated using the PROC CORR model. Then, the best linear unbiased predictor (BLUP) calculation for each trait was implemented using a PROC MIXED model, with genotype, location, genotype by location, and replications as random effects . All anap values of the SNPs were less than the cutoff value.The BLUP values of all agronomic traits for each line across different environments and 43,252 SNPs were used to perform a phenotype-genotype GWAS, which was implemented in the R package GAPIT  using thhttp://www.maizegdb.org/). The physical positions of each IBD segment, QTL, and QTN were queried against the B73 reference genome (RefGen_v2). On this basis, consistency analysis was conducted by projecting IBD segments, QTLs, and QTNs onto the identical genetic frame diagram. IBD segments containing both QTLs and QTNs were treated as important candidate genetic regions and were analyzed in depth.QTLs associated with the 15 agronomic traits were collected from public data and the literature (2(FPKM) [For each important IBD region, genes predicted by the Filtered Gene Set model  and loca2(FPKM) , ANOVA b2(FPKM) . IntegraThe 304 lines were clustered into four groups  correspoBased on the data of the 15 main agronomic traits in the 180 lines, specific variation characteristics were found in different FP groups . For exavgt1 on chromosome 8 and was significantly associated with DTP, DTS and DTT.The values of BLUPs for the 15 agronomic traits of the 180 lines across 15 environments were used in phenotype-genotype association mapping. The major results are shown in vs. B73, 207 vs. HZS and 207 vs. Mo17, respectively; 168 and 137 segments were common in B73 vs. HZS and B73 vs. Mo17, respectively; and 183 segments were common in HZS vs. Mo17. Triple comparison showed that 133 segments were common among B73, 207 and HZS; 130 among B73, 207 and Mo17; 135 among HZS, 207 and Mo17; and 132 among B73, HZS and Mo17. Quadruple comparison showed that 128 segments were common among these four groups. By contrast, 116 IBD segments were specific to the B73 group, 111 to 207, 190 to HZS, and 105 to Mo17  varying from -3.45 to 5.39, where a minus sign indicates a downward change , ear diameter (ED), ear row number (ER), and kernel number per row (KNPR). “b” shows flowering time-related traits, including DTP, DTS, DTT, and ASI. “c” shows kernel shape-related traits, including 10-kernel length (10KL), 10-kernel width (10KW), 10KL/10KW, and 10-kernel thickness (10KT). “d” shows plant architecture-related traits, including plant height (PH), ear height (EH), and EH/PH. “e” shows kernel weight-related traits, including 100-kernel weight (KWE), 100-kernel volume (KV), and kernel density (KD).(TIF)Click here for additional data file.S2 FigRed and gray lines are defined as 0.05 and 0.01 divided by the SNP number of 43,252, respectively. “a” shows ear-related traits, including ear length (EL), ear diameter (ED), ear row number (ER), and kernel number per row (KNPR). “b” shows flowering time-related traits, including DTP, DTS, DTT, and ASI. “c” shows yield-related traits, including 10-kernel length (10KL), 10-kernel width (10KW), kernel density (KD) and 100-kernel volume (KV).(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 TableEH, PH, EL, ED, KD, KNPR, DTT, DTP, DTS, ASI, KV, 10KW, 10KL, and ER are the abbreviations for ear height, plant height, ear length, ear diameter, kernel density, kernel number per row, days to tasseling, days to pollination, days to silking, interval between silking and anthesis, 100-kernel volume, 10-kernel width, 10-kernel length and ear row number, respectively.(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 TableFPKM is the abbreviation for fragments per kilobase of exon per million fragments mapped.(XLSX)Click here for additional data file.


ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg=−0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness.The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes ( The hippocampus in mammalian brain varies in size across individuals. Here, Hibar and colleagues perform a genome-wide association meta-analysis to find six genetic loci with significant association to hippocampus volume. Brain structural abnormalities in the hippocampal formation are found in many complex neurological and psychiatric disorders including temporal lobe epilepsyEnvironmental factors, such as stress, affect the hippocampusHere we perform a GWAS meta-analysis of mean bilateral hippocampal volume in 33,536 individuals scanned at 65 sites around the world as a joint effort between the Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortia. Our primary goal is to find common genetic determinants of hippocampal volume with previously unobtainable power. We make considerable efforts to coordinate data analysis across all sites from both consortia to maximize the comparability of both genetic and imaging data. Standardized protocols for image analysis and genetic imputation are freely available online (see URLs). In the most powerful imaging study of the hippocampus to date, we shed light on the common genetic determinants of hippocampal structure and allow for a deepened understanding of the biological workings of the brain's memory centre. We confirm previously identified loci influencing hippocampal volume, identify four novel loci and determine genome-wide overlap with Alzheimer's disease.n=26,814 individuals of European ancestry) revealed six independent, genome-wide significant loci associated with hippocampal volume (P=1.42 × 10−11), rs7020341 , rs2268894 , and rs2289881 . The other two loci have been previously characterized in detail: with index SNPs rs77956314 , in linkage disequilibrium (LD)  with our previously identified variant at this locus (rs7294919) and rs61921502 , in LD (r2=0.459) with previous top locus rs17178006 : 2.6% of the variants accounted for 43.3% of the 18.76% variance explained (Common variants genotyped from across the whole-genome explained as much as 18.76% (s.e. 1.56%) of the observed variance in human hippocampal volume, based on LDSCORE regressionxplained .n=5,368). We found that the top SNP from our primary analysis, rs77956314, has a broad, nonspecific effect on hippocampal subfield volumes with the greatest effect in the right hippocampal tail (P=1.27 × 10−8). rs61921502 showed strong lateral effects across right hippocampal subfields with the largest effect in the right hippocampal fissure (P=6.45 × 10−9). rs7020341 showed greatest effects bilaterally in the subiculum . rs2268894 show left-lateralized effects across hippocampal subfields with the strongest effect in the left hippocampal tail (P=1.76 × 10−5). The remaining two variants (rs11979341 and rs2289881) did not show significant evidence of association across any of the hippocampal subfields. The full set of results from the hippocampal subfield analysis is tabulated in To test for differential effects on individual subfields of the hippocampal formation, we examined the six significant variants influencing whole hippocampal volume in a large cohort (rg=−0.155 (s.e. 0.0529), P=0.0034; see Methods).We used LDSCOREP=1.42 × 10−11), rs7020341 , rs2268894  and rs2289881 . We previously discovered two of the novel loci, rs7020341 and rs2268894  gene (ASTN2 have been observed in patients with autism spectrum disorder and attention-deficit/hyperactivity disorderDPP4 (r2=0.83) with a genome-wide significant locus associated with a decreased risk for schizophrenia (rs2909457)sonic hedgehog (SHH) gene, crucial for neural tube formationSHH in adult human hippocampusP=0.0089). Finally, variant rs2289881 lies within an intron of the microtubule-associated serine/threonine kinase family member 4 (MAST4) gene  gene  which enrderDPP4  that encT4) gene . The pror2>0.99; n=22,761) that removed all patients diagnosed with a neuropsychiatric disorder. Observed effects are therefore not likely to be driven by inclusion of patients with brain disorders. All significant loci are tabulated in n=6,722; Effect sizes from the full sample were almost identical to those obtained from a subset meta-analysis (Pearson's rg=−0.155 (s.e. 0.0529), P=0.0034), through which loci associated with decreased hippocampal volume also increase risk for AD. This confirms a shared etiological component between AD and hippocampal volume whereby genetic variants influencing hippocampal volume also modify the risk for developing AD.We estimated that 18.76% (s.e. 1.56%) of the variance in hippocampal volume could be explained by genotyped common genetic variation. This effect was only tested within populations of European ancestry and does not necessarily reflect the level of explained variance in other populations worldwide. This is a substantial fraction of the overall genetic component of variance determined by twin heritability studies, and the heritability of hippocampal volume is relatively high at around 70% refs , 20, 21.MSR3B, a gene related to oxidative stressMSR3B may influence neurogenesis specifically within the dentate regions of the hippocampal body, where cell proliferation is known to continue into adulthood in healthy humansAs the hippocampal formation is a complex structure comprised of diverse functional units, we sought to examine the genetic variants identified in our analysis for focal effects on hippocampal subfield volumes. When assessing 13 subfields of the hippocampus  we found that two of the top variants from our analysis (rs77956314 and rs7020341) had largely non-specific effects: most of the subfield volumes showed significant evidence of association . The varHere we identified four novel loci associated with hippocampal volume and examined each variant for localized effects in hippocampal subfields. When partitioning the full genome-wide association results into functionally annotated categories, we discovered that SNPs in evolutionarily conserved regions were significantly over-represented in their contribution to hippocampal volume. Further, we found significant evidence of shared genetic overlap between hippocampal volume and Alzheimer's disease. This large international effort shows that by mapping out the genetic influences on brain structure, we may begin to derive mechanistic hypotheses for brain regions causally implicated in the risk for neuropsychiatric disorders.High-resolution MRI brain scans and genome-wide genotyping data were available for 33,536 individuals from 65 sites in two large consortia: the ENIGMA Consortium and the CHARGE Consortium. Full details and demographics for each participating cohort are given in r=0.74)3). Full details of image acquisition and processing performed at each site are given in Hippocampal volumes were estimated using the automated and previously validated segmentation algorithms, FSL FIRSTP<1 × 10−6), and poor genotyping call rate (<95%). Cleaned and filtered datasets were imputed to the 1000 Genomes Project reference panel  using freely available and validated imputation software . After imputation, genetic data were further quality checked to remove poorly imputed SNPs (estimated R2<0.5) or low MAF (<0.5%). Details on filtering criteria, quality control, and imputation at each site may be found in Genetic data were obtained at each site using commercially available genotyping platforms. Before imputation, genetic homogeneity was assessed in each sample using multi-dimensional scaling (MDS). Ancestry outliers were excluded by visual inspection of the first two components. The primary analysis and all data presented in this main text were derived from subjects with European ancestry. Replication attempts in subjects of additional ancestries are presented in 2, sex, intracranial volume and diagnosis (when applicable). For studies with data collected from multiple centres or scanners, additional covariates were also included in the model to adjust for any scanning site effects. Sites with family data  used mixed-effects models to account for familial relationships, in addition to covariates stated previously. The primary analyses for this paper focused on the full set of individuals, including datasets with patients, to maximize power. We re-analysed the data excluding patients to verify that detected effects were not due to disease alone. The regression coefficients for SNPs with P<1 × 10−5 in the model including all patients were almost perfectly correlated with the regression coefficients from the model including only healthy individuals (Pearson's r=0.996). Full details for the software used at each site are given in GWAS were performed at each site, as follows. Mean bilateral hippocampal volume ((left+right)/2) was the trait of interest, and the additive dosage value of a SNP was the predictor of interest, while controlling for 4 MDS components, age, ageThe GWAS of mean hippocampal volume was performed at each site, and the resulting summary statistics uploaded to a centralized site for meta-analysis. Before meta-analysis, GWAS results from each site were checked for genomic inflation and errors using Quantile–Quantile (QQ) plots . GWAS rerg) between any two trait pairs. Here we estimated the common genetic overlap between hippocampal volume and Alzheimer's diseaseThe common genetic overlap, total variance explained by the GWAS, and the partitioned heritability analyses were estimated using LDSCORE25h2g) can be partitioned into specific subsets of variants. The functional annotation partitioning used the pre-prepared LDSCORE and annotation (.annot) files available online (see URLs) following the method of Finucane et al.et al., page 4)et al.et al.h2g explained by that partition divided by the proportion of variants in the GWAS that fall into that partition. All LDSCORE analyses used non-genomic controlled meta-analyses.As well as estimating the total variance explained, the genomic heritability =7.45 × 10−5.Gene annotation, gene-based test statistics, and pathway analysis were performed using the KGG2.5 software packageSNPs (top 5%) shows the top 5% associated SNPs within the locus and are coloured by their correlation to the top SNP. Genes shows the gene models from GENCODE version 19. Hippocampus gives the predicted chromatin states based on computational integration of ChIP-seq data for 18 chromatin marks in human hippocampal tissue derived from the Roadmap Epigenomics Consortiumhippocampus track are as follows: TssA (Active TSS), TssFlnk (Flanking Active TSS), TssFlnkU (Flanking TSS Upstream), TssFlnkD (Flanking TSS Downstream), Tx (Strong transcription), TxWk (Weak transcription), EnhG1 (Genic Enhancers 1), EnhG2 (Genic Enhancers 2), EnhA1 (Active Enhancers 1), EnhA2 (Active Enhancers 2), EnhWk (Weak Enhancers), ZNF/Rpts (ZNF genes & repeats), Het (Heterochromatin), TssBiv , EnhBiv , ReprPC (Repressed PolyComb), ReprPCWk (Weak Repressed PolyComb), Quies (Quiescent/Low). Additional information about the 18 state chromatin model is detailed elsewhereConservation is the basewise conservation score over 100 vertebrates estimated by PhyloP from the UCSC Genome Browser Human hg19 assembly.In n=4,491) and HUNT (n=877) cohorts. Volumes from the 26 subfield regions (13 in each hemisphere) were the phenotypes of interest and individually assessed for significance with the top variants from our primary analysis while correcting for the following nuisance variables: 4 MDS components, age, age2, sex, intracranial volume. Association statistics from each of the tests in the Rotterdam and HUNT cohorts were meta-analysed using a fixed-effects inverse variance-weighted model yielding the final results. We declare an individual test significant if the P value is less than a Bonferroni-corrected P value threshold accounting for the total number of tests: Pthresh=0.05/(26 subfields × 6 SNPs)=3.21 × 10−4.We segmented the hippocampal formation into 13 subfield regions: CA1, CA3, CA4, fimbria, Granule Layer+Molecular Layer+Dentate Gyrus Boundary (GC_ML_DG), hippocampal-amygdaloid transition area (HATA), hippocampal tail, hippocampal fissure, molecular layer (HP), parasubiculum, presubiculum and subiculum using a freely available, validated algorithm distributed with the FreeSurfer image analysis packageThe genome-wide summary statistics that support the findings of this study are available upon request from the corresponding authors MAI and PMT (see URLs). The data are not publicly available due to them containing information that could compromise research participant privacy/consent.https://github.com/bulik/ldschttp://enigma.usc.edu/protocols/genetics-protocols/http://gump.qimr.edu.au/general/gabrieC/LocusTrack/http://enigma.ini.usc.edu/download-enigma-gwas-results/http://www.internationalgenome.org/dataHow to cite this article: Hibar, D. P. et al. Novel genetic loci associated with hippocampal volume. Nat. Commun.8, 13624 doi: 10.1038/ncomms13624 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures 1-3, Supplementary Notes 1-3Demographic description of cohorts included in the analysis


Genome-wide association studies have made substantial progress in identifying common variants associated with human diseases. Despite such success, a large portion of heritability remains unexplained. Evolutionary theory and empirical studies suggest that rare mutations could play an important role in human diseases, which motivates comprehensive investigation of rare variants in sequencing studies. To explore the association of rare variants with human diseases, many statistical approaches have been developed with different ways of modeling genetic structure . Nevertheless, the appropriate strategy to model genetic structure of sequencing data and its effect on association analysis have not been well studied.We investigate 3 statistical approaches that use 3 different strategies to model the genetic structure of sequencing data. We proceed by comparing a burden test that assumes independence among sequencing variants, a burden test that considers pairwise linkage disequilibrium (LD), and a functional analysis of variance (FANOVA) test that models genetic data through fitting continuous curves on individuals’ genotypes.Through simulations, we find that FANOVA attains better or comparable performance to the 2 burden tests. Overall, the burden test that considers pairwise LD has comparable performance to the burden test that assumes independence between sequencing variants. However, for 1 gene, where the disease-associated variant is located in an LD block, we find that considering pairwise LD could improve the test’s performance.The structure of sequencing variants is complex in nature and its patterns vary across the whole genome. In certain cases , ignoring the genetic structure in the association analysis could result in suboptimal performance. Through this study, we show that a functional-based method is promising for modeling the underlying genetic structure of sequencing data, which could lead to better performance. Advancements in sequencing technology have enabled researchers to sequence exome regions or even the whole genome at affordable cost . The emeVarious statistical methods have been proposed to group SNVs with or without considering the underlying genetic structure . However, the impact of different strategies of modeling genetic structure on association results has rarely been investigated. If empirical evidence suggests that use of genetic structure in association analysis does not increase power, it gives us a basis for excluding this factor from statistical modeling. On the other hand, if it is important to consider LD among SNVs, then we need to investigate appropriate strategies for characterizing the underlying genetic structure. As an initial step to investigate this issue, we chose 3 tests with different ways of modeling LD between SNVs: (a) a weighted burden test assuming independence among SNVs (BT) ; and (c)L is the number of SNVs, gij is the number of low-frequency alleles of the jth SNV for the ith individual. The weight is defined to emphasize the effect of rare variants with nj is the number of controls and qj is the minor allele frequency (MAF) of the jth SNV in controls. Analysis of variance (ANOVA) is then used to assess the association between summary genetic scores and the binary phenotype. Because the test simply adds the genotype of each SNV weighted by a function of its MAF, it does not consider LD between SNVs.We consider a burden test developed by Madsen and Browning  that asswj and gij are defined in the same manner as BT. However, unlike the conventional burden test, the test statistic of BTCOV is given by Y denotes the vector of disease status and Vs is the covariance matrix, where In addition to the above burden test, we also consider another type of burden test proposed by Schaid et al , which cgik(t), the smoothed function of genetic variants at the genomic position t for an individual i in the kth group. The FANOVA can then be used to compare the difference of curves in cases and controls. The FANOVA model can be written as:FANOVA considers LD by fitting a continuous function (curve) on the genotype data of an individual . While vi, k, nk and t denote the individual, the group , the total number of individuals in the kth group, and the genomic position of a genetic variant, respectively. G. P stands for the gaussian process, where γ is the covariance function, ϵik is the error term and μk is the mean function for the kth group. We test the following hypotheses:where t.for some Similar to ANOVA, the test statistic for the hypothesis can be constructed as,where Simulations were conducted to compare the performance of the 3 methods using a simulation model specified here as well as on the Genetic Analysis Workshop 19 (GAW19) simulated phenotype data. First, we selected a subset of 142 unrelated individuals from the GAW19 family-based sequencing data. For each replicate, we randomly chose a 30-kb segment from the 1.4 Mb region (chromosome 3: 33100124 to 34539295). From each segment, we randomly selected a specified proportion of SNVs  from the first simulation out of the 200 simulations was used.The above simulations only evaluated 1 genetic region. To investigate the performance of the 3 methods on regions with different genetic structures, we also applied them to the subset of 142 unrelated samples from the GAW19 family-based simulated data, of which 24 samples are cases. This data consists of 294 genes, including Type I error rates of the 3 tests are well controlled at the 0.05 level . As we observe from Table p values from the association analysis. Consistent with the result from simulations, we find that in general FANOVA attains smaller p values, while the p values of BT and BTCOV were comparable.Table THRA, BTCOV attains a lower p value than the other 2 tests. In the follow-up analysis, we observe a small LD block in this gene (Fig. Through this study, we find that overall BT and BTCOV have comparable performance. However, for 1 gene, p value than the other 2 tests in the analysis of RELB. From Fig. Variants may have more complex structure than pairwise LD. Hence, in most cases we find that FANOVA has comparable or better performance than the other 2 tests. For example, FANOVA attained a lower Our observations indicate that the performance of tests depends on the underlying genetic structure; hence, ignoring LD in the association analysis may not be ideal. It is advisable to use function-based approaches to explore and model the genetic structure. As illustrated by Fig.


The advent of affordable sequencing has enabled researchers to discover many variants contributing to disease, including rare variants. There are methods for determining the most informative individuals for sequencing, but the application of these methods is more complex when working with families. Sets of large families can be beneficial in finding rare variants, but it may be unfeasible to sequence all members of these family sets.Using simulated data from the Genetic Analysis Workshop 19, we apply multiple regression to identify cases and controls. To find the best controls for each case, we used kinship coefficients to match within families. Selected cases and controls were analyzed for rare variants, collapsed by gene, associated with hypertension using the family-based rare variant association test (FARVAT).MAP4, did not meet the Bonferroni corrected significance threshold. However, analysis of cases and controls using our selection method substantially improved the significance of MAP4, despite the reduction in sample size.The gene with the strongest simulated effect, Taking the additional steps to select the optimal cases and controls from large family data sets can help ensure that only informative individuals are included in analysis and may improve the ability to detect rare variants. Whole-genome sequencing (WGS) is an important tool in the discovery of rare variants that influence disease. Family-based association studies have likewise been crucial in the fine-mapping of genetic variants contributing to complex disease. Decreased sequencing costs have made it increasingly feasible to sequence large families or even large sets of families, but WGS remains too expensive for most studies. To address this, a subset of family members may be selected for WGS, but it can be difficult to determine which configuration of family members will have the greatest power to detect rare variants. Extreme phenotyping is an approach that compares individuals at opposite ends of the phenotypic spectrum with the thought that rare causal variants will be enriched for in the extremes of complex traits –3. The aWe analyzed replicate 1 of the simulated data set from the Genetic Analysis Workshop 19 (GAW19) T2D-GENES Project 2, a family data set with WGS data , with knn = 170; see Table n = 277; see Table To define cases and controls, we modified an approach that selects participants with variation in their phenotype that is unexplained by known nongenetic risk factors, and thus are most likely to have a genetic component , 4. UsinThe process for control selection is outlined in Fig. n = 91 founders) at a p value cutoff of less than 2.9 × 10−8  leaving a total of 1,539,595 sites. Sites that did not pass QC were then removed from the data set of imputed genotypes that included 959 subjects . This data set contained 1,215,399 imputed sites, of which 87,555 sites were removed as a result of the aforementioned QC process, leaving 1,127,844 sites for analysis.In addition to the quality control (QC) performed by the organizers of Genetic Analysis Workshop prior to release , furtherGene-based annotation was performed with the sites remaining after QC using ANNOVAR (Annotate Variation)  and the Sequencing data from chromosome 3 for each set of cases and controls  were analyzed using FARVAT . FARVAT p = 0.05/1389 = 0.000036), none of the genes reached significance for any of the 3 sets of cases and controls. Because MAP4 was simulated to be significantly associated with SBP, Table MAP4, along with the 10 most significant genes for each analysis, which tended to vary. Of the genes on chromosome 3, only MAP4, FLNB, and ABTB1 were simulated to have an effect on SBP, explaining 7.79 %, 0.29 %, and 0.13 % of the total variance in SBP. Although MAP4 did not meet the Bonferroni-corrected significance threshold, analysis of potential cases and controls showed improved significance for MAP4 over the analysis of all individuals in the base population, and analysis of selected cases and selected controls further improved the significance of MAP4. Figure p values, indicating that type I error was controlled.Table MAP4, the gene with the strongest simulated effect on SBP, improved with each step of the selection process, indicating that our selection process overcame the loss of power because of a decrease in sample size and dichotomization of a quantitative trait.The potential power of family data is appealing for the discovery of rare variants that contribute to complex disease. Family data sets can contain hundreds or thousands of individuals, and WGS may not be feasible for every individual in every family. Frequently, researchers will select some family members for sequencing and then impute sequencing data for the remaining family members using existing genome-wide SNP data, however, this can still be costly and the accuracy of imputation varies depending on the approach used , 15. As Family data can be useful for the detection of rare variants, but must be carefully analyzed. There are options to prioritize the selection of cases and controls for sequencing and analysis. Careful case definitions, combined with information on family structure, can help ensure that only the most informative individuals are chosen for sequencing. This can help keep costs low and, potentially, improve the ability to detect rare variants. However, loss of power is a real concern, meaning the selection process may only yield meaningful results if there is a large base population from which to select.


The output ofcophesim can be used as a direct input for different genome wide association study tools.cophesim is available fromhttps://bitbucket.org/izhbannikov/cophesim.Simulation is important in evaluating novel methods when input data is not easily obtainable or specific assumptions are needed. We present Genome-wide association studies (GWAS) are routine in population research. New methods are being developed for better accessing complex associations between genotypes and phenotypes, uncovering genotype structures or testing evolutionary hypotheses. Testing the novel methods requires experimental data, which may not be easily obtainable. One solution is to use artificial data simulated with specific assumptions.GENOME1,Plink2,phenosim3,CoaSim4,Fregene5,ForSim6,QuantiNemo7,GCTA8,HapGen9,SeqSimla10, andSimRare11 offer qualitative and dichotomous simulated phenotype. But the known phenotype simulation software tools have some limitations, which may prevent customers from using them: (i) the majority, if not all, of the phenotype simulation software tools do not offer simulation of survival traits/time-to-event outcome, making it impossible to test respective hypotheses of associations; (ii) some of the tools are not easy to use, due to wide range of parameters, which the user has to provide and control , making them unnecessarily difficult to use and preventing the user from future use of the tool; (iii) phenotype simulation is often offered as an auxiliary part of the genetic simulation routine, and therefore the user first has to perform a time-consuming unavoidable genetic simulation in order to obtain the phenotype; (iv) in situations when the genetic data is already simulated from other tools, onlyphenosim andGCTA offer adding simulated phenotype to such data. Consequently, it is necessary to have a new, simple and flexible phenotype simulation tool with plain algorithmic assumptions.The best existing phenotype simulators, such as:cophesim, a comprehensive phenotype simulation tool that was developed to add a phenotype to corresponding genotypes simulated by other simulation tool  effect sizes of causal variants, with the ability to simulate epistatic interactions. It also can simulate phenotype within gene-environment interaction assumptions using up to 10 covariates.Consequently, we presenton tool .cophesiThe workflow see includescophesim accepts the genotype output data fromPlink,MS12 andGENOME software applications. Phenotypes  are then added according to the following simulation scenarios.Currentlythi individual  is simulated according to the logistic model :ip is the probability of a particular outcome. Incophesim, it is a probability of a “case”  forthi individual. Ifip is greater than the some thresholdp0 ), then the phenotype forthi individual is set to “1” and to “0” otherwise. The variablez is determined with the following equation:jE – effect size forthj variant, user-defined;ijg – value ofthj genetic marker forthi individual;jα - effect size forthj covariate andijX is a value ofthj covariate for athi individual (the term∑j=1KαjXij is added to represent gene-environment iterations);iϵ – a standard normal residual,i Nϵ , computed forthi individual,M is a total number of genetic variants andK is a total number of covariates used.Dichotomous phenotype forIf the user did not provide the effect sizes for causal variants, the following strategy is then used:ijw is a weight and computed as follows:wij=gij−2MAFj(2MAFj(1−MAFj))1/2  or (3) (in case if effect sizes were not supplied).Qualitative (continuous) phenotype for13: ifU is uniform in  and ifS(·|z) is the conditional survival function derived from the proportional hazards model:S(t|z) =eH0(t)ez–, then the random variableS(·|z). In this equation,H0(t) is a cumulative baseline hazard. By default, we use the Weibull cumulative baseline hazard:H0(t) =λρtρ–1;z is the same parameter that defined above, for each individual, and depends on whether the user provided effect sizes for causal variants or not. We also implemented exponential and Gompertz hazards.We model a survival phenotype from the proportional hazards model using the inverse probability methodg1 andg2, with effect sizesE1 andE2 is to replace some portion ofg2 withg1 values according to providedr122 coefficient, which reflects a correlation between two SNPs. We also consider applying other techniques, such as copulas, in order to simulate LD.The simplest way to simulate collinearity between two SNPs,thi individual:E12gi1gi2 is the interaction term in whichE12 is the epistatic effect size ; αj is the effect size forthj covariateX.These are modeled with the following equation forEMMAX14,Blossoc4,Plink (.ped file),QTDT15,TASSEL16,GenABEL17 . Then (Step 2) we convert a binarysim.plink.bed file tosim.plink.ped (option--recode in Plink). This step is not required since cophesim can handle binaryPlink files , but we provide this step in order to show the ability of the program to deal withPlink PED format. Finally (Step 3), we simulate dichotomous (by default), continuous (option-c) and survival (option-s) traits from previously simulated data stored in filessim.plink.ped andsim.plink.map. Note that we simulate survival trait with Gompertz hazard function (option -gomp); effect sizes for causal variants are provided in fileeffects.txt (to include this file we use option-ce).In this example, we first (Step 1) simulate genetic data usingPlink suite. The following parameters were used:N = 10,000 individuals,N.snp.c = 100 causal, with totalN.snp = 1,000 variants. Causal variants were labeled with ‘1’ and the other  variants were labeled with ‘0’. These labels are then used later as true identifiers during calculation of TPR (true positive rate) and FPR . Dichotomous, continuous and survival phenotypic traits were simulated withcophesim. Then association tests were performed withPlink for dichotomous and continuous traits . Association tests for survival trait were performed with the R packageGenABEL. Then calculatedp-values provided by association tests for each variant were compared to the significance threshold. Those variants passed the threshold were recognized as causal and associated with simulated phenotype. These classification results later were compared to the true identifiers (defined above) in order to obtain TPR and FPR. For all these tests, we varied the significance threshold from 0 to 1 with the increment of 0.001.We provide Receiver-Operating Characteristic (ROC) curves  construchttps://bitbucket.org/izhbannikov/cophesim_data/ROC/roc.RThe R code to construct ROC curves is provided in the file “roc.R”. This file is attached to this computer note and also in the data repository:cophesim for phenotype simulation from genetic data obtained either from simulation or real data collecting.cophesim makes it possible to simulate various demographic models under user-defined scenarios.In this work we presented thehttps://bitbucket.org/izhbannikov/cophesimTool and source code available from:10.5281/zenodo.81019518Archived source code as at time of publication: doi:License: MIThttps://doi.org/10.5281/zenodo.80409019.The example script and output files for the software are available at:cophesim we provided a repository “cophesim_data”:https://bitbucket.org/izhbannikov/cophesim_data. Download or clone this repository to be able to run tests.To test the cophesim software that they have developed for simulating phenotypic data using genotype information. The input and output file formats are compatible with many of the most commonly used computer programs for genome wide association studies. The software is flexible, well documented and fills a gap in existing tools, especially for simulating time-to-event phenotypes. The paper is well written and easy to follow, and we only have some minor comments and suggestions.Zhbannikov and co-workers present theClosing bracket missing in the sentence below equation (3)In equation (4), if the user does not provide gene effects then the phenotype is built by the sum of the standardized genotypes for each individual. Could you motivate this choice a bit and explain why it would be useful?In equation (5), the subscripts look wrong. a_i should be a_jIn the Linkage Disequilibrium section the term “copula” is used. We do not think most readers of this paper can be expected to be acquainted with copulas and a reference is needed. Consider adding a short paragraph where you discuss limitations and the possibility to add further functionality in the future, including:Dominance effectsProbit link for binary dataSimulation of correlated traitsAlternative ways to simulate LD including a copula approach   Minor commentshttps://bitbucket.org/izhbannikov/cophesim_data/ROC/roc.R (we were able to retrieve the code fromhttps://bitbucket.org/izhbannikov/cophesim_data/src/) Check that the following link (at the end of the paper) works:We have read this submission. We believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. In the manuscript “Cophesim: a comprehensive phenotype simulator for testing novel association methods”, I. Zhbannikov and colleagues from the Duke University, presented a software that allowed to generate genotype data prepared with a genetic simulator for the use in the investigations of the genome wide association study (GWAS) tools. The rational for development of the software is clearly explained. The idea of the study is to use computer simulations to model data with specific assumptions. Similar simulators are known but all of them do not allow simulate survival. There are several other disadvantage with the existing simulators reviewed by the authors.  The description of the software is technically sound. The methods section is clearly presented. Dichotomous phenotype are simulated according to the logistic model with the covariates being genetic variants and covariates. Continuous phenotypes are simulated using the linear regression. Survival phenotype is modeled using the proportional hazards with inverse probability method. The details of the code, methods and analysis allow replication of the software and its use by the others. The methods section is clearly presented. Dichotomous phenotype are simulated according to the logistic model with the covariates being genetic variants and covariates. The output formats are compatible with the other applications (Table 1). It is useful example if using the simulator and the other examples are available in the manual. The ROC curve example is also very useful. The information provided is quite sufficient to allow interpretation of the expected results. In short, the Cophesim is a useful tool that can be helpful in the genetic analyses. The article is scientifically sound, the methods are described with details – this article will greatly help the researcher interested in the application genetic analyses.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


Genomic wide selection is a promising approach for improving the selection accuracy in plant breeding, particularly in species with long life cycles, such as Jatropha. Therefore, the objectives of this study were to estimate the genetic parameters for grain yield (GY) and the weight of 100 seeds (W100S) using restricted maximum likelihood (REML); to compare the performance of GWS methods to predict GY and W100S; and to estimate how many markers are needed to train the GWS model to obtain the maximum accuracy. Eight GWS models were compared in terms of predictive ability. The impact that the marker density had on the predictive ability was investigated using a varying number of markers, from 2 to 1,248. Because the genetic variance between evaluated genotypes was significant, it was possible to obtain selection gain. All of the GWS methods tested in this study can be used to predict GY and W100S in Jatropha. A training model fitted using 1,000 and 800 markers is sufficient to capture the maximum genetic variance and, consequently, maximum prediction ability of GY and W100S, respectively. This study demonstrated the applicability of genome-wide prediction to identify useful genetic sources of GY and W100S for Jatropha breeding. Further research is needed to confirm the applicability of the proposed approach to other complex traits. Jatropha curcas L.) has become a potential crop for producing biofuel due to the high oil content found in its seeds and the ability to transform this oil into biofuel [Currently, many countries have invested a lot of money into researching promising species for biofuel production  due to t biofuel , 4. Jatr biofuel .Moreover, Jatropha has several agronomic morphological traits that make it a useful crop for producing biofuel and feeding animals, such as drought tolerance , rapid gAlthough marker-assisted selection (MAS) has played an important role in plant breeding for disease and pest resistance , 11, itsThe complexity of traits related to GY and lack of a simple MAS approach warrant the testing of other breeding approaches. Genomic wide selection (GWS), as proposed by Meuwissen et al. , has becCryptomeria japonica breeding [Pinus taeda demonstrated the value of GWS when the models were used at the relevant selection age in accordance with the breeding zone where marker effects were estimated [A few studies have evaluated the use of GWS in forestry breeding. Wong and Bernardo  first evbreeding . The probreeding . A firststimated . Similarstimated . Althoug179 Jatropha half-sib families from the Embrapa Cerrados germplasm bank were evaluated in this experiment. The Brazilian region where each family were collected can be found in the Oidium spp., toxic and resistant to Oidium spp., and toxic and susceptible to Oidium spp.  were from the germplasm bank experiment, and 42 plants (14 plants per crossing) were from the diallel experiment .-1.Total genomic DNA was extracted from younger leaves using the protocol of  with minDiversity Arrays techonology (DArT PL) was the company responsible to obtain DarTs and SNPs. Many methods have been developed to reduce genome complexity, however the DArT methods provide a significant advantage via an intelligent selection of genome fraction corresponding predominantly to active genes. This selection is achieved through the use of a combination of Restriction Enzymes which separate low copy sequences (most informative for marker discovery and typing) from the repetitive fraction of the genome. While the initial DArT implementation on the microarray platform involves fluorescent labeling of representations and hybridization to dedicated DArT arrays, the DArTseq method deploys sequencing of the representations on the Next Generation Sequencing (NGS) platforms. The advantage of DArTseq over the array version of DArT is currently limited to applications requiring very high marker densities (tens of thousands of markers). This technology is therefore positioned in the area of high resolution mapping and detailed genetic dissection of traits. As modern breeding, moves rapidly in this direction, especially in larger organizations, DArTseq is increasingly used in crop improvement applications. DArTseq for a new organism starts with optimization of complexity reduction method(s). While the choice of restriction enzyme combinations is large, DArT PL has invested considerable effort in testing various combinations on a significant number of organisms and has developed sets of complexity reduction methods (representations) that are performing quite well compared to other methods. The optimization process usually selects one dominant method of complexity reduction for the crop, but in many cases several methods were identified which offer application-specific advantages. The difference between the methods can be both quantitative (different number of unique fragments in the representation) as well as compositional (different sets of fragments captured in the representations). These differences in representation size and composition translate to different efficiencies in marker detection rate and quality  and can be further optimized for performance in different applications. It used 1,248 SNPs and DArTs markers.Genetic and environmental parameters were estimated by restricted maximum likelihood (REML) analysis using the Selegen software . ExperimBecause the second experiment was a diallel, the dominance effect should be fitted into the mixed model. Therefore, experiment 2 was analyzed using the follow model:Genetic diversity was estimated by multidimensional scaling analysis (MDS)  using thNarrow sense heritability was calculated for each experiment as the additive genetic variance divided by the total phenotypic variance. The genetic variance was calculated using the equation proposed by Falconer et al. .Prediction ability was assessed as the Pearson Correlation of the genomic estimate breeding value (GEBV) and phenotypic value in the validation population.th line; ni is the number of replicates used for computing the mean value of the ith genotype; μ is an intercept; gi is the genetic value of the ith genotype; and εi is a model residual. The genomic selection models differed in how molecular marker information was included in gi.Eight GWS methods were used for analysis in the field experiment: RR-BLUP, G-BLUP, Bayesian Ridge Regression (BRR), Bayes A, Bayes B, Bayes Cπ, Bayesian LASSO (BLASSO) and Reproducing Kernel Hilbert Spaces Regression (RKHS). In all models, the phenotypic records were described asG/M, where VG is the genetic variance and M is the number of markers. In the Bayes A method, each effect i is drawn from a normal distribution with its own variance: N from a mixed model . This waG/M, where VG is the genetic variance and M is the number of markers. The variance was assigned an inverse chi-square ).BRR assumes that each marker had a variance equal to VBayes Cπ assumes common marker variances and allows some markers to have no effect . Additioλ2 is assigned a Gamma prior with a rate (δ) and shape (r), which, in this study, were the default in the BGLR package in R. Finally, independent scaled inverse chi-square priors were assigned to the variance parameters, and the scale and degree of freedom parameters were set to Su = Se = 1 and d:f:e = d:f:u = 4, respectively. BLASSO is described by De Los Campos et al. [In the BLASSO method, marker effects are assigned independent Gaussian priors with marker-specific variances variance function proportional to the evaluations of a reproducing kernel, K, evaluated in marker genotypes; here, xi and xj are vectors of marker genotype codes for the ith and jth individuals, respectively. All hyper parameters were assumed following De Los Campos et al. [In RKHS, genetic values were viewed as a Gaussian process. When markers and a pedigree were available, genetic values were modeled as the sum of two components:s et al. .The effect of numbers of markers on prediction ability was determined through five-fold cross-validation by excluding, after each interaction, the marker that had the smallest effect. Therefore, the number of markers decreased from 1248 to 2. Each interaction was repeated 50 times to avoid sampling bias for markers, and the average of these replications was used to represent the prediction ability of each interaction.The prediction ability average with standard error as the error bars was plotted versus the number of markers in each interaction using Boxplot. G-BLUP was used to perform these analyses because it was the fastest GWS method.All statistical modeling was performed in R. RR-BLUP and G-BLUP were performed using the rrBLUP package . The Bayes A, Bayes B, Bayes Cπ and RKHS models were performed using the BGLR package (function BGLR), and BLASSO and BRR were performed using the BLR package (function BLR).A total of 20,000 burn-ins  and 40,000 saved iterations, as obtained from the Markov chain Monte Carlo (MCMC) method, was used in all Bayesian methods. The convergence of Bayesian models was checked by inspecting trace plots of the variance parameters.th generation, Intel Xeon E5-26 processor, 3.30 GHz, 64 or 96 GB RAM, 1024 GB hard drive) were used to perform all analyses.Two high-performance computers , and 3 times greater in experiment 1 for weight of 100 seeds (W100S) . The herCluster analysis by MDS showed the diversity between Jatropha genotypes, and only one group was detected . This grFive-fold cross validation was performed using the full set of 1,248 markers to predict GY and W100S in Jatropha. Prediction ability was estimated as the correlation of GEBV and phenotype values in the validation population.The prediction ability was similar between GWS methods for GY and W100S , except There were no differences between the GWS methods to estimate heritability for both traits, except in that Bayes Cπ estimated a higher heritability .The processing time ranged from 0.08 and 0.07 (RR-BLUP) to 753.09 and 684.23 seconds (BL) for GY and W100S, respectively. We observed that RR-BLUP and G-BLUP were the fastest methods, followed by RKHS: G-BLUP and RR-BLUP were, respectively, 500 and 100 times faster than the fastest Bayesian method (RKHS) and 7000 and 1400 times faster than the slowest Bayesian method (BLASSO).The number of markers did not affect the prediction accuracies that presented values close to 1, except when the number of markers was less than 50 or greater than 1,000  for bothGenetic variance presented a cubic shape, decreasing beyond 100 markers, increasing beyond 1,000 and 800 for GY and W100S, respectively, and decreasing when more than 1,000 and 800 markers were used to train the model . The resThe genetic variance found in our study for GY was similar to those of previous reports , 25 whilIt was observed that the genetic variance for GY was greater than that for W100S. This difference was possibly due to a scale effect: whereas GY ranged from 0 to 999, W100S ranged from 0 to 96.78.r) [g and CVe is greater than 1, the selection gain will be high [r was greater than 1 for W100S in experiment 1. However, CVr was lower than 1 for GY, which indicated that phenotypic selection may not provide any genetic gain for this trait.Another parameter commonly used to evaluate the genetic variability between families is the ratio coefficient of variation (CVr) . When th be high . In thisBased on the parameters estimated by REML analysis, selecting superior genotypes for GY and W100S based on phenotypic values would not provide a good selection gain for the next generation because approximately 73% and 65% of the phenotypic variance is not genetic for GY and W100S, respectively. Thus, it is necessary to use more accurate methodologies to predict genetic effects. Therefore, based on these results and on previous research , 25, we Several studies have shown that compared with ridge regression methods, more complex statistical methods give only a small increase in the accuracy of genomic prediction for polygenic traits –47. HoweY is the cycle length of GWS, rA is the genomic prediction accuracy, HX is the phenotypic selection accuracy, and LX is the cycle length of phenotypic selection. Substituting the values estimated for GY and W100S in the formula, it can be observed that GWS must be superior to phenotypic selection if the cycle length of GWS is the same cycle length of phenotypic selection for GY and less than 49% the cycle length of phenotypic selection for W100S. Therefore, because the cycle is half the length when using GWS  instead of a traditional breeding cycle , GWS is a useful tool to reduce the breeding cycle.Moreover, it has now been demonstrated that predictive models built on the basis of genome-wide markers allow breeders to obtain higher selective accuracy, even for traits of low heritability, such as GY and seed oil content in Jatropha. In addition, genomic breeding values may be estimated at the seedling stage, which can reduce the Jatropha breeding cycle by at least five years (6 years for breeding cycles with GWS versus 12 years for breeding cycles without GWS). Because selection response is inversely proportional to breeding cycle length, we calculated the expected impact of GWS on Jatropha breeding. Considering the accuracies and cycles reported here, GWS may increase the selection efficiency in Jatropha breeding by more than 100% , 24, 54.In the case of perennial crops such as Jatropha, they need several years, ranging from 10 to 14 years, to obtain suitable phenotypic evaluations. Based on practical considerations and the theoretical equation presented above, GWS may improve the efficiency of breeding programs. The main step in which GWS will be useful is shortening the length of the breeding cycle. This will occur because the progeny testing phase can be omitted when GWS is applied, and breeders will thus be able to perform early selection at the seedling stage. Then, selected individuals can be immediately propagated by micropropagation protocols; consequently, optimized clonal trials with several years of anticipation can be established compared to a classical breeding.The selection response per time unit may be drastically increased (by as much as 50%) when the breeding cycle is reduced because the selection response is inversely proportional to the breeding cycle length, as theoretically and experimentally demonstrated . For ins2), and the effective number of quantitative trait loci (QTL) or effective number of chromosome segments underlying the trait (Me) [2 increases [The effectiveness of GWS depends on the correlation between the predicted genotypic value and the underlying true genotypic value . This coait (Me) , 58. Simncreases , 23, 49 ncreases , 60. Peincreases  showed, Models fitted using over 1000 and 800 markers were capable of predicting GY and W100S, respectively . The comHowever, before Jatropha breeding programs incorporate GWS on a large scale, the results found in our study must be validated across years and by evaluating progenies. Because the genetic material used in this study consisted of diverse accessions from the germplasm bank , the resAcrocomia aculeate), and pongamia (Pongamia pinnata), are perennial crops, genomic breeding is one of the most promising ways to foster the development of perennial crops dedicated to biodiesel production.Because the demand for biodiesel is constantly increasing, the development of dedicated crops has been suggested as a strategic action. Thus, biodiesel production is expected to become much more efficient if not only conversion processes themselves are improved but also oil feedstocks are optimized to this end. In that context, genomics offers innumerable technologies for collecting genetic information that could be potentially integrated into Jatropha breeding to aid in the development of cultivars with outstanding performance for biodiesel production. Because genomics offers a platform to learn more about the relationships of genes and phenotypes, the long-term goal of applying genomics to breeding is to link genomic information with the field research that is currently underway, with the purpose of developing accurate predictive models. Such models could then be operationally used by breeders to estimate the performance and adaptability of genotypes across locations or ecosystems based on genetic data alone, i.e., without the need for conducting laborious and expensive phenotyping trials at the beginning of the breeding cycle. In the context of a long-lived perennial crop, with long breeding cycles and late-expressing traits, the achievement of such a long-term goal promises to revolutionize selective breeding . BecauseIn the near future, GWS can improve the efficiency of producing Jatropha oil, but many studies are needed to prove all such theories in practice experiments because no study to date has evaluated GWS in biofuel traits. Therefore, studies should evaluate how the GWS method is better to capture high accuracy for oil production, how many individuals and markers are needed to train the model, and how the GxE interaction can influence prediction accuracy.There was genetic variance between the genotypes evaluated, and it was possible to obtain selection gain using GWS;All genomic selection methods tested in this study can be used to predict the grain yield and weight of 100 seeds in Jatropha;Training models fitted using 1,000 and 800 markers are sufficient to capture the maximum genetic variance and, consequently, the maximum prediction ability for grain yield and weight of 100 seeds, respectively.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Prediction accuracy for the models tested varied from 0.138 (seed yield) to 0.912 (100 seed weight), whereas performance of models did not show any significant difference for estimating prediction accuracy within traits. Kinship matrix calculated using genotyping data reaffirmed existence of two different groups within selected lines. There was not much effect of population structure on prediction accuracy. In brief, present study establishes the necessary resources for deployment of GS in chickpea breeding.Genomic selection (GS) unlike marker-assisted backcrossing (MABC) predicts breeding values of lines using genome-wide marker profiling and allows selection of lines prior to field-phenotyping, thereby shortening the breeding cycle. A collection of 320 elite breeding lines was selected and phenotyped extensively for yield and yield related traits at two different locations  during the crop seasons 2011–12 and 2012–13 under rainfed and irrigated conditions. In parallel, these lines were also genotyped using DArTseq platform to generate genotyping data for 3000 polymorphic markers. Phenotyping and genotyping data were used with six statistical GS models to estimate the prediction accuracies. GS models were tested for four yield related traits  Cicer arietinum) is the second largest cultivated grain legume globally which plays vital role in ensuring food and nutritional security in Asian and sub-Saharan African regions of the world. Because of its higher protein content chickpea serves as an important source of protein in vegetarian diet. Chickpea also fits well in crop rotation programs because of its ability to fix atmospheric nitrogen and improve the soil nutritional profile. It is a self-pollinated, diploid (2n = 16) annual crop with genome size of ~740 Mbp  has also become available  and high-throughput genotyping technologies provide an opportunity for translating genomics information in crop breeding  population are not phenotyped but genotyped, and therefore breeding cycle time is reduced and genetic gain per unit time is enhanced. In plants, Bernardo and Yu  were the−1. Semagn et al. , which was tested for yield and yield related traits at several locations for many years were used in this study Table . These lFollowing four traits were selected for detailed analysis for each year at both the locations.Days to flowering (DF): Number of days to achieve 50% flowering in whole plot.Days to maturity (DM): Number of days to achieve the maturity in whole plot.100 seed weight (SDW): Plants were harvested and random 100 seeds were weighed.Seed yield (SY): Plants from each plot were harvested and weighed to measure the seed yield.All these four traits were further used for random cross validation prediction using GS models.Plant leaves were collected from 15 days old seedlings and genomic DNA was isolated using high throughput mini-DNA extraction method as described by Cuc et al. . DNA washttp://www.diversityarrays.com/dart-application-dartseq-data-types).All selected lines were genotyped using sequencing-based DArT genotyping platform known as DArTseq as described in Sansaloni et al. . In brieyijk is the phenotypic trait analyzed; μ is the grand mean; ri is the fixed effect of replication i; (r/b)ij is the random effect of block j nested with replication i with N gk is the random effect of entry k with N ijk is the random residual effect with N yijkl is the phenotypic trait analyzed; μ is the grand mean; ei is the fixed effect of year i; (e/r)ij is the random effect of replication j in year i with N e/r/b)ijk is the random effect of block k nested with replication j in year i with N gl is the random effect of entry l with N eg)il is the random effect of the interaction between entry l year i with N ijkl is the random residual effect with N Phenotyping data on 320 lines generated at IARI and ICRISAT was curated and used for further analysis. Analysis of Variance (ANOVA) for phenotypic data was performed for the targeted traits  using SAS software version 9.4 . Best Linear Unbiased Predictors (BLUP) of entries were estimated considering replication as fixed and nested block effect and entry factor as random using modelr2 calculated asr2 and LD decay were computed using an R package Synbreed.Genotyping data for 3000 polymorphic markers including SilicoDArTs and DArT-SNP markers were analyzed collectively for 315 elite lines. Marker statistics such as polymorphism information content (PIC) value, gene diversity, minor allele frequency (MAF) and missing percentage were calculated using PowerMarker V3.0 , Kinship GAUSS (semiparametric model), Bayes Cπ, Bayes B, Bayesian Least Absolute Shrinkage, and Selection Operator (Bayesian LASSO) and Random Forest (RF)  were used for prediction of GEBVs. RR-BLUP assumes that all markers have common variances with small but non-zero effect and therefore shrinks equally for each marker effect  were used with all six different GS models for estimating prediction accuracies. For calculating the prediction accuracy, marker effects were calculated using standard linear model using following equation:y denotes the vector of adjusted phenotypes of order and ψ is a px1 vector of fixed effects, F is a known incidence matrix corresponding to fixed effects, X is a matrix of genotypes for markers, β is the vector of marker effects and e is a vector of random residual terms.In order to assess the impact of marker attributes viz. DM, DF, SDW, and SY for both locations and seasons as well. To estimate the prediction accuracy of GEBV, the approach of cross-validation (CV) was employed. Five-fold cross-validation was performed to predict the breeding values in different environments and also in pooled environment conditions. Five-fold CV was performed by randomly assigning 80% of the lines as training population and the remaining 20% as testing candidates. The whole process was repeated 20 times, resulting in a total of 100 CV runs. The prediction accuracy was measured as Pearson correlation between the observed adjusted phenotypic values  and the prediction values computed by the different models.For fitting the GS model, separate analyses were performed for four yield and yield related traits In order to assess the effect of population structure and population size on GEBV/prediction accuracy, population structure was considered as one of the factor for calculating the prediction accuracy. With genotyping data on 315 elite lines, number of natural genetic groups (K) and the distribution of individuals among these groups were estimated using STRUCTURE 2.3  and rainfed (RF) at ICRISAT and IARI for five seasons were used to calculate coefficient of variation (CV), genetic variance (GV), phenotypic coefficient of variation (PCV), genotypic coefficient of variation (GCV), and broad sense heritability (H2) and environmental coefficient of variation (ECV) for yield and yield related traits viz. Days to flowering (DF); Days to maturity (DM); 100 seed weight ; Seed yield  and for the rest two seasons Random Forest model was found producing highest prediction accuracies . While the least prediction accuracy was observed with RR BLUP , Bayes B (ICRISAT-IR-13: 0.647); IARI-IR-13: 0.673 , root length density (RLD), and root dry weight (RDW)] as compared to the recurrent (JG 11) as well as donor parent (ICC 4958)  with mapped markers extended upto 500 kb (CaLG02)–2500 kb (CaLG04). Training population used in the present study included the elite breeding lines, LD analysis using genotyping data for these lines suggested presence of the huge LD blocks which could be one of the reasons for such high prediction accuracy (Table Another important factor affecting the prediction accuracy is extent of LD decay which defines the minimum number of markers required for estimation of prediction accuracy. In the case of non-inbred lines there is a significant decrease in LD therefore requires large number of markers to compensate the fast decay in LD (Liu et al., cy Table .In general higher prediction accuracies are observed for the traits with less complexity while accuracy decreases with increase in the trait complexity (Zhang et al., Further higher prediction accuracies could be the design of the study in such a manner that training set as well as testing set were phenotyped in same environment (Burstin et al., Cattle breeding is one of the major beneficiaries of GS revolution and similar approach is being implemented in plant breeding for enhancing the rate of genetic gain by reducing the long duration selection cycles and increasing the selection intensity and efficiency. GS can play significant role in improving the traits with longer generation cycle and complex mechanism involving large number of small effect QTLs. Preliminary work in genomic selection for chickpea improvement has produced encouraging results with application of DArT markers system. It further opens a possibility to deploy high density genotyping methods like GBS and SNP arrays, which may result in more improvement in prediction and finally enhancement in the rate of genetic gain in chickpea.MR generated the genotyping data; MS, SS, PG, BC, and ST generated phenotyping data; MR, AR, and RD conducted phenotyping, genotyping data analysis including the statistical modeling; MR, AR, AJ, YL, JH, AL, TS, JC, JJ, and RV contributed to analyze and interpret data; MR, JC, and RV wrote the manuscript; RV conceived, designed, and supervised the study and finalized the manuscript.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.