Brassica napus canola need to be broadened for exploitation of heterosis at a greater level in the breeding of F1 hybrid canola cultivars. In this study, we evaluated 228 inbred B. napus canola lines derived from six B. napus × B. oleracea interspecific crosses and following two breeding methods (F2- and BC1-derived lines) to understand the effect of the B. oleracea alleles on heterosis for different agronomic and seed quality traits. Test hybrids of the inbreds derived from crosses involving vars. botrytis (cauliflower), alboglabra  and capitata (cabbage) cv. Badger Shipper, on an average, gave about 10% mid-parent heterosis (MPH), and about 67% of the test hybrids gave higher seed yield than the common B. napus parent indicating that B. oleracea alleles can contribute to heterosis for seed yield in spring B. napus canola hybrids. This was also evident from a positive correlation of the genetic distance of the inbred lines from the common B. napus parent with MPH for seed yield (r = 0.31) as well as with hybrid yield (r = 0.26). Almost no correlation was found between genetic distance and MPH for seed oil and protein content as well as with the performance of the test hybrids for these two traits. The occurrence of positive correlation between seed yield of the inbred lines and test hybrids suggested the importance of the genes exerting additive effect for high seed yield in the hybrids. Very little or almost no heterosis was found for the other agronomic traits as well as for seed oil and protein content. While comparing the two breeding methods, no significant difference was found for seed yield of the test hybrids or the level of MPH; however, the BC1-derived inbred and test hybrid populations flowered and matured earlier and had longer grain-filling period than the F2-derived population. Thus, the results suggested that the B. oleracea gene pool can be used in the breeding of spring B. napus canola to improve seed yield in hybrid cultivars.The genetic base of  Brassica napus L.  canola, also known as oilseed rape, is one of the most important oilseed crops in the world. It is an amphidiploid species as a result of hybridization between B. oleracea  and B. rapa   . B. napuears ago . Spring,ears ago . Currentears ago . To deveears ago . The narears ago ; therefoearchers .B. napus. Genetic divergence of the parental lines is thought to be related to the superior performance of the F1 hybrid  of the parents has been investigated by different researchers in oilseed 1 hybrid ; however1 hybrid . GCA of 1 hybrid .B. napus. For example, in case of the primary gene pool, 1 hybrids developed by crossing of spring B. napus lines, carrying alleles introgressed from Chinese semi-winter type, with natural spring B. napus. Similarly, B. napus can improve seed yield in spring B. napus canola hybrids. In case of the secondary gene pool, B. rapa can be a valuable gene pool for alleles for increasing biomass yield in B. napus, and greater biomass at vegetative and maturity stage was found to be associated with higher seed yield in spring canola hybrids  developed by the Canola Program of the University of Alberta. All B. oleracea accessions were high-erucic (~40% erucic acid) and high glucosinolate (>60 µmol glucosinolate g−1 seed) type. Seeds of the B. oleracea accessions var. alboglabra line-NRC (PBI) was collected from the National Research Council, Saskatoon, Canada; var. botrytis cv. BARI cauliflower from the Bangladesh Agricultural Research Institute, Bangladesh; var. italica cv. Premium Crop from Dr. Ron Howard, Alberta Agriculture and Forestry, Brooks, Canada; var. capitata cv. Balbro from Hazera Seeds of Growth, Netherlands; and var. capitata cvs. Badger Shipper and Bindsachsener from germplasm collection of the Canola Program of the University of Alberta A04-73NA × B. oleracea var. botrytis cv. BARI cauliflower A04-73NA × B. oleracea var. capitata cv. Badger Shipper A04-73NA × B. oleracea var. capitata cv. Bindsachsener A04-73NA × B. oleracea var. capitata cv. Balbro A04-73NA × B. oleracea var. italica cv. Premium Crop A04-73NA × 1 plants were self-pollinated to produce the F2 seeds and backcrossed to the B. napus parent to produce BC1 seeds. The F2 and BC1 population were subjected to pedigree breeding with selection for spring growth habit, plant fertility and the two canola quality traits, zero erucic acid in seed oil and low glucosinolate in seed meal. All 228 (110 + 118) inbred lines were confirmed to be spring type euploid B. napus (2n = 38) possessing the canola quality properties (nAnCn/oCn/o) was expected to be composed of the C genome of B. napus (AnAnCnCn) and the C genome of B. oleracea (C°C°), where the proportion of B. oleracea alleles was expected to be 50% in case of the F2-derived population and 25% in case of the BC1-derived population.The Foperties . TheoretTest hybrid seeds were produced by manual crossing of the 228 inbred lines as male and the B. napus line A04-73NA as female. For this, a total of 110 F7 plants of A04-73NA × B. oleracea and 118 BC1F6 plants of (A04-73NA × B. oleracea) × A04-73NA of the six crosses were grown in a greenhouse  of the Department of Agricultural, Food and Nutritional Science, University of Alberta in 2014–15 winter, and test hybrid seeds as well as self-pollinated F8 and BC1F7 generation seeds were produced for field trial in 2015. Two to three plants of each of the F8 and BC1F7 lines were grown in greenhouse in 2015-16 winter and their test hybrid seeds were produced for field trial in 2016. In the same way, test hybrid seeds were produced in greenhouse for 2017 and 2018 field trials.B. napus parent A04-73NA were grown in field plots in the summer of 2015, 2016, 2017, and 2018. In 2015, the trial was grown at Edmonton Research Station (ERS), and in the remaining years, the trials were grown at St. Albert Research Station of the University of Alberta.The test hybrids, their self-pollinated male parent lines, and the common In 2015, seeding was done manually in 3-row plots of 1.0 × 1.2 m (length × width) size where 66 seeds were seeded in 22 spots in the middle row with 5 cm spacing between the spots, while 44 seeds were seeded in each of the two guard rows. After crop establishment (two leaf stage), thinning was done in the middle row where 22 ± 2 plants were retained. Plot size in 2016 was 2.0 m × 1.3 m (length × width) and in 2017 and 2018 was 3.0 m × 1.3 m; however, the number rows per plot in 2016 and 2017 was four while in 2018 it was two. In all these three years, seeding was done by a plot seeder. Amount seed used per plot was 1.5 g in 2016, and 2.0 g in 2017 and 2018. The difference in the size of the plots in different years was due to the availability of the test hybrid seeds.Field plots were laid out in a way that the test hybrids were bordered by their respective parents, where the hybrid and its two parents constituted one experimental unit. This design enabled direct comparison of the test hybrids with their parents and precise estimation of mid-parent heterosis (MPH); however, this design had also increased the total number of plots in a replication to 513. To accommodate this large number of plots in a best uniform piece of a land, field plots were laid out in an incomplete randomized block design, where each replication was divided into multiple blocks. Number of replications in all year was two, and randomization of the experimental units (hybrid and parents) was done using Cropstat version 7.2 and Microsoft Excel 2007.−1), and seed oil (%) and protein (%) contents. In addition to this, the duration of flowering time and grain-filling period data was collected in 2017 and 2018. Days to flowering data were collected when about 50% plants in a plot had at least one open flower. The end of flowering data was collected when about 90% plants in a plot did not have flower. Days to flowering and end of flowering data was collected two times in a week. Duration of flowering time was calculated by subtracting days to flower from the end of flowering date. Plant height (cm) data was collected at the end of flowering on a whole plot basis by measuring height of the plants from soil surface. Days to maturity data was collected from the middle rows when silique color changed from green to light yellow or brown and seed color of the silique on the main branch (examined by opening the silique) changed to brown or black. Grain-filling period was calculated by subtracting the end of flowering date from the maturity date. Plots were harvested with a plot combine and plot yield data was converted to kg/ha.The following agronomic and seed quality traits were collected from the middle rows on plot basis: Days to flowering, plant height (cm), days to maturity, seed yield  in the Analytical Laboratory of the Canola Program of the University of Alberta. For this, five to eight gram bulk open-pollinated seeds harvested from the field plots were used. A calibration equation available in this laboratory was used for quantification of oil and protein contents using the software WinISI II . Oil and protein contents were calculated on whole seed basis at 8.5% moisture and reported as percent of the whole seed.2- and BC1-derived inbred lines and their seven parents (B. napus A04-73NA and six B. oleracea cultivars/lines) was extracted using SIGMA DNA extraction kit  following manufacture's instruction. A total of 95 polymorphic SSR markers , 10 mM each dNTP , 5 µM of each forward and reverse primer, and 5 µM tag F . PCR was carried out in a GeneAmp PCR System 9700 thermal cycler  with the following program: 1 cycle of 5 min at 95°C for initial denaturation, 35 cycles where each cycle consisted of 1 min at 95°C for denaturation, 1 min at 58°C for annealing and 1.5 min at 72°C for extension, and the final extension time was 15 min at 72°C. Size-based separation of the amplified DNA fragments was done using a capillary electrophoresis AB Genetic Analyzer No. 3730 .Genomic DNA of the above-mentioned 227 (110 + 117) F markers  from ninBest linear unbiased prediction (BLUP) was used to estimate genotypic value of the inbred lines and their test hybrids for different agronomic and seed quality traits. In this analysis, replication, block nested in replication and genotype were considered as random effects, and the analysis was done using the statistical software program of SAS .Heterosis: MPH (mid-parent heterosis) and NPH (heterosis over the B. napus parent) was calculated for different agronomic and seed quality traits using the estimates from BLUP and using the following formulas:Analysis of variance (ANOVA): ANOVA for different traits was performed using data of the inbred lines and test hybrids, and MPH and NPH values with the statistical software program of SAS using PROC MIXED through the option METHOD = Type 3 sums of squares. In this analysis, environment consisted of four field trials conducted over four years  as detailed in the Field Trial section. The inbred lines used in this study to produce the test hybrids were developed from six crosses  and following two breeding methods (F2- and BC1-derived). The six crosses, the two breeding methods and their interaction (cross × method) were considered as fixed effect; while the environment and genotype nested in cross × method were considered as random effect. The following linear model summarizes the sources of variation:ijkpY is the trait value observed for the pth genotype (inbred line) from the ith  cross and the jth  breeding method grown in the kth  environment; µ, iC, j,T (CT)ij, kE, p(ij)G and kp(ij)GE are the overall mean and effects due to the ith cross, the jth breeding method, the ijth cross × breeding method, the kth environment, the pth genotype within the ijth cross × breeding method and the kpth genotype × environment interaction, respectively; ijkpϵ is the random residual for the ijkpth observation. All random effects G and kp(ij)GE) and random residual are assumed to be independently and identically distributed with mean zero and variances being kE ~ NG ~ NG ~ N between the Lsmeans was done following Tukey's test. Pearson's correlation coefficient (r) values were calculated using cor.test function and chart.correlation of the PerformanceAnalytics package  was used to facilitate the identification of the test hybrids with differentiated performance when accounting for differences and interrelationships among the multiple agronomic and seed quality traits. Lsmean values of the test hybrids (obtained above) across the environments for different agronomic and seed quality traits were used for the PCA and data standardized  prior to this analysis. Data standardization and PCA were performed using R package vegan ; however, all genotyping results were confirmed manually as well. The absence (0) or presence (1) of the amplification products were scored based on fragment length, and data recorded in a 0/1 matrix for the presence/absence of the marker amplicons. The 0/1 matrix was used to calculate Nei's genetic distance using the software GENALEX 6 (r) between genetic distance of the inbred lines from the common B. napus parent and performance of the inbred lines and test hybrids was calculated for different agronomic and seed quality traits using cor.test function and chart.correlation in R statistical computing program  higher yield than their corresponding inbred populations  for seed yield, no significant difference was found between these methods for the development of the inbred lines as well as in their test hybrid populations  earlier, had shorter duration of flowering and took longer grain-filling period than the inbred populations—these factors might have contributed to the greater seed yield in the test hybrid populations  and matured (106.0 ± 1.3) significantly (P < 0.05) earlier than the F2-derived population. Test hybrid populations of the BC1-derived lines still flowered earlier  and had longer grain-filling period (29.5 ± 5.4) than test hybrid population of the F2-derived lines  taller than the inbred populations  with duration of flowering and days to maturity, while it showed a negative correlation (P < 0.001) with grain-filling period in both inbred and test hybrid populations (P < 0.001) negative correlation with duration of flowering, and a positive correlation with grain-filling period and seed oil content in both inbred and test hybrid populations. This suggests that high yielding lines or test hybrids with high oil content and earliness of flowering and maturity and longer grain-filling period can be obtained from this population.Days to flowering showed a positive correlation  with the test hybrid population for all agronomic and seed quality traits  negative correlation with MPH; however, correlation between the performance of the inbred lines and NPH was significant (P < 0.001) and positive for all traits, including seed yield (r = 0.30)  with seed yield in the inbred population; however, this correlation was positive (r = 0.26) in the test hybrid population as well as with MPH (r = 0.31) and NPH (r = 0.24) (r = 0.30), duration of flowering (r = 0.35), and days to maturity (r = 0.29) in the inbred population; however, this correlation was negligible in the test hybrid population. A positive correlation of the genetic distance of the inbred lines with days to flowering, duration of flowering and days to maturity indicate that B. oleracea alleles delayed flowering and maturity in the inbred lines; however, the negative effect of these B. oleracea alleles has been repressed to some extent by the alleles of B. napus in the test hybrids. Genetic distance showed a negative correlation (r = −0.29) with grain-filling period in the inbred population; however, this correlation was positive (r = 0.32) for MPH. A moderate positive correlation of genetic distance was found with plant height in the inbred (r = 0.30) and test hybrid population (r = 0.47) as well as for MPH and NPH. Almost no correlation was found between genetic distance and seed oil or protein content in both inbred and hybrid population (To understand the effect of genetic distance of the inbred lines from the  = 0.24) Figure 5B. oleracea and B. napus parents. The position of the 95 SSR markers used in this study together with heterozygosity of the markers in the test hybrid population, deduced from marker genotype of the inbred lines and the common B. napus parent A04-73NA, is presented in It was expected that the test hybrid population to be heterozygous at different loci for the alleles originating from the 1 hybrids  for different agronomic and seed quality traits. PC1 showed high correlation with various traits  for the agronomic and seed quality traits. PC1 showed high correlation with all traits except plant height  trials, we also found similar results (B. napus parent (data not shown). This indicate that this variant of B. oleracea might carry less heterotic alleles for seed yield, or depletion of favorable heterotic alleles might have occurred during the development of this population.Since identification of the phenomenon heterosis or hybrid vigour in maize, the development of Fn Canada . Howevery traits . TherefoA genome , is need results . Howeverr = −0.14) correlation with seed yield in the inbred population; however, this correlation was positive in the test hybrid population as well as with MPH. This indicates that several alleles of B. oleracea in homozygous condition gave poor yield in the inbred population; however, at least, some of the alleles were capable of contributing to heterosis through non-additive genetic effect. Involvement of non-additive gene effect for high seed yield in hybrids was also evident from a weak correlation between the performance of the inbred lines and the hybrids, as well as from a weak correlation of the performance of the inbred lines with MPH and NPH. Involvement of both additive and non-additive genes in the genetic control of seed yield in B. napus hybrids has also been reported by several researchers . This is not a large number of markers when compared with SNPs; however, limited number (e.g. 18 to 55) of SSRs can provide good information of genetic diversity, as has been reported by several researchers . Geneticearchers . While ws parent Figure 1Brassica can induce a number of genetic change in the genome through homoeologous recombination between the chromosomes . Stronger selection on the F2-derived population as compared to the BC1-derived population for spring growth habit and the two canola quality traits (zero erucic acid and low glucosinolate) might be one of the reasons for this genetic similarity as well as similar seed yield of the populations developed following these two breeding methods. In contrast, 1-derived population as compared to F2-derived population of B. napus × B. juncea interspecific cross.In the present study, backcrossing of the FAlmost no heterosis was found for seed oil and protein contents in the test hybrid populations of the inbred lines derived from the six interspecific crosses. These two traits are mainly controlled by additive genes  which coB. napus × B. oleracea crosses flowered significantly earlier than the common B. napus parent indicating that the alleles of the C genome of B. oleracea can contribute to earliness in B. napus. Test hybrid populations also flowered and matured slightly earlier than their inbred populations. This agree with the results reported by B. napus depending on the types of materials used and test environmental condition. In contrast, the duration of flowering and grain-filling period, respectively, exhibited a significant negative and positive correlation with seed yield. The negative correlation between the duration of flowering and seed yield might have resulted from the failure of the late flowering lines and hybrids to reach physiological maturity at the time of harvest as all plots were desiccated at the same time, and this might had penalized the late flowering ones for seed yield. On the other hand, the longer grain-filling period might had resulted fully developed seeds and, thus, contributed to the positive correlation of this trait with seed yield. Several inbred lines of the B. oleracea alleles introgressed into spring B. napus canola inbred lines can exhibit heterosis for seed yield in B. napus hybrids. Among the different variants of B. oleracea used in this study, cauliflower, Chinese kale and some of the cabbages showed great potential to increase seed yield in spring canola hybrids. However, improvement of the seed yield of the hybrid parent lines will also be needed to increase seed yield in the hybrids as evident from positive correlation of the performance of inbred lines with hybrid yield as well as with NPH. In this regard, alleles introgressed from broccoli can also contribute to hybrid breeding. Thus, introgression of genome content of B. oleracea can broaden the genetic base of the C genome of B. napus for the development of improved spring B. napus canola hybrid cultivars.In conclusion, results from this study showed that the All datasets generated for this study are included in the article/AN carried out the experiment, collected the data, performed analysis, designed the figures and wrote the first draft of the manuscript which was edited by HR. JP supervised the multivariate analysis. BK, JB, and XW contributed to data collection. BK, JP, R-CY, and HR provided critical feedback and helped to shape the research, data analysis, and read the manuscript. HR conceived the original idea and supervised the research.Financial support for research has been provided by Natural Sciences and Engineering Research Council of Canada (NSERC) and Nutrien Ag Solutions.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.


Newcastle Disease (ND) is a continuing global threat to domestic poultry, especially in developing countries, where severe outbreaks of velogenic ND virus (NDV) often cause major economic losses to households. Local chickens are of great importance to rural family livelihoods through provision of high-quality protein. To investigate the genetic basis of host response to NDV, three popular Tanzanian chicken ecotypes  were challenged with a lentogenic (vaccine) strain of NDV at 28 days of age. Various host response phenotypes, including anti-NDV antibody levels , and viral load (2 and 6 dpi) were measured, in addition to growth rate. We estimated genetic parameters and conducted genome-wide association study analyses by genotyping 1399 chickens using the Affymetrix 600K chicken SNP chip. Estimates of heritability of the evaluated traits were moderate (0.18–0.35). Five quantitative trait loci (QTL) associated with growth and/or response to NDV were identified by single-SNP analyses, with some regions explaining ≥1% of genetic variance based on the Bayes-B method. Immune related genes, such as ETS1, TIRAP, and KIRREL3, were located in regions associated with viral load at 6 dpi. The moderate estimates of heritability and identified QTL indicate that NDV response traits may be improved through selective breeding of chickens to enhance increased NDV resistance and vaccine efficacy in Tanzanian local ecotypes. Newcastle disease (ND) is a major threat to poultry globally, and severe outbreaks of the velogenic strains of Newcastle disease virus (NDV) in African local chicken populations often have devastating economic impacts on households. In Africa, local chicken production is characterized by low input production systems ,2 and seParamyxoviridae and genus Avulavirus and is a devastating poultry pathogen [Newcastle Disease Virus is an avian paramyxovirus type 1 (APMV-1) virus that belongs to the family pathogen . Chickenpathogen . NDV strpathogen ,11. Lentpathogen . Mesogenpathogen .African local chicken ecotypes/breeds are characterized by production under limited management of low feed supply and are well adapted to harsh climatic conditions, as evidenced by the presence of selection signatures across their genomes ,15. HoweAll animal procedures were approved by the Institutional Animal Care and Use Committee of University of California, Davis (#:17853)ad libitum access to feed and water.Breeder chickens consisted of 65 sires and 324 dams from three popular Tanzanian chicken ecotypes, Ching’wekwe (Ching), Kuchi, and Morogoro Medium (MoroMid) that were randomly collected from the five regions of Tanga, Shinyanga, Morogoro, Singida, and Mwaza, representing the central, coastal, and lake zones of Tanzania . Sires a7, 50% embryonic infectious dose (107EID50) of a live attenuated type B1 LaSota lentogenic NDV strain. This lentogenic vaccine strain induces an immune response without causing severe disease and death, allowing for observation of phenotypic responses to the virus. To quantify viral load, viral RNA was isolated from lachrymal fluid samples at 2 and 6 days post infection (dpi) and quantified using qPCR, as described by Rowland et al. [10 for downstream analyses. Viral clearance was computed as the difference between viral loads at the two time points divided by viral load at 2 dpi. Blood samples were collected and ELISA was used to measure the anti-NDV antibody levels at 10 dpi, which is the time required for birds to generate an acquired immune response [10. Body weights were measured at hatch and at 7, 14, 21, 28 (0 dpi), 34 (6 dpi), and 38 (10 dpi) doa. Pre- and post-infection growth rates were calculated as grams per day from these weights using linear regression of weight on age. Outliers greater than three standard deviations from the mean were removed for all response traits.Blood samples were collected from all birds at 27 doa and IDEXX NDV ELISA was used to quantify maternal antibody levels . At 28 doa, all birds were challenged via the oculo-nasal route with 10d et al. . The mearesponse ,23. The ® 600k Array at GeneSeek . Genotyping Array annotation files were based on the chicken Gallus gallus genome version 5 . Genotype data quality filtering was performed with Axiom™ Analysis Suite 3.1  and included single nucleotide polymorphism (SNP) call rate ≥99% and minor allele frequency ≥0.05. Other Affymetrix genotype metrics used for filtering, with their corresponding cutoffs, are listed in Blood samples were collected from chicks before challenge using Whatman FTA cards . Genomic DNA was isolated from the FTA cards and genotyping was conducted using the Affymetrix AxiomPopulation structure was examined by constructing a Multi-dimensional Scaling (MDS) plot in two dimensions using the cluster algorithm in the PLINK v1.9 software . Shared Y is the dependent phenotype variable: pre- and post-infection growth rate, antibody at 10 dpi, viral load at 2 dpi, and viral load at 6 dpi. Fixed effects included death prior to 10 dpi (D = 0/1), trial , and sex . Only one covariate, population proportion (C), obtained as described above, was fitted. Random effects included animal genetic effects (A), using the genomic relationship matrix computed based on procedures reported by [X to account for maternal effects, and residuals (e). The dam effect (X) was removed for some traits because it was not significant. For viral load at 2 and 6 dpi, and antibody at 10 dpi, qPCR plate  and replicate plate (46), respectively, were added as fixed effects. Phenotypic variance was obtained by summation of variance due to animal genetic, dam, and residuals. Heritability was computed as a ratio of the estimates of animal genetic to phenotypic variance. Bivariate animal models were used to estimate pairwise genetic and phenotypic correlations between traits, with the same fixed and random effects as specified for the univariate analyses.Estimation of variance components and heritabilities was done using ASReml 4  based onorted by , X to acTwo approaches were used for whole genome association analyses. First, a Bayesian approach called Bayes B , as implpolygenic_hglm” function, with a genomic kinship matrix that was created using the ibs function. To test for association between a trait and genotypes at a SNP for related individuals, the “mmscore” function was used and residuals were obtained from the polygenic_hglm analysis. The mmscore function was performed using the formula described by Rowland et al. [For the second approach, single SNP association analyses were conducted using the R package GenABEL , with a d et al. .To determine significance thresholds for the GenABEL analyses, multiple test correction was performed. Suggestive genome-wide significance thresholds of 10 and 20% were computed using a modified Bonferroni correction as 0.1 or 0.2 divided by the number of independent tests. To determine the number of independent tests, the SNP genotype data were separated by chromosome and then further divided to form chromosomal segments such that the number of SNPs were equal to half the number of animals, as described by Waide et al. . The numhttps://www.ncbi.nlm.nih.gov/genome/gdv/)Gene annotation for 1-Mb windows that explained more than 1% of genetic variance was obtained using NCBI’s Genome Data Viewer on the chicken genome version Gallus gallus 5  . Viral cp-value = 2.05 × 10−6) and 20%  were used to declare suggestive associations in the single SNP GenABEL analyses. Markers significantly associated with pre- and post-infection growth rates, antibody, and viral load at 2 and 6 dpi from single-SNP GenABEL analyses are presented in A total of 1399 animals and 396,055 SNPs remained after quality control and were utilized for GWAS analyses. Because the chicken ecotypes were highly heterogeneous and admixed, analyses were performed across ecotypes by fitting population proportion (C) to account for the structure of the populations. Using principle component analysis, 71,374 components accounted for 95% of the variance of SNP genotypes across the genome. Bonferroni corrected thresholds of 10  indicated a common genetic background of the Tanzanian local chicken ecotypes that were evaluated. This admixture can be attributed to inter-mating (breeding) among chickens across different areas of the country and movement of chickens between country districts through trading and market chains. The MoroMid and Ching ecotypes were sampled from districts (regions) that neighbor each other, compared to Kuchi sample districts. The complimentary Admixture  and MDS Estimates of heritability were moderate for most traits, ranging from 0.18 for viral load at 2 dpi to 0.35 for pre-infection growth rate. Viral clearance had the lowest heritability estimate, at 0.04. These results agree with previous findings from commercial egg laying chickens that were challenged with the LaSota NDV strain . HeritabGrowth rates were negatively and positively genetically correlated with viral load at 2 and 6 dpi, and viral clearance, respectively. Although these correlations were low, this indicates that selection for decreased viral load at 6 dpi and increased viral clearance could increase pre- and post-infection growth rates. In addition, the positive genetic correlations between anti-NDV antibody levels and growth rates indicate that selection for increased antibody levels would also increase growth rate. Our results contrast with the findings of Rowland et al.  in commeTwo analysis platforms, Gensel and GenABEL, were used for GWAS analyses. We utilized the Bayes B approach in the Gensel software to compute genetic variance accounted for by a 1-Mb window for the NDV response traits. The Gensel software does not allow for fitting random effects in the model, except SNP effects. An R package, GenABEL, was used to identify individual SNPs associated with the NDV response traits. GenABEL allows fitting single SNPs one-by-one during the association analyses.Salmonella enterica serovar Enteritidis. The SBDS gene could be important in the regulation of cellular processes during disease challenge in chickens.One suggestive QTL region was identified for post-infection growth rate based on a significantly associated SNP on chromosome 19 at 1.6 Mb. The location of the QTL identified by GenABEL analyses corresponded with a 1-Mb window  that expSingle-SNP analyses revealed one significant suggestive SNP associated with antibody level. A window explaining >1% of genetic variance for antibody level contained 16 genes . HoweverMkp-1 mRNA basal levels to control p38 MAPK activity during lipopolysaccharide stimulation [One significant suggestive QTL for viral load at 2 dpi was identified on chromosome 5 by the GenABEL analyses. This SNP corresponds in location with a 1-Mb window that explained 2% of genetic variance based on the Gensel analyses. The significant SNP was within the intron of the PLEKHH1 gene, which interacts with the MYC transcription factor to activate the transcription of growth-related genes . These rmulation . SLC39A9mulation . SLC39A9mulation .The QTL on chromosome 24 associated with viral load at 6 dpi had the most significant and largest number of SNPs identified in our study. The approximate 1-Mb window that contained these SNPs also had the highest genetic variance explained and contained 36 genes . Some ofOur results revealed that heritability estimates for most NDV response traits were moderately high. Growth rates were positively genetically correlated with anti-NDV antibody level and were negatively correlated with viral load at 6 dpi. The genetic correlation between anti-NDV antibody levels and viral load at 2 and 6 dpi were not different from zero. Six suggestive QTL for NDV response traits were identified, some of which were consistent with QTL identified in previous independent studies. The strongest QTL region was identified on chromosome 24 and contained several candidate genes, including EST1, TIRAP, and KIRREL3, that could be vital in the chickens’ response to NDV infection. The moderate estimates of heritability and the identified QTL for NDV response traits, suggest that NDV response traits can be improved through selective breeding of chickens to enhance NDV resistance and vaccine efficacy of chickens in regions where NDV is endemic. The variants and important genomic regions identified in this study warrant further investigations to comprehensively understand underlying molecular mechanisms of NDV challenge.


Polygenic risk scores (PRS) have shown promise in predicting human complex traits and diseases. Here, we present PRS-CS, a polygenic prediction method that infers posterior effect sizes of single nucleotide polymorphisms (SNPs) using genome-wide association summary statistics and an external linkage disequilibrium (LD) reference panel. PRS-CS utilizes a high-dimensional Bayesian regression framework, and is distinct from previous work by placing a continuous shrinkage (CS) prior on SNP effect sizes, which is robust to varying genetic architectures, provides substantial computational advantages, and enables multivariate modeling of local LD patterns. Simulation studies using data from the UK Biobank show that PRS-CS outperforms existing methods across a wide range of genetic architectures, especially when the training sample size is large. We apply PRS-CS to predict six common complex diseases and six quantitative traits in the Partners HealthCare Biobank, and further demonstrate the improvement of PRS-CS in prediction accuracy over alternative methods. Polygenic risk scores (PRS) have the potential to predict complex diseases and traits from genetic data. Here, Ge et al. develop PRS-CS which uses a Bayesian regression framework, continuous shrinkage (CS) priors and an external LD reference panel for polygenic prediction of binary and quantitative traits from GWAS summary statistics. Although this approach has advantages in terms of computational and conceptual simplicity, and has been used to predict genetic liability across a broad phenotypic spectrum, recent studies have shown that this conventional method for PRS construction discards information and limits prediction accuracy4. More sophisticated Bayesian polygenic prediction methods that rely on GWAS summary statistics, including LDpred4 and the normal-mixture model recently developed6, can incorporate genome-wide markers and accommodate varying genetic architectures, and thus have enhanced performance and flexibility. However, the type of prior on SNP effect sizes used in these methods, known as discrete mixture priors, imposes daunting computational challenges and may result in inaccurate adjustment for local LD patterns.To date, most applications calculate PRS from a subset of the genetic markers after pruning out single nucleotide polymorphisms (SNPs) in LD and applying a 12. However, all previous work required individual-level data and was limited to small-scale analyses (both in term of the sample size and number of genetic markers). PRS-CS only requires GWAS summary statistics and an external LD reference panel, and therefore can be applied in a broader range of settings.In this work, we present a polygenic prediction method, PRS-CS, which utilizes a Bayesian regression framework and places a conceptually different class of priors—the continuous shrinkage (CS) priors—on SNP effect sizes. Continuous shrinkage priors allow for marker-specific adaptive shrinkage , and thus can accommodate diverse underlying genetic architectures. In addition, continuous shrinkage priors enable conjugate block update of the SNP effect sizes in posterior inference , and thus can accurately model local LD patterns and provide substantial computational improvements. Several special cases of continuous shrinkage priors have been applied to quantitative trait prediction or gene mapping14, and demonstrate that PRS-CS dramatically improves the predictive performance of PRS over existing methods across a wide range of genetic architectures, especially when the training sample size is large. We apply PRS-CS to predict six curated common complex diseases (breast cancer (BRCA), coronary artery disease (CAD), depression (DEP), inflammatory bowel disease (IBD), rheumatoid arthritis (RA), and type 2 diabetes mellitus (T2DM)) and six quantitative traits  in the Partners HealthCare Biobank15, and further demonstrate the potential of PRS-CS for the clinical translation of polygenic prediction.We conduct simulation studies using the UK Biobank genetic dataN and M denote the sample size and number of genetic markers, respectively, y is a vector of traits, X is the genotype matrix, β is a vector of effect sizes for the genetic markers, and ε is a vector of residuals. By assigning appropriate priors on the regression coefficients β to impose regularization, additive PRS can be calculated using posterior mean effect sizes.We consider a Bayesian high-dimensional regression framework for polygenic modeling and prediction:β can be represented as scale mixtures of normals:N is a normal distribution with mean μ and variance σ2, and G is a mixing distribution. For example, if G places all its mass at a single point, i.e., δ• is the Dirac delta measure, then marginally 16. To create a more flexible model of the genetic architecture, a discrete mixture of two or more point masses or densities can be used, which allows for a wider effect size distribution than a normal prior can produce. For example, π is the mixing probability , produces the point-normal prior on effect sizes, βj ~ (1−π)δ0 + πN, which was used in LDpred4. Although discrete mixture priors offer a natural and intuitive approach to model non-infinitesimal genetic architectures, posterior inference requires a stochastic search over an exponentially large discrete model space, and does not allow for multivariate block update of effect sizes, which limits computational efficiency and may result in inaccurate modeling of local LD patterns.Essentially all widely used prior densities for ϕ is a global scaling parameter that shares across genetic markers and controls the degree of sparseness of the model, and g is an absolutely continuous density function, in contrast to a discrete mixture of atoms or densities. By appropriately choosing the continuous mixing density g, this modeling framework can produce a variety of shapes of the prior distribution on βj. In particular, g can be designed to introduce a prior distribution on the SNP effect sizes that has a sizable amount of mass near zero to impose strong shrinkage on noise, while at the same time has heavy tails to avoid over-shrinkage of truly non-zero effects. The marker-specific local shrinkage parameter ψj can then adaptively squelch small noisy estimates towards zero, while leaving data-supported large signals unshrunk. In this work, we investigate a specific g , and present two versions of the algorithm, which differ in the way to learn the global scaling parameter ϕ. In PRS-CS, we search a small number of fixed ϕ, select the ϕ value that produces the best predictive performance in a validation data set, and evaluate the algorithm in an independent testing set. In the second version of the algorithm, which we call PRS-CS-auto, we use a fully Bayesian approach and place a standard half-Cauchy prior on the global shrinkage parameter20: ϕ1/2 ~ C+, such that ϕ is automatically learnt from data and no validation data set is needed.In this work, we investigate a conceptually different class of priors—the continuous shrinkage priors. In particular, we consider the following prior on SNP effect sizes, which can be represented as global-local scale mixtures of normals:22. Here we enable posterior inference of SNP effect sizes from GWAS summary statistics under continuous shrinkage priors using an efficient Gibbs sampler with multivariate block update of the effect sizes (see Methods section).Individual-level Bayesian regression models (1) with a prior on SNP effect sizes can often be approximated using an external LD reference panel and turned into summary statistics based methodsP-value thresholding (P+T), LDpred and LDpred-inf4. Throughout the paper, we use the 1000 Genomes Project (1 KG) European sample (N = 503) as the external LD reference panel, but also assess the impact of using an in-sample LD reference panel on prediction accuracy in Supplementary Information.We compare PRS-CS and PRS-CS-auto with four polygenic prediction methods that rely on GWAS summary statistics in both simulations and real data analyses: polygenic scoring based on all genetic markers (unadjusted PRS), informed LD-pruning  and P-value threshold in P+T, fraction of causal SNPs in LDpred, and global shrinkage parameter in PRS-CS) were selected in a validation data set (N = 3000). Prediction accuracy for all methods was quantified by R2 between the observed and predicted traits in an independent testing set (N = 3000).We first compared the predictive performance of six polygenic prediction methods across different genetic architectures and training sample sizes  in simulation studies Fig. . SNP effFigure In addition to prediction accuracy, we assessed the calibration of polygenic prediction methods by regressing the true phenotype onto the PRS predictor and inspecting the regression slope. A slope close to one indicates that a predictor is correctly calibrated. Consistent with predictive performance, as the training sample size grows, our Bayesian approach provides the best calibration among all methods examined  a point-gamma model with different numbers of causal variants produced similar patterns of prediction accuracy  as an in-sample LD reference panel in the point-normal simulations produced, in general, slightly higher prediction accuracy for methods making use of LD information  the point-normal model with different total heritability 0.2 and 0.8); (2) a point- the poin and 0.8;23 are presented in Supplementary Table We applied PRS-CS, PRS-CS-auto, and alternative methods to predict six curated common complex diseases , and six quantitative traits  in the Partners HealthCare Biobank. Large-scale GWAS summary statistics for each disease and trait were downloaded from public domains Table . SNP herR2 (for disease phenotypes) and R2 (for quantitative traits) is summarized in Fig. Predictive performance measured by Nagelkerke’s R2 compared to LDpred) and rheumatoid arthritis (28.62% relative increase in Nagelkerke’s R2 compared to LDpred). For coronary artery disease, depression and type 2 diabetes mellitus, LDpred and PRS-CS had similar predictive performance, and both performed dramatically better than P+T. PRS-CS was only inferior to LDpred in the prediction of inflammatory bowel disease (10.24% relative decrease in Nagelkerke’s R2). However, we note that inflammatory bowel disease has the smallest training sample size among all diseases and traits  instead of the 1KG reference panel slightly increased the prediction accuracy but the improvement was marginal , and the Bayesian computation with 1000 MCMC iterations on the longest chromosome can be completed within an hour using one Intel(R) Xeon(R) CPU core and 2 GB of memory. Expanding the size of LD blocks may improve prediction accuracy but increases computational cost (as each MCMC iteration requires inverting an L × L matrix where L is the block size), while reducing the size of LD blocks has the potential risk of missing long-range LD. Therefore, the partition we chose represents a balance between modeling accuracy and computational burden. Including multi-million SNP predictors may increase prediction accuracy28 but requires further work.Although continuous shrinkage priors enable multivariate modeling of the LD structure, simultaneous updating of the effect sizes for genome-wide markers remains computationally infeasible. In this work, we used a genome partition computed and validated by prior work30, the normal-inverse-gaussian prior29, the generalized t  prior32, and the normal-exponential-gamma prior34, among others. In addition, most frequentist regularization procedures, such as LASSO, elastic net and bridge regression, have a Bayesian counterpart that can be represented as global-local scale mixtures priors in combination with posterior mode inferences. Each of these priors uses a different continuous mixing density to produce a different marginal prior on the SNP effect sizes. These alternatives may perform equally well or better than the Strawderman-Berger prior for certain genetic architectures. However, we found that as long as the prior on the effect sizes places a sizable amount of mass around zero and has heavier-than-exponential tails, variation in the shape of the prior does not seem to have a large impact on prediction accuracy. Therefore, we believe that the primary gain of PRS-CS over existing methods lies in its more accurate multivariate modeling of local LD patterns and its block-updated Gibbs sampling that can improve the mixing and convergence rate of the Markov chain. We thus recommend using the Strawderman-Berger prior as a default choice. A systematic investigation and comparison of different continuous shrinkage priors is a direction of future work.We note that the prior we investigated in this work, i.e., the Strawderman-Berger prior on the local marker-specific shrinkage parameter, is only one of the possible choices within the class of continuous shrinkage priors, which includes the normal-gamma prior40, BayesR42, BVSR43, BSLMM44, and DPR45, have used discrete mixture priors on SNP effect sizes, we expect that PRS-CS can provide substantial improvements in computational efficiency and prediction accuracy for genomic prediction that leverages individual-level data. Second, jointly modeling multiple genetically correlated traits and including functional annotations in polygenic modeling are expected to increase the predictive performance of PRS, as shown by recent studies47. Lastly, current research on polygenic prediction has largely been restricted to European samples. Heterogeneity between the GWAS, LD reference and testing samples may reduce prediction accuracy as recently demonstrated in genetic correlation analysis and fine-mapping49. Expanding genomic prediction methods to handle unknown ancestry of the target sample  and enable transethnic risk prediction is critical to maximize the value of PRS in a diverse population.We note several additional directions for further technical developments that may be useful. First, although this paper is focused on polygenic prediction methods that only require GWAS summary statistics, PRS-CS, and PRS-CS-auto can be straightforwardly applied to individual-level data. Given that a majority of the existing Bayesian genomic prediction models, including Bayes alphabetic methodsR2 less than 1%), likely due to a combination of its relatively low heritability, extremely polygenic genetic architecture, and the heterogeneous nature of the disorder. A recent study projected that a GWAS with multi-million subjects is needed to identify genetic variants that explain 80% of the SNP heritability for major depressive disorder5. In contrast, it may be easier to produce a clinically useful prediction for some autoimmune diseases or late-onset chronic diseases , due to the existence of SNPs with moderate to larger effect sizes. With these being said, as the GWAS sample size continues to grow, we believe that the predictive value of PRS will keep increasing, and PRS-CS(-auto) will demonstrate bigger advantages over existing methods with larger training sample sizes.Although PRS-CS provides a substantial improvement over existing methods for polygenic prediction, current prediction accuracy of PRS is still lower than what can be considered clinically useful, and much work is needed to further improve the predictive performance and translational value of PRS. In theory, the utility of PRS depends on multiple factors, including the GWAS sample size, and the heritability and genetic architecture of the disease. For example, among the six complex diseases we analyzed, depression had the lowest prediction accuracy , β is a vector of effect sizes, ε is a vector of independent environmental effects, and we have assigned a non-informative scale-invariant Jeffreys prior on the residual variance σ2. In contrast to discrete mixture priors, we consider a conceptually different class of priors:βj scales with the residual variance and the sample size, ϕ is a global scaling parameter that is shared across all effect sizes, ψj is a local, marker-specific parameter, and g is an absolutely continuous mixing density function. This type of prior is known as global-local scale mixtures of normals.We consider the following phenotype model:σ2, ϕ and ψj, j = 1,2,…, M, and the marginal least squares effect size estimates of the regression coefficients β isT = diag{ϕψ1,ϕψ2,…, ϕψM} is a diagonal matrix, and D = ZΤZ/N is the LD matrix. It can be seen that the posterior mean is a matrix shrinkage version of the least squares estimate. In the degenerative special case where ψj ≡ 1, the model becomes Ridge regression and all effect sizes are shrunk towards zero at the same constant rate controlled by the overall shrinkage parameter ϕ. The introduction of the local shrinkage parameter ψj thus allows heterogeneity in the scales of effect sizes.We first note that, given variance parameters D = I and thusτj = 1/(1 + ϕψj) is the shrinkage factor for the j-th marker, which relies on both ϕ and ψj, and describes the amount of shrinkage from the marginal least squares solution towards zero; τj = 0 indicates no shrinkage while τj = 1 yields total shrinkage. Therefore, ϕ controls the overall sparsity level of the model and plays a similar role as the regularization parameter in penalized regression, while ψj adaptively modifies the amount of shrinkage for each marker. By assigning a prior on ψj, which can produce a marginal prior density on βj that has both a sharp peak at zero and heavy tails, the model can pull small effects towards zero, while asserting little influence on larger effects.To provide further intuitions, assuming that all genetic markers are unlinked , we have ψj:α,β) denotes the gamma distribution with shape parameter α and scale parameter β. By using change of variables, it can be verified that placing a gamma-gamma prior on ψj is equivalent to placing a three-parameter beta (TPB) prior on the shrinkage factor τj33:x < 1, a > 0, b > 0 and ϕ > 0. When ϕ = 1, the TPB distribution becomes a standard Beta distribution. For a fixed value of ϕ, a controls the behavior of the TPB prior near one, and thus the behavior of the prior on βj around zero; b controls the behavior of the TPB prior near zero, and thus affects the tails of the prior on βj. Figure τj (upper panel) and βj (middle and lower panels) with ϕ = 1, b = 1/2, and three different values of a: a = 1/2, a = 1 and a = 3/2. It can be seen that when a = 1/2 and b = 1/2, the TPB prior has substantial mass near zero and one  can be easily derived. By using LD information from an external reference panel, the method can be applied to GWAS summary statistics and does not require individual-level data. We describe the Gibbs sampler in ψj : a = 1 and b = 1/2. This particular specification is known as the Strawderman-Berger prior18 or the quasi-Cauchy prior51, and appears to work well across a range of simulated and real genetic architectures.For all continuous shrinkage priors that take the general form in Eq. , Gibbs s27 [http://bitbucket.org/nygcresearch/ldetect-data], and conduct multivariate update of the effect sizes within each LD block . We use ρ = 1 throughout this paper.In practice, we partition the genome into 1703 largely independent genomic regions estimated using data from the 1KG European samplelock see . To avoiϕ, and setting ϕ1/2 roughly to the proportion of causal variants52 works well. If a prior guess of the sparseness of the genetic architecture is not available, we provide two ways to learn ϕ. In PRS-CS, we search a small number of ϕ values: ϕ1/2 ∈ {0.0001, 0.001, 0.01, 0.1, 1}, and select the ϕ that produces the best predictive performance in a validation data set, which is independent of the testing set where prediction accuracy of the algorithm is evaluated. In PRS-CS-auto, we use a fully Bayesian approach and assign a standard half-Cauchy prior on ϕ1/220, such that ϕ is automatically learnt from GWAS summary statistics and no validation data set is needed. See ϕ.We find that the predictive performance of the model is not sensitive to the global shrinkage parameter For both PRS-CS and PRS-CS-auto, the Gibbs sampler usually attains reasonable convergence after 1000 Markov Chain Monte Carlo (MCMC) iterations and produces prediction accuracy close to what can be achieved by much longer MCMC runs. We thus use 1000 MCMC iterations with the first 500 steps as burn-in in simulation studies to reduce computational cost. In practice, we recommend using longer MCMC runs when time and computational resources permit. In the Partners HealthCare Biobank analysis, we report the predictive performance of PRS-CS and PRS-CS-auto based on 10,000 MCMC iterations in total and 5000 burn-in steps.i-th individual is M is the total number of genetic markers, Xij is the genotype for the i-th individual and the j-th SNP, and j-th SNP.The unadjusted PRS is the sum of all genetic markers across the genome, weighted by their marginal effect size estimates. More specifically, the unadjusted polygenic score for the P-value thresholding. In this study, we use the implementation of the P+T method in the software package PRSice-253 [https://choishingwan.github.io/PRSice] and its default parameter settings. Specifically, for any pair of SNPs that have a physical distance smaller than 250 kb and an R2 greater than 0.1, the less significant SNP is removed. The polygenic score is then calculated as the sum of the remaining, largely independent SNPs with a GWAS association P-value below a threshold PT, weighted by their marginal effect size estimates. We consider PT ∈ {1E−8, 1E−7, 1E−6, 1E−5, 3E−5, 1E−4, 3E−4, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1} in this paper. The PT value that produces the highest prediction accuracy in a validation data set is selected, and the predictive performance is assessed in an independent testing set.The P+T method refers to the calculation of PRS using informed LD-pruning  and https://github.com/bvilhjal/ldpred] is a method that infers the posterior mean effect size of each genetic marker from GWAS summary statistics while accounting for LD, using a point-normal prior on the SNP effect sizes and LD information from an external reference panel4. Consider the linear model y = Zβ + ε, where both the phenotype y and the genotype matrix Z have been standardized. LDpred places an independent point-normal prior on each regression coefficient βj:π is the fraction of causal variants. Given π and an estimate of 23 to the GWAS summary statistics, LDpred employs an MCMC sampler to approximate the posterior mean of βj, conditioning on marginal least squares effect size estimates and LD information from a reference panel. In this paper, we consider π ∈ {1E−5, 3E−5, 1E−4, 3E−4, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1}. The π value with the highest prediction accuracy in a validation data set is selected, and the predictive performance is assessed in an independent testing set.LDpred .The genetic data for the UK Biobank comprises 488,377 samples and was phased and imputed to ~96 million variants with the Haplotype Reference Consortium (HRC) haplotype resource and the UK10K + 1KG reference panel. We leveraged the QC metrics provided by the UK BiobankN = 503) as an external LD reference panel. SNP effect sizes were simulated using (1) a point-normal model as specified in Eq. , the simulation was repeated 20 times.We performed simulation studies using real genetic data from the UK Biobank and the 1KG European sample (d in Eq.  with dift model with heavy tails (a mixture of a point mass at zero and a Student’s t-distribution with 4 degrees of freedom); (3) sampling SNP effect sizes using a point-gamma model (a mixture of a point mass at zero and a gamma distribution with the shape parameter set to 2), which produces an effect size distribution that is asymmetric about zero and positively skewed with the right tail being long and thin and the left tail being short and fat; (4) using the combined UK Biobank validation and testing data sets (N = 6000) as an in-sample LD reference panel in the point-normal simulations. For each setting and training sample size considered , and the simulation was repeated 20 times.In order to systematically compare polygenic prediction methods across a wide range of settings, we conducted a number of secondary simulation studies: (1) sampling SNP effect sizes using a point-normal model with heritability fixed at 0.2 or 0.8; (2) sampling SNP effect sizes using a point-https://biobank.partners.org] is a collection of plasma, serum, DNA and buffy coats samples collected from consented subjects, which are linked to their electronic health records (EHR) and survey data on lifestyle, environment, and family history55. To date, Partners Biobank has enrolled more than 96,000 participants, and released genome-wide genetic data for 25,482 subjects. A study protocol is not required for Partners investigators to obtain de-identified data sets from Partners Biobank.The Partners HealthCare Biobank [P < 1 × 10−6 were discarded. We then removed SNPs that showed significant batch associations with P < 1 × 10−6, and merged genotyping batches for subsequent processing and analyses.We performed QC on each genotyping batch separately with the following steps: (1) SNPs with genotype missing rate >0.05 were removed; (2) samples with genotype missing rate >0.02 or absolute value of heterozygosity >0.2, or samples that failed sex checks were excluded; (3) SNPs with missing rate >0.02, or HWE test N = 503) included TSI, IBS, GBR, CEU, and FIN subpopulations. The random forest model was then applied to the Partners Biobank participants, and identified 19,136 unrelated subjects  of the genotype data in all the 1KG samples, and trained a random forest model using the top 4 PCs on the super population labels , in which EUR , RA, and T2DM—for which there are more than 500 cases in the Biobank that have been genotyped, and external large-scale GWAS summary statistics are publicly available. For all the diseases, cases have an algorithm-based positive predictive value (PPV) of having current or past history of the disease greater than 0.90, and controls have a negative predictive value (NPV) of having no history of the disease greater than 0.99.In addition, we selected six quantitative traits—height (HGT), body mass index (BMI), high-density lipoproteins (HDL), low-density lipoproteins (LDL), cholesterol (CHOL), and triglycerides (TRIG)—that have been measured in the Partners Biobank healthy control population with a Charlson age-comorbidity index 0–2 and the predicted 10-year survival probability greater than 90%. We predicted these quantitative traits in a relatively heathy population to avoid measurements affected by severe diseases or medications. For participants that have multiple measurements of a trait of interest, we used the median value. Table 23. Heritability estimates for diseases on the observed scale were transformed to the liability scale as described in Lee et al.58 using the assumed population and sample prevalences shown in Supplementary Table GWAS summary statistics for all the diseases and quantitative traits are publicly available  were selected in the validation set, and the predictive performance was evaluated in the testing set. We use the average R2 between the observed and predicted phenotypes across 100 random splits to assess the predictive performance for the quantitative traits, and report the average Nagelkerke’s R2 metric across 100 random splits for disease (case–control) phenotypes. Nagelkerke’s R2 is defined as N is the sample size. We define the relative increase or decrease in R2 of a polygenic prediction method A compared to method B as R2 or Nagelkerke’s R2, we also report area under the ROC curve (known as AUC), area under the precision-call curve, and the odds ratio (OR) comparing top 10% of the participants having high polygenic risk with the remaining 90% of the sample. We adjusted for current age, sex and top 10 PCs of the genotype data in the calculation of all predictive performance metrics.For each curated disease and quantitative trait, the Partners HealthCare Biobank sample was repeatedly and randomly split into a validation set comprising 1/3 of the data and a testing set comprising 2/3 of the data. Tuning parameters (Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting Summary


Studies on genetic effects of coffee consumption are scarce for Asian populations. We conducted a genome-wide association study (GWAS) of habitual coffee consumption in Japan using a self-reporting online survey.N = 6,264) and confirmed in a replication cohort . Two loci achieved genome-wide significance (P < 5 × 10− 8) in a meta-analysis of the discovery and replication cohorts: an Asian population-specific 12q24 , which harbors CUX2, and 7p21 , in the upstream region of the aryl hydrocarbon receptor (AHR) gene, involved in caffeine metabolism. Subgroup analysis revealed a stronger genetic effect of the 12q24 locus in males (P for interaction = 8.2 × 10− 5). Further, rs79105258 at the 12q24 locus exerted pleiotropic effects on body mass index (P = 3.5 × 10− 4) and serum triglyceride levels (P = 8.7 × 10− 3).Candidate genetic loci associated with habitual coffee consumption were searched within a discovery cohort  contains supplementary material, which is available to authorized users. Many epidemiologic studies have investigated the health benefits of drinking coffee, which is one of the most popular beverages globally. These benefits include a reduced risk of type 2 diabetes mellitus , 2, cardCYP1A1 and CYP1A2) loci on chromosome 15 and in a region close to the aryl-hydrocarbon receptor (AHR) gene on chromosome 7, as well as several novel genetic variants , 1.007–1.011) in the genome-wide scan   was tested, with adjustment for age and sex . After meta-analysis of the discovery and replication cohorts, two of these loci (7p21 and 12q24) achieved genome-wide significance (P < 5 × 10− 8). Heterogeneity in genetic effects between the discovery and replication cohorts was not significant for the 7p21 and 12q24 loci  were not replicated. The remaining three  were significantly associated with habitual coffee consumption , which harbors several genes, including aldehyde dehydrogenase 2 (ALDH2) and cut-Like homeobox 2 (CUX2). The second strongest association was found for 7p21 , an upstream region of the aryl hydrocarbon receptor (AHR) gene.Additional file P = 1.4 × 10− 10) (Table P = 9.1 × 10− 9). These results confirmed the association between habitual coffee consumption and the 12q24 and 7p21 loci.For sensitivity analysis, we categorized subjects according to habitual coffee consumption: (i) no coffee , (ii) 0.00 to 1.00 cups of coffee per day, (iii) 1.00 to 3.00 cups of coffee per day, and (iv) more than 3.00 cups of coffee per day. The cut-off values were determined according to previous studies –42. NextP > 5 × 10− 8)  with others (< 3.00 cups of coffee per day). As a result, 12q24 and 7p21 loci achieved genome-wide significance, although no additional loci were significantly associated with coffee consumption  were considered the younger subgroup and the remaining subjects were categorized as the older subgroup.P for interaction = 8.2 × 10− 5), suggesting that the genetic effect of the 12q24 locus is stronger in males than in females. The effect size in the younger subgroup  was not different from that in the older subgroup   of the rs79105258 variant (at the 12q24 locus) was 0.254  cups per day in males and 0.083  in females  and TG (P = 8.7 × 10− 3) after adjustment for age, sex, and cohort region  was significantly associated with higher habitual coffee consumption after adjustment for age, sex, and cohort region. As the 12q24 locus harbors the ion GWAS .P = 4.4 × 10− 2) was not significantly different from that before menopause  (P for interaction = 0.85). Further large-scale genetic studies are needed to reveal whether sex steroids play any role in the sex difference of the genetic effects of the 12q24 locus.Our study found a remarkable sex difference in the genetic effect of the 12q24 locus on coffee consumption. This difference might be related to sex steroid hormones. To examine whether the sex difference could be explained by sex hormones, we compared the genetic effect of the 12q24 locus on habitual coffee consumption between females before and after menopause  studies have investigated the causal role of coffee or caffeine use on health outcomes, including risks for type 2 diabetes, cardiovascular disease, and cancers . These MIt is important to highlight the methodological limitations of this study. Details regarding cup size, coffee type , and precise chemical composition (caffeine content) were not available. As discussed in a previous study , habituaIn conclusion, this study consolidates the association of habitual coffee consumption with the 12q24 and 7p21 loci. We have for the first time revealed that the genetic effect of the 12q24 locus is stronger in males than in females. Our present findings contribute to our understanding of the genetic factors and sex differences influencing coffee consumption, which has implications for lifestyle guidance based on genetic data for East Asian populations.Additional file 1:Table S1. Results of genome-wide association analysis that compares heavy coffee consumers with others. Table S2. Results of genome-wide association analysis based on a dominant model. Table S3. Results of genome-wide association analysis based on a recessive model. Table S4. Adjustment for potential confounding factors. Table S5. Pleiotropic effects with adjustment for age, sex, cohort region, and alcohol consumption. Table S6. Pleiotropic effects with adjustment for age, sex, cohort region, and alcohol frequency. (PPTX 65 kb)Additional file 2:Figure S1. Quantile-quantile plot for the genome-wide analysis of coffee consumption. Figure S2. The genomic regional plot from the association analysis of coffee consumption. Figure S3. Effects of a 7p21 variant on habitual coffee consumption stratified by sex and age. Figure S4. Genetic effect of the 12q24 locus in females before and after menopause. (PPTX 748 kb)


Background: The expected genetic variance is an important criterion for the selection of crossing partners which will produce superior combinations of genotypes in their progeny. The advent of molecular markers has opened up new vistas for obtaining precise predictors for the genetic variance of a cross, but fast prediction methods that allow plant breeders to select crossing partners based on already available data from their breeding programs without complicated calculations or simulation of breeding populations are still lacking. The main objective of the present study was to demonstrate the practical applicability of an analytical approach for the selection of superior cross combinations with experimental data from a barley breeding program. We used genome-wide marker effects to predict the yield means and genetic variances of 14 DH families resulting from crosses of four donor lines with five registered elite varieties with the genotypic information of the parental lines. For the validation of the predicted parameters, the analytical approach was extended by the masking variance as a major component of phenotypic variance. The predicted parameters were used to fit normal distribution curves of the phenotypic values and to conduct an Anderson-Darling goodness-of-fit test for the observed phenotypic data of the 14 DH families from the field trial.Results: There was no evidence that the observed phenotypic values deviated from the predicted phenotypic normal distributions in 13 out of 14 crosses. The correlations between the observed and the predicted means and the observed and predicted variances were r = 0.95 and r = 0.34, respectively. After removing two crosses with downward outliers in the phenotypic data, the correlation between the observed and predicted variances increased to r = 0.76. A ranking of the 14 crosses based on the sum of predicted mean and genetic variance identified the 50% best crosses from the field trial correctly.Conclusions: We conclude that the prediction accuracy of the presented approach is sufficiently high to identify superior crosses even with limited phenotypic data. We therefore expect that the analytical approach based on genome-wide marker effects is applicable in a wide range of breeding programs. U = μ + iσgh , or were a registered variety (ANT) carrying resistance to net blotch, powdery mildew (Blumeria graminis) and scald (Rhynchosporium commune). By crossing each registered elite variety with each donor line, respectively, a 5 × 5 factorial cross was attempted. However, not all crosses were successful and yielded viable offspring , KWS Meridian , Otto , Etincel , and Quadriga  were crossed with the resistance donor lines BAZ 2L101 (101), BAZ 2L146 (146), DH 33 (D33), DH 37 (D37) developed by the Julius Kühn Institute and the registered variety Antonella . The resistance donor lines carried resistances to either barley yellow dwarf virus , net blotch , Harzhof , Irlbach , Lenglern , Morgenrot . The parental lines were used as checks and were replicated five times.The field data were analyzed with the mixed linear modelwhere the common mean μ and genotypes were treated as fixed factors, whereas blocks, locations, and heteroscedastic model errors were assumed as random. The resulting adjusted entry means for yield for each DH line were used in further calculations.All 250 resulting DH lines and the ten parental lines were genotyped with the 50 k iSelect Chip . All SNP markers with more than two recorded alleles, more than 10% missing values and a gene diversity of <10% were excluded from the analysis, as well as all individuals with more than 15% missing marker information. As a result, 9,597 SNP markers and 259 genotypes  remained for the analysis.For the prediction of marker effects, we used ridge-regression best linear unbiased prediction  to 15.20 dt2/ha2 (D37QUA). The differences between the predicted yield means 2/ha2 and 15.20 dt2/ha2. In contrast, for the five crosses with donor line 146, 2/ha2 and 12.12 dt2/ha2 . The estimate of the masking variance based on the average standard error from the field data was 2/ha2 and thus was higher than the observed phenotypic variances for ten out of 14 crosses (data not shown).The crosses D33ETI and D33QUA showed downward outliers which resulted in high observed phenotypic variances p = 0.01).The Anderson-Darling goodness-of-fit test indicated that there is no evidence to reject the null hypothesis that the observed yield values . The correlation between the observed phenotypic variance r = 0.34 for all 14 crosses (data not shown). However, when the two crosses D33ETI and D33QUA with downward outliers were removed, this correlation increased to r = 0.76 (data not shown).The correlation between the observed yield means A comparison of the ranking of crosses based on the observed yield data of the best resulting DH line from each cross with the ranking of the crosses based on the criterion s Figure .A negative covariance existed between s Figure . HoweverDespite the recent large interest in methods of cross prediction and the selection of promising crossing partners based on marker data in the plant breeding community  is greater than the number of observations . This potential overfitting was not quantified by the analyses we present here.The present study uses a set of intercrossed lines as a training set, and we evaluate the genetic variance in the same data set. Consequently, the results presented here cannot be regarded as an independent validation. Instead, we are rather investigating the fit of the model to the data. If the model does not fit the data in such an analysis, the conclusion can be drawn that the model is not suitable to explain the data. If the model is able to explain the data, however, a considerable overfitting of the model might still be present, because genomic prediction is an We are using only small numbers of lines per cross. The estimates of the phenotypic variances within each cross are therefore not estimated with a high precision, but instead they have large standard errors and large confidence intervals. In an experiment designed to validate the formulas for variance prediction, larger family sizes would be desirable.Due to their large standard errors, we decided not to further decompose the per-cross variances into genetic variance and within-cross residual variance. Such an analysis would have the advantage of being able to compare genetic within-cross variances, and in addition would be able to model cross-specific residuals. Nevertheless, the estimation errors of genetic variances are large, even for experiments that were designed specifically for that purpose, and in the present data set we consider the precision of per-cross estimates of genetic variances as too low for drawing valid conclusions. For this reason, we decided to present only the phenotypic per-cross variances, and to compare those with the masking variance estimated across all crosses. This enables an explorative comparison of the magnitudes of the variance components. In a purposely designed experiment, the estimation of per-cross genetic variances and their comparison with the predicted genetic variance would provide not only an explorative comparison but rather would allow more stringent hypothesis testing.The field trial in our experiment consisted of five replications for each genotype, this resulted in a limited precision of the phenotypic data. As a consequence, the masking variance in our experiment still amounts to considerable size. In a validation experiment carrying out replicated trials in more than five locations and more than one year would result in a smaller masking variance. Ideally, the design of the validation experiment should result in a masking variance that is smaller than the within-family variance. This would allow an effective within-family selection. Further, it would be desirable if the validation experiment was of a size that allowed heteroscedastic error variances for locations or even for the location:cross combinations.A further issue that is not addressed with our experimental setup is the question of whether random genetic drift or selection during the DH process might have an effect on the estimated variances, this might also be addressed in a validation experiment.Our motivation to use the present data set in spite of its limitations and in spite of the fact that it was not specifically designed for validation of formulas for variance prediction was, that it actually originates from a practical breeding program. Our argumentation is that the results presented here have a high transferability to applied breeding programs, whereas the results of a pure validation study would have only a limited transferability due to differences in the experimental setup.2/ha2 and 15.20 dt2/ha2. For the five crosses with donor line 146 , a direct comparison of While a direct comparison of s2 of a normal distribution is defined as Bronshtein et al. . However, this was also mainly due to the crosses D33ETI and D33QUA, which each displayed outliers in the form of two very low yield values . From these findings, we draw two conclusions. First, low correlations between the predicted genetic variances This overestimation could be expected, as precise field trials to assess the yield are only carried out for a limited number of pre-selected individuals, while the analytical approach yields estimates for infinite unselected population sizes. Moreover, the crosses D33ETI and D33QUA featured downward outliers that might have inflated the average standard error for the adjusted treatment means and consequently the derived masking variance Despite the fact that the predicted genetic variances More importantly, we argue that the main application of cross prediction in practical breeding programs is not so much to provide 100 percent accurate predictions of s Figure . In thisIt has been postulated that a negative covariance exists between 2 Figure . For exa Jannink , geneticThus, we observed that the negative covariance between o Figure . If the In order to provide breeders with a fast and easy-to-use tool to implement the presented approach in their breeding pipelines, routines for data pre-processing, estimation of marker effects and cross prediction with the formulas of Osthushenrich et al.  have beeThe analytical approach of Osthushenrich et al.  yields pThe datasets generated and/or analyzed during the current study are not publicly available due to the confidential genotypic data of the donor lines from an ongoing research project but are available from the corresponding author on reasonable request.HJ, MS, LC, KK, SB, AH and DK developed the genetic materials, conducted the field and greenhouse experiments. CZP and MF planned the field experiments and analyzed the field data. TO analyzed the genotypic data. TO and EH wrote the manuscript. MF, FO, and EH directed the project, contributed to the analyses and manuscript revisions. All authors proof-read the draft and approved the final 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.