Motivated by observational studies that report associations between schizophrenia and traits, such as poor diet, increased body mass index and metabolic disease, we investigated the genetic contribution to dietary intake in a sample of 335,576 individuals from the UK Biobank study. A principal component analysis applied to diet question item responses generated two components: Diet Component 1 (DC1) represented a meat-related diet and Diet Component 2 (DC2) a fish and plant-related diet. Genome-wide association analysis identified 29 independent single-nucleotide polymorphisms (SNPs) associated with DC1 and 63 SNPs with DC2. Estimated from over 35,000 3rd-degree relative pairs that are unlikely to share close family environments, heritabilities for both DC1 and DC2 were 0.16 (standard error (s.e.) = 0.05). SNP-based heritability was 0.06 (s.e. = 0.003) for DC1 and 0.08 (s.e = 0.004) for DC2. We estimated significant genetic correlations between both DCs and schizophrenia, and several other traits. Mendelian randomisation analyses indicated a negative uni-directional relationship between liability to schizophrenia and tendency towards selecting a meat-based diet (which could be direct or via unidentified correlated variables), but a bi-directional relationship between liability to schizophrenia and tendency towards selecting a fish and plant-based diet consistent with genetic pleiotropy. Affected individuals have a life expectancy that is reduced by an average of 14.5 years relative to the general population2. The primary factor contributing to increased mortality is cardiovascular disease (CVD)3. Weight gain and obesity, which are common in schizophrenia4, are important risk factors for CVD5. Notably, evidence of shared genetic factors between schizophrenia and obesity has been reported, but not in the direction expected from epidemiological data. Genetic correlations estimated from genome-wide association study (GWAS) results from independently collected schizophrenia case-control samples and other traits show a significant negative genetic correlation (rg) of schizophrenia risk with body mass index (BMI) 6. There is no evidence for a genetic relationship between schizophrenia and Type 2 diabetes  or coronary artery disease 7. These results imply that if genetic factors also contribute to the associations between metabolic syndrome and schizophrenia, this is a likely complex relationship.Schizophrenia is a chronic mental disorder with typical onset in early adulthood and a lifetime risk of approximately 0.7–0.9%8. We hypothesised that there might be an underlying genetic susceptibility to the self-selected dietary composition in individuals with schizophrenia and that this would be manifest as a significant genetic correlation between schizophrenia and self-selected diet measured in a community sample. A twin study of 18–19-year-olds (N = 2865) reported heritability (h2) estimates for vegetable eating of 54% (95% CI: 47–59%) and for meat or fish-eating of 44% (95% CI: 38–51%)9. These estimates may be inflated by shared family environment. Meta-analyses of GWASs for macronutrient intake  have confirmed associations between consumption of carbohydrates, fat and protein with the fibroblast growth factor 21 (FGF21) gene and associations of consumption of protein intake with the fat mass and an obesity-associated locus (FTO)12. Significant genetic correlations between protein intake and BMI (rg = 0.23) have been reported, but no significant evidence for genetic correlations between any macronutrient types and schizophrenia (rg = < 0.07)10. Larger samples are needed to replicate these findings and to elucidate further how diet correlates with other traits at the genetic level.Dietary intake has a causal association with obesity and people with schizophrenia tend to have an unhealthy diet, higher in fat and refined sugar and low in fruit and vegetables13; (2) whether there is shared genetic susceptibility between dietary intake and schizophrenia and (3) if so, whether there was any statistical evidence consistent with a causal relationship between SNPs are bi-directional using Mendelian Randomisation. We also explored genetic correlations of dietary intake with a number of other traits with available GWAS summary statistics.Our study aimed to investigate (1) genetic influences on dietary intake using GWAS data from the UK Biobank13.The United Kingdom Biobank (UKB) is a major community-based longitudinal study with extensive genetic and phenotypic information of over 500,000 participants aged 40–69 years from across the UK during 2006–2010. The study design and sample characteristics have been extensively described elsewhereThis research has been conducted using the UK Biobank resource under application number 12505 and follows UK Biobank’s Ethics and Governance Framework.All participants completed a generic diet questionnaire  that was used to estimate the average consumption of fruit, vegetables (raw and cooked), fish (oily and non-oily), meat , bread, cheese, cereal, tea, coffee and drinking water.14. Since questions about bread, cheese, cereal, tea, coffee and drinking water had low loadings on the components (<0.08), we excluded these questions from the PCA and repeated the PCA using only the questions about fruit, vegetables, fish, and meat consumption. Three eigenvalues were greater than 1. We selected the first two factors, factor 1 , explaining 23% of the variance of the included questionnaire items and representing a meat-related diet , and factor 2 , explaining 18% of the variance and representing a fish and plant-related diet  15  and passing additional QC filters . Genotyped SNP used by the UKB had already been LD pruned (r2 < 0.1) and had long-range LD regions removed  of the UKB data have been described extensively by the UKB groupThe number of individuals with complete phenotypic and genotypic data was 335,576.15. We identified likely 100 twin, 12,957 full-sibling, 3354 parent–offspring, 6092 second- and 37,947 third-degree relationship pairs in our European individuals following the procedure outlined in Bycroft et al.18. We then estimated the phenotypic correlation (rp) for each group or relatives and approximated the heritability of DC1 and DC2  within each class as rp/aR; where aR is the average coefficients of relationship  relatives). This approximation assumes that the rp reflects only shared additive genetic contributions. Since close family member share non-additive genetic and family/social networks, contributions from such factors would generate higher rp/aR for close relatives compared to more distant relatives. We also estimated rp/aR for BMI, as a benchmark.The UK Biobank provided KING kinship coefficients and the fraction of markers, which share no alleles identical-by-descent19 using the whole sample vs. the sample on unrelated Europeans after regressing out the covariates  year of birth, (2) sex, (3) month of assessment, 4) assessment centre, 5) batch and (6) 100 genetic PCs. To obtain a better understanding of DC1 and DC2, we also compared their geographical distribution assessme batch an20 to model the associations between SNPs and the residuals of the two phenotypes, DC1 and DC2. BOLT-LMM uses a mixed model that uses genetic data to account for population structure and relatedness between individuals. The threshold for significance of associations was a p-value < 5 × 10−8.We used the BOLT-LMM software packagep-value (<5 × 10−08) that are in approximate linkage disequilibrium with each other at r2 < 0.1 and to generate Manhattan and Quintile–Quintile plots and achieve SNP functional annotations21.We used the FUMA web application to identify independent significant SNPs with a genome-wide significant 21 using the association analysis summary statistics. Taking into account that the UK Biobank imputation used both 1000 genomes and Haplotype Reference Consortium (HRC) reference panels while FUMA only uses 10,000 genomes as a reference panel, it is likely that our gene-set analyses may be based on an incomplete set of variants. Gene expression analysis was obtained from GTEx v6  integrated by FUMA22  score regression through LD Hub v1.9.0 (http://ldsc.broadinstitute.org/centers/)7 and GWAS summary statistics. 235 traits were examined using LD hub, and the Bonferroni corrected p-value threshold for significance is 0.05/470 = 1 × 10−4. For traits that had estimated genetic correlations significantly different from 0 with DC1 or DC2, we used the gsmr R-package to implement Generalised Summary-data-based Mendelian Randomisation to test for bi-directional genetic associations24. Heterogeneity in dependent instrument (HEIDI) outlier analyses were implemented to exclude SNPs that have significant pleiotropic effects.We estimated genetic correlations between schizophrenia25 to examine if the association of an SNP with the phenotype is mediated through gene expression, a tool to help prioritise GWAS results for follow-up functional studies. We used the following summary data expressed: (1) brain-expressed: expression quantitative trait loci (eQTL) meta-analysis data where we tested 7324 probes and DNA methylation quantitative trait loci (mQTL) summary data where we tested 92,867 probes from26. The Bonferroni adjusted p-value threshold for eQTL analyses was 0.05/7324 = 6.8 × 10−6 and for the mQTL was 0.05/92,867 = 5.4 × 10−7; (2) blood-expressed: eQTL summary data from the CAGE data set27, where we tested 8468 probes and mQTL data from the Brisbane Systems Genetics Study and the Lothian Birth Cohorts of 1921 and 1936 from28 where we tested 92,867 probes. The Bonferroni adjusted p-value threshold for eQTL analyses was 0.05/8468 = 5.9 × 10−6 and for mQTL was 0.05/91,578 = 5.4 × 10−7. We conducted a number of sensitivity analyses (We used the summary-data-based Mendelian randomisation (SMR) softwareanalyses .  and females were more likely to have a lower DC1 score  , but in contrast to DC1, females were more likely to have a higher DC2 score   had complete genotypic and phenotypic data and were included in a PCA to generate two independent diet components (DCs) . As expected, rp increased with the coefficient of relationship (aR), and the rp/aR estimates were higher for close relatives. For 3rd-degree relatives, coefficients of non-additive genetic relationship and influences of a shared common environment are expected to be small, so rp/aR provides estimates of the trait heritability (h2). We estimate h2 of 0.16 for DC1 and 0.16 for DC2 , with the APOE protein a significant cholesterol transporter that has been directly related to low-density lipoprotein cholesterol30 and mostly known for its associations with Alzheimer’s disease31. Here, the C allele, associated with higher risk of Alzheimer’s disease32 was negatively correlated with the meat-related diet. Another strongly associated locus was the chromosome 22, rs429358 SNP that maps to the FGF21 gene, replicating previous GWASs on macronutrient intake12. This locus was also associated with the individual questionnaire items (Supplementary Table NEGR1) gene (p = 5.7 × 10−15) a BMI-related gene35. Seven gene-sets reached statistical significance  gene (p = 4.4 × 10−17), one of the most extensively studied genes in the field of food consumption and obesity, was associated with DC2 at the gene analysis further supporting previous candidate gene studies40.Fat mass and the obesity-associated protein (p = 1.4 × 10−08).Two gene-sets achieved statistical significance  and the ribosomal protein L31 pseudogene 12 (RPL31P12), both of which have been consistently associated with BMI42, educational attainment43, intelligence44 and major depressive disorder45. In addition SMR association analyses link DC1 and Histone Cluster 1 H2B Family Member F (HIST1H2BF) that has been associated with hip circumference46 and body height47, and DC2 with IKAROS family zinc finger 3 (IKZF3) gene that has been associated with inflammatory bowel disease48 and family with sequence similarity 167 member A (FAM167A) gene that has been associated with systemic lupus erythematosus49.Integration of association results with GTEx gene expression across 30 tissue types showed that SNP associations were significantly enriched in the brain tissue gene-set for both DC1 and DC2 while DC1 was also significantly enriched in the pituitary gland tissue gene-set  and positive genetic correlation between schizophrenia and DC2 . Since DC1 and DC2 are orthogonal , they were also genetically uncorrelated .Genetic correlations between DC1 or DC2 with other traits estimated from publicly available GWAS summary statistics Figs.  show sigrg = −0.18, s.e. = 0.03, p = 5.02 × 10−08) and positive genetic correlations with waist circumference  and waist-to-hip-ratio   , chronotype , BMI  and other BMI-related traits while negative genetic correlations were found with age at menarche  and intelligence . We also conducted genetic correlations analyses for the DC1 per sex separately but the results were similar for both sexes (Supplementary Tables DC2 was positively correlated to bipolar disorder (50) to investigate if there is statistical evidence consistent with uni- or bi-directional relationships between the correlated traits.Given the significant genetic correlations between the DCs and other traits we used MR analyses  but were not significant when DC1 was the exposure  = 118). When DC2 was an exposure, for 1 standard deviation change in DC2 the odds of liability to schizophrenia increased 1.43 fold .MR analyses between DC2 and schizophrenia were bi-directionally significant , but not when DC2 was an exposure   consequence of high BMI, which may reflect that, in general, individuals with high BMI consume larger quantities of all types of food.Interestingly, BMI was associated with DC2 when DC2 was an outcome , replicating previous GWA studies on macronutrient intake12. Moreover, 4 out of 7 brain mQTL SNPs and 6 out of 12 blood-expressed mQTL SNPs were associated with DC1. Five mQTL SNPs and one eQTL SNP expressed in the brain and eight mQTL SNPs expressed in the blood were associated with DC2 were at the 19q13.3 locus, including the izumo sperm-egg fusion 1 (IZUMO1) gene, the MEF2 activating motif, and SAP domain containing transcriptional regulator (MAMSTR) gene and the RAS-interacting protein 1 (RASIP1) gene supporting a role of the 19q13.3 locus in diet intake12.Variation between people in dietary intake as represented by DC1 and DC2 is predominantly driven by non-genetic factors, consistent with it being primarily influenced by a variety of socioeconomic and psychological factors, including lifestyle, culture and health beliefsn = 91,114) that did not find evidence of a phenotypic relationship of macronutrient intake and schizophrenia10. Based on the diet questionnaire data available, our DCs reflect self-selected diet composition and quantity.Our primary hypothesis that there would be a genetic correlation between dietary intake and schizophrenia was supported. It is important to note that we chose to examine food type consumption rather than macronutrient intake, a decision informed by a published GWA meta-analysis  or drug treatment (where drugs targeting schizophrenia pathways could impact DC2 pathways). Mendelian Randomisation analyses indicated that while results were consistent with schizophrenia being related with a reduction of DC1, that the relationship between schizophrenia and DC2 is more likely to reflect pleiotropy.We found that genetic factors contributing to high DC2 values, indicating higher consumption, mainly, of fish (oily and non-oily) and cooked vegetables were positively and significantly correlated with genetic factors associated with schizophrenia   and the conditioning trait  to be zero. In that case, all the previously significant genetic correlations between DC1, DC2 and the other traits became non-significant , such as inflammation66, oxidative processes67, and brain plasticity68, it is important to further examine potentially shared pathways between dietary intake and disease to gain a better understanding of the underlying biology. Although the direction of the association results between the DCs and schizophrenia was unexpected, similar seemingly paradoxical results have been type found for 2 diabetes (T2D). Zhu et al.50 recently used GSMR Mendelian randomisation to report a negative association between BMI and type 2 diabetes (T2D), despite BMI being a known risk factor . They interpret the relationship between BMI and T2D as a complex mixture of causality, reverse causality and pleiotropy. It seems that this broad interpretation is likely to represent the relationship between dietary intake, BMI and schizophrenia.The low-heritability estimates of DC1 and DC2 attest to dietary intake being a mostly environmental, and therefore potentially modifiable, behavioural trait70, there is evidence that higher BMI is associated with under-reporting of the quantity of food consumption72 and that individuals tend to under-report unhealthy foods and over-report consumption of fruit and vegetables73. The assessment we used is subjective and reflects a significant limitation of population and community studies of nutritional science in general74. Another limitation is that diet changes over time75. The UK biobank participants are not representative of the population in certain sociodemographic characteristics76. Finally, ascertainment bias could lead to collider bias 77. Therefore, our findings need to be tested in other cohorts.We used a self-report questionnaire to assess dietary intake. Although this is the typical assessment method used in large population-based studiesAlthough the heritability of diet intake measures is low, we identified many independent genome-wide significant loci associated with our two DC traits, genetic correlations, as well as possible causal and shared genetic pathways with schizophrenia and many other traits. Our study adds evidence-based results to the growing recognition of the need of a holistic approach in the context of disorders of the brain. Further studies are needed to help gain a better understanding on the role of diet, nutrition and metabolic traits in disease onset, disease progression and treatment.Supplementary NoteSupplementary TablesSupplementary FiguresRegional association plots


Inbreeding control is a key concern in managing local endangered breeds, which often have developed unique adaptation features. Ramo Grande is a local cattle breed raised in the Azores archipelago under very harsh conditions, with a census of about 1300 cows dispersed by various islands. This fragmentation is a challenge when the goal is to keep inbreeding under control. Currently, panels of genetic markers are available which enable the assessment of inbreeding and the occurrence of previous bottlenecks in a population. These panels also allow the identification of genes associated with specific production traits, if reliable phenotypic information is available. We used a panel of genetic markers and estimated that the degree of inbreeding was approaching a level of concern, while some exotic gene inflow may have occurred in the past. We were able to identify genetic markers significantly associated with longevity, which reflects the ability of these cattle to remain productive under severe environmental conditions. Genetic markers were also identified as significantly associated with age at first calving and calf growth rate. The results indicate that genomic information can be used to control inbreeding and to implement genomic selection in Ramo Grande cattle to enhance adaptation and production traits.Ramo Grande is a local cattle breed raised in the archipelago of Azores, with a small and dispersed census, where inbreeding control is of utmost importance. A single nucleotide polymorphism (SNP) Beadchip array was used to assess inbreeding, by analysis of genomic regions harboring contiguous homozygous genotypes named runs of homozygosity (ROH), and to estimate past effective population size by analysis of linkage disequilibrium (LD). Genetic markers associated with production traits were also investigated, exploiting the unique genetic and adaptation features of this breed. A total of 639 ROH with length >4 Mb were identified, with mean length of 14.96 Mb. The mean genomic inbreeding was 0.09, and long segments of ROH were common, indicating recent inbred matings. The LD pattern indicates a large effective population size, suggesting the inflow of exotic germplasm in the past. The genome-wide association study identified novel markers significantly affecting longevity, age at first calving and direct genetic effects on calf weight. These results provide the first evidence of the association of longevity with genes related with DNA recognition and repair, and the association of age at first calving with aquaporin proteins, which are known to have a crucial role in reproduction. The diversity of animal genetic resources plays a crucial role in meeting human needs and is essential for maintaining the efficiency of production systems, particularly as they are threatened by climate change . MaintaiIn local breeds, often with small population size, the increase of inbreeding is one of the major concerns, because of the associated loss of genetic diversity and the deleterious impact of inbreeding depression . The RamNe) based on pedigree information was 28 [A program aimed at the genetic management of Ramo Grande cattle has been in place for nearly 20 years, essentially based on the collection of phenotypic and pedigree data, with the goal of selecting for some production traits while maintaining inbreeding under control . Still, n was 28  which isn was 28 .F), even in the absence of pedigree information, and provides insight into the levels of linkage disequilibrium (LD) and the corresponding Ne [FROH) [FROH provides a good indication of individual autozygosity at the genome-wide level and allows the distinction between ancient and recent inbreeding, as recombination tends to disrupt long chromosome segments [Ne, which is a useful way to evaluate the loss of genetic diversity over time [The use of medium-density single nucleotide polymorphism (SNP) arrays allows an alternative but accurate estimation of inbreeding (nding Ne . Currente [FROH) , which ae [FROH) . FROH prsegments  and longver time .In addition to conserving genetic diversity of local breeds, the improvement of their productive performance while maintaining adaptability to local environmental conditions is desired, with the goal of making them more competitive and thus self-sustaining. Therefore, identifying polymorphisms in genes that may influence important traits is also crucial for the current management/improvement program of this breed.Genomic tools developed in recent years may provide insight and a thorough understanding of the threats to genetic diversity of local breeds , and revPedigree data has been collected for Ramo Grande cattle since 1982, and currently there are 8931 accumulated records in the Herdbook. DNA samples were obtained from 44 Ramo Grande bulls with semen available for commercial use. These bulls were sampled from several farms located in different islands across the archipelago of Azores . Given the small number of sires existing in the breed, the sampled bulls were inevitably related, with an average relationship among them of 4.12 ± 1.81%.n = 11) or semen (n = 33) using QIAsymphony robot (QIAGEN) according to manufacturer instructions. All animals were genotyped with the Bovine SNP50v3 Analysis Beadchip , obtaining genotypes for about 54K SNPs. Data quality control was performed using PLINK v1.90 [Bos taurus genome (assembly UMD 3_2), or assigned to chromosomes X and Y were excluded. Markers were then selected according to the following quality criteria: (i) call frequency (≥0.95), minor allele frequency (MAF ≥ 0.01) and Hardy-Weinberg equilibrium . SNPs that did not satisfy these criteria were excluded from further analyses. Moreover, considering that LD can lead to the detection of ROH that are not truly identical by descent (IBD), LD pruning was performed before ROH call in order to increase power, as advised by Purcell et al. (2007) [r2 threshold of 0.5.DNA was extracted from either blood (NK v1.90 . All SNP. (2007)  and appl. (2007) ,17,18. UFROH). In addition, to investigate the effect of other criteria in the definition of FROH, we also considered FROH as the proportion of ROH segments >8 Mb relative to the full chromosome length [The following criteria were used to define the ROH: (i) sliding window of 15 SNP loci; (ii) minimum number of 40 SNPs for ROH identification; (iii) minimum SNP density of 1/100 kb (1000 bp); (iv) maximum gap between consecutive SNPs of 1 Mb; (v) Minimum thresholds of ROH length of 4 Mb. These were investigated as advised by Ferenčaković et al. 2013)  and appl13  and ae length .r2) between SNP pairs within the same chromosome was obtained using PLINK [r2 ≥ 0. For assessment of the relationship of LD with genomic distance, the mean LD was computed in bins of 10K. The effective population size was then estimated using LD measurements according to Corbin et al. (2012) [The estimate of linkage disequilibrium   using SN. (2012) .(i)Type score—total sum of points attributed to various body conformation traits assessed at the time of registration;(ii)Temperament—also assessed at the time of registration in the Herdbook, where animals are subjectively scored from very docile (score = 1) to unyielding (score = 5);(iii)Age at first calving—age in days when a cow had its first calf;(iv)Calving interval—interval in days between two consecutive calvings of a cow;(v)Number of calvings through life—total number of calvings in the lifetime of a cow;(vi)Longevity—computed as the age in months at the last registered calving plus 7 months, assuming that the cow lasted at least up to calf weaning;(vii)Calf weight adjusted to 4 months of age—this weight was assumed to receive both direct and maternal genetic influences;(viii)Carcass weight per day of age, computed as carcass weight divided by age at slaughter—this growth rate was assumed to receive both direct and maternal genetic influences.A Herdbook has been in place for the Ramo Grande breed since 1995, where animals are registered after being classified according to morphology and type at around 2 years of age. Animals are scored for nine body conformation traits, and to enter the Herdbook they must receive a total type score of at least 75 in a maximum of 100 possible points. As part of the on-going management program of this breed, phenotypic data has been collected since 1996, including measurement of the following traits:Estimated breeding values (EBV) are routinely predicted for these traits using univariate Animal Models with fixed and random effects specific for each trait. The linear mixed models used for analyzing the various traits were as follows.(1) Analysis of type score, temperament, age at first calving, longevity and number of calvings:(2) Analysis of calving interval:(3) Analysis of live weight at 4 months and carcass weight/day of age:‘y’ is the vector of observations;‘b’ is the vector o fixed effects;‘a’ is the vector of additive direct genetic effects;‘m’ is the vector of additive maternal genetic effects;‘p’ is the vector of permanent maternal environmental effects;‘e’ is the vector of residual effects;a, Zm, e, Zp’ are known incidence matrices relating the fixed effects (X) and each of the random effects  with the vector of observations ‘y’.‘X, ZVariance components were estimated and breeding values predicted with the BLUPF90 package .For the genome-wide association analyses, EBVs of the genotyped bulls were obtained from the published annual evaluations, which are based on phenotypic records collected on an average of 55.5 ± 91.3 offspring per bull. These EBVs were then used to compute deregressed EBV (DEBV), calculated by applying the procedure described by Garrick et al. (2009) , consideegscore function [p-values were then corrected by multiplying observed p-values by the deflation factor λ obtained in GWAS [−6 and 5 × 10−5, where the first corresponds to 10,000 independent Bonferroni corrected tests and the second was proposed as the threshold allowing the detection of moderate associations [Using the obtained DEBV as phenotypes, single SNP genome-wide association analyses (GWAS) were performed using the GenABEL toolset, accounting for stratification using the principal component analysis method with the function . The relfunction . The p-v in GWAS . We used in GWAS  and used in GWAS ,29,31. Tciations . The prociations http://www.ncbi.nlm.nih.gov/projects/mapview/). The Genemania package [www.reactome.org) to perform analyses of pathway enrichment as described in [Putative candidate genes overlapping significantly associated SNPs for the studied traits were identified based on the ARS-UCD1.2 assembly, using the NCBI Map Viewer . These criteria resulted in a set for LD and GWAS analyses comprising 37,727 markers in 42 animals. On the other hand, for ROH estimates we further prepared a subset of genotypes that was processed for LD pruning, which included 21,884 SNPs.Using the subset of data that was pruned for SNPs in LD, we have performed the identification of ROH segments in order to estimate the inbreeding level in this breed. Using the criterion of one ROH being at least 4 Mb long, a total of 639 ROH were identified, and all individuals displayed at least one ROH segment. As shown in The mean number of ROH by chromosome was 15.2  and the FROH per individual was 0.091 ± 0.073 when a minimum threshold of 4 Mb was used (FROH = 0.080 ± 0.068 (results not shown). Overall, the variability in FROH between animals was large  and distance between markers, for SNPs located on the same chromosome and estimated using the GWAS subset of markers. We observed that for distances of 1 Mb the r2 was about 0.1 while for a distance of 4 Mb the values of r2 were about 0.075. The estimation of ancestral effective population size from LD decay indicates that 5 generations ago the effective population size of Ramo Grande was 44 animals  decay can contribute to better understand the extent of ROH as well as to enable the estimation of effective population size in breeds for which the pedigree information is not very deep or is unreliable.  animals . We alsoIn the selection program of Ramo Grande, a single-trait genetic evaluation is carried-out yearly for several production traits (see methods). The descriptive statistics and genetic parameters estimated by REML for the various traits are in p-values were then corrected by multiplying observed p-values by the deflation factor λ obtained in GWAS (see methods). We observed strong significant associations for three of the studied traits, i.e., longevity, age at first calving and direct genetic effect on calf weight at 4 months . The other observed significant associations for this trait where we could infer functionality are located in BTA8, upstream of PRUNE2 that encodes for protein homolog2, and in the intron region of RAD23B gene, that encodes for the ubiquitin protein receptor RAD23B.We have identified significant associations of longevity with SNPs located in BTA1 and BTA8 . The BTAPRUNE2 and RAD23B have strong interactions, and do not display connections with GMNC  and CTNNA2 (Catenin Alpha 2). These genes are pivots of a gene network composed by two hubs  when compared with Italian local cattle breeds with a census comparable to Ramo Grande, where the mean FROH was about 0.05 [FROH in Ramo Grande was similar to that currently observed in American Holstein [FROH, the mean for Ramo Grande was 0.079, which is close to the mean reported using the same threshold in Brown Swiss (FROH = 0.074), but higher than the means reported for Fleckvieh, Norwegian Red and Tyrol Grey (mean FROH ranging from 0.019 to 0.036) [The level of out 0.05 . On the Holstein . When a o 0.036) .FROH for the studied population indicates that some individuals have extremely high levels of inbreeding , which may lead to inbreeding depression and loss of genetic variability in the near future [The distribution of r future ,37. ThisNe), and pairwise values of r2 were averaged and plotted against genomic distance. We observed that the extent of LD in Ramo Grande is higher in comparison with other European local breeds with similar census [The extent of LD was used to estimate current and past effective population size  are crucial proteins required to ensure the proper follicular development of the ovary [STAT5A [ZNF395 [AQP5 [AQP5 as being significantly associated with a trait related with reproductive development and that is functionally associated with a previously identified QTL. Finally, we have studied the trait direct genetic effects for calf weight at four months, for which our estimate of heritability was 0.22, in close agreement with the mean heritability estimate of 0.24 for direct genetic effects for weaning weight [KCNMA1 and CTNNA2. While KCNMA1 gene encodes for a protein that forms the potassium channel [CTNNA2 encodes for a protein that may function as a linker between cadherin adhesion receptors and the cytoskeleton to regulate cell-cell adhesion and differentiation in the nervous system [KCNMA1 have consistently been associated with obesity [CTNNA2 has been associated with plasma levels of vitamin K [We have further identified, although with a lower degree of significance, SNPs significantly associated with age at first calving and with direct genetic effects for calf weight at four months. In relation with age at first calving, our estimate of heritability (0.20) is within the wide range of heritability estimates that have been reported, between 0.02 and 0.47 ,50,51, a channel , playinghe ovary , which ihe ovary  we found [STAT5A , and thi [ZNF395 , which i95 [AQP5 . This isg weight . We have channel , CTNNA2 s system . In huma obesity ,62,63, aitamin K , that plitamin K . The funThis study reports for the first time the use of genomic information for the maintenance and selection of Ramo Grande cattle, a breed with small census and fragmented dispersion in the archipelago of Azores. The obtained results highlight that the levels of inbreeding currently observed are high in comparison with other European local breeds, and some sires are carriers of very large ROH segments, which may compromise their performance and use. These results are crucial to improve the on-going program aimed at maintaining genetic diversity of this population, in collaboration with local breeders. The analyses of linkage disequilibrium indicate that the estimated effective population size several generations ago is very high, which probably reflects the inflow of exotic germplasm in Ramo Grande in the past. Our results support the existence of various genetic markers associated with longevity, age at first calving and direct genetic effects on calf weight at four months, which are functionally relevant and important for a hardy breed such as Ramo Grande. To our knowledge, these associations have not been previously identified in cattle, and they should be further investigated to confirm that relevant associations with economically important traits also exist in other cattle breeds. Overall, our study confirms the usefulness of genomic information to enhance the genetic management of local populations, both in terms of conservation of their genetic diversity and selection for production efficiency, including adaptation traits.


The aim was to assess the efficiency of gaits characteristics in improving jumping performance of sport horses and confront accelerometers and judge scores for this purpose. A sample of 1,477 young jumping horses were measured using accelerometers for walk, trot, and canter. Of these, 702 were genotyped with 541,175 SNPs after quality control. Dataset of 26,914 horses scored by judges in breeding shows for gaits and dataset of 142,682 horses that performed in jumping competitions were used. Analysis of accelerometric data defined three principal components from 64% to 89% of variability explained for each gait. Animal mixed models were used to estimate genetic parameters with the inclusion to up 308,105 ancestors for the relationship matrix. Fixed effects for the accelerometric variables included velocity, gender, age, and event. A GWAS was performed on residuals with the fixed effect of each SNP. The GWAS did not reveal other QTLs for gait traits than the one related to the height at withers. The accelerometric principal components were highly heritable for the one linked to stride frequency and dorsoventral displacement at trot (0.53) and canter (0.41) and moderately for the one linked to longitudinal activities . Low heritabilities were found for the walk traits. The genetic correlations of the accelerometric principal components with the jumping competition were essentially nil, except for a negative correlation with longitudinal activity at canter (-0.19). The genetic correlation between the judges’ scores and the jumping competition reached 0.45 for canter . But these correlations turned negative when the scores were corrected for the known parental breeding value for competition at the time of the judging. In conclusion, gait traits were not helpful to select for jumping performances. Different gaits may be suitable for a good jumping horse. Gait characteristics have been widely used in sport horse breeding for decades. The aim is both to improve gaits as a direct trait or to use them as a tool for an indirect prediction of success in competition . SpecifiIn this study, gaits were measured by accelerometry in a large sample of young jumping horses during regular competitions as an extra test. Half of them were genotyped. The aim was to calculate heritability and to perform a genome-wide association study of the gait traits measured by accelerometers. We then compared the use of these traits with the traditional use of assessment scores of young horses in the breeding evaluation of horses when the selection goal is the performance in jumping competitions.Research did not used additional manipulation of the horses than the one required in ordinary official jumping competition. Consequently, it was not necessary to refer to an ethics committee. We received permissions from the owners of the horses as well as the show organizers to collect the data.® accelerometer [During the spring of both 2015 and 2016, 1,477 young sport horses aged four (42%) and five (58%) years were recorded with an Equimetrixerometer  at walk,® accelerometer device attached to the girth. Data were recorded at competition events for young jumping horses after the competition was over. Riders were asked to perform a quick gait test including the three gaits in an arena with diagonal lines of 60 meters in order to have a sufficiently long measurement time. For trot and canter, two diagonals were measured: one performed at working gait and the other one at medium gait according to the rider’s feeling. The difference between the two gaits was mostly specific to the horse and lead to two different velocities for each gait. Velocity was measured using a chronometer on this diagonal. A sample of 10 seconds for each gait was extracted for the gait measurement calculations. The raw data were the acceleration recorded at 100 Hz per axis in the dorsoventral, longitudinal, and lateral axes of the horse. Five gaits were distinguished: walk (W), working trot, medium trot, working canter, and medium canter. Eight measurements were then calculated:Acceleration of the horses was recorded with the Equimetrixvelocity (V),stride frequency (SF),regularity (R),symmetry ,dorsoventral displacement (DVD),dorsoventral power or activity (DVA),longitudinal activity (LGA),lateral activity (LTA).The calculation methods are described in –16. LogaNearly all of these horses (94%) performed in jumping competitions between 2014 and 2019. They spent between one and six years in competition, with an average of 4.0. Analysis of the jumping competition data was performed with the complete dataset of official jumping competitions from 2002 to 2019 since the birth year 1998, including 142,682 horses, and 612,449 annual performances. Horses born abroad and with a potential partial career outside France were excluded from the analysis. Success in competition was measured by the repetition of an annual performance. The annual performance was the logarithm of the annual sum of points distributed according to the rank and the event. Points were awarded for each event based on the height of the obstacles and the technical difficulty (a Grand Prix event has more points than a Table C event for the same height of obstacles). The distribution of points awarded for an event was done on a traditional exponential basis . Points Only twenty nine percent of the horses measured for accelerometry also participated in breeding shows where conformation and gaits were scored. The analysis of the judges’ scores included all of the data recorded by the Stub Book Selle Français (SBSF) during regular breeding shows for young horses (two to three years old) from 2005 to 2018. They involved nine conformation traits and three gait traits judged in hand for the walk and during free presentation for trot and canter. There were 26,914 horses that were recorded once (for 52% of the horses), twice (37% of the horses), and up to a maximum of nine times for the same horse in 1,540 local, regional, and national shows.Pedigrees were provided by the IFCE  on behalf of the breeding organizations. According to the different analyses performed (see below), the number of horses in a pedigree that were used varied from 10,907 to 308,105 over four generations. Links between traits  were mainly provided by the horses that had the different traits measured and sires with progeny that had the different traits measured, as well as all relationships between the horses. There were 410 sires with progeny in the three traits .−6, call rate > 90%), 541,175 SNPs were retained.Of the horses measured by accelerometry, 702 were genotyped with the Affymetrix Axiom Equine genotyping array that includes 670,806 SNPs. After quality control , age (four or five years), velocity , event (26 levels), a the random polygenic effect , and e the vector of residuals. X and Z are incidence matrices. A bi-trait analysis was then performed where the variance-covariance matrices of random effects were: V(a) = A ⊗ G and V(e) = I ⊗ R, with ⊗ the direct product, A the relationship matrix from pedigree, G the 2 x 2 matrix of genetic variance-covariance between medium and working gait for each trait, I the identity matrix, and R the 2 x 2 matrix of residual variance-covariance.To answer this question, a bi-trait model between working and medium trot was devised for each of the eight trot measurements and between working and medium canter for the seven canter measurements. The following model was applied for each of the measurements for working gait on the one hand and medium gait on the other hand:To summarize the main characteristics of the 38 measurements representing gaits, we performed a principal component analysis with phenotypes corrected for the identified fixed effects.y represents the vector of measurements for the walk (eight traits), trot (eight traits) or canter (seven traits), b the vector of fixed effects with fixed effect of the type of gait (working/medium) for trot and canter, velocity within type of gaits , gender , age (four or five years) and event (26 levels), u the random effect of the 1,477 horses for the repeated measurements in trot and canter , and e the vector of residuals. X and Z are incidence matrices. The variance-covariance matrices of random effects were: We used:The principal component analysis was performed using the FactoMineR package  with as y representing the vector one of the nine PCs, b the vector of fixed effects, which included gender , age (four or five years), velocity (covariate), event (26 levels), a the random polygenic effect , p the random permanent environmental effect (for trot and canter repeated two times), and e the vector of residuals. X and Z are incidence matrices. The variance-covariance matrices of random effects: V(a) = A ⊗ G, V(p) = I ⊗ P, and V(e) = I ⊗ R, with ⊗ the direct product, A the relationship matrix from pedigree, G the 9 x 9 matrix of genetic variance-covariance between each PC trait, P the 9 x 9 matrix of genetic variance-covariance between the permanent environmental effect of the PC trait, I the identity matrix, and R the 9 x 9 matrix of residual variance-covariance. The residual covariances between trot and canter were null by construction: the environmental correlation was considered in the correlation between the permanent environmental effects.We performed a genome-wide association study (GWAS) for the nine PCs. Scores for each of the three principal components of each gait were computed for each horse using the linear combination given by eigenvectors obtained by the principal component analysis. A mixed model including both the fixed effect of the SNP and the random genetic value of the horse would be the correct model to analyze these scores. The GWAS was performed using the GenABEL package . GenABELIn the second step the GWAS was performed using:R matrix was a 10 x 10 matrix with 1) zero values between gaits because the environmental correlation was considered by the 10x10 P matrix of variance-covariance between the permanent environmental effect p and 2) zero values between height at withers and gait variables to ensure that the environmental correlation between height and gaits was in the regression coefficient 3) non-zero values between the variables for the same gaits as they were computed from the same data.In a second step, we included height at withers in our model. The multivariate analysis of the gaits involved 10 traits: the nine PCs and the height at withers. The model included as covariates the velocity and the height at withers for the gait components, so that the model was a structured equation model , 23 and a the vector of the genetic additive values and p the vector of the permanent environmental effect. The variance matrices were V(a) = A ⊗ G, V(p) = I ⊗ P, and V(e) = I ⊗ R, with ⊗ the direct product, A the relationship matrix from the pedigree , G the 3 x 3 matrix of the genetic variance-covariance between the PC trait, the jumping performance, and the judges’ scores, P the 3 x 3 matrix of the genetic variance-covariance between the permanent environmental effect of the three traits, I the identity matrix, and R the 3 x 3 diagonal matrix of the residual variance-covariance.Due to the amount of jumping data, the analysis was performed repeatedly with three traits: one of the PCs, one of the judges’ scores, and the overall jumping performance. The same model as before was used for the PCs, as in previous section including height. The model for the jumping performance included as fixed effects a combination (leading to 125 levels) of year (from 2002 to 2019) and age effects  and gender . The model for the judges’ scores included fixed effects of age , year (from 2005 to 2018), local/regional/national type, category (six levels according to age in year and gender), gender , and show . There were two random effects: An additional analysis of the judges’ scores alone was performed to estimate the genetic correlation between them. The model included the already mentioned fixed effects, genetic values, and permanent environmental effects. The residual variance-covariance matrix included the covariance residuals between the scores obtained in the same event. The pedigree restricted to horses measured in breeding shows included 75,528 horses.https://www.ifce.fr/). We calculated the EBV for all horses judged in breeding shows obtained the year of the show, deleting results in competitions obtained after this date from the whole dataset . These EBV obtained at two or three years of age were, therefore, calculated only from parent information, rather than from their own performances, and are referred to as the parent average (PA) EBV. We then performed a regression with, as the dependent variable, the judges’ scores in breeding shows and, as the independent variable, the PA obtained the year of scoring. Residuals of this regression were used as a new scoring: the trait was the supplemental information provided by the assessment of conformation and gait, knowing the estimated genetic value for jumping competition. This new trait was used in bivariate analysis with the jumping competition performance to compute new genetic correlations, with the same models as in the previous section. In this case, the genetic correlation expected was lower than the initial genetic correlation between the two traits (when positive) and decreased with the increase of PA reliability: ra the initial genetic correlation and CD0 the reliability of the parental evaluation used to calculate the residual of scores  for the jumping competition trait are available to the public for all horses in France since birth  as a single gait, with two repeated measurements. The same strategy was applied for the canter (C).The principal component analyses of the horse effects on the eight measurements at each gait (seven for canter) are illustrated in Figs p = from 1.0 x 10−6 to 1.0 x 10−8) for the region on chromosome 3  close to the one linked to the height at withers already found by Signer-Hasler et al. [p = 4 x 10−35. However, the objective of measuring gait characteristics was not to distinguish tall and small horses. Thus, we decided to differentiate the two traits with a structured equation model: gait components were analyzed using the height effect, and height was added to the genetic analysis as a supplementary trait. No other significant regions were found on gait characteristics after height correction  had a negative effect on all of the variables, albeit non-significant for walk. Females had a lower PCT1_DVD and PCC1_DVD, and a higher PCW2_SF, meaning a higher stride frequency in each case (even when corrected for their lower height). They had a higher PCC3_LGA. Geldings were not significantly different from stallions, except for their PCT1_DVD and PCT2_LGA, which were lower than in stallions but higher than in mares for DVD. The event effect was substantial: the range was often higher than one phenotypic standard deviation unit.The effect of velocity on PCs was described with the estimates of effect of the type of gaits (medium versus working), knowing that medium gaits had a higher velocity than working gaits in average, and with the linear regression coefficients for velocity within each type of gait . The linThe results of the 10 multiple traits analysis are shown in The genetic correlations are presented in The relatively high genetic correlations between gait scores and jumping competition performance, although these are very different traits, prompted us to look at genetic correlations between conformation and gait scores and between conformation and jumping competition performance. High genetic correlations between the scores for conformation traits and gait traits were found : from 0.This homogeneity in the correlations led us to create new variables to characterize the scores: the residuals from the regression of scores as a predicted variable and genetic evaluation for the jumping competition performance from ascendants as known at the time of the scoring. The mean reliability of these genetic evaluations was 0.312. For example, with this reliability and the formula given in the material section, expected genetic correlation for an initial genetic correlation of 0.40 would be 0.28. In practice, the genetic correlation between the jumping competition performance and the residual scores  became sNo significant genetic correlation was found between the height at withers and the jumping performance .Accelerometry provided new insight regarding the description of gaits. We found that trot and galop were similarly characterized at medium and at working speed. Three principal components were enough to define each of the gaits: trot, canter, and walk. As expected –30 activThe measurement retained to describe gaits should be understood as velocity-corrected variables, not raw measurements. Activities mean the amount of acceleration and deceleration along the axis concerned developed to maintain the same velocity. Genomic analysis of the gaits measured by accelerometry revealed no major QTLs, except the one related to height. The power of GWAS was not very high with only 702 genotyped horses, therefore only very major genes as the one related to height could have been found. A major gene on gaits, a mutation in DMRT3 gene, was found for pace and trot in gaited breeds and breeds for trotting races , 32. ButEstimates of heritabilities based on kinematic analysis (video or accelerometers) of gaits can be found in a handful of publications –37 but tg = -0.19, SE 0.10). At first glance, it is surprising that lower activity gave better results in jumping competition. Longitudinal activity is linked to deceleration and acceleration along the longitudinal axis. After correction for velocity, longitudinal activity is therefore linked to the amount deceleration and acceleration necessary to maintain a similar velocity. If these acceleration or deceleration increase without increase of velocity, it may represent loss of efficacity in the movement of canter. This can explain the negative correlation. Other gait characteristics were mostly genetically independent of the jumping performance.The genetic relationships between gaits measured by accelerometers and jumping performance in competition were weak. Only one principal component for canter was significantly genetically linked to performance in jumping competition: a lower longitudinal activity at canter correlated with a slightly higher performance in competition (rhttps://www.ifce.fr/). This is why we also calculated the genetic correlation between the jumping performance and the residual of scores adjusted for the EBV calculated at the age at which the breeding show took place so before any of their own performances in jumping competition. We found negative genetic correlations, even for the gait scores. The scores did not add supplementary information genetically positively related to jumping performance over the parental EBV.This was not the case for gaits measured by judges’ scores during breeding shows. The genetic correlation between the score for walk and jumping performance was low  but relatively high for trot  and especially high for canter 0.45, SE 0.03). At first glance, it can be interpreted as an accurate assessment of gaits, specifically for jumping ability and not only for dressage, and it is hence able to predict the potential for a good jumping performance in competition. This is in accordance with the literature. In Sweden, Wallin et al. , and the, SE 0.03Nevertheless, genetic correlation between the gait scores and the accelerometric variables helped to gain an understanding of the assessment criteria. A good score for trot and canter was genetically linked to high dorsoventral displacement, low stride frequency, and low longitudinal and lateral activities. For walk, the link was with high dorsoventral activities and displacement, regularity, and low stride frequency and longitudinal activity. These genetic correlations were high up to 0.75. And coherence was found despite everything in the detailed genetic correlation between the gait scores and the accelerometric variables for the relationship with jumping competition performance. The genetic correlation between jumping performance and PCC3_LGA (-0.19) was confirmed by the genetic correlation between the canter score and PCC3_LGA (-0.34) and the canter score and jumping performance (0.45).The main conclusion is the genetic independence between gaits characteristics and jumping performance. In consequence, selection on gaits traits, whatever they are, will no improve or deteriorate the jumping ability. The only exception is the following. Low longitudinal activity and a low stride frequency at canter for a given velocity are slightly favorable traits for jumping competition performance. Distinct selection for height, irrespective of the goal (increase or decrease), is possible when the genetic evaluation for gaits is based on a structural equation model, as was done here. No other QTLs were found that could help with selection, other than the one linked to the height at wither, which naturally led to a lower stride frequency and a higher dorsoventral displacement at trot and at canter.Judges’ scores for gaits were clearly related to specific gait variables quantifiable by accelerometers. But they were genetically correlated much more efficiently to performance in jumping competition than the accelerometric variables. This may be because judges see more qualities and characteristics in gaits than can be produced with accelerometers, but perhaps also because they know the genealogy of the horse and thus their parental EBV for jumping competition. The contribution of information regarding gaits produced by the judges who are cognizant of this EBV is not positively genetically correlated to jumping competition performance.Measuring horses with an accelerometer will not be helpful for selecting horses for jumping performance. However, it may be useful if the performance in competition is not the only goal of the breed and if gait traits are a goal themselves. In that case, accelerometers provide objective, easy to record and heritable traits to select.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S1 File(PDF)Click here for additional data file.S2 File(PDF)Click here for additional data file.


Previous work has shown that higher resistance against PD has underlying additive genetic components and can potentially be improved through selective breeding. To better understand the genetic basis of PD resistance in Atlantic salmon, we challenged 4506 smolts from 296 families of the SalmoBreed strain. Fish were challenged through intraperitoneal injection with the most virulent form of the virus found in Norway . Mortalities were recorded, and more than 900 fish were further genotyped on a 55 K SNP array.Pancreas disease (PD) is a contagious disease caused by The estimated heritability for PD resistance was 0.41 ± 0.017. The genetic markers on two chromosomes, ssa03 and ssa07, showed significant associations with higher disease resistance. Collectively, markers on these two QTL regions explained about 60% of the additive genetic variance. We also sequenced and compared the cardiac transcriptomics of moribund fish and animals that survived the challenge with a focus on candidate genes within the chromosomal segments harbouring QTL. Approximately 200 genes, within the QTL regions, were found to be differentially expressed. Of particular interest, we identified various components of immunoglobulin-heavy-chain locus B (IGH-B) on ssa03 and immunoglobulin-light-chain on ssa07 with markedly higher levels of transcription in the resistant animals. These genes are closely linked to the most strongly QTL associated SNPs, making them likely candidates for further investigation.The findings presented here provide supporting evidence that breeding is an efficient tool for increasing PD resistance in Atlantic salmon populations. The estimated heritability is one of the largest reported for any disease resistance in this species, where the majority of the genetic variation is explained by two major QTL. The transcriptomic analysis has revealed the activation of essential components of the innate and the adaptive immune responses following infection with SAV3. Furthermore, the complementation of the genomic with the transcriptomic data has highlighted the possible critical role of the immunoglobulin loci in combating PD virus. Salmo salar) and rainbow trout (Oncorhynchus mykiss), affecting fish during the sea-water phase of the production. The aetiological agent, salmonid alphavirus (SAV), is a single-stranded RNA virus belonging to the Togaviridae family [Pancreas disease (PD) is a severe contagious disease of farmed Atlantic salmon  . The samSAV usually infects salmon at the smolt stage during the first year in the sea. Clinical manifestation of PD infection might include sudden loss of appetite and lethargy, reduction in growth, abnormal swimming behaviour and increased mortality . MortaliSo far, a few studies have aimed to evaluate a host immune response to infection with PD . It has t), a monovalent vaccine by PHARMAQ (ALPHA JECT micro® 1PD) and a DNA vaccine by Elanco (Clynav) are a few examples. Although the efficiency of vaccines has been questioned regarding their failure to eradicate the disease, in addition to their high costs, various side effects and negative impacts on slaughter quality, vaccination helps to reduce the mortality and alleviate some of the associated signatures of the infection, such as weight loss and feed conversion rate [A possible solution and a common practice to enhance resistance against PD is vaccination. Several vaccines including a multivalent vaccine by MSD Animal Health  model of infection , mortaliWe observed considerable variation in the rates of survival between the challenged families, ranging from 3 to 100% Fig. . The pedIn contrast, in a controlled experiment, all mortalities, with high certainty, can be attributed to the specific pathogen of interest. Furthermore, one can monitor mortalities regularly, and various environmental parameters can be controlled and adjusted. In this regard, our experiment and estimates are more comparable and similar to those reported by Gonen et al. . In theip < 1.024e− 06 . The two most significant SNPs on this genomic block are at both ends of the segment, each explaining approximately 3.4% of the phenotypic variance, suggesting the possibility of two different QTL on ssa03 . The genotyped fish were from 65 families, with a minimum of 10 and an average of 14 animals per family. The inflation factor was estimated to be 1.07, an indication of no confounding effect due to population stratification. A total of 17 genetic markers, nine located on chromosome ssa03 and eight on chromosome ssa07 exceeded the genome-wide significance level of The finding of a QTL on ssa03 corroborates well with the reported QTL on the same chromosome in the two populations investigated by Gonen et al. . In that= 0.106 ± 0.05). The most strongly associated SNP on this genomic block is an intergenic SNP , explaining about 5.8% of the variation  within the QTL segment of ssa06 (0.46) compared to that of ssa03 (0.54).While no marker on ssa06 passed the genome-wide significant threshold, two markers on the p arm of this chromosome  passed the chromosome-wide significance level. The proportion of phenotypic variation explained by each of these markers is about 2%. This finding is interesting, as the p arm of ssa06 shares homeology  with the q arm of ssa03 , where wWe obtained, on average, 15 million paired-end reads transcriptome sequence data, per animal, from the apex of the heart. Following trimming and filtering the low-quality reads, sequences with multiple hits against the genome and removal of rRNA and Illumina adapter contaminations, approximately 10 million uniquely aligned paired-end reads per fish retained for assessment of the gene expression profiles. Both cluster and principal component analysis (PCA) of the gene expression data, separated and grouped animals based on the survival status of the fish Fig. a and b, The transcriptome of the most severely sick fish had signatures of high levels of expression of genes associated with innate immune response and IFN-related antiviral genes. On the other hand, fish, which survived till the termination of the challenge, exhibited higher expression of genes specific for adaptive immunity  and the NOD-like receptor gene (NLR family member X1), both of which being part of the PRR. In response to a viral infection, TLR13 can activate a MyD88-dependent pathway, an essential element for signalling within the immune cells, resulting in the activation of nuclear factor κB (NF-κB) and type I IFN via interferon regulatory factor 7 (IRF7) . IRF4 an3 and theChemokines are important in triggering inflammation in virus-infected tissues. Many genes involved in the chemokine-mediated signalling showed significantly higher levels of expression in the moribund animals. Some examples are chemokine ligand 17 , chemokine ligand 19 , chemokine receptor-like 2 , chemokine ligand 1 , chemokine ligand 11 , chemokine ligand 4 , chemokine ligand 25  and chemokine ligand 20 . These genes play essential roles in the recruitment of leukocytes to sites of infection and inflammation and guiding the lymphocytes throughout the body . It is i2-fold change of 9.877). The role of this gene in salmonids, which have also shown to be down-regulated in several other viral infections, is not very well understood. However, it is unlikely that its lack of expression is a sign of suppression of a classical complement pathway, but rather an indication of a connecting bridge between the innate and the acquired immunity [The most significantly down-regulated gene, not only among the immune-related genes but within the entire dataset, is the complement component C1q-like protein 2, with almost no expression among the animals that survived the challenge  and ssa06 (IGH-A) . The iniAlthough there are various lines of evidence regarding the crucial role of antibodies in defence against viruses, the early activation of antiviral immunity and inflammatory responses does not necessarily prevent the propagation of the pathogen and the subsequent development of the disease. Clearance of pathogens and disease symptoms usually take place shortly after the initiation of the adaptive immunity , 36. In The findings reported in this study provide knowledge, necessary for helping us better understand the genetic basis of resistance against PD. First, our data confirm that PD resistance has a high heritable, additive genetic component. The estimated heritability of 0.41 on the observed binary scale, is among the highest estimates for any disease resistance in Atlantic salmon. One might also expect this finding to be irrespective of the mode of infection, as recently we have shown that resistance against SAV3 is not influenced by the means at which the virus enters the host, whether it is through injection or via cohabitation . Second,https://nofima.no/en/) to the challenge facility in VESO Vikan . The group consisted of all families from the entire 2015 year-class of the SalmoBreed nucleus, approximately 15 individuals per family, to assure reliable estimates of genetic parameters [4.8 TCID50 per mL and the challenge continued in the same tank. The SAV3 isolate, R-1-2007 (GenBank: LT630447.1), used in this study, is one of the most pathogenic forms of the virus found in Norway [p × (1 - p), where p is the frequency of surviving animals). At the end of the experiment, approximately 4 weeks after the infection when the mortalities stabilized, the remaining fish were euthanized with an overdose of anaesthesia .In November 2015, 4506 PIT-tagged (passive integrated transponder) smolts were transported from NOFIMA MARINE AS in Sunndalsøra, Norway , and the random variables, the common-environmental full-sib family prior to tagging (c) and the animal (u) effects. The residuals are defined in the e vector. The random effects are assumed to have the following distributions, , where I is an identity matrix, A is the additive genetic numerator relationship matrix, The pedigree-based variance components, i.e., the additive genetic . We used a 55 K custom made Affymetrix Axiom array and the Affymetrix Axiom analysis suite software to genotype the fish and call the SNPs. Additional genotype quality measures and filtering were applied using the SNP & Variation Suite v8.8.3 . Samples and SNPs with call rates < 98%, Hardy-Weinberg p-value (Fisher’s exact test) < 10–10 or minor allele frequency < 0.05% were excluded, leaving 903 animals and about 49 K markers for the subsequent genomic analysis.Adipose fin-clip from 930 of the challenged animals, representing 65 families, were collected throughout the trial. With an average of 15 fish per family, the choice of family selection was set to maximize the genetic diversity, based on the pedigree information, while utilizing the within-family genetic variations , 54–56. i. the gender effect, predicted according to the Y-specific linked markers [ii. the A matrix was replaced with the genomic relationship matrix , computed as described previously [) [Rk) and those covering the rest of the genome (Gk). The genomic relationship matrices  , 41 by fr2 threshold 0.5) to construct an n × n genetic distance relationship matrix based on the identity-by-state (IBS). The relationship information was then used to perform a single-locus mixed-model association analysis, using the full set of quality-controlled markers, by applying Efficient Mixed-Model Association eXpedited (EMMAX) [y is the vector of survival scores, X, Z1, Z2 and Z3 are the incidence matrices assigning phenotype to the vectors of fixed effects (b), i.e., the animal weight at tagging and gender, the common-environmental family effect (c), additive genetic effect , assigned to the matrix for marker genotypes and the residual  using a mixed linear model. The methodological details of the association analysis have been described previously . In brie (EMMAX) , 61. TheSNPs per , 63. AltSNPs per . NonetheSNPs per , where wr2 function in plink v1.9 [mrssh0 is the Mahalanobis total sum of squares, proportional to the variance of the data, and mrssj is the Mahalanobis residual sum of squares for marker j [We estimated the linkage disequilibrium (LD) within the QTL regions, for SNP pairs separated by up-to 100 kb, using the ink v1.9 . The infink v1.9 . The promarker j , 67, 68.www.illumina.com). Sequencing was done on an Illumina HiSeq 4000 platform as paired-end (PE) 150 bp reads.We investigated the host transcriptomic response to SAV3 infection by collecting the apex of the heart from six challenged fish at a moribund state during the early stage of the challenge (11 dpi) and six individuals that survived till the termination of the experiment (29 dpi). Heart rather than pancreas was chosen as the target tissue, as it is usually more damaged due to infection and it is less affected by cross-tissue contamination during sampling , 50. Thewww.bioinformatics.babraham.ac.uk/projects/fastqc/). The sequencing adapters and low-quality nucleotides were removed by Trimmomatic (v.0.36) [https://www.ncbi.nlm.nih.gov/assembly/GCF_000233375.1/) using TopHat (v.2.0.13) [https://www.ncbi.nlm.nih.gov/assembly/GCF_000233375.1). The expression data were scaled using the median of the geometric means of fragment counts across all samples [p-value of less than 0.05. More information regarding details of sequence handling, assessment and analysis have been outlined previously [The initial sequence quality assessment was done by fastQC ((v.0.36) . The rem.2.0.13) . Only re samples . Estimateviously , 73.Additional file 1: Supplementary Figure 1. Heatmap of the immune-related genes, with different expression profiles between the moribund (M) and survived animals (S) within the QTL containing regions of a. ssa03 and b. ssa07.Additional file 2: Supplementary Table 1. Differentially expressed genes between the moribund animals at 11 dpi (sample_1) and animals that survived the challenge (sample_2). Supplementary Table 2. Functional enrichment analysis of differentially expressed genes between the moribund animals at 11 dpi and animals that survived the challenge.


Functional analysis of longitudinal phenotypic data for five and 10 time points saturates QTL detection power and genomic predictive ability for canopy height in wheat. Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches  had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities , while the effect of including significant QTLs in prediction models was relatively minor (<1–4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops. In recent years, field-based high-throughput plant phenotyping (HTPP) tools have been used extensively in crop trials, aiming to reduce or eliminate manual measurements, increase the amount and quality of data for temporally dynamic phenotypes, and, ultimately, translate ‘big data’ collected using various sensors into knowledge  in mapping families  and genoPrevious studies of wheat plant height, an important agronomic trait, have identified several dynamic QTLs  comprising 197 lines derived from crossing the Chinese Spring (CS) and Paragon (PAR) genotypes was used in this study (A recombinant inbred line (RIL) population . Subsequently, an average canopy height was estimated for each sub-region of the RIL and then averaged together to obtain the plot canopy height (R2) between manual and automatic measurements was 0.59, 0.78, 0.85, and 0.81 for 70, 155, 208, and 233 DAS, respectively   . Thus, wWe used the ASReml package  variance–covariance structure (VCOV) was used for the genotype effects , with covariance matrix σ 2g=Var(g)ΣT + Ψ , A was the numerator relationship matrix (we used the I identity matrix), ε=ξ+η, where ξ and η refer to the spatially correlated and independent errors, respectively. The variance of residuals was assumed to be 2ξ was the variance due to local tendency and Dη was a t×t diagonal VCOV matrix, with each TP having a specific spatially independent variance component. Matrices c and Φ r and order given by the number of columns (c) and rows (r), respectively  among traits (i.e. heights at different TPs) . Finally, broad-sense heritability (h2) was estimated based on an entry-mean for each TP, following the equation: h2=σ 2g/(σ 2g+σ 2ε), where σ 2g=Vg and σ 2ε=Vε were the genetic and residual variances (V), respectively. The SEs of heritability estimates were estimated using the ‘delta’ method (http://www.homepages.ed.ac.uk/iwhite/asreml/uop).0⋱000Ψt] . In addient TPs) , followi’ method  using thWe applied data smoothing followed by dimensionality reduction of genotype BLUPs across all TPs following the approach described by 1, R2, R5, and R6 in 3, R4, R7, and R8 in We compared two approaches for selecting five or 10 TPs from the 26 TPs available for the 2017 data by sampling TPs in two different ways . In the ® HD wheat single nucleotide polymorphism (SNP) array  were removed, and the remaining missing SNP data were imputed using Beagle 4.1 , X is an n×2 matrix of QTL genotype probabilities, β is a 2×p matrix of QTL effects, and ε is an n×p matrix of random residuals following a multivariate normal distribution. At each position λ (putative QTL location), a multivariate regression model with a single QTL was fitted. Thus, the log10 likelihood ratio of the HK model was 0| was the matrix determinant for the null model with no QTL, and |RSS(λ)| was the matrix determinant for the model with a single QTL at position λ.where tLOD, and In contrast, the MLOD and SLOD approaches used the maximum and average LOD scores across TPs, respectively: MLOD(λ)=maxFinally, for multiple functional mapping , we followed the methodology proposed by p is the estimated frequency of an arbitrarily chosen allele and k is the simulated additive effect divided by the SD of the BLUPs for the respective TP. Finally, statistical power was estimated as the proportion of iterations in which the simulated additive effects were detected as QTLs at α=0.05 based on the permutation thresholds used in our analyses of empirical data (see above).We performed data perturbation simulations  to alter1–R8, The phenotypic traits used for prediction were the (i) individual TP BLUPs; (ii) the last two B-spline coefficients; and (iii) the first functional PCs from analyses of five, 10, and 26 TPs, including different TP selection scenarios R1–R8, .We used the additive genomic best linear unbiased prediction (GBLUP) model:β was the vector of fixed effects, a was the vector of random additive genetic effects of the SNP markers, and ε was a vector of random residuals. The incidence matrices for β and a were X and Z, respectively. The distributions of random effects were assumed to be a~N and ε~N, where I was the identity matrix and Ga was the additive genomic relationship matrix calculated using the first formula proposed by where 1 and R5 scenarios, as well as the full set of 26 TPs . This model is expected to be advantageous assuming that height is controlled by relatively few genes of large effect and many genes of smaller effect  was calculated as the Pearson correlation between adjusted values and genomic estimated breeding values in 50 replications from independent validation scenarios (n=148) to form a training set, while the remaining 25% (n=49) were used as a validation set. We applied Fisher’s Z transformation of the predictive abilities and compared them among scenarios using Scott–Knott’s test (2a=Vga) and residual (σ 2ε=Vgε) variances, enabling the calculation of genomic heritability as h2g=σ 2a/(σ 2a+σ 2ε).Predictive ability   . The proP<0.01) .From the IM and CIM scans of the 2017 data , we detected a total of six QTLs . Three oBased on the 2016 data, we detected one QTL on chromosome 2B across multiple TPs, whereas another QTL on chromosome 6A was detected in a single TP, in a different growth stage from 2017 . FurtherUsing single functional mapping at five, 10, and 26 TPs based on the multivariate LOD test statistic HKLOD score), we identified two, three, and four QTLs, respectively . ResultsLOD scoreAs expected under the presumed genetic model of highly complex temporal dynamics of height growth, both absolute effect sizes and proportions of PVEs varied substantially across the growing season, with clear examples of early , late (7A), or persistent (2B) QTLs . FurtherGenetic and residual variance components from the GBLUP model generally followed the same temporal patterns as those from the FA model, with increasing values after the tillering growth stage . ConsequThe predictive ability for B-spline coefficients of phenotypes that had been smoothed was generally higher than that for individual TP or PC data . Based oSetaria  and the additional computational cost  that may not necessarily translate into improved genomic prediction or more complete understanding of trait architecture. Based on our results, we formulated the following two practical recommendations, which are naturally only applicable in situations similar to our study. First, when the emphasis is on dissecting trait architecture, we recommend using both individual TP and functional mapping approaches to maximize the overall number and types of QTLs detected. Secondly, for more applied purposes (e.g. in breeding), analyses of spline-smoothed phenotypes selected based on phenological information may be sufficient to maximize genomic predictive ability. Further slight improvements may be achieved through the inclusion of QTLs as fixed-effect covariates, though it is not currently clear if the additional complexity (and consequently delay) associated with QTL detection is justified.JXB online.Supplementary data are available at Fig. S1. Illustration of steps for processing of point clouds.Fig. S2. Manual ground height versus height from the 3D point cloud.Fig. S3. Heatmap of genetic correlations of height between 26 TPs in 2017.Fig. S4. Genetic map consisting of 2330 SNPs.Fig. S5. Temporal pattern of canopy height growth in 2016.Fig. S6. Smoothed and dimension-reduced height in 2017.Fig. S7. Interval and composite QTL mapping at 26 TPs in 2017.Fig. S8. Signed LOD scores for each time point in 2017 and 2016.Fig. S9. Summary of individual TP mapping results in 2016.Fig. S10. Functional QTL mapping analysis for five, 10, and 22 TPs in 2016.Table S1. Likelihood ratio and Wald tests at 26 TPs in 2017.erz545_suppl_Supplementary_Figures_S1-S10_and_Table_S1Click here for additional data file.


Polygenic risk scores (PRS) use the results of genome-wide association studies (GWAS) to predict quantitative phenotypes or disease risk at an individual level, and provide a potential route to the use of genetic data in personalized medical care. However, a major barrier to the use of PRS is that the majority of GWAS come from cohorts of European ancestry. The predictive power of PRS constructed from these studies is substantially lower in non-European ancestry cohorts, although the reasons for this are unclear. To address this question, we investigate the performance of PRS for height in cohorts with admixed African and European ancestry, allowing us to evaluate ancestry-related differences in PRS predictive accuracy while controlling for environment and cohort differences. We first show that the predictive accuracy of height PRS increases linearly with European ancestry and is partially explained by European ancestry segments of the admixed genomes. We show that recombination rate, differences in allele frequencies, and differences in marginal effect sizes across ancestries all contribute to the decrease in predictive power, but none of these effects explain the decrease on its own. Finally, we demonstrate that prediction for admixed individuals can be improved by using a linear combination of PRS that includes ancestry-specific effect sizes, although this approach is at present limited by the small size of non-European ancestry discovery cohorts. Genome-wide association studies (GWAS) have proved remarkably successful at identifying the genomic basis of complex traits. For example, 3,290 genome-wide significant loci explain approximately 25% of the phenotypic variation in height in European ancestry individuals . For WHI, JHS, and HRS, we flipped alleles to the positive strand using the appropriate strand files from https://www.well.ox.ac.uk/∼wrayner/strand/. Because the different cohorts each contain different ancestry groups, we initially identified individuals with admixed African ancestry in each cohort using a combination of genetic clustering and self-reported ancestry as described below. For these individuals, we then inferred local ancestry, as described in the next section.We obtained genotype and phenotype data from the UK Biobank  (JHS), aUKB: This dataset contains several ancestry groups. We selected 8,813 individuals with African or admixed African and European ancestry-based on PCA . After ancestry inference, we further filtered this set to contain individuals with at least 5% genome-wide African ancestry proportion and phenotype availability, resulting in 8,700 individuals that we refer to as UKB_afr  from the mean , resulting in 6,863 individuals , and at most 0.05 of the second ADMIXTURE component . We further filtered this set to contain individuals with at least 5% genome-wide African ancestry and sex-corrected height not less than 2 sd below the mean , resulting in 2,270 individuals (referred to as “HRS_afr”). We also identified 10,486 individuals who self-reported “White/Caucasian” ancestry and had at most 0.05 of each of the first two ADMIXTURE components, of which 10,159 had sex-specific height above the -2 sd cutoff .https://www.nealelab.is/uk-biobank, accessed April 2, 2019). We used a set of 13,586,591 autosomal SNPs that passed QC filters of INFO score > 0.8 and MAF > 0.0001. For some analyses, we used between-sibling effect sizes estimated at a subset of 1,284,881 SNPs .For the admixed cohorts, we estimated local and genome-wide ancestry. We merged each dataset with CEU (Utah residents with Northern and Western European ancestry) and YRI  individuals from The 1000 Genomes Project (2015) and phased each dataset separately using HAPI-UR  were: 1,000, 500, 100, 75, 50, 25, 10, 5. Genetic window sizes (in cM) were: 1, 0.5, 0.3, 0.25, 0.2, 0.15, 0.1. We considered SNPs below the p-value thresholds: 5×10−7, 5×10−6; 5×10−5 5×10−4, 5×10−3. For each set of parameters, we followed these steps: 1) retain only SNPs below the p-value threshold, 2) select the lowest p-value SNP, 3) remove SNPs within the window around that SNP, 4) repeat steps 2 and 3 until there are no SNPs left. We also used a strategy of clumping based on empirical LD structure. We used PLINK2 (r2 between pairs of SNPs using UKB_eur (–clump-p1 0.01 –clump-r2 0.5–clump-kb 250|100| 50). Finally, we applied a strategy where we clumped SNPs in approximately independent LD blocks ((Table S1).We first intersected the ∼13.5 million SNPs from the UK Biobank summary statistics and the genotyped SNPs in each dataset . Next, wD blocks  . We repeated these steps for analyses using effect sizes re-estimated from sibling pairs and imputed genotypes, except restricting the initial set of SNPs (before pruning/clumping) to those present in the sibling or imputed dataset. For imputed genotypes, we performed the 40 c+t strategies using the physical windows described above (Table S1).For the unweighted PRS, we tested prediction for the same 80 sets of SNPs 2, and the first 10 principal components, computed using smartpca  of individual j at SNP i, and i. To calculate unweighted PRS, we set where the sum is over all 2, genome-wide European ancestry proportion  based on R2 for each bin. To infer confidence intervals, we used the R package “boot”  and repeated the analysis of partial-2R as a function of We evaluated the predictive power of Finally, we constructed a combined PRS where Admixed African effect sizes are used for SNPs falling in African ancestry regions of the genome, and European effect sizes are used for SNPs falling in European ancestry regions. For African ancestry segments, effect sizes from admixed Africans are weighted by a constant, α. So, for each haplotype in each individual, we have:iG is the genotype of the i-th SNP, and EUR and AFR are the sets of European and African ancestry regions of the genome . We then sum where q-quantile of height conditional on being in the upper q-quantile of PRS as:We estimated the odds ratio for being in the upper q-quantile of height and q-quantile of PRS who are also in the upper q-quantile of height. We used standardized residuals of height after regressing out age, 2age, sex for each dataset.where R2, and 95% confidence intervals obtained by the percentile bootstrap on 3,000 replicates over samples for each bin, as described above. We then divided the values for each bin by those obtained for the full dataset, thus obtaining a relative partial-R2. In another approach, we tested for correlation between We used recombination maps estimated in African Americans (AA_Map)  for each PRS SNP as a function of the mean squared frequency difference in the window surrounding each SNP. We then repeated the analysis for 6kb windows.https://github.com/mathilab/PRS_Height_Admixed_Populations. Genotype and phenotype data were obtained from UK Biobank or dbGaP. File S1 contains 14 supplementary figures. Table S1 contains 20 sheets. Sheets 1-6: Different SNP sets generated by clumping and their PRS values. Effect sizes from 360K European ancestry individuals from the UK Biobank. Data from genotype arrays. Sheets 6-12: Different SNP sets generated by clumping and their PRS values. Effect sizes estimated from ‘White British’ sibling pairs from the UK Biobank. Data from genotype arrays. Sheet 13: Different SNP sets generated by clumping and their PRS values. Data from imputed datasets. Sheets 14-19: Different SNP sets generated by clumping and their unweighted PRS values. Sheet 20: Difference between PRS values for 1000G super-populations  for different sets of SNPs. Supplemental material available at figshare: https://doi.org/10.25387/g3.12795887.Scripts developed specifically to perform the data analyses reported in this work are available at: r2 and LD blocks inferred for African and European populations, and the infinitesimal model of LDpred. Among the clumping and thresholding (c+t) strategies, increasing the p-value cutoff and window sizes improves prediction . LD clumping yields higher predictive power but depends on prior knowledge of the population-specific LD structure and has the highest difference in PRS between 1000 Genomes European and African Populations (Table S1). Approximately independent LD blocks .We tested 81 approaches to PRS construction, including five different p-value cutoffs and 15 window sizes, pairwise D blocks  yields sD blocks  and obta2R was 22.4% in UKB_eur, and 15.6% in HRS_eur  to be 1.5% (S.E.=0.3%). This result is robust to the set of SNPs used in the PRS, with intercepts ranging between -1% and 2.5%, depending on the pruning strategy . We observe the same pattern when using the LDpred infinitesimal model . Relevant for clinical interpretation, the odds-ratio for ‘tallness’ in the tails of the PRS distribution is also lower in the admixed populations than in the European ancestry population, although only 2.3-fold on average across populations between the highest and lowest 5% of the European ancestry spectrum (95th quantile of PRS distribution), compared to the 3.8- to 5-fold difference in partial-2R.Stratifying individuals in each cohort by their average genome-wide ancestry, we find that partial-. If the predictive power of the PRS came entirely from these segments, then we would expect the relationship between ancestry and partial-R2 to be the same as when we used the whole genome  would be multiplied by the proportion of the genome in European ancestry segments . Our observations are intermediate to these extremes (We next restricted the PRS SNPs to those found in segments of the genome inferred to have European ancestry . If the extremes . We concR2 in WHI_afr (3.6–3.9%), JHS_afr (3.8–4.1%), and HRS_afr (3.1–3.2%) . With larger African ancestry GWAS, we expect that we would be able to improve the PRS performance in the admixed populations with this approach.Next, we explored whether combining ancestry-specific PRS could improve predictive power, as suggested by .1–3.2%) . Weighti.1–3.2%) . Finally.1–3.2%) . This apSeveral explanations have been proposed to explain why the predictive power of PRS is lower in non-European ancestry populations. These include differences in LD patterns, the allele frequency of PRS variants, additive genetic variance, gene-gene (G×G) and gene-environment (G×E) interactions in different populations, and biases in the discovery cohort. In this section, we evaluate the impact of some of these factors on PRS-based phenotypic prediction.R2 values across ancestries. Because GWAS have more power to detect more common variants, one hypothesis is that the PRS will tend to contain variants that are more common in European than African ancestry backgrounds–resulting in systematically lower predictive power in African ancestry populations. We tested this by comparing the ratio of the additive genetic variance contributed by the variants used in the PRS in the European and the admixed datasets (Equation 7). This ratio is the relative difference we would expect if the effect sizes and LD structure were the same across ancestries, and only allelic frequencies differed. We estimate this ratio to be: 0.78 (UKB), 0.92 (HRS), 1.04 (JHS), and 1.07 (WHI), suggesting that at most 8% of the decrease in partial-2R (in non-UKB samples) can be explained by differences in the site frequency spectrum (SFS). JHS and WHI have fewer SNPs genotyped than HRS and UKB .A related possibility is that SFS differences might affect not just the variance explained by the PRS SNPs, but also the total genetic variance. Genome-wide heterozygosity in European ancestry populations is approximately 30% lower than in West African ancestry populations . If thisR2 in admixed African populations when compared to European ancestry populations.Variants identified by GWAS are not generally themselves causal but rather are linked to the causal variant(s). Linkage disequilibrium patterns are known to differ between populations, suggesting that tag SNPs discovered in the original European ancestry GWAS may be less efficient at tagging the causal variants on non-European ancestry haplotypes. Using GWAS variants detected in an exclusively European ancestry cohort would thus result in a reduced partial-R2 for the admixed cohorts tends to decrease between the first and fourth quantiles of recombination , suggesting that differences in LD do play a role in reducing prediction in non-Europeans. On the other hand, we note that, even for the quartile of lowest recombination, the reduction in partial-R2 for admixed individuals is substantial – 76% on average across datasets – compared to 84% for the fourth quantile . Thus, even if all PRS variants were from low recombination regions, we would still observe a substantial reduction in predictive power. We performed the same analysis using a recombination map derived from the 1000 Genomes CEU population . One potential confounding factor in this analysis is that causal variants in low recombination regions might be better tagged than those in high recombination regions, which would reduce the relative performance in high recombination regions. However, since we find little evidence of difference, we conclude that this is unlikely to be a major factor.If LD differences between African and European haplotypes drive the pattern seen in bination , suggestquantile . Thus, epulation  and founP = 0.97) between P = 0.0379) .A second prediction is that the difference between effect sizes estimated in European and African ancestry populationsct sizes  and whetion rate , and a sR2 are driven by differences in ability to tag the causal variant, then PRS constructed from imputed genotypes should see a smaller decrease in predictive power than those constructed from genotype array data. Using imputed genotypes for the HRS and UKB cohorts, we find that the relationship between ancestry and partial-R2 is the same for imputed and array data suggesting that this is not the case . We conclude that residual population structure in the UK Biobank GWAS results does not drive differences in predictive power across ancestries.Despite statistical methods to control for population stratification, it continues to be a confounding factor in the analysis of GWAS results . More sophisticated approaches provide limited improvement in predictive power, require additional assumptions about LD structure or other parameters, and do not necessarily lead to substantial improvements in predictive power or transferability  (cis-epistasis or allelic heterogeneity – which mimics epistasis (We show that differences in LD structure and SFS affect the transferability of PRS but do not explain the full magnitude of the decrease. Our results are broadly consistent with simulation studies showing that these two factors are expected to decrease variance explained when the test cohort has different ancestry from the GWAS cohort and, specifically, that together they explain up to 72% of the loss of accuracy in prediction between European and African ancestry (By incorporating effect sizes from admixed populations in a linear combination of PRS, we are able to improve predictive power, in agreement with previous findings (Our approach has several limitations. In order to disentangle the factors affecting PRS generalizability across ancestries, we focused on height – a model trait due to its high polygenicity and heritability – in recently admixed cohorts of highly diverged European and African ancestry in the US and the UK. We expect that our insights will transfer to some extent to other traits, ancestries, and cohorts, but there may be significant differences. For example, genetic architecture, local adaptation, and environmental factors, to name a few, might differ, and our results might not directly apply to the same extent. A related issue is that there is variation across the African ancestry samples used in this study, although we treated African ancestry as derived from a single population. On the other hand, most of the ancestry of the populations that our cohorts are drawn from is West African, even other African ancestries are largely symmetrically related to Europe, and in these cohorts, the admixture proportion is the major component of variation (e.g., Another limitation of our approach is the difficulty of distinguishing the effects of correlated variables. For example, the site frequency spectrum, the recombination rate, the density and effect sizes of GWAS hits, and the effectiveness of tag SNPs are all correlated. While we can separate these effects in some cases, our results are likely still confounded to some extent. In simulations, we can control and quantify these effects. However, this requires realistic simulations of complex traits in admixed populations. Developing such simulations is another important future goal. A related issue is that there are both biological and environmental factors that are correlated with ancestry. Local ancestry analysis can control for many of these effects e.g., , but it In summary, leveraging information about each associated variant’s local ancestry background is a promising way to improve transferability, albeit that, too, requires larger non-European cohorts to estimate effect sizes. Though we focused on cohorts of recent admixed European and African ancestry, additional work is required to characterize the transferability of PRS both in populations with more complex recent admixture, as well as in populations that are more anciently admixed. While we showed that different factors each play a modest role in PRS generalizability, there is much room for advances in approaches such as ours as more diverse GWAS datasets become available.