Eimeria and cestode parasitism, and productivity [body weight and body condition score (BCS)]. Combined data from the two chicken ecotypes, Horro (n = 384) and Jarso (n = 376), were jointly analyzed for genetic parameter estimation, genome-wide association studies (GWAS), genomic breeding value (GEBVs) calculation, genomic predictions, whole-genome sequencing (WGS), and pathways analyses. Estimates of across-ecotype heritability were significant and moderate in magnitude (0.22–0.47) for all traits except for SG and BCS. GWAS identified several significant genomic associations with health and productivity traits. The WGS analysis revealed putative candidate genes and mutations for IBDV , MDV (GRM7), SG (MAP3K21), Eimeria (TOM1L1) and cestodes  parasitism, which warrant further investigation. Reliability of GEBVs increased compared to within-ecotype calculations but accuracy of genomic prediction did not, probably because the genetic distance between the two ecotypes offset the benefit from increased sample size. However, for some traits genomic prediction was only feasible in across-ecotype analysis. Our results generally underpin the potential of genomic selection to enhance health and productivity across-ecotypes. Future studies should establish the required minimum sample size and genetic similarity between ecotypes to ensure accurate joint genomic selection.Poultry play an important role in the agriculture of many African countries. The majority of chickens in sub-Saharan Africa are indigenous, raised in villages under semi-scavenging conditions. Vaccinations and biosecurity measures rarely apply, and infectious diseases remain a major cause of mortality and reduced productivity. Genomic selection for disease resistance offers a potentially sustainable solution but this requires sufficient numbers of individual birds with genomic and phenotypic data, which is often a challenge to collect in the small populations of indigenous chicken ecotypes. The use of information across-ecotypes presents an attractive possibility to increase the relevant numbers and the accuracy of genomic selection. In this study, we performed a joint analysis of two distinct Ethiopian indigenous chicken ecotypes to investigate the genomic architecture of important health and productivity traits and explore the feasibility of conducting genomic selection across-ecotype. Phenotypic traits considered were antibody response to Infectious Bursal Disease (IBDV), Marek’s Disease (MDV), Fowl Cholera (PM) and Fowl Typhoid (SG), resistance to  Village poultry are important in low- and middle-income countries around the world  and, theEimeria and cestode parasitism) and two production traits (live body weight and body condition score) (We previously used a high-density genome-wide array to genotype two distinct unselected Ethiopian indigenous chicken ecotypes (village chickens from the Jarso and Horro geographic regions) and performed genomic studies on each ecotype separately to investigate the genetic architecture of six major infectious diseases  . The outn score) .In the present study, we extended the previous work of All work was conducted with the approval of the University of Liverpool Research Ethics Committee (reference RETH000410).Details of the bird populations used in the present study, sampling strategy implemented and phenotyping of individual birds have been described in detail in Eimeria spp. oocysts and cestode spp. eggs were counted with a modified version of the concentration McMaster technique as described in Pasteurella multocida, henceforth PM) and Fowl Typhoid  were measured using in-house developed ELISAs, as described in 10-transformed in order to normalize the respective distributions.Fresh feces were collected for parasite egg measurements, and brachial blood collected into tubes with sodium citrate for serology and on FTA cards for DNA extraction from each of the birds. ® Axiom <® HD) consisting of 580,954 SNPs uniformly distributed across the genome (P < 10–6). After quality control, 359,470 SNP markers were kept for further analyses, distributed across all chromosomes.All birds were genotyped using the high density single nucleotide polymorphism (SNP) genome-wide DNA array (Affymetrixe genome . These de genome : minor aVariance components and genetic parameters were estimated for the combined population of the two ecotypes as described previously in detail in P ≤ 1.39E-07 and 2.78E-06 for genome-wide significant (P ≤ 0.05) and suggestive  levels, respectively, corresponding to −log10(P) of 6.85 and 5.55.For each studied trait, a genome-wide association analysis (GWAS) was performed using combined data across the two ecotypes. The GEMMA algorithm  and the 1 within the Ensembl database and the galGal6 assembly. We chose these 200-kb windows based on the average Linkage Disequilibrium (LD) calculated previously for the Horro and Jarso chicken populations (r2 ∼ 0.2) rarely exceeds 100 kb. This allowed us to catalog and create lists of all genes located around the identified significant markers that are potentially associated with antibody response, resistance to parasitic infection and production traits.All significant and suggestive significant SNP markers identified in the GWAS were annotated using the galGal4 assembly according to the Affymetrix map file and their locations were then re-mapped from galGal4 to galGal6 using UCSC liftOver tool . In addiulations ; mild LDSix Horro and 14 Jarso birds were whole-genome sequenced  on an Illumina HiSeqX platform. The sequencing reads were mapped to galGal6 using BWA-MEM  and variBased on the GWAS results, the candidate regions (200 kb windows around each of the significant SNP) for each of the studied traits were extracted from the WGS data for both the Horro and Jarso chickens. The identified variants in these regions were annotated and their predicted effects on the encoding protein were assessed using the Ensembl Variant Effect Predictor tool . The varP-values obtained using Fisher’s exact test), after correcting for a baseline threshold  regions would contain genes contributing to common pathways associated with each of the studied traits. Therefore, initially the lists of annotated genes located within the QTL regions for each bird phenotype were analyzed using the Ingenuity Pathway Analysis (IPA) program . We soughreshold .Genomic breeding values (GEBVs) were simultaneously estimated for all birds of the two ecotypes using a genomic best linear unbiased prediction model including the same effects as the genetic parameter estimation model , with Jarso animals being the smaller ones, which may explain the number of predicted high impact variants found only in Jarso birds in candidate regions linked to live body weight and growth. This suggestion is in accordance with previous studies of humans, mice and cattle, which have reported that many lncRNAs play crucial roles in the development of skeletal muscle and cardiac lineages  under accession number All work was conducted with the approval of the University of Liverpool Research Ethics Committee (reference RETH000410).RC, OH, PW, PK, and TD conceived the experimental design and secured funding. AP, OH, and GB designed the genetic studies. TTD and JB performed the collection of samples and the phenotyping. GB, VL, and AP with input from ESM and OM performed the genetic parameter and the genomic prediction analyses. GB and AP collated and edited the genotyping data and performed the GWAS analysis. AVT and OH generated the WGS data and VL with input from AP analyzed the genetic variants. AP performed the pathway analysis. AP, OH, RC, PW, TD, GB, VL, and ESM interpreted these results. GB and VL wrote the manuscript with input from AP. All other co-authors provided manuscript editing and feedback and read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


APOLD1, PLBD1, PDE6H, GPRC5D and GPRC5A) and lipid metabolic processes (MTMR2). The EWSR1 gene, underlying the OCU13 region, is linked to the development of brown adipocytes. The findings suggest that there is a large component of polygenic effect behind the differences in IMF content in these two lines, as the variance explained by most of the windows was low. The genomic regions of OCU1, OCU8 and OCU13 revealed novel candidate genes. Further studies would be needed to validate the associations and explore their possible application in selection programmes.Intramuscular fat (IMF) is one of the main meat quality traits for breeding programmes in livestock species. The main objective of this study was to identify genomic regions associated with IMF content comparing two rabbit populations divergently selected for this trait, and to generate a list of putative candidate genes. Animals were genotyped using the Affymetrix Axiom OrcunSNP Array (200k). After quality control, the data involved 477 animals and 93 540 SNPs. Two methods were used in this research: single marker regressions with the data adjusted by genomic relatedness, and a Bayesian multiple marker regression. Associated genomic regions were located on the rabbit chromosomes (OCU) OCU1, OCU8 and OCU13. The highest value for the percentage of the genomic variance explained by a genomic region was found in two consecutive genomic windows on OCU8 (7.34%). Genes in the associated regions of OCU1 and OCU8 presented biological functions related to the control of adipose cell function, lipid binding, transportation and localisation ( Intramuscular fat (IMF) contributes to improve organoleptic properties and sensory attributes of the meat, as demanded by consumers  were applied to confirm the identified relevant genomic regions.All experimental procedures were approved by the Ethical Committee of the Universitat Politècnica de València, according to Council Directives 98/58/EC  muscle, after adjusting data for systematic effects  and a common litter random effect. The high‐IMF line had a mean of 1.27 g/100 g of LTH with 0.21 standard deviations, and the low‐IMF line had a mean of 0.83 g/100 g of LTH with 0.07 standard deviations. Details about the IMF divergent selection experiment can be found in Martínez‐Álvaro et al. , obtained after slaughter of the animals, were used for DNA extraction using a standard protocol  and 93 540 SNPs. In addition, the SNP density was described in this research because the rabbit SNP array is new , sex (two levels), order‐parity (three levels) and line (two levels). The inclusion of a common litter random effect in the model was evaluated owing to the importance of this effect in previous studies of IMF in rabbits (Martínez‐Álvaro Single marker regression (SMR) with the data adjusted by genomic relatedness. The analysis was implemented using a family‐based score test for association (FASTA). The SNP effects were evaluated with FASTA based on a polygenic‐lineal mixed model that included the genomic kinship matrix to explain relatedness in the sampled population , Z is the design matrix for random polygenetic effects, u is the vector of random polygenic effects with a normal distribution N and e is the vector of random residual effects with a normal distributionN; σu2 is the genomic variance and G is the genomic kinship matrix computed using the genomic data by the method of Astle & Balding  calculated for 10 Mb LD blocks according to LD analysis implemented in plink . This method is more robust to population structure than SMR approaches , αj is the random substitution effect for SNPj and δj is the random 0/1 variable that represents the presence (δj = 1 with probability 1 − π) or absence (δj = 0 with probability π) of SNPs in the model for a given iteration. The value of π is defined as the proportion of SNPs with zero effects in the model. The value of π in our study was 0.9988, which means that between 100 and 200 SNP markers have non‐zero effects for every iteration. The parameters of the model were estimated with marginal posterior distributions using Markov chain Monte Carlo. After some exploratory analysis, a total of 825 000 iterations were performed, with a burn‐in period of 225 000 iterations. Only one sample every 60 iterations was saved to avoid the high correlation between consecutive samples. Gensel® version 4.90 software . The gene annotations were determined using Ensembl Genes 96 Database in biomart  version 6.8  threshold for SMR method.Two methods were used in this research: SMR with the data adjusted by genomic relatedness and a BMMR (Bayes B method). We employed the term of "relevant" in order to denote those SNPs and genomic windows that we considered as true positive associations. In this research, we understand the GWAS as an exploratory analysis, which works as a mechanism for deriving promising genomic regions associated with IMF, and retrieving annotated rabbit genes. Table hod Fig. . The twoRegarding the LD analysis, we found that in our data the rabbit genome could be divided into 2338 LD blocks and 6158 independent sites, with the longest LD blocks having a maximum length of 10 Mb. The associated SNPs on OCU13 and on OCU8 displayed a high LD within the chromosomal region Fig. . The assAfter the previous analysis (GWAS and LD), four relevant genomic regions were used to continue searching for putative candidate genes based on the functional annotation analysis Table . In thesThe final objective of our study was to generate a list of putative candidate genes, in order to guide further research for investigating the genetic determination of IMF content. Overall, 46 genes were annotated to the four relevant genomic regions Table .APOLD1) shows functions related to lipid binding, transportation and localisation. The ‘phospholipase B domain containing 1’ (PLBD1) and ‘phosphodiesterase 6H’ (PDE6H) genes show functions linked to hydrolase activity (phospholipases) and lipid metabolic processes. In humans, several functional annotations, including the sphingolipid signalling pathway, have been found for the ‘K‐RAS proto‐oncogene, GTPase’ (KRAS) gene. Moreover, two members of the retinol‐induced G protein‐coupled protein receptors also stand out in OCU8: ‘G protein‐coupled receptor class C group 5 member D’ (GPRC5D) and ‘G protein‐coupled receptor class C group 5 member A’  gene displays biological functions linked to lipid metabolic processes. In addition to the biological and molecular functional annotations, a list of pathways that include these genes was generated from david, the KEGG and Wiki pathways databases (Table Only three genes (two non‐coding‐protein genes and one protein‐coding gene) mapped to the genomic region on OCU13  or Bayes factors below thresholds . Moreover, this region presented a strong and long LD block between windows 841 and 842, which could imply a selective sweep process owing to divergent selection Fig. . The MAFet al. (FABP4 (OCU3) candidate gene and IMF. Our results are not in agreement with the studies for FTO (OCU5)   . However, these associations should be taken with caution as the significance threshold was more liberal  than in our GWAS . In agreement with GWAS studies for IMF in swine, our results suggest that there is a large polygenic component influencing the trait . This transcription factor activates the expression of the gene BMP7 (‘Bone Morphogenetic protein 7’), which in turn promotes the development of brown adipocytes  were found in the associated regions. In OCU13, orthologues of a novel gene (Ensembl gene ID: ENSOCUG00000027270) have been reported in other species. In rabbits, there are no functional annotations related to lipid metabolism or IMF linked to this gene. However, in humans and mice this gene is known as APOLD1, PLBD1, PDE6H and GPRC5A were involved in functions of lipid transport, localisation and binding or in the control of adipose cell function. Two of these genes (PLBD1 and PDE6H) participated in the catabolism of phospholipids, which are the major components of cell membranes and have important implications in adipocyte hypertrophy  was associated with the control of fat deposition in chickens . In particular, MTMR2 (OCU1) was linked to the metabolic process of lipids. This gene has been proposed as a functional candidate gene for IMF in GWAS and signatures of selection studies in a Duroc pig population selected for IMF , OCU8  and OCU1 (MTMR2) related to IMF. Nevertheless, further research would be necessary in order to corroborate these results; for instance, a genotype refinement or sequencing of promoter and exonic regions of the candidate genes and its validation in independent populations of rabbits. Our results could be important for further studies to discover polymorphisms that can assist in IMF genetic improvement.This is the first GWAS study for IMF in rabbits and hence provides a benchmark for continuing research in the field. Our findings support the hypothesis that four genomic regions  influence IMF content. The genomic variance explained by these associated regions is important although no major causal variants seem to segregate in the analysed rabbit populations. Therefore, according to what we observed in these divergently selected lines, it seems that IMF content is mainly driven by a polygenetic effect. In addition, we identified some candidate genes on the associated genomic regions of OCU13 . NIB was supported with a “Ramon y Cajal” grant provided by Ministerio de Ciencia e Innovación (RYC‐2016‐19764). CSH and PN were supported by the Medical Research Council . CSH was supported by Biotechnology and Biological Sciences Research Council .BSS carried out the statistical analyses and drafted the manuscript. AB, PH, LF and MAS conceived of the study and secured substantial funding. PH, BSS and RP performed the phenotypic data recording and collected DNA samples. PN, CSH and NIE supervised analyses and helped draft the manuscript. All authors read and approved the final manuscript.Figure S1 LD block of the associated genomic region on OCU13.Click here for additional data file.Figure S2 LD block of an associated genomic region on OCU8.Click here for additional data file.Figure S3 Assessment of genotypes for the three relevant SNPs within genomic regions associated with intramuscular fat.Click here for additional data file.Table S1 Genes found in the genomic regions associated with intramuscular fat.Click here for additional data file.Table S2 Functions of genes identified in this study through enrichr and david.Click here for additional data file.Table S2 Functions of genes identified in this study through enrichr and david.Click here for additional data file.


The alcohol consumption GWAS signals removed by the MLC corrections are enriched in metabolic/cardiovascular traits. Almost all the previously reported negative estimates of genetic correlations between alcohol consumption and common diseases become positive/non-significant after the MLC corrections. We also observe MLC biases for smoking and physical activities in the UK Biobank. Our findings provide a plausible explanation of the controversy about the effects of alcohol consumption on health outcomes and a caution for future analyses of self-reported behavioural traits in biobank data.Genome-wide association studies (GWAS) have discovered numerous genetic variants associated with human behavioural traits. However, behavioural traits are subject to misreports and longitudinal changes (MLC) which can cause biases in GWAS and follow-up analyses. Here, we demonstrate that individuals with higher disease burden in the UK Biobank ( Conflicting reports have found disease to sometimes be positively and sometimes negatively correlated with alcohol consumption. Here, the authors show that misreporting and reduction of alcohol consumption is associated with disease, leading to misleading associations between alcohol and disease. This conclusion, however, is contradictory to the negative estimates of the genetic correlation (rg) between AC and several diseases such as obesity7, major depressive disorder (MDD)7, Parkinson’s disease5, and type 2 diabetes (T2D)5 reported in the recent genome-wide association studies (GWAS) and also contradictory to the protective effects of moderate drinking reported in observational studies9. Different hypotheses have been proposed to explain these discrepancies, including (1) heavy AC might alter metabolism or impair nutrient absorption11, meaning that the effect is dosage-dependent; (2) people who have health problems may quit or reduce drinking, or underreport their intake level12; and (3) some other common explanations include confounding factors6  and collider bias14. Nevertheless, to date, no study has provided an in-depth investigation into the causes of the discrepancies.Behaviours and lifestyles are modifiable risk/protective factors for common diseases in humans. In the past few decades, one of the most controversial debates in public health is on the effect of alcohol consumption (AC) on common diseases, especially cardiovascular/metabolic diseases. Large-scale meta-analyses of epidemiological studies18. These phenotypes are also subject to change during lifetime22, for instance in response to disease diagnosis, but data to track such longitudinal variations are rarely available. Both misreports and longitudinal changes (hereafter referred to as MLC) could change the distribution of the phenotypes and thus may affect the results of both epidemiological and genetic studies.In epidemiological or genetic studies, phenotypic data of behavioural and lifestyle traits are often collected from self-reported questionnaires, which are subject to misreports , especially for questions related to smoking, drinking, and drug use23. The UKB includes detailed questionnaires of these behavioural traits, providing a unique resource to investigate the potential pitfalls in the analyses of self-reported phenotypes. We demonstrate that MLC could induce biases in GWAS of these traits and follow-up analyses that use summary statistics from the GWAS. We then propose a correction procedure to mitigate the MLC biases. Using AC as an example, we identify and remove the participants whose self-reported AC is inconsistent with their intake frequency, medical records or online follow-ups and the participants who reduced their AC intake because of illness or doctor’s advice during the past 10 years. Then, we stratify the participants into three longitudinal change groups  and run a GWAS analysis in each group separately followed by a meta-analysis. We also elaborate on why some of the previous studies might suffer from MLC biases.In this study, we set out to investigate biases due to MLC in genetic analyses of self-reported behavioural traits including AC, tobacco smoking, and physical activities in the UK Biobank (UKB)18 but often overlooked in genetic analyses. Here, we focused on the analyses of AC because (1) its relationship with common diseases is controversial; (2) the data required by our investigations and corrections are available; (3) the sample size is large . In this study, our definition of misreports for AC includes misreporting about drinking status24, underreporting the AC level17, and selective recall of the question about AC level25, all of which might occur in the UKB. These kinds of misreports are mainly attributed to27 social desirability29  and recall bias31 . First, 14,488 UKB participants identified themselves as never drinkers, but data from follow-up questionnaires and medical records32 for 3,627 of these participants suggested that at least 10% of the individuals were very likely to have drinking history, e.g., previously diagnosed as having alcoholic hepatitis or alcohol use disorder  unless specified elsewhere. Second, 9,064 individuals (2.1%) were classified as current drinkers but reported zero consumption level, indicating possible underreporting. Third, 66,058 individuals (15.6%) reported their alcohol intake frequency and other related questions but did not report their actual AC levels, suggesting a potential selective recall bias. It has been shown previously that heavy drinkers tend to be less responsive25, and a high non-response rate could lead to an underestimate of the average AC level in the sample33. To investigate the characteristics of the suspected misreporting individuals, we examined the phenotypes of 18 common diseases in the UKB and used disease count (the number of diseases carried) as an indicator of disease burden for each participant  also showed a significantly higher mean disease count than the remaining current drinkers .Misreports are common in self-reported data setsn = 424,507) were asked a question “compared to 10 years ago, do you drink less/the same/more nowadays?” (Methods), and 262,107 (61.7%) of them reported “less” or “more”. We denoted the three groups of individuals as LESS, SAME and MORE, respectively. The LESS group  had a lower average AC level, higher disease prevalence for several common diseases, and higher mean disease count than individuals in the other two groups , the prevalence of cardiovascular disease (CVD) was 0.411, ~2.7 times higher than that in all current drinkers (0.154), providing strong evidence of disease ascertainment of AC , we found that 44.9% of associated phenotypes  using AC GWAS data from each of the three longitudinal change groups or the whole sample (Methods). Before the MLC corrections, we observed substantial differences between s.e. 36) and found that the Biases in GWAS results due to MLC are expected to carry over to follow-up analyses using summary statistics of the GWAS, such as the genetic correlation  negative 4 showed negative 5 showed that AC is negatively genetically correlated with several common diseases including Parkinson’s disease, obesity, and T2D. We show that the negative estimates could be replicated in our study using data without the MLC corrections but most of them turned to positive after the MLC corrections  Fig. . Howeverentclass1pt{minimaentclass1pt{minimaentclass1pt{minimat P < 0.0 negative38. Clarke et al.4 and Liu et al.5 reported positive Socioeconomic status (SES) has been shown to affect people’s alcohol use and health outcomes, and several studies have shown that people with higher SES tend to have higher AC levels and lower disease risks than people with lower SESentclass1pt{minima39 which is more robust to collider bias than the conventional covariate adjustment approach. We found that before the MLC corrections, the t = 67.9, −log10(P) = 841.0) and between the LESS and MORE groups  = 879.7). The observation is in line with one of our conclusions above that participants with cardiometabolic diseases tend to reduce AC because these diseases are often associated with higher BMI. This observation is unlikely to be driven by EA because the differences remain highly significant  = 1288.9 between LESS and SAME and Welch-t = 79.5, −log10(P) = 1344.8 between LESS and MORE) after adjusting BMI for EA.To test the effects of EA and HI on the mentclasspt{minimamentclasspt{minima41. As the MR analysis relies on GWAS data, it might also be affected by the MLC biases as described above. We used BMI in the UKB as an example to demonstrate the performance of MR in the presence of MLC, based on several commonly used MR methods including IVW (inverse variance weighted)42, Robust43, MR-Egger44, GSMR39, weighted median45, simple median45, mode46, MR-PRESSO47, MRMix48, Con-Mix49, RAPS50. While the estimates from some methods  were all significantly positive and consistent across all the analyses with or without the MLC corrections, the estimates from IVW, simple median, MR-PRESSO, MRMix, Con-Mix seemed to be sensitive to MLC with some of them being negative  is a method that uses genetic variants as instrumental variables (IVs) to infer causal relationship between exposure and outcomeive Fig. . We also5 with a sample size of ~1 million consisting of ~42.9% of the sample from 23andMe and ~33.0% from the UKB (Methods). The results were similar to those from the analyses above using the AC GWAS data from the UKB only , often used as the reference group to compute the effect  of AC on disease risk, was enriched with individuals from the LESS group which had a higher CVD incidence than the SAME and MORE groups  into the LESS, SAME, and MORE groups  of METT was only 0.431, implying substantial longitudinal changes. Unfortunately, these changes were undocumented for the majority of UKB participants, so we were not able to perform correction as we did for smoking and drinking. It is known from prior work that device-measured PA show consistent inverse relationship with BMI, blood pressure, and adiposity53, while self-reported PA show inconsistent genetic correlation patterns at different intensity levels54, and that self-reported records in elder cohorts could suffer more from recall bias due to the high proportion of cognitive impairment55. Together with the evidence from the literature, our results suggest that IPAQ and OAA are better PA indicators than METT in the UKB, and that the Finally, we investigated physical activities (PA) in the UKB. The PA traits included self-reported METT  scores, IPAQ , and overall acceleration average . We investigated these three measures in this study because they are the most commonly used PA indicators and available in the UKB. IPAQ is a derived categorical trait  that utilises information from the METT and its three subsets: walking, moderate, and vigorous activities (Methods). We first estimated the In this study, we raised concerns that genetic analyses of human behavioural traits could be biased by misreports and longitudinal changes. AC in the UKB was used as the main example to demonstrate the detrimental effects of MLC on several genetic analyses commonly used to identify variant-trait associations or estimate the genetic or causative relationship between traits. Our results showed that disease ascertainment was likely to be the main cause of the MLC biases, which can be largely corrected for using additional information  and coarse longitudinal data . Our results also showed that the MLC corrections proposed in this study added value to the routine quality controls (QC) in GWAS for behavioural traits. Additionally, biases due to longitudinal changes appeared to be larger than that due to misreports, because the longitudinal changes were observed in more than half of the participants, while misreports only accounted for 10~20% of the UKB sample (at least for those we have identified thus far), as verified in our simulations 5, suggesting heterogeneity in drinking behaviours among different regions or populations. Second, since we have demonstrated that biases from MLC were mainly attributable to disease ascertainment, different disease prevalence between populations may lead to different patterns of MLC. Third, as MLC are associated with other factors, such as SES, studies with ascertainment of any of the MLC-associated factors would lead to a change of the MLC pattern. Last but not least, the pattern of MLC could vary in different age groups. For instance, disease ascertainment is expected to have a larger influence in middle-aged populations than in younger populations because younger populations are less likely to be affected by common diseases investigated in this study56. This is supported by the observation that the older UKB participants had a higher mean disease count with a higher proportion of them reducing AC due to illness or doctor’s advice , giving rise to differences in MLC biases between males and females , influences from family members or friends, religious experience, self‐evaluation and legal problemey Table . Any of In conclusion, we advise awareness of the pitfalls when analysing data on behavioural traits in biobank data sets such as the UKB. Misreports and longitudinal changes of behavioural traits by disease ascertainment could create biases and thereby induce spurious signals and a loss of power in GWAS. Biases in GWAS summary statistics due to MLC could further lead to biased estimates in follow-up analyses such as genetic correlation and Mendelian randomisation. As more biobank data sets have become accessible, it is important to identify, investigate, and correct for these biases in all kinds of behavioural traits including smoking, drinking, diet, physical activity, sleep, and self-rated health status. A longitudinal study of 1 million individuals for several decades seems impractical at present, but we have shown that the biases in AC can be largely corrected for by phenotypic QC and longitudinal adjustment when additional phenotype information  are available. Questionnaires on lifetime use may provide more accurate estimates of the effects of behaviours on health outcomes at a much lower cost than a longitudinal follow-up study. Researchers should be more careful regarding these biases when conducting analyses for behavioural and other modifiable traits using biobank data sets with self-reported records.23. The UK Biobank has approval from the North West Multicentre Research Ethics Committee (MREC), and informed consent has been obtained from all participants. There were 455,607 individuals of European ancestry with complete information on sex, age and principal components (PCs). The self-reported drinking statuses (data-field ID: 20117) were: never drinkers , previous drinkers , current drinkers , and unknown (446 participants preferred not to answer and 254 provided no response). We removed “former drinkers” from all the analyses in this study, considering the occurrence of the “sick quitter phenomenon”61. Among the 424,507 current drinkers, 358,449 individuals reported their intake level. The AC level was summed up as a weekly total intake score (units/week) of all the alcoholic drink subtypes including beer plus cider, red wine, champagne plus white wine, spirits, and fortified wine. The mean of AC was 10.67 units per week (s.d. = 10.23). One unit was defined as one measure for spirits, one glass for red wine/white wine/champagne, or one pint of beer/cider. The raw AC units were transformed by log2 (raw AC units + 1) to avoid having a heavily skewed distribution. The smoking intensity was measured in cigarettes per day (CPD) in all current smokers . Physical activity traits in the UKB were collected from both self-reported questionnaires and devices (wrist-worn accelerometers). METT is a total score of the Metabolic Equivalent Task (MET) minutes per week for walking, moderate activity, and vigorous activity . IPAQ is a derived categorical trait that utilises the information from the METT and its three subsets mentioned above (see transformation criteria in https://biobank.ndph.ox.ac.uk/showcase/refer.cgi?id=540). The three IPAQ categories are denoted as low, moderate, and high . The participants were voluntary, and the measurements were collected for seven consecutive days .We obtained behavioural and disease traits from the UK Biobank (UKB) data39, we extracted phenotypic data of common diseases based on the primary (data-field: 41202) and secondary (data-field: 41204) ICD 10 codes and self-reported diseases (data-field: 20002) , (2) current drinkers with reported weekly consumption of zero (n = 9064), and (3) current drinkers who provided no response to AC , and retained a total of 349,385 individuals. The second step is to account for self-reported longitudinal changes compared to 10 years ago (data-field ID: 1628). We partitioned the individuals who passed the QC above into three groups based on the longitudinal change  and conducted GWAS within each group. In the LESS group, we further removed individuals who reduced their AC because of being ill or doctor’s advice, i.e., longitudinal change due to disease ascertainment . We then performed an inverse-variance weighted meta-analysis of the GWAS results from the three groups . This partitioning strategy efficiently removed any difference in mean or variance between the three groups. More details of the MLC corrections are shown in Supplementary Figs. Our MLC corrections consist of two steps. The first step is a phenotypic quality control (QC) procedure used as an attempt to minimise the effects of misreports. We removed the individuals who self-reported as 1) never drinkers ,  current 63 panel by the UKB team23. We selected a subset of the sample of European ancestry  from the whole UKB cohort by projecting the individuals onto the PCs from the 1000 Genome Project (1KGP). Genotype posterior probabilities were converted to hard-call genotypes using PLINK2 64. We removed SNPs with a minor allele count <5, Hardy-Weinberg equilibrium test P-value < 1 × 10−6, missing genotype rate >5%, or imputation info score <0.3. For binary traits, we performed BOLT-LMM analysis65 with sex, age and the first 10 PCs fitted as covariates and then transformed the estimates of SNP effects on the observed 0-1 scale to odds ratios (OR) by LMOR66. For quantitative traits, we adjusted the phenotypic values for sex and age, standardised the adjusted phenotypes to z-scores, excluded individuals with 65 with the first 10 PCs as fitted as covariates in the model.The UKB genotype data were cleaned and imputed into the Haplotype Reference Consortium (HRC)Considering a loss of power due to decreased sample size by MLC corrections, we randomly down-sampled the GWAS data by 21,940 individuals and repeated this process 30 times. We used a z-statistic to test if the number of loci that became non-significant (or changed from non-significant to significant) after the MLC corrections is significantly different from that expected by random down-sampling. The average number of loci that became non-significant due to down-sampling was 10.03 (standard error of the mean s.e.m. = 0.85), significantly (67 (LDSC) to estimate SNP-based heritability for a trait and the bivariate-LDSC35 to estimate genetic correlation between traits using ~1.2 million SNPs in common with those in HapMap 3 (ref. 68). For the 234 traits for which we obtained GWAS summary data from LD Hub (http://ldsc.broadinstitute.org/ldhub/), the LDSC analyses were performed online in LD Hub36. Note that due to the restricted access to the full summary statistics of the 23andMe data sets, we did not perform the genetic correlation analysis for AC using the full GSCAN data5.We used the LD score regression41. MR assumes that the IVs are independent of possible confounders that may associate with both the exposure and outcome. Also, the IVs are assumed not to be associated with the outcome other than mediated through the exposure. However, in real data, these assumptions can be violated, leading to a biased estimate of the causal effect69. We performed MR analyses to test the causal effect of AC on BMI using IVW, Robust, MR-Egger, simple median, weighted median, mode, and Con-Mix implemented in the R package ‘MendelianRandomization’ v0.4.2, MR-PRESSO v1.0, MRMix v0.1.0 and RAPS v0.2 in R, and GSMR implemented in GCTA v1.91.8beta (http://cnsgenomics.com/software/gsmr/). The IVs were selected from a clumping analysis of the GWAS summary statistics in GCTA-GSMR  is a method to estimate causal effect of an exposure on an outcome using instrumental variables (IVs) associated with the exposurerb approach developed in a previous study70 as well as the causal effect of Y on D using GSMR. To demonstrate the effectiveness of our MLC correction procedure in correcting for disease ascertainment bias, we divided the individuals into two groups (LESS and SAME) and then conducted the GWAS separately, followed by a meta-analysis. Details of the simulation process and parameter specifications can be found in Supplementary Note We carried out simulations to mimic the bias due to disease ascertainment in GWAS and its follow-up analyses. If individuals who are affected by a disease tend to change a behaviour, such a change would lead to a spurious correlation between the disease and behaviour. We considered four scenarios in the simulation: (I) the disease liability (D) is independent of the behavioural trait (Y), and 100 SNPs are associated with Y only; (II) Y had a causal effect on D, and 100 SNPs are associated with Y (and D mediated through Y); (III) Y and D are independent, and 100 SNPs are associated with D only; (IV) Y had a causal effect on D, 100 SNPs affected Y (and D mediated through Y), and another set of 100 SNPs affected D directly. In each scenario, to mimic the disease ascertainment, we reduced the values of Y for the individuals who had high values of D. More specifically, if the D value of an individual passed a threshold , the corresponding Y value would be subtracted by a constant. We set the disease ascertainment threshold to be 10%, 20%, 30%, or 40% and considered a subtraction from Y value by 1, 2, 3, 4 or 5 standard deviations. We then conducted GWAS and estimated SNP effect correlation between Y and D using the Further information on research design is available in the Supplementary InformationPeer ReviewReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15


French beef producers suffer from the decrease in profitability of their farms mainly because of the continuous increase in feed costs. Selection for feed efficiency in beef cattle represents a relevant solution to face this problem. However, feed efficiency is a complex trait that can be assessed by three major criteria: residual feed intake (RFI), residual gain (RG) and feed efficiency ratio (FE), which involve different genetic determinisms. An analysis that combines phenotype and whole-genome sequence data provides a unique framework for genomic studies. The aim of our study was to identify the gene networks and the biological processes that are responsible for the genetic determinism that is shared between these three feed efficiency criteria.A population of 1477 French Charolais young bulls was phenotyped for feed intake (FI), average daily gain (ADG) and final weight (FW) to estimate RFI, RG and FE. A subset of 789 young bulls was genotyped on the BovineSNP50 single nucleotide polymorphism (SNP) array and imputed at the sequence level using RUN6 of the 1000 Bull Genomes Project. We conducted a genome-wide association study (GWAS) to estimate the individual effect of 8.5 million SNPs and applied an association weight matrix (AWM) approach to analyse the results, one for each feed efficiency criterion. The results highlighted co-association networks including 626 genes for RFI, 426 for RG and 564 for FE. Enrichment assessment revealed the biological processes that show the strongest association with RFI, RG and FE, i.e. digestive tract  and metabolic processes (cellular and cardiovascular). Energetic functions were more associated with RFI and FE and cardio-vascular and cellular processes with RG. Several hormones such as apelin, glucagon, insulin, aldosterone, the gonadotrophin releasing hormone and the thyroid hormone were also identified, and these should be tested in future studies as candidate biomarkers for feed efficiency.The combination of network and pathway analyses at the sequence level led to the identification of both common and specific mechanisms that are involved in RFI, RG and FE, and to a better understanding of the genetic determinism underlying these three criteria. The effects of the genes involved in each of the identified processes need to be tested in genomic evaluations to confirm the potential gain in reliability of using functional variants to select animals for feed efficiency. Improving feed efficiency of beef cattle is a major concern for beef producers. In France, on the one hand, the carcass weight of Charolais beef bulls has increased by 13% between 1996 and 2016 . CurrentEstimates of the heritability of FCR, RFI and RG are moderate, i.e. on average 0.23, 0.33 and 0.28, respectively (see review in ). Thus, A previous study  that esthttps://www.legifrance.gouv.fr/eli/decret/2013/2/1/2013-118/jo/texte).During this experiment, all animals were kept indoors, handled with care following the Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) ethics policy in accordance with the guidelines for animal research of the French Ministry of Agriculture  . Means and standard deviations of feed efficiency and production traits for this subset are in Table Bos taurus individuals from different cattle breeds, most of them purebred. However, to reduce the time necessary for imputation to WGS, a subset of 18 cattle pure breeds was selected to build the reference population, by ensuring a sufficient representativeness of each breed and the genetic proximity with the French cattle pure breeds [see Additional file The genotypes of the young bulls were imputed to WGS as described in . Briefly2) as calculated by the Minimac software. To remove variants with the lowest accuracies of imputation, only those with a R2 higher than 30% and a MAF higher than 1% were retained for further association analyses, i.e. 8,602,123 variants.The accuracy of the imputation from HD to WGS was assessed by the coefficient of determination , age of the dam  and twinning (single or twin), and age at the end of the test was included as a covariate. All association analyses were computed with the GCTA software (version 1.26) , using tWe applied the AWM approach  to detecThe proportion of variance explained by the SNPs retained after the three AWM analyses was estimated using the GCTA software. To do that, a new genomic relationship matrix was computed for each feed efficiency trait with SNPs from the AWM analysis only. Then, these SNPs were used to perform a genome-based restricted maximum likelihood (GREML) analysis to estimate the SNP-based heritability for each trait . This anEnrichment in gene ontology (GO) terms and pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) was investigated for the genes selected by the AWM approach. The plug-in ClueGO 2.5.5  for Cyto10), because of the huge number of markers and tests at the sequence level. Considering this very stringent threshold, no variant exceeded this threshold for the three GWAS performed here. However, in a first approach, the 100 variants with the lowest P-value for RFI, RG and FE were selected to find the most likely associated genes (variants included in the genes). This analysis revealed seven genes for RFI, eight for RG and 11 for FE (see Table CAPN7 and CACNA1E) and two with both RG and FE (ENSBTAG00000030623 and MROH7). No gene was associated with both RFI and RG.For feed efficiency traits, the Manhattan plots from the GWAS are in Figure S1 [see Additional file 0.05/8,60,123), beAll the genes retained after the AWM analyses are in Tables S1, S2 and S3 [see Additional file The AWM analysis with RG as key phenotype decreased the number of variants from 8,602,123 after the GWAS to 11,169 SNPs. Feed intake was not correlated (using SNP z-scores) with RG and was not in the AWM analysis. Of the 11,169 SNPs, 3750 that were located in 426 genes were retained  with FE and used in the AWM analysis. Among the 13,751 SNPs, 4139 were located in 564 genes  was identified with RFI, RG and FE in the AWM analyses with RFI or FE as key phenotype and with RG, FE and ADG in the AWM analysis with RG as key phenotype.The RFI, RG and FE AWM analyses highlighted the same five genes that were associated with the three feed efficiency criteria: After each AWM analysis, genomic correlations were calculated using the variant’s z-score of the additive effects of the retained variants and compared with the genetic correlations computed from pedigree data  and three KEGG pathways   are set to detect significant associations. For instance, in [In the past, most studies have focused their GWAS on a P-value approach, using a Bonferroni or false discovery rate (FDR) threshold. These methods attempt to find the good balance between highly conservative thresholds that result in strong but few SNPs, and relaxed thresholds that result in more SNPs but with potential false positive results. In our study, at the whole-genome scale, this approach would have been too stringent and no SNPs would have passed the thresholds. This absence of major effects for SNPs associated with feed efficiency has already been observed in the literature and, usually, very weak thresholds  and “vascular smooth muscle contraction” pathway were enriched by the three feed efficiency networks. In addition, the RFI and FE networks also included thrombin-activated receptor. Thrombin is a serine protease that regulates platelet aggregation, endothelial cell activation and several processes in vascular biology such as conversion of circulating fibrinogen to fibrin monomer . Linked ected in , were foected in , 38, 55.Both RG and FE networks enriched the gonadotrophin releasing hormone (GnRH) signalling pathway, which is known to be the most significantly enriched KEGG pathway for growth and RFI enriched networks . This paMetabolisms related to glycoproteins were enriched in the three feed efficiency networks. Glycoproteins are subjected to post-transcriptional modifications that catalyse covalent cross-linking of oligosaccharide chains to amino acids . GlycoprOur results show that RFI is specifically associated with the ossification process, inflammatory response, cellular component disassembly, protein localization and melanogenesis. Melanogenesis has been shown to be related to RFI , 64. ForOur results revealed both common and specific biological processes associated to RFI, RG and FE and allow a better understanding of the genetic determinism of these feed efficiency criteria. Each of the three gene networks, one for each trait, confirmed the strong association between FE and RFI and between FE and RG and also the weaker association between RFI and RG. The enrichment analysis highlighted the complexity of the genetic architecture of feed efficiency, especially for RG that enriched the largest number of processes. Indeed, a huge number of cardio-vascular and cellular processes were highlighted for this trait and several others such as the immune system, reproduction, protein regulation and signalling systems. Interestingly, RFI and FE seemed to be more associated with energy functions, which confirms the role of the energy metabolism on feed efficiency. The three feed efficiency criteria were also associated with digestive tract processes such as salivary, gastric and mucin secretion. Moreover, our study identified several potential markers that could be used to predict feed efficiency e.g. apelin, glucagon, insulin, aldosterone, GnRH or the thyroid hormone. Our findings confirm the real interest of using gene network analyses to identify genes that have small effects on traits. The effects of these genes need to be tested in genomic evaluation to confirm the potential benefit of using functional SNPs to select animals for feed efficiency.Additional file 1. List of breeds included in the reference population panel for whole-genome imputation.Additional file 2: Fig. S1. Manhattan plots of residual feed intake (a), residual gain (b) and feed efficiency ratio (c).Additional file 3: Table S1. List of genes retained after the AWM analysis with RFI as key phenotype. Table S2. List of genes retained after the AWM analysis with RG as key phenotype. Table S3. List of genes retained after the AWM analysis with FE as key phenotype.Additional file 4: Table S4. List of GO terms and KEGG pathways enriched by genes retained after the AWM analysis with RFI as key phenotype. Table S5. List of GO terms and KEGG pathways enriched by genes retained after the AWM analysis with RG as key phenotype. Table S6. List of GO terms and KEGG pathways enriched by genes retained after the AWM analysis with FE as key phenotype.Additional file 5: Fig. S2. Network interaction between GO terms, KEGG pathways and genes from the AWM analysis with RG as key phenotype. Fig. S3. Network interaction between GO terms, KEGG pathways and genes from the AWM analysis with FE as key phenotype.


Functional and evolutionary patterning converges at the level of macroscale cortical structural organization. n = 1113), we determined structural covariance of thickness to be organized along both a posterior-to-anterior and an inferior-to-superior axis. Both organizational axes were present when investigating the genetic correlation of cortical thickness, suggesting a strong genetic component in humans, and had a comparable organization in macaques, demonstrating they are phylogenetically conserved in primates. In both species, the inferior-superior dimension of cortical organization aligned with the predictions of dual-origin theory, and in humans, we found that the posterior-to-anterior axis related to a functional topography describing a continuum of functions from basic processes involved in perception and action to more abstract features of human cognition. Together, our study provides important insights into how functional and evolutionary patterns converge at the level of macroscale cortical structural organization.The topology of the cerebral cortex has been proposed to provide an important source of constraint for the organization of cognition. In a sample of twins ( A fundamental question in neuroscience is how the structure of the cortex constrains its function. Over the course of almost a century, numerous studies have shown that the cerebral cortex is organized along dimensions that reflect systematic variations in features of brain structure and function such as laminar differentiation, gene expression, and structural and functional connectivity  macroscale patterns in cortical organization make an important contribution to human cognition and (ii) cortical thickness covariance reflects shared maturational and genetic processes and is organized in multiple communities. However, the reason for their particular spatial relationship remains unclear. Our current study sought to directly examine the genetic origin of spatial organization of macroscale cortical features. We used unsupervised learning methods to construct large-scale organizational gradients that underpin the structural covariance across the cortex. In contrast to clustering-based decompositions of the brain into discrete communities  and paleocortex (olfactory cortex) distance and the inferior-to-superior organization gradient in humans and macaques, aligning covariance topology with the dual origin theory. Together, these analyses highlight the important role that genetic processes play in determining the large-scale organization of cortical structure and so provide an important window into the innate architecture supporting human cognition.P < 0.001, and visual, sensory-motor, fronto-parietal, and default mode networks had higher covariance within network relative to random networks [false discovery rate (FDR) q < 0.05], in line with previous work .r = 0.47, P_spin < 0.005) using non-parametric spin tests to account for spatial autocorrelation .r = 0.05, P > 0.1) (The second gradient (G2) followed an inferior-superior pattern with end points in superior parietal lobe and lingual gyrus, respectively, and explained 13% of the variance. Binning G2, we observed highest covariance within and between superior regions, negative covariance between both gradient end points, and a positive diagonal indicating covariance patterns where highest within and not between bins. The inferior-superior covariance gradient did not show a relation with the principal functional gradient (P > 0.1) .n = 799, age 8 to 85 years] using different preprocessing pipelines of thickness (CIVET and FreeSurfer 6.0), and parcellation methods . Environmental correlations were organized along a posterior-anterior and inferior-superior axis as well, explaining 13 and 11% of the variance, respectively (fig. S5).The second gradient explained 13% of the variance and reflected a similar inferior-superior axis as was seen in the structural covariance gradients. Genetic correlation was strongest between regions at similar levels of the inferior-superior gradient, with regions in both apexes showing stronger genetic correlations. Similar to our observations in structural covariance, we observed a positive relationship between G1 and the principal gradient of functional organization  , P < 0.0001] and inferior-superior gradient  in humans and macaques. Similarities were stronger than between posterior-anterior gradient in humans and inferior-superior gradient in macaques , P = ns] or inferior-superior gradient in humans and posterior-anterior gradient in macaques , P = 0.001].Last, using a recently-proposed cross species alignment   or the hippocampus (archicortex). The theory was established on histological investigations of the adult cortex of various reptiles and mammals . This suggests that macroscale structural organization varies gradually as a function of paleocortex distance. In contrast, there was negative relationship between inferior and superior proportions of the inferior-superior gradient and archicortex distance  and a negative, but nonsignificant, linear relationship between this gradient and archicortex distance . We did not observe a consistent association between the dual origin and the posterior-anterior gradient . Evaluating genetic correlation as a function of paleo- and archicortex distance, we observed that genetic correlation varied as a function of distance from both origins.We computed the geodesic distance from the archicortex and paleocortex in humans motivated by previous anatomical descriptions  (3) and P < 0.002, Spearman’s r = −0.63, P_spin < 0.0001) and a negative association with paleocortex distance . Again, we did not observe a consistent association between the dual origin and the posterior-anterior gradient .We performed a similar analysis in macaque monkeys using a previous approximation of the distance from archi- and paleocortex  and G2 , although neither survived spin corrections .Last, comparing the covariance gradients in humans against the laminar differentiation as a proxy for sensory-fugal patterning, we observed some correspondence between this model at the main gradients of genetic correlation G1  patterning is extensively described during embryonic development of the central nervous system , which comprised data from 1206 individuals , 298 monozychotic (MZ) twins, 188 dizychotic (DZ) twins, and 720 singletons, with a mean age of 28.8 years . We included individuals for whom the scans and data had been released (humanconnectome.org) after passing the HCP quality control and assurance standards. The full set of inclusion and exclusion criteria are described elsewhere  protocols of the HCP are previously described (www.bic.mni.mcgill.ca/ServicesSoftware/CIVET). The nonuniformity artefacts were corrected with the N3 algorithm using the recommended N3 spline distance of 125 mm for 3T T1-weighted scans. Cortical thickness was then measured as the distance between the estimated “white” and “gray” cortical surfaces, in the native space framework of the original MR images, using the same approach that is used in FreeSurfer.In addition to assess robustness and replicability of the results across different surface estimation pipelines, cortical thickness estimates were further estimated using FreeSurfer version 6.0 and CIVET  represents the portion of the phenotypic variance (σ2p) accounted for by the total additive genetic variance (σ2g), i.e., h2 = σ2g/σ2p. Phenotypes exhibiting stronger covariances between genetically more similar individuals than between genetically less similar individuals have higher heritability. Within SOLAR, this is assessed by contrasting the observed covariance matrices for a neuroimaging measure with the structure of the covariance matrix predicted by kinship. Heritability analyses were conducted with simultaneous estimation for the effects of covariates. For this study, we included covariates including global thickness, age, sex, age2, and age × sex.Heritability (g) and environmental (ρe) correlations, on the basis of the phenotypic correlation (ρp), between cortical thickness parcels in the following formula: h21 and h22 are the heritability of the parcel-based cortical thickness. The significance of these correlations was tested by comparing the log likelihood for two restricted models  against the log likelihood for the model in which these parameters were estimated. A significant genetic correlation is evidence suggesting that (a proportion of) both phenotypes are influenced by a gene or set of genes (phg) divided by the phenotypic correlation. For the relative contribution of environmental correlation to the phenotypic correlation, we computed phe) divided by the phenotypic correlation , and we assessed the amount of variance explained.To compute macroscale organizational gradients, we performed several analysis steps. The input of the analysis was the structural covariance/genetic correlation matrix, which was cut off at 90% similar to previous studies  data acquired through the HCP  running through the cortical mantle using an approach invariant to mesh configuration (https://github.com/brian-lau/multdist/blob/master/minentest.m), and statistical significance was assessed with permutation tests (1000). Thus, spin tests are used to assess significances of similarity of continuous spatial maps that have spatial autocorrelations, whereas statistical energy tests were used to compare two distributions.To make comparisons across gradient and distance maps, we used spin tests to control for spatial autocorrelation when possible (http://fcon_1000.projects.nitrc.org/indi/indiPRIME.html). Three cohorts of macaque monkeys were included in the present study .We used the MRI data from the recently formed non-human primate (NHP) data sharing consortium PRIME-DE (Macaca mulatta) scanned on a 3T scanner with four-channel coil. The data were collected while the animals were under anesthesia. Briefly, the macaque was sedated with intramuscular injection of ketamine (10 mg/kg) combined with either xylazine (0.125 to 0.25 mg/kg) or midazolam (0.1 mg/kg) and buprenorphine (0.01 mg/kg). In addition, macaques received injections of atropine , meloxicam , and ranitidine . The anesthesia was maintained with isoflurane. The details of scan and anesthesia procedures are described in (http://fcon_1000.projects.nitrc.org/indi/PRIME/oxford.html). Protocols for animal care, MRI, and anesthesia were carried out under authority of personal and project licenses in accordance with the UK Animals (Scientific Procedures) Act (1986)  scanned on a Siemens Skyra 3T with four-channel clamshell coil. All the animals were scanned under anesthesia. In brief, the macaques were sedated with injection of ketamine (10 mg/kg), dexmedetomidine (0.01 mg/kg), and buprenorphine (0.01 mg/kg). The anesthesia was maintained with isoflurane at 1 to 2%. The details of the scan and anesthesia protocol can be found at http://fcon_1000.projects.nitrc.org/indi/PRIME/ucdavis.html. The neuroimaging experiments and associated procedures were performed at the California National Primate Research Center under protocols approved by the University of California, Davis Institutional Animal Care and Use Committee (The full dataset consisted of 19 rhesus macaque monkeys (M. mulatta) scanned on a Vertical Bruker 4.7T primate dedicated scanner. We restricted our analysis to 10 animals  for whom two awake resting-state fMRI scans were required. The structural T1-weighted images were acquired using MDEFT sequence with resolution of 0.6 mm by 0.6 mm by 0.6 mm; TE, 6 ms; and TR, 750 ms. All nonhuman animal scans and associated procedures were performed at Newcastle University, UK and were approved by the Animal Welfare and Ethical Review Body at Newcastle University and by the UK Home Office (The full dataset consisted of 14 rhesus macaque monkeys (www.itksnap.org)  spatial denoising by a nonlocal mean filtering operation (We excluded macaque monkeys that showed a hemispheric difference of more than 0.2 cm  for our final analysis, as gradient models were estimated on the basis of covariance of ipsi- and contralateral covariance.First, we constructed a covariance matrix, controlling for dataset site and global thickness. Next, we performed gradient analysis analog to described in humans.To evaluate the similarity between human and macaque gradients, we transformed the human gradient to macaque cortex based on a functional alignment techniques recently developed. This method leverages advances in representing functional organization in high-dimensional common space and provides a transformation between human and macaque cortices . For our phenotypic analyses, we selected individuals with complete cortical thickness data. Our sample for phenotypic correlations consisted of 799  individuals with a mean age of 41.1 years .To evaluate the cross-sample reproducibility of observations, we additionally investigated cortical thickness covariance in the enhanced Nathan Kline Institute-Rockland Sample (NKI). The sample was made available by the Nathan-Kline Institute  (http://fcon_1000.projects.nitrc.org/indi/enhanced/studies.html. All T1 scans were preprocessed using the FreeSurfer software library (https://surfer.nmr.mgh.harvard.edu) version 6.0.0 . Moreover, cortical thickness estimation using CIVET was performed using version 2.1.1 (www.bic.mni.mcgill.ca/ServicesSoftware/CIVET).Cortical thickness estimates of the individuals of the HCP S1200 release using differing processing pipelines were computed as part of an independent study  for a given pair of parcels i and j is defined in Eq. 2s is a participant and n is the number of participants. We used the MPCMRI to compute the gradient of microstructure.We estimated microstructural profile covariance (MPC) using myelin-sensitive MRI, in line with the previously reported protocol (Eq. 1www.humanconnectome.org/) (www.ncbi.nlm.nih.gov/pmc/articles/PMC3472598/) (http://fcon_1000.projects.nitrc.org/indi/enhanced/studies.html). The raw data may not be shared by third parties due to ethics requirements but can be downloaded directly via the above web links. Macaque data were obtained from the recently formed NHP data sharing consortium PRIME-DE (http://fcon_1000.projects.nitrc.org/indi/indiPRIME.html). Three cohorts of macaque monkeys were included in the present study . Genetic analyses were performed using Solar Eclipse 8.4.0 (www.solar-eclipse-genetics.org), and data on the KING pedigree analysis are available at https://www.nitrc.org/projects/se_linux/ (https://brainspace.readthedocs.io/en/latest/). Surface-wide statistical comparisons and visualizations were carried out using SurfStat (https://github.com/MICA-MNI/micaopen/tree/master/surfstat) in combination with ColorBrewer (https://github.com/scottclowe/cbrewer2). Both structural covariance and genetic correlation gradients are available at https://github.com/sofievalk/projects/tree/master/Structure_of_Structure.This study followed the institutional review board guidelines of corresponding institutions. All human data analyzed in this manuscript were obtained from the open-access HCP young adult sample (HCP;


We identify 281 distinct variant-trait-associations at genome wide significance that replicate known associations. Allele frequencies for replicated loci show higher correlations with European (r = 0.94) than with African (r = 0.85) or Japanese (r = 0.80) populations. We find differences in linkage disequilibrium patterns and in effect sizes of the replicated loci compared to previous reports. We also report 17 novel and Qatari-predominate signals providing insights into the biological pathways regulating these traits. We observe that European-derived polygenic scores (PGS) have reduced predictive performance in the Qatari population which could have implications for the translation of PGS between populations and their future application in precision medicine.Clinical laboratory tests play a pivotal role in medical decision making, but little is known about their genetic variability between populations. We report a genome-wide association study with 45 clinically relevant traits from the population of Qatar using a whole genome sequencing approach in a discovery set of 6218 individuals and replication in 7768 subjects. Trait heritability is more similar between Qatari and European populations ( The genetic basis for traits can vary between populations. Here the authors report a genome wide association study with 45 clinically-relevant traits in individuals from Qatar, replicating many known loci and identifying new Qatari-predominant signals. However, most of the published GWAS studies performed to-date are from European, or East Asian populations4. Middle Eastern populations are under-represented. Also, all GWAS conducted so far used genotyping arrays imputed on genome sequencing data from studies in which only few, if any, Middle Eastern genomes were present and therefore miss all population-specific signals. Large-scale GWAS of many traits and complex diseases in Africans and Asians indicated differences in the genetic architecture between populations, but included few, if any, study participants from Arab ethnicities. In addition, many trait-associated variants show differences in allele frequencies and effect sizes across populations7 which may complicate the derivation of polygenic scores. Recent studies have shown that polygenic risk scores derived from studies in European populations have lower predictive performance when applied to non-European populations8, providing strong argument for conducting GWAS in non-European populations that are less represented in previously published studies.Genome-wide association studies (GWAS) have provided new insights into the genetic determinants of many clinically relevant traits and identified thousands of disease- or trait-associated genetic variantsHere, we report the first comprehensive GWAS of 45 clinically relevant traits in a Middle Eastern population using a whole genome sequencing approach. We unveil differences in heritability of certain life-style related traits between populations, investigate differences in the genetic architecture of replicating loci, assess the performance of European-derived polygenic scores in Qatari population, and report novel trait associations that are predominant to the Middle Eastern population of Qatar.9 with the aim to gain insights into the population structure and the genetic architecture of clinically relevant phenotypes in the Middle Eastern Qatari population. The present study is based on whole genome sequence data from 6218 participants of QBB and further replication in 7768 subjects from the second batch of QBB data. We performed a comprehensive heritability and genome-wide association study for 45 clinically relevant traits in the Middle Eastern population of Qatar. The investigated traits cover the following categories (Table N = 3), electrolytes (N = 7), measures of enzyme activity or abundance (N = 5), blood coagulation-related traits (N = 4), blood cell composition (N = 9), lipid traits (N = 4), and other clinically-relevant biochemistry measurements (N = 13). A detailed description of the study population and phenotype assessment is provided in the methods section, Supplementary Data The QGP is a population-based study designed to perform whole genome sequencing of the Qatar Biobank (QBB) participants11. A recent study in the Ugandan population of Africa showed marked differences in heritability estimates for many complex traits compared to European populations6. For example, estimates of heritability for body height in Ugandan populations was significantly lower (49%) than those from European populations (77%) suggesting differences in genetic loci and/or proportion of environmental contribution. The heritability of most traits in Middle Eastern populations remains undetermined. We therefore performed a comprehensive assessment of the heritability (h2) of the 45 traits in the QGP data. We found that h2 estimates ranged from 13% for serum iron levels (Fe) to 59% for body height  compared to the correlation between African and Middle Eastern populations  was lower than in European  but not significantly different from African populations . Similarly, heritability for BMI in QGP (31%) was lower than in Europeans; namely Sardinia  and Iceland . Interestingly, h2 for the liver enzyme GGT in QGP data (35%) was similar to the European populations  but significantly higher than in African populations . In contrast, estimates of heritability for cholesterol in QGP data  was significantly lower than values from European  or African  populations.The proportion of variation that can be attributed to genetic factors (heritability) has been investigated for many clinically-relevant traits, but mainly in populations of European descentability h of the 4ability h of the 4N = 7,880,618) using linear mixed models correcting for age, sex, population principal components and relatedness (see methods). The genomic inflation factor (λGC) ranged between 0.99 and 1.13 . Considerable inflation was only detected for traits that are well-known to have large polygenic architecture such as adult height (λGC = 1.13) and BMI (λGC = 1.09). Manhattan and quantile-quantile plots for the studied traits are presented in Supplementary Data We performed genome-wide association analyses of 45 clinically-relevant quantitative traits using whole genome sequencing data for 6218 individuals from the QGP study. We focused on common and low frequency variants  with any other variant within a window of 10 Mb. Of the 301 identified genetic signals, 281 were located within ±500 kb of a previously reported variant for the same trait. We replicated many loci that are known to have consistent association in studies across various population ancestry2. Examples include the SLC2A9 locus for uric acid , the UGT1A4 locus for total bilirubin  and the APOE locus for low density lipoprotein-cholesterol . Of the 281 genome-wide significant variant-trait associations, 51 were observed for the same SNP as reported in the PhenoScanner12. For these SNPs it was possible to assess the direction of association  and all showed directionality of association consistent with previous reports. We also observed multiple distinct signals for many loci  for which 20 distinct genome-wide significant signals were detected in a 615 kb region on chromosome 13  patterns between QGP data and other populations can account for differences in signal patterns. LD analysis of the Tbil and PT loci shows marked differences in LD patterns and allele frequencies between the European, East Asian and QGP populations  or very rare  in the East Asian population whereas only two of the variants are very rare in Europeans.Figure −8 Table . For eac 13 Fig.  which ha7 as a reference. We selected this study because it represents the largest and most comprehensive published GWAS of similar traits, and because the traits in the BBJ study were transformed similarly to our analysis, which allows a direct comparison of variant-level effect sizes for the identified loci . In the BBJ study, Kanai et al.7 performed a GWAS of 58 clinically-relevant traits in study participants of East Asian descent. Of the 45 traits analyzed in the present study, 28 traits overlapped with those analyzed by the BBJ project. For these traits, the BBJ study identified a total of 907 trait-variant associations which include known loci from previous studies in various populations  as well as new loci identified in the Japanese population (N = 332). Of the 907 association, we could evaluate 898 in QGP: 659 for which the same genetic variant was available in our data set (designated as group A variants) and 239 for which at least one proxy variant within 1 Mb in our data was in strong LD  or exhibited some degree of LD  with the variant reported in BBJ 5. Since polygenic risk scores estimated from one population may not be precisely applicable to other populations, we assessed replication, allele frequency and effect size for group A variants in QGP data. We found 29 loci that replicated at a Bonferroni-corrected significance threshold of P < 5.6 × 10−5 (0.05/898). All had consistent direction of effect. Comparison of effect size for the replicated loci showed a significant trend for higher effect sizes in QGP compared to BBJ  compared to African (r = 0.85) or Japanese (r = 0.80) populations , 180 of the 659 group A variants show nominal evidence of replication and all but seven of these have consistent direction of effect with the variant reported in BBJ. Analysis of these loci revealed significant differences in the distribution of effect size compared to BBJ data  compared to BBJ . Conversely, a larger number of loci with small effects (Beta < 0.05) was observed in BBJ (n = 97) compared to our data  but no significant difference was found for loci with an effect size > 0.1 (P > 0.05). Comparison of allele frequencies for these loci also shows higher correlation with European (r = 0.94) compared to African (r = 0.75) or Japanese population (r = 0.70). Colocalization analysis showed that 16 out of the 180 loci (8.9%) had distinct signals between QGP and BBJ  contained a signal within ±500 kb with significant P-values for the same trait (N = 115), after correction for multiple testing (Table P = 3.97 × 10−49) but not in QGP (P = 0.21), however, a SNP (rs1042445) located 63 kb upstream and not in LD with rs5030081 (r2 = 0.05) is significantly associated (P = 1.30 × 10−48) with the same trait in QGP. In total, of the 898 variants identified in the BBJ study, we identified 355 variants that showed evidence of replication either directly, through a proxy, or located in a region previously reported for the same trait.A number of group B (11 out of 149) and group C variants (2 out of 90) showed evidence of replication for the same trait after correction for multiple testing . We focused our analysis on PGS derived from European populations since our heritability and allele frequency comparison showed higher correlation between QGP and Europeans. In addition, robust data with enough information to allow comparison with our data was mainly available for European populations. The predictive performance of PGS from 11 traits was tested on QGP data and results are presented in Fig. To assess the translatability of polygenic scores (PGS) derived from other populations to the Qatari population, we assessed the predictive performance of PGS for traits with available scoring data in the Polygenic Score Catalog  in the discovery set were tested for replication in an additional 7768 subjects from the second data release of QBB. Eight of the 20 novel loci replicated at a Bonferroni level (P < 0.05/20). Meta-analysis of discovery and replication results are shown in Table N = 3) or with a minor allele frequency that is three- to seven-fold lower (N = 5) than what is observed in European population ancestries13. These include novel loci for coagulation-related traits , blood cell traits (WBC) and other biochemical traits . Regional association plots (RAP) for novel loci are shown in Supplementary Data CSTB gene with a relatively large effect size . CSTB is a member of the cystatin superfamily that encodes the Cystatin B protein that functions as protease inhibitor. It has been shown that Cystatin C, another member of cystatin superfamily, is a determinant of serum levels of Hcyst15. We also identified a novel locus associated with two related coagulation traits (PT and INR) on chromosome 13 situated near LINC01070, a gene that encodes a long noncoding RNA. Another novel association was identified with serum total bilirubin (Tbil) near ARL4C gene. Serum level of Tbil is routinely used to assess liver function and studies have shown that ARL4C is highly expressed in primary hepatocellular carcinoma tumors and its expression is associated with poor prognosis16. We also identified four novel loci for white blood cell count trait  shows that heterozygous Nhlh1 knockout mice have decreased basophil white blood cell number compared to wild type  in QGP but rare (MAF < 1%) in all other population ancestries reported by 1000 Genome project13. These variants were pruned based on LD  and their association with the clinical traits was investigated. For these loci, we used Bonferroni-adjusted significance threshold of P < 1.15 × 10−5 correcting for the number of tested variants. Loci showing significant association in the discovery set were tested in the replication set to confirm their association. Meta-analysis of discovery and replication results identified 9 Qatari-predominantvariant-trait-associations  and the first comprehensive GWAS of 45 clinically-relevant traits from a Middle Eastern population. Heritability estimates for the studied traits in QGP were generally correlated with previous estimates in other populations. However, significant differences in heritability were observed for some traits suggesting differences in the genetic architecture resulting from population-specific past events such as genetic drift and selection. In addition, variations in environmental factors and their interaction with genetic factors could explain the observed differences in heritability. For example, the heritability of GGT, an enzyme used clinically to assess liver function, was similar to that reported in European populations but significantly higher than in African populations. This observation could be due to a larger contribution of environmental factors explaining the phenotypic variations in GGT in African populations. High prevalence of liver diseases such as cirrhosis and Hepatitis B virus infection in sub-Saharan African populations17 indicating substantial contribution of environmental factors leading to lower heritability estimates. In addition, heritability estimates for cholesterol (TCH and LDL-C) in our data were significantly lower compared to values reported in European or African populations suggesting higher contribution of environmental influences such as diet and lifestyle. Consistent with this, heritability estimates for BMI in Qatar was also lower when compared to two European populations. The prevalence of obesity in Qatar is among the highest in the world18 and this obesity endemic caused (at least in part) by fat-rich diet and lifestyle factors plausibly leading to lower heritability estimates of BMI and cholesterol traits. However, technical variations in measurements could also contribute to the differences in heritability between the studies.We performed one of the largest GWAS using whole genome sequence data to date  aged 18 years and older. The study covers extensive baseline sociodemographic data, clinical and behavioral phenotypic data, biological samples, as well as clinical biomarkers. All QBB participants signed an informed consent form prior to their participation; and the study was approved by Hamad Medical Corporation Ethics Committee and QBB institutional review board. Heritability and GWAS analyses were performed on data from the first QBB data release (6218 QBB participants). Replication analyses were based on an additional 7768 QBB participants from the second QBB data release.The present study was performed on the QBB study participants. QBB is an on-going longitudinal population-based study aiming to recruit 60,000 subjects from the Qatari population with follow up every 5 yearsAll QBB participants attended an assessment session in which physical measurements were collected and each participant filled a standardized questionnaire reporting information on lifestyle, diet, and medical history. Collected physical measurements included anthropometry , body composition, grip strength, arterial stiffness, blood pressure, electrocardiogram data, respiratory function and cardiorespiratory fitness. Additional phenotypes were also collected, such as 3D carotid ultrasound, full body dual energy X-ray absorptiometry (iDXA), “microscopic” features of the optic nerve and macula, and brain magnetic resonance imaging (MRI). During the assessment session, participants provided biological samples  for analysis and storage. Part of the biological samples were transferred to the diagnostic laboratories at Hamad General Hospital where clinical diagnostic biomarkers were measured. The present study focused on 45 clinically-relevant traits as listed in Table https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Reads were then aligned to GRCh37 (hs37d53) reference genome using bwa.kit (v0.7.12) (https://github.com/lh3/bwa/tree/master/bwakit). Quality control on mapped reads was performed using Picard (v1.117) [CollectWgsMetrics] (https://gatk.broadinstitute.org/hc/en-us). Variant calling was performed following GATK 3.4 best practices (https://software.broadinstitute.org/gatk/documentation/article?id=3238): Indel realignment and base recalibration was performed on the initial bam file then HaplotypeCaller was run on each sample to generate an intermediate genomic variant call file (gVCF). Joint variant calling was performed using all generated gVCF files at once. We first run GenomicsDB8 to combine the different samples by regions, then for each region, we ran GenotypeGVCFs, applied SNP/Indel recalibration and then merged all regions.DNA was extracted from peripheral blood using the automated QIASymphony SP instrument according to Qiagen MIDI kit protocol’s recommendations . Genomic DNA integrity was assessed using the Genomic DNA assay on the Caliper Labchip GXII . DNA quantification was done using Quant-iT dsDNA Assay  on the FlexStaion 3 . Whole genome libraries were prepared from 150 ng of DNA using the Illumina TruSeq DNA Nano kit. Genomic libraries were sequenced on HiSeq X Ten  following the manufacturer’s recommended protocol to achieve a minimum average coverage of 30x. Library construction and sequencing was performed at the Sidra Clinical Genomics Laboratory Sequencing Facility. Quality control of Fastq files was performed using FastQC (v0.11.2) , duplicates (N = 10), call rate < 95% (N = 1), and gender ambiguity (N = 65). To identify population ancestry outliers, we performed multidimensional scaling (mds) analysis as implemented in PLINK19. Pairwise identity by-state (IBS) matrix was determined based on a pruned set of independent autosomal SNPs  using a window size of 200 SNPS and LD threshold of r2 = 0.05. Subjects with more than four standard deviation units (±4 SD) away from the mean of the first two mds components were identified as population outliers (N = 87) and removed before analysis  was defined as the proportion of phenotypic variance attributed to genetic factors estimated from genome-wide SNP genotype data. h2 was calculated using the polygenic model implemented in GenABEL ver. 1.8-020. The model included age, sex, and the first four principal components (PC) as covariates. Genomic kinship matrix was used to correct for relatedness and was determined using IBS analysis implemented in GenABEL. To enable comparison of heritability with previously published work, we also calculated heritability using the restricted maximum likelihood (GREML) method21 implemented in the software package GCTA22. Age, sex, and the first four PCs were included as covariates in the GREML model. Linear regression analysis was used to assess correlation between heritability values across population ancestries.Heritability 24. Regional association plots were generated using the locusZoom tool25 using linkage disequilibrium data calculated from QGP data using PLINK. Genomic inflation factor, Quantile-Quantile plots and Manhattan plots were generated using R ver. 3.4.0.Genome wide association testing was performed using the variance component-based method GRAMMAR-Gammar2 < 0.1) within a window size of 10 Mb into distinct signals described by the SNP with the lowest p value. We annotated the SNPs representing the distinct loci using PhenoScanner12 allowing for proxy SNPs reported in five populations  using an LD cut-off of r2 > 0.1, a window size of ±500 kb, and we used Experimental Factor Ontology (EFO) terms (https://www.ebi.ac.uk/ols/ontologies/efo) to map phenotypes (Supplementary Data 2 and PubMed literature searches (accessed on 31 January 2020).We lumped all associations on a given trait based on LD , group C: a SNP with LD (0.1 < r2 < 0.8) is available. To identify any other signal, we also queried the region ±500 kb for association with the relevant trait. Correlation of effect size and allele frequency was performed using linear regression analysis. To assess differences in associated signals between QGP and BBJ we performed colocalization analysis using the Coloc R-package ver. 3.2-126. We tested two hypotheses: H3; the locus is associated with the trait in both BBJ and QGP but the association is driven by different variants, H4; the locus is associated with the trait in both BBJ and QGP and the association is driven by the same variants. Meta-analysis of discovery and replication results was performed using the inverse variance-weighted method implemented in METAL27.To assess the degree of replication of known signals, we compared our results to previously published work focusing on a single large GWAS of similar traits from the Biobank Japan project (BBJ)http://www.pgscatalog.org) for traits with available data. We selected PGS scores derived from the largest published study in European populations. PGS were available for 11 traits from European populations and details are presented in Supplementary Table 19. Pearson’s correlation (R) between the trait values and PGS were calculated using R.Polygenic scores (PGS) scoring files were downloaded from the Polygenic Score catalog (Further information on research design is available in the Nature Research Reporting Summary linked to this article.Supplementary informationDescriptions of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Reporting Summary