Gene associations with behavioural traits were found on all chromosomes except for chromosome 13, with associated SNPs reported on all chromosomes except 5, 13, 17, 18 and 23. Generally, it was found that correlations between behaviour and production traits were low or negligible, suggesting that genetic improvement can be undertaken without negatively affecting production. There was variation between the results of the studies examined, and this underlines that any genetic study is population specific. Thus, to assess the heritability, genetic associations with production and genomic areas of interest for behavioural traits, a large-scale study of the population of interest would be required.Cattle have the potential to seriously injure humans and cause damage to property. The risk of cattle reacting in a dangerous manner can be reduced through genetic selection for cattle which have a better temperament. A literature search was undertaken which returned papers which met the criteria of “Bovine”, “Genetics” and “Behaviour” or terms therein. Behavioural traits were grouped and their heritability, genomic associations and correlations with production traits examined. It was found that heritability estimates were more accurate in studies with large populations (n = 12) of these studies measured over 1000 animals. The heritability estimates were found to vary between studies. Gene associations with behavioural traits were found on all chromosomes except for chromosome 13, with associated SNPs reported on all chromosomes except 5, 13, 17, 18 and 23. Generally, it was found that correlations between behaviour and production traits were low or negligible. These studies suggest that additive improvement of behavioural traits in cattle is possible and would not negatively impact performance. However, the variation between studies demonstrates that the genetic relationships are population specific. Thus, to assess the heritability, genetic associations with production and genomic areas of interest for behavioural traits, a large-scale study of the population of interest would be required.People who work with cattle are at severe risk of serious injury due to the size and strength of the cattle. This risk can be minimised by breeding less dangerous cattle, which have a more favourable reaction to humans. This study provides a systematic review of literature pertaining to cattle genetics relating to behaviour. The review protocol was developed using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework, with Population, Exposure and Outcome components identified as Bovine, Genetics and Behaviour respectively. Forty-nine studies were identified in the sifting and assigned non-exclusively to groups of heritability (22), genomic associations (13) and production traits related to behaviour (24). Behavioural traits were clustered into the following groups: “temperament, disposition and/ or docility”, “aggression”, “chute score”, “flight speed”, “milking temperament”, “non-restrained methods” and “restrained methods”. Fourteen papers reported high accuracy (Standard Error ≤ 0.05) estimates of heritability, the majority ( Cattle were domesticated around 10,000 years ago . Cattle Natural selection and human-directed breeding strategies have led to the development of phenotypically distinct breeds adapted to specific breeding goals. Genetic improvements cannot happen in an individual animal’s lifetime but have the advantage of being additive over subsequent generations. Genetic selection in cattle has often focused on production KPIs, which are easier to evaluate than behavioural traits which have subjective measurement and variation in how they are defined . Animal Bos taurus species, which are the most populous cattle type in Europe. The objective of this paper is to summarise exciting scientific evidence for researchers, breeders, policymakers and farmers to direct breeding goals; with an aim to ultimately improve the behaviour of cattle, which in turn can improve handler safety.This study reviews the genetic aspect of cattle behaviour and will examine three areas of interest: heritability of temperament traits, correlation of temperament traits with production traits, and finally genomic aspects of behavioural traits. This review focuses on contemporary papers published since the year 2000 and cattle of The Preferred reporting items for the systematic reviews and meta-analyses (PRISMA) framework  was usedBos taurus’, where the subject was Bos indicus or an animal of a different species; ‘cattle health and welfare’, referring to cattle disease or temperament with no genetic aspect; ‘food safety and supply’, meat quality, food supply chain and spoilage; ‘production and breeding’, genetic studies with no temperament aspect or focusing on animal management. Narrative reviews which had no original research were also excluded from the search criteria. Selected papers were sifted into three categories: heritability of temperament traits; correlation of temperament traits with production traits; and molecular/ genomic areas related to temperament traits. The categories were not mutually exclusive.Discarded papers were sifted into four categories: ‘Non Articles for inclusion in the review were independently appraised by two authors to ensure suitability and assigned to one or more categories as necessary. Once category allocation had been discussed and agreed upon by both authors, the data from the heritability and genomic association studies were tabulated. Due to the variation in statistical methods used by the authors of the different studies to assess correlation and association between production and behavioural traits, this information could not be directly compared. The relationships were categorised as favourable or non-favourable and the strength of the relationship was derived from the description given of the relationship in each study by the original authors and, where available, the correlation coefficient. The heterogeneous mix of breeds and animal types which were reported in the genomic association studies meant that a meta-analysis was not possible.Bos taurus, including Bos indicus cattle and other non-bovine species. Some studies were related to cattle welfare and health, and included behaviour but no production, conversely, cattle production and breeding studies focused on the animals’ KPIs and did not record any behaviour traits. The inclusion in the search protocol of “beef” and “dairy” led to food safety and production studies which did not include the animal stage of production being returned by the protocol.The search criteria returned a total of 2211 papers once duplicates were removed. The majority of these papers were rejected during screening. The main reason for discarding a paper was that the subject was not Twenty-six review papers were removed, demonstrating that this is an area of great scientific interest. The reviews were examined to ensure the current was necessary and had not been undertaken previously. It was found that 17 of the reviews were carried out before 2016, and as such were considered outdated. The nine reviews which were carried out in the most recent five-year period did not fulfil the aims of this study, some were general reviews of domestic animals and not specifically focused on cattle ,16,17. OSixty-seven full texts were assessed. One non-English language text was discarded . FourteeThe traits found in this review are listed in Many of the studies tested the behavioural traits over two periods. This allowed the animal’s habituation to different testing methods to be assessed. Habituation is important as an animal’s reactions to humans can be improved through positive experiences. Furthermore, repeating the behavioural tests allows comparison at different ages, if there is a high correlation between the same trait when measured at different ages, it can be surmised that the same set of genes controls the trait at the ages recorded ,34.The most reported trait was temperament, this was generally a subjectively measured trait, however, some objective measures were included such as movement in a weighing device  or a comThe subjective nature of not only the measurement but of the nomenclature of assessing behavioural traits is demonstrated by a study which reports the temperament of cattle  citing aThe studies examined reported aggression to be a subjective trait which assessed either how the animal interacted with other cattle in the herd  or humanThis review will use the term ‘Chute score’ (CS), however, the terms chute and crush are interchangeable and generally reflect the terms used in the geographic region where the study was undertaken. Chute score was generally measured subjectively with observers scoring an animal’s reaction when restrained in a chute ,56,57,58Flight speed (FS) and exit velocity (EV) both describe the behaviour of an animal leaving a crush or squeeze chute. One study assessed this subjectively on a scale of 1 to 4, with the animal judged to have walked, trotted, ran or jumped, respectively, . Most stFive studies reported on the milking temperament or actions whilst milking of cows, with two studies using the same dataset ,43. ThreNon-restrained measures of temperament were recorded when the animal was in an enclosed pen but was able to move freely. An animal’s reactivity can be assessed by its reaction to novel objects or humans  where thRestrained behaviours included the animal’s reaction when tethered ,70, avoiTwenty-two papers which reported the heritability of the traits of interest in this review were returned in the search. This included a range of different breeds and composite breeds of both dairy and beef origin. Estimates of heritability were found to vary for each behavioural trait. Some of the variations in the estimated heritability can be explained by the variation in measuring traits previously described. However, the studies also varied by the numbers and types of cattle included. Heritability is a population parameter; therefore, reported heritability of a trait is dependent on several factors such as the experimental design, the model used for estimation, number of sampled animals, population size, breed of animals, genetic variation within animals used in the study and environmental factors. As a result, the estimated heritability will vary between studies. However, the estimated heritability can be constant across populations and species in some traits .Heritability is a parameter expressing the proportion of phenotypic variation explaining genetic factors in the population . The herEstimates of heritability of temperament varied, ranging from low heritability for cows milked by an AMS  to moderThere is also a notable variation in estimated heritability observed between different breeds of beef cows . This isThe importance of external environmental effects on the heritability of temperament was demonstrated by allocating embryo transfer full siblings to non-related dams, thus removing the confounding effect of dam behaviour . This stFour studies reported the heritability of aggression, all estimates were low, with the highest estimate of heritability for aggressiveness at parturition. Aggression could be measured at different events or stages in the animal’s life, one study reported the estimated a low heritability of aggressiveness during gestation and a moderate heritability of aggression during parturition . In thisDirect heritability of CS in a study of American Hereford cattle had a moderate heritability . This waNo estimated heritability for FS was considered accurate. Variation between breeds for FS was reported in the same study, ranging from 0.11 ± 0.07 in Limousin to moderate in Charolais, German Simmental and Hereford (0.25–0.36 ± ≤0.08)  using a Heritability of milking temperament was reported to be low . It is sNo studies were returned which assessed non restrained behavioural measures for heritability.No highly accurate heritability estimates were found for restrained measures of behaviour. Only one study in the restrained behavioural measures had more than 1000 cattle , which rThirteen papers were allocated to the genomics review, three of these did not report any significant associations. Significant associations are listed in Milking temperament also had multiple gene associations on chromosome 27, however, the seven reported associations were all from the same study previously detailed . This stThis spread of associations across the genome is attributed to the behaviour being influenced by multiple genes . Many beTwenty four studies reported the correlations or associations between production and behavioural traits. It is difficult to assess from the correlation coefficient whether a relationship is favourable or not as some studies had an inverse scale for behavioural traits and some did not. For the purposes of this review, it is stated that a study is ‘favourable’ if animals with a better temperament performed better. The correlation can be between 0, no relationship and 1 (or −1), a perfect relationship. This paper considers a correlation of 0 to 0.10 as no clear correlation, 0.11 to 0.30 as weak, 0.31 to 0.80 as moderate and 0.81 or greater as a strong correlation. Where correlation coefficient is not reported, the category of the relationship was derived from the text in the original study. Three groups of production traits  were conA principle components analysis found that the calf’s personality traits would dictate its feeding behaviour, with more curious calves starting to consume starter diets at an earlier age . AnimalsDocility scores were found to have no clear correlation with body weight at 200 days or 400 days of age in Angus and Limousin cattle , or withAssociations between growth traits and behavioural traits ranged from no correlation to moderately favourable. Growth efficiency was reported to be associated favourable and moderately with FS . WhereasA favourable relationship was reported between carcass weight and FS, however, this was not found to be significant , a signiUnfavourable relationships between rib eye area, subcutaneous fat thickness and temperament ; tendernFor a cow to be considered fertile she must be able to get in calf, gestate the calf to full term and deliver a live calf. The cow must then be able to produce sufficient quality and quantity of milk to sustain her calf or for a dairy enterprise. In order to get into the calf, a heifer must reach sexual maturity. It was reported that both docility and aggression had unfavourable associations with age at puberty, whereby aggressive and/or less docile heifers reached puberty earlier . AlthougA small pelvic opening can lead to dystocia which is dangerous for both cow and calf . It was Behavioural reactivity of dual-purpose Simmental cows significantly influenced milk yield and composition, with calmer cows having a higher milk yield . NervousThe study discarded one paper which was not published in English. This paper had an English abstract which appeared to support the other studies included in the review. However, the wide range of studies included which were in general agreement suggest that the coverage of the review was adequate.Some environmental aspects are taken into account through the analyses of automatic and conventional milking or the use of embryo transfer to remove parental effects on temperament. However, to direct improvement of temperament and animal behaviour it is necessary to take a multifaceted approach which considers environmental and genetic effects.n < 1000) which limits their reliability and how they can be applied to novel populations.There were varying results for different behavioural measures in different studies but also within the same study . AlthougBos taurus cattle was developed and returned 2238 papers. Relevant papers were assigned non-exclusively to groups of heritability (22), genomic associations (13) and production traits related to behaviour (24). The heritability of behavioural traits means that additive improvement of temperament is possible and beneficial to animal producers. Correlations between temperament and production traits suggested that selection against animals that are highly reactive to improve welfare and ease of handling would not have detrimental impacts on productivity and reproductive outputs. Although there were genes and SNPs reported, the breed and animal type differences between studies meant that a meta-analysis was not possible. The findings of this paper can inform breeding decisions and if employed with suitable management lead to the improvement of animal behaviour and, in turn, farm safety. Furthermore, the variation in heritability estimates, correlations with production and genomic associations of behavioural traits reported in the studies reviewed highlights that these relationships are population specific. There was also found to be variation in management systems and the methods of behaviour assessment and scoring. Thus, to accurately assess the heritability, genetic associations with production and genomic areas of interest for behavioural traits, a large-scale study of the population of interest using uniform, comparable methods would need to be required.A search protocol to return studies which described the heritability, potential associations with production or the genomic areas of interest of behavioural traits in


We found substantial genetic correlations between the body composition measures and a range of cardiometabolic diseases, with the strongest correlation between liver fat and type 2 diabetes . These findings show that MRI-derived body composition measures complement conventional body anthropometrics and other biomarkers of cardiometabolic health, highlighting the central role of liver fat, and improving our knowledge of the genetic architecture of body composition and related diseases.Obesity and associated morbidities, metabolic associated fatty liver disease (MAFLD) included, constitute some of the largest public health threats worldwide. Body composition and related risk factors are known to be heritable and identification of their genetic determinants may aid in the development of better prevention and treatment strategies. Recently, large-scale whole-body MRI data has become available, providing more specific measures of body composition than anthropometrics such as body mass index. Here, we aimed to elucidate the genetic architecture of body composition, by conducting genome-wide association studies (GWAS) of these MRI-derived measures. We ran both univariate and multivariate GWAS on fourteen MRI-derived measurements of adipose and muscle tissue distribution, derived from scans from 33,588 White European UK Biobank participants . Through multivariate analysis, we discovered 100 loci with distributed effects across the body composition measures and 241 significant genes primarily involved in immune system functioning. Liver fat stood out, with a highly discoverable and oligogenic architecture and the strongest genetic associations. Comparison with 21 common cardiometabolic traits revealed both shared and specific genetic influences, with higher mean heritability for the MRI measures (h A GWAS study of European individuals uncovers genetic associations between whole-body MRI derived measures and cardiometabolic diseases and highlights the key role of liver fat in cardiometabolic health. Over one-third of the United States adult population qualifies for a diagnosis of metabolic syndrome3, characterized by excessive visceral adiposity, insulin resistance, hypertension, low high-density lipoprotein cholesterol, and hypertriglyceridemia5. Metabolic syndrome substantially increases the risk of coronary artery disease, type 2 diabetes, cancer, and metabolic associated fatty liver disease 11. As shown by our previous work, body composition is also strongly associated with brain structure and brain disorders, which are among the most costly and debilitating medical conditions in the world14. An improved understanding of the genetic and biological determinants of body composition is needed to provide insights into the complex interplay between metabolic factors, prevent and treat multiple highly prevalent conditions, and improve public health outcomes10.Obesity and associated cardiometabolic diseases are currently considered one of the largest global public health concerns15. In susceptible individuals, excessive energy intake, stored as visceral adipose tissue (VAT), combined with insulin resistance, leads to heightened lipolysis and release of free fatty acids16. Increased free fatty acid flux to the liver results in hypertriglyceridemia, which in turn contributes to dyslipidemia and atherosclerosis. Lipolysis in VAT further promotes insulin resistance and gluconeogenesis and increases pro-inflammatory reactions that exacerbate endothelial dysfunction and hypertension16. This is reflected in heightened levels of pro-inflammatory markers among individuals with metabolic syndrome17. Muscle mass is also a determinant of cardiometabolic health18, as skeletal muscle constitutes the largest insulin‐sensitive tissue in the body and is the primary site for insulin‐stimulated glucose utilization19. Still, the nature and extent of overlap between these different determinants of cardiometabolic functioning remain unclear.Body composition is partly determined by a complex constellation of interacting metabolic processes and inter-organ cross-talk that may become dysregulated and lead to metabolic syndrome23. Body anthropometrics such as waist circumference and body mass index (BMI) lack a direct connection to pathophysiology24 Measures of regional adipose tissue, most accurately and comprehensively identified through MRI26, may offer sensitive proxies of cardiometabolic health and therefore complement these common measures27. This is further suggested by research indicating they have independent associations with cardiometabolic diseases and improve risk prediction beyond body anthropometrics30.Measures of localized adipose tissue, liver fat and regional muscle volume can now be accurately extracted from whole-body MRI scans31, genetically determined individual differences play a substantial role in regulating body composition34. Cardiometabolic risk factors have both unique and shared genetic correlates35. Much less is known about the genetics of specific MRI-derived body composition measures. We aimed to map the unique and shared genetic architectures across the MRI-derived body composition to provide a holistic understanding of the interplay between different tissue types and their role in metabolic syndrome and cardiometabolic health. We further sought to identify the extent of genetic overlap between these measures and common medical conditions, as such information promotes research into shared molecular pathways and therefore a better understanding of the underlying biology.In addition to social and physical environmental factorsz-score 36. See Table α = 5 × 10−8/14 = 3.6 × 10−9. Our sample for the main analyses consisted of 33,588 unrelated White European participants of the UK Biobank (UKB), with a mean age of 64.5 years (standard deviation (SD) 7.5 years), 51.4% female. We pre-residualized all measures for age, sex, test center, genotyping array and the first twenty genetic principal components to control for population stratification37.We conducted GWASs of fourteen MRI-derived muscle and adipose tissue distribution measures and investigated the genetic link to conventional cardiometabolic risk factors. We included six measures of adipose tissue distribution: abdominal subcutaneous adipose tissue, VAT, abdominal fat ratio, anterior and posterior thigh muscle fat infiltration, and liver protein density fat fraction. Additionally, we investigated three measures related to thigh muscle tissue, namely anterior and posterior thigh muscle volume and weight-to-muscle ratio. We further analyzed visceral and abdominal adipose tissue, and anterior and posterior muscle volume, divided by standing height in meters squared, and total thigh muscle volume −9. Two loci stood out with highly significant p-values, on chromosome 19  and chromosome 22 , both identified in the GWAS on liver fat. Using converging positional, eQTL, and chromatin interaction information (see Methods), we mapped these loci to genes previously coupled to MAFLD 38 as well as inflammatory processes and cancer 39. Supplementary Fig. Univariate GWASs on the individual measures revealed a total of 82 loci, including 50 unique, surpassing the study-wide significance threshold of 3.6 × 10p < 1 × 10−16). Thus, our results suggest a cross-ethnicity generalization of these genetic associations with MRI-derived measures of body composition, despite the known high variability of body anthropometrics across ethnicities24.Additionally, we assessed the generalization of the discovered loci in an additional set of 5042 non-White European UKB participants with identical processing steps. Of the 79 lead single-nucleotide polymorphisms (SNPs) available in this set, 77 had effects in the same direction as the main analyses  identified 31 genome-wide significant genes, including the three primary MAFLD genes 44, further confirming the strong biological validity of this liver fat measure and its connection to MAFLD. Functional annotation of the set of 31 genes revealed differential expression in the liver, pancreas, and subcortical brain regions and significant enrichment among Gene Ontology (GO) biological processes specifically related to lipid homeostasis and metabolic processes. Supplementary Data In total, we identified eight study-wide significant loci for liver fat, validating those found in a previous GWAS of organ tissue using different measurement protocols46. The results are summarized in Fig. 48. However, the notable exception is liver fat, with substantially lower polygenicity and higher discoverability than the other measures, in line with the relatively few highly significant associations we identified through the GWAS.Next, we estimated the polygenicity and effect size variance (‘discoverability’) by fitting a Gaussian mixture model of null and non-null effects to the GWAS summary statistics using MiXeRp < 1 × 10−16); see the diagonal of Fig. Figure 49, which increases statistical power in a scenario of shared genetic signal across the univariate measures51. After applying a rank-based inverse normal transformation, we performed MOSTest on the residualized measures, yielding a multivariate association with 9.1 million SNPs included.Gene variants are likely to have distributed effects across these measures of body composition, as they are correlated components of the same biological system. To identify variants influencing body composition as a whole, we also jointly analyzed all measures through the Multivariate Omnibus Statistical Test (MOSTest)MOSTest revealed 100 significant independent loci across all MRI-derived measures Fig. . Figure α = 0.05/18,203), with highly significant differential expression in the liver, pancreas, heart, muscle, and several other tissues , see Supplementary Data MAGMA identified 241 significant genes after multiple comparison correction α = 0.0/18,203, N = 33,588). These analyses showed that the large majority of loci were indeed novel; Supplementary Fig. To establish whether the loci discovered through the univariate GWAS of the body MRI measures are novel compared to related measures of cardiometabolism, we additionally ran univariate GWAS on 21 secondary measures of anthropometric and cardiometabolic factors  was significantly higher than the body anthropometrics and other biomarkers , p = 1.8 × 10−7. The heritability per measure is further provided in Supplementary Fig. The heritability of the MRI-derived measures  with type 2 diabetes. Coronary artery disease was found to have highly significant positive genetic correlations with visceral and subcutaneous adipose tissue. Overall, we found weak negative genetic correlations with muscle tissue measures and stronger positive genetic correlations with adipose tissue measures, with two exceptions; anorexia nervosa showed the opposite direction of correlation compared to the other conditions, and there was no discernible pattern for schizophrenia. Genetic correlations with the anthropometric and metabolic measures are provided in Fig. Next, we analyzed the genetic overlap of the MRI-derived measures with medical conditions previously linked to cardiometabolic health, selecting recent GWAS with adequate powerGiven that the body composition of men and women differs substantially, we provide sex-stratified GWAS summary statistics besides those produced through the primary analyses. Further, Supplementary Table Here, we reported results from a comprehensive, large-scale GWAS of MRI-derived measures of body composition. Joint analyses of measures of regional adipose and muscle tissue distributions revealed extensive genetic overlap and led to the identification of a large number of shared genetic risk loci across traits. We further showed genetic overlap with body anthropometrics and cardiometabolic measures as well as medical conditions linked to cardiometabolic health. Our findings illustrate how MRI-derived measures can be leveraged to improve our understanding of the biology underlying the metabolic system, emphasizing liver fat as a particularly promising measure, highlighting the integral role of steatosis and MAFLD in cardiometabolic health.60.The genetic correlations of body composition measures with common medical conditions underlined that they may complement conventional measures to better understand cardiometabolic health. Liver fat showed a stronger genetic correlation with type 2 diabetes than conventional measures. While causality needs to be established, this correlation could suggest that the amount of liver fat and its genetic determinants may play a central role in type 2 diabetes development, and at a minimum robustly positions MAFLD onto the map of relevant comorbidities of type 2 diabetes alongside cardiovascular disease, kidney disease and diabetic retinopathy. Further, we found significant positive genetic correlations between coronary artery disease and visceral and subcutaneous adipose tissue, adding genetic evidence to the well-established relation between this disease, obesity, and body fat distribution47. This was reflected in the GWAS yield, with a few highly significant loci coupled to lipid homeostasis explaining the majority of genetic variance for this measure. These loci should be scrutinized for the biological link between liver fat and cardiometabolic conditions61, and may potentially point to fundamental processes that become dysregulated in these diseases. Indeed, all components of metabolic syndrome correlate with liver fat content62. Evaluation of MAFLD risk has been recommended for any individual with metabolic syndrome and related morbidities 62, and the large effects of these liver fat-associated loci even may suggest potential as features for individual risk stratification in MAFLD64. These findings also attest to the accuracy and clinical relevance of MRI-derived measures of liver fat, and support the notion that MAFLD should be considered an integral component of obesity and metabolic syndrome and a key non-communicable disease11.Liver fat also stood out from the other measures with regard to its genetic architecture. While all traits investigated were substantially heritable, the genetic discoverability of liver fat was much higher, with an oligogenic architecture as opposed to the polygenic architectures of the remaining traits and other complex biomedical measures65, and has been implicated in aging and frailty66. It has also been coupled to metabolic syndrome67 and cardiovascular mortality68. Recent literature focused on liver disease and its progression have also highlighted the importance of muscle health69. Muscle fat infiltration has been linked to higher comorbidity within MAFLD and decreased muscle fat infiltration has been correlated with improvement in steatohepatitis71. Our findings suggest a strong genetic component to these associations, indicated by the large degree of shared genetic architecture with related diseases. Interestingly, fat accumulation in the muscle arises through specific pathways, including the intramyocellular accumulation of lipid65, which is associated with insulin insensitivity and inflammation72.Another key finding was that the highest number of significant loci were found for muscle fat infiltration in the anterior and posterior thighs, two measures not previously genetically studied. Fatty infiltration of skeletal muscle reduces the muscle mass and strength48. Indeed, adipose and muscle tissue are known to have complex regulatory cross-talk, both releasing metabolism-regulating molecules to maintain a balanced weight-to-muscle ratio73. The increased yield from the multivariate GWAS analysis, nearly doubling the number of unique loci discovered, is in line with the hypothesis of strong biological interplay and shared molecular mechanisms. The multivariate GWAS allowed for identifying loci that have distributed effects across the included body composition measures. These may help to explain the complexity of metabolic syndrome and the frequent comorbidity between diseases associated with body composition. Our findings that a substantial portion of the genetic determinants of these measures are related to the immune system fit with a large body of literature indicating that adipose tissue is an active metabolic and endocrine organ that secretes a host of pro- and anti-inflammatory factors, and with the characterization of obesity as a state of chronic low-grade inflammation74. Thus, the current genetic findings can form the basis for functional follow-up studies to determine the molecular mechanisms involved in the complex relations between lipids and the immune system.The genetic correlations between the MRI-derived body composition measures indicate partly overlapping biological processes with some unique genetic determinants. The correlation structure further suggests that adipose tissue distribution is genetically largely independent from muscle tissue. However, it should be noted that global correlations underestimate overlap when a mixture of genetic effects in the same and opposing directions cancels each other out75.There was high genetic overlap between the sets of MRI-derived measures of body composition and the conventional measures of body anthropometrics and cardiometabolic health, indicating that they tag similar biological processes. The body anthropometrics were correlated with both muscle and adipose tissue, indicating little specificity, in line with the long-standing recognized limitations of these global measures that they fail to distinguish between specific body types that differ widely in risk for disease40. We further combined the study of individual measures with a multivariate approach to genetic discovery, allowing for greater GWAS locus yield and insight into the overall architecture of these complementary indicators of body composition and associated diseases. However, further studies are needed to clarify the role of putative moderators such as sex76, age, and ethnicity77.Strengths of this study are the large number of whole-body MRI scans and the use of state-of-the-art, precise body composition measures, including multiple measures not previously investigated. With this, we were able to replicate loci reported earlier for MRI-derived measures of organ tissue, with different measurement protocols and different study focusLimitations of this study include the fact that the approaches employed do not allow for causal claims beyond genetic associations. Establishing the directionality of causal effects underlying the genetic correlations between the studied measures and diseases will therefore require follow-up investigation, e.g., through Mendelian randomization. Our investigation of traits was further not comprehensive, and we lacked a sufficiently powered GWAS of MAFLD for inclusion in the analyses of genetic correlation. Further, the limited locus yield from the univariate GWAS and the low percentage of explained genetic variance for the body MRI measures, with the exception of liver fat, point towards low statistical power. The collection of larger sample sizes, as now underway through several large-scale initiatives such as the UK Biobank, and the use of more powerful statistical approaches, such as MOSTest, will be required to improve discovery.4. Genetic data is less subjected to reverse causation and confounding than environmental factors. Knowledge about shared and specific genetic determinants is therefore central to develop effective strategies that optimally treat the individual. Contributing to this, we showed that accurate MRI-derived measures of liver and regional adipose and muscle tissue characteristics have strong genetic components. However, our findings have also made clear that the majority of these measures are highly complex and polygenic, leading to limited yield with current sample sizes. Further, when combined, they tag similar sets of biological processes as widely available measures of anthropometrics and blood markers. This raises the question whether it is worthy to collect costly MRI scans to obtain these measures, which is hard to answer firmly with current knowledge. This study does show that the individual measures have their own unique patterns of genetic correlations and that they lead to the identification of novel loci, indicating they capture unique information, which may prove important to tease apart the influences of complex biological processes on body composition. Further, we prove that the shared influences can be leveraged to boost discovery. As such, aided by their growing availability, these measures have the potential to substantially enhance our understanding of body composition and related diseases, provide drug targets for MAFLD and related traits, and contribute to combatting a large, increasing threat to public health.To conclude, the high prevalence of cardiometabolic diseases, combined with substantial morbidity and mortality, indicates a strong need for new therapeutic targets. While these diseases are often comorbid, they are treated separately, with this polypharmacy bringing along increased risk of adverse drug reactions78. It has received ethics approval from the National Health Service National Research Ethics Service , leaving N = 33 588, mean age 64.5 years (SD = 7.5) at the time of the MRI scan, 51.4% female. For the replication analyses, we made use of data from non-White Europeans, with otherwise same exclusion criteria, leaving N = 5042, mean age 63.0 years (SD = 7.7), 52.9% female. This ethnic grouping was based on self-report and confirmed by genetic principal component analysis (UKB data field 22006). For the secondary analyses of measures of cardiometabolic health, we included all White Europeans with complete genetic and covariate data. After excluding one of each pair of related individuals , this sample consisted of 377,950 individuals with a mean age of 57.4 years (SD = 7.9), 53.7% female. The lower age of this sample reflects that we used age at baseline assessment, when these measures were taken.We made use of data from participants of the UKB population cohort, obtained from the data repository under accession number 27412. The composition, set-up, and data gathering protocols of the UKB have been extensively described elsewherevice ref /NW/0382,80. We acquired the data as processed by AMRA , subsequently released by UKB. We bridged with UKB project accession #6569 to obtain early access to this data, which was then obtained from the UKB data repositories and stored locally at the secure computing cluster of the University of Oslo.Body and liver MRI scans were collected from four scanning sites throughout the United Kingdom, all with identical scanners and protocols. They were acquired on 1.5 T Siemens MAGNETOM Aera scanners using a body dual-echo Dixon Vibe protocol and a single-slice multi-echo gradient Dixon acquisition, respectively. The UKB core neuroimaging team has published extensive information on the applied scanning protocols and procedures, which we refer to for more details6. Briefly, the process for fat and muscle compartments includes the following steps: (1) calibration of fat images using fat-referenced MRI, (2) registration of atlases with ground truth labels for fat and muscle compartments to the acquired MRI dataset to produce automatic segmentation, (3) quality control by two independent trained operators including the possibility to adjust and approve the final segmentation, and (4) quantification of fat volumes, muscle volumes and muscle fat infiltration within the segmented regions. For liver proton density fat fraction (PDFF), nine regions of interest (ROI) were manually placed, evenly distributed in the liver volume, while avoiding major vessels and bile ducts.The methods used to generate the MRI-derived measurements has been described and evaluated in more detail elsewhere7. Liver PDFF was calculated depending on the protocols implemented by UK Biobank. The liver protocol was initially based on a single-slice symmetric 10-point acquisition with IDEAL reconstruction, but after 10.000 scans it was replaced by the whole-body dual echo Dixon images with an additional T2* and proton density correction, where the T2* values were estimated from a separate single-slice asymmetric 6-point acquisition. The latter method has been described and validated against IDEAL-based liver PDFF previsouly4. 1.338 scans were acquired with both liver protocols to assess the switch, and a good agreement was found between the protocols—the mean difference in liver PDFF was 0.30% with a standard deviation of the differences of 0.80%.Muscle volumes were calculated as fat-tissue free muscle volumes. Muscle fat infiltration was calculated as the average T2*-corrected fat fraction and converted to PDFF9. A recent study, investigating both regional fat and muscle volumes as well as muscle fat infiltration and liver fat fraction, showed high repeatability and reproducibility on five different 1.5 T and 3 T scanners from three different vendors4.Test-retest reliability of the MRI-derived measures included in this study is high, with a nearly perfect intraclass correlation coefficient, and the automated processing performs better than manual segmentation of muscle and fatty tissuehttp://biobank.ctsu.ox.ac.uk/showcase/)). Specifically, we extracted the following measures of adipose tissue: VAT, defined as the adipose tissue within the abdominal cavity, and abdominal subcutaneous adipose tissue (ASAT), defined as the adipose tissue between the top of the femoral head and the top of T9. We also extracted measures of muscle fat infiltration (MFI) derived from the anterior and posterior thighs , averaged over both legs, and liver PDFF. As measures of muscle tissue, we included anterior and posterior thigh fat-free muscle volume (ATMV and PTMV). We extracted two ratios from the UKB repository, namely weight-to-muscle ratio (WMR), defined as weight/TTMV, and abdominal fat ratio (AFR), which is (VAT + ASAT)/(VAT + ASAT + TTMV). For VAT, ASAT, ATMV, and PTMV, we computed index measures by dividing these measures by the squared standing height in meters . This is done since weight, adipose tissue, and lean tissue compartments scale to approximate height squared. In addition, a sex-, height-, and weight invariant normalized z-score for TTMV (TTMVz) was calculated. Including adjustment for sex, height, and weight has been shown to strengthen the association between muscle volume and hospitalization/function, and TTMVz has previously been associated with poor function, hospitalization, and all-cause mortality in general population, as well as poor function and metabolic comorbidity in MAFLD12.We extracted a selection of body composition measures , which has undergone extensive quality control procedures as described by the UKB genetics teamhttps://github.com/precimed/mostest), with the approaches employed being identical for both the primary analyses of the body MRI data and the secondary analyses of cardiometabolic health metrics. The procedure has been extensively validated49. GWAS on each of the pre-residualized and normalized measures were carried out using the standard additive model of linear association between genotype vector, gj, and phenotype vector, y. Independent significant SNPs and genomic loci were identified in accordance with the PGC locus definition, as also used in FUMA SNP2GENE83. First, we selected a subset of SNPs that passed genome-wide significance threshold 5 × 10−8, and used PLINK to perform a clumping procedure at linkage disequilibrium (LD) r2 = 0.6 to identify the list of independent significant SNPs. Second, we clumped the list of independent significant SNPs at LD r2 = 0.1 threshold to identify lead SNPs. Third, we queried the reference panel for all candidate SNPs in LD r2 of 0.1 or higher with any lead SNPs. Further, for each lead SNP, its corresponding genomic loci were defined as a contiguous region of the lead SNPs’ chromosome, containing all candidate SNPs in r2 = 0.1 or higher LD with the lead SNP. Finally, adjacent genomic loci were merged if separated by less than 250 KB. Allele LD correlations were computed from EUR population of the 1000 genomes Phase 3 data. We made use of the Functional Mapping and Annotation of GWAS (FUMA) online platform (https://fuma.ctglab.nl/) to map significant SNPs from the MOSTest analyses to genes. For this, we combined the default positional mapping with eQTL and 3D chromatin interaction mapping, including all available tissue types.We carried out GWAS through the freely available MOSTest software  and j, MiXeR incorporates LD information and allele frequencies for 9,997,231 SNPs extracted from 1000 Genomes Phase3 data to estimate the expected probability distribution of the signed test statistic, N is the sample size, Hi indicates heterozygosity of i-th SNP, rij indicates an allelic correlation between i-th and j-th SNPs, and zj, allowing us to calculate |Zj| from MOSTest p-values. Finally, given the estimated parameters of the model, the power curve S(N) is then calculated from the posterior distribution We applied a causal mixture modelh2), we applied LD score regression (LDSR)84 to the univariate GWAS summary statistics. For this, each set of summary statistics underwent additional filtering, including the removal of all SNPs in the extended major histocompatibility complex region (chr6:25–35 Mb). We further used these munged summary statistics to perform cross-trait LDSR to estimate genetic correlations between the measures85.For estimates of SNP-based heritability (n = 7522), as listed in the Molecular Signatures Database .We carried out gene-based analyses using MAGMA v1.08 with default settings, which entails the application of a SNP-wide mean model and the use of the 1000 Genomes Phase 3 EUR reference panel. Gene-set analyses were done in a similar manner, restricting the sets under investigation to those that are part of the Gene Ontology biological processes subset (Further information on research design is available in the Peer Review FileSupplementary Information-NewDescription of Additional Supplementary DataSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting Summary


Considering relatives’ health history in logistic regression for case–control genome-wide association studies (CC-GWAS) may provide new information that increases accuracy and power to detect disease associated genetic variants. We conducted simulations and analyzed type 2 diabetes (T2D) data from the Framingham Heart Study (FHS) to compare two methods, liability threshold model conditional on both case–control status and family history (LT-FH) and Fam-meta, which incorporate family history into CC-GWAS.2 = 0.28), variant minor allele frequency ranging from 1% to 50%, and 1% of phenotype variance explained by the genetic variants, Fam-meta had the highest overall power, while both methods incorporating family history were more powerful than CC-GWAS. All three methods had controlled type I error rates, while LT-FH was the most conservative with a lower-than-expected error rate. In addition, we observed a substantial increase in power of the two familial history methods compared to CC-GWAS when the prevalence of the phenotype increased with age. Furthermore, we showed that, when only the phenotypes of more distant relatives were available, Fam-meta still remained more powerful than CC-GWAS, confirming that leveraging disease history of both close and distant relatives can increase power of association analyses. Using FHS data, we confirmed the well-known association of TCF7L2 region with T2D at the genome-wide threshold of P-value < 5 × 10–8, and both familial history methods increased the significance of the region compared to CC-GWAS. We identified two loci at 5q35 (ADAMTS2) and 5q23 (PRR16), not previously reported for T2D using CC-GWAS and Fam-meta; both genes play a role in cardiovascular diseases. Additionally, CC-GWAS detected one more significant locus at 13q31 (GPC6) reported associated with T2D-related traits.In our simulation scenario of trait with modest T2D heritability (hP-values in real data application, highlighting the benefits of including family history in genetic association studies.Overall, LT-FH and Fam-meta had higher power than CC-GWAS in simulations, especially using phenotypes that were more prevalent in older age groups, and both methods detected known genetic variants with lower The online version contains supplementary material available at 10.1186/s12864-022-08897-8. Traditional genome-wide association studies assessing the association with binary traits using a logistic regression (CC-GWAS) are based on each study individual’s genotypes and case–control status. Phenotypic status of relatives who are not genotyped is also expected to be associated with participants’ genotypes based on the rules of genetic inheritance. While readily available but often ignored, family history provides additional valuable information that may increase the accuracy and power of association tests. To leverage family history in genetic association studies, different approaches have been proposed. A popular technique focuses on imputing missing genotypes for individuals based on their relatives’ genotypes and a known pedigree structure –4. AccorIn this paper, we examined two proposed approaches to directly integrate the phenotypes of ungenotyped relatives into association analysis, without any additional time-consuming imputations or further costly genotyping. The “liability threshold model conditional on both case–control status and family history” (LT-FH) method  replacesDetails regarding the LT-FH method can be found elsewhere . In LT-FWhich includes the environmental (Fam-meta considers two independent association tests that have been previously described elsewhere . The firwhere N = 1800). Sex was randomly assigned for the children, and age was assigned using the following rules: each child’s age was generated under a continuous uniform distribution between 18 to 45 years old; the mother was 20–45 years older than the oldest child; the father was within five years of the mother’s age, and had to be at least 20 years older than the oldest child.We conducted simulations to directly compare the performance of LT-FH and Fam-meta, while CC-GWAS results served as a reference. Three main models were utilized to generate phenotypes: a null model without SNP effects (model 1), a model with variants contributing to disease liability (model 2), and a model with an additional age by genotype interaction (model 3). To evaluate each model, we first generated 400 nuclear families consisting of parents and offspring for each iteration, where 200 families have two children, and 200 families have three children (For the null model (model 1), we simulated one continuous disease trait using the following formula:Age and sex explained 10% and 5% of the total phenotypic variance, respectively. The polygenic component 2, 2pq, q2, respectively, where p is the MAF. Then, we determined the children’s genotypes through gene dropping, assuming that each parent passes down one of their alleles to their offspring with an equal chance of selecting either allele. Phenotypes were generated as follows:In the second model (model 2), we incorporated eight independent causal variants with the following minor allele frequencies (MAFs): 1%, 2%, 5%, 10%, 20%, 30%, 40%, 50%  an interaction between age and genotype of causal variants in addition to the eight causal variants:Causal variants were generated the same way as in model 2. We reduced the proportion of phenotypic variance explained by each causal variant from 1% to 0.5% so that the genotype by age interaction term and the genotype term each explained 4% of the total phenotypic variance  for CC-GWAS, as if the parents’ genotypes were unavailable but their phenotypes (case–control statuses) were. LT-FH and Fam-meta each leveraged the phenotypes of parents accordingly (see descriptions of each method above).The continuous trait Y was transformed to a binary case–control status for all three models using a threshold model with a disease prevalence of 0.3. To evaluate type I error rate of each model, we generated eight independent, non-causal variants with the same allele frequencies as the causal variants, and assessed their individual association with case–control status using CC-GWAS, LT-FH, and Fam-meta. For model 1, we completed 50,000 simulation replicates under HN = 3400). Using the new pedigree, we simulated phenotypes under model 2, and ran association tests using the offspring (N = 1000) for CC-GWAS. In terms of the relatives regression conducted in Fam-meta, we considered two sample sizes: first, all grandparents (N = 1600), and second, only one pair of randomly selected grandparents (N = 800). The purpose of this scenario was to compare whether including a larger number of grandparents in association analyses would increase power. For each sample size, we ran 5000 iterations.To test the influence of leveraging more distant relatives’ information compared to first-degree relatives, we used the phenotypes of grandparents instead of parents as the available family history information. We only tested this scenario with CC-GWAS and Fam-meta, because LT-FH is not designed to incorporate second-degree relatives. A new family structure was utilized, where each family still comprised two parents with two or three offspring, but each parent also had their respective parents, totaling four grandparents. Similar to previous simulations, there were 200 families with two grandchildren and 200 families with three grandchildren  and grandchildren (Gen 3 cohort), while numerous health conditions have been monitored to assess cardiovascular diseases and their risk factors. Both LT-FH and Fam-meta were applied to the same three generations of FHS participants, with T2D case–control status available for most participants. T2D was defined as having at least one of the following conditions: fasting glucose ≥ 7 mmol/L after ≥ 8 h, Hemoglobin A1c ≥ 6.5%-units, 2-hour glucose by an oral glucose tolerance test ≥ 11.1 mmol/L, non-fasting glucose ≥ 11.1 mmol/L, physician-diagnosed diabetes, or use of antidiabetic medication. Participants with known type 1 diabetes were excluded. In the case where a second-generation participant’s parent was not part of the original FHS cohort, but the participant reported T2D family history during a follow-up exam, we used those records to expand available family information. For participants who were genotyped, genetic variant dosages from the Haplotype Reference Consortium release 1.1 based imputations were used in all analyses. Imputation was performed on the Michigan Imputation Server using minimac3 and the Haplotype Reference Consortium reference panel release 1.1 April 2016 using genetic variants passing the following criteria: call-rate ≥ 97%, Hardy–Weinberg From the full set of FHS participants, we retrieved T2D status and T2D family history of 8362 genotyped individuals, excluding participants of non-European ancestry, defined using principal component (PC) analysis.PCs were first computed with HapMap samples. Mean and standard deviation for PC1 and PC2 were computed for White samples, including those in FHS and CEU (Utah residents with ancestry from northern and western Europe). Participants were labeled as non-European if the value for PC1 or PC2 was greater than 6 standard deviations from the mean value for White samples. Using the LT-FH software, we calculated the posterior mean genetic liability scores for each participant based on the participant’s T2D status as well as any available parents’ or siblings’ T2D status. Then, we used a linear mixed-effects model to evaluate the association between T2D liability phenotypic scores and each imputed variant, adjusting for participant’s last exam age, sex, and the first ten PCs. We additionally adjusted for smoking status (never/former/current smoker) in a sensitivity analysis  who have both T2D disease status and genotypes available, while the other included relatives (n = 3780) who only have T2D status but no genotypes available. For the first sample, we used a logistic mixed-effects regression model to evaluate the association between proband’s T2D case–control status and individual genetic variants, adjusting for last exam age, sex, and the first two PCs that were significantly associated with T2D. We accounted for relatedness using a kinship matrix based on the FHS pedigree. The first regression analysis corresponds exactly to CC-GWAS. In the second sample, we first matched the relatives with their corresponding probands using Framingham family identifiers. If a relative was related to multiple probands, the probands’ genotypes and phenotypes were averaged within the same family. Then, we used a logistic mixed-effects model to evaluate the association between relative’s T2D case–control status and imputed genetic variants based on probands’ genotypes, adjusting for proband’s phenotype, relative’s last exam age, relative’s sex, and the first two PCs. A kinship matrix was included to account for familial relatedness. For both regression models, we additionally adjusted for smoking status in a sensitivity analysis  and were included in the final analyses. Quantile–Quantile plots and Manhattan Plots of T2D association results using each familial history method, compared to CC-GWAS, are shown in Figs. GC, there was little to no evidence of type I error inflation . ADAMTS2 is primarily expressed in fibroblast cells, with low tissue specificity [ADAMTS2 and body fat distribution to the trunk [ADAMTS2 expression in heart tissues has been described in cardiac hypertrophy induced by pressure overload [PRR16 (proline rich 16), which encodes a cell-size regulator and is predominantly expressed in fibroblast cells [PRR16 have been reported to be associated with maternal BMI and coronary heart disease [GPC6 (glypican 6), has higher levels of expression in the aortic and pituitary gland tissues [From our simulations comparing LT-FH and Fam-meta to CC-GWAS, we concluded that Fam-meta was the most powerful, while LT-FH was the most conservative. Moreover, we observed a greater increase in power from CC-GWAS to the two familial methods when an interaction between age and genotype was added to the phenotype model. This indicates that LT-FH and Fam-meta are more powerful when age has a larger influence on the phenotype. In the scenario where we considered more distant relatives, the power increase from CC-GWAS to Fam-meta when using four grandparents’ phenotypic information was approximately twice of the increase in power when only two grandparents were used, but half of the increase in power when two parents were used. Our conclusions should still hold if SNPs in LD are analyzed, since both LT-FH and Fam-meta described in this paper evaluate the association of each variant individually, regardless of whether there is any correlation among the variants. Using data from the FHS, we detected several genome-wide significant hits. First, the l groups , we expeq23 Fig.  that pascificity . Associahe trunk , and dowoverload . The 5q2st cells . Differe disease . Interes tissues . Multipl tissues , pulse p tissues , and smoTCF7L2 region. However, considering that most study cohorts already collect family health history information, and LT-FH and Fam-meta are both easy to implement, it is only beneficial to include family history in association studies, even if the increase in power is small. We expect the gain in power to be much greater when analyzing a disease that is more strongly correlated with aging, such as T2D or dementia. If a study sample does not include many elderly participants, parents’ disease status can be much more informative for studying age-dependent penetrant diseases. Such a scenario was demonstrated by our interaction model in the simulations. In addition, LT-FH has been extended to LT-FH ++ that adopts an age-dependent liability threshold model, which accounts for disease age of onset in addition to family history [Further examining known T2D loci previously identified in the literature, we did not observe any notable increase in significance using LT-FH and Fam-meta compared to CC-GWAS, besides the apparent  history .In terms of practicality and application, LT-FH and Fam-meta both had their advantages as well as drawbacks. LT-FH provides a posterior genetic liability score even for individuals who had missing disease status, as long as the disease status was available for parents and/or siblings. In addition, the format required of sibling status was either “at least one sibling with disease” or “no disease”, so incomplete sibling status could still be informative in LT-FH. On the other hand, for Fam-meta, the logistic regressions required phenotypes available for all probands and relatives. Fam-meta could be modified to incorporate all relatives, while LT-FH could only use parents and siblings. Fam-meta was also easier to implement, because it is an extension of the logistic regression model (CC-GWAS). The LT-FH software was more computationally intensive when the sample size was large.There are several limitations to our study. First, there could be some inaccuracy in the T2D family history information in the FHS that was self-reported. Secondly, when performing simulations, we needed to limit the sample size for each iteration because LT-FH was computationally intensive. Moreover, given the predominantly European ancestry of participants in the FHS cohort, our results may not generalize to other population groups. Finally, we did not study the effect of incorporating family history into rare variant association analysis. A recently published new method, family history aggregation unit-based test (FHAT), further extended LT-FH to analyze rare variants . FHAT waIn this paper, we examined two innovative approaches that extend GWAS to incorporate familial history, regardless of genotyping status. LT-FH uses liability threshold modeling, while Fam-meta meta-analyzes summary statistics from two logistic regressions. As presented throughout our analyses in simulations and application to FHS data, both LT-FH and Fam-meta outperformed, or performed equally well as CC-GWAS. Therefore, we highly recommend the inclusion of family disease history in GWAS using the two approaches addressed in this study. Researchers can use our results to determine whether LT-FH or Fam-meta is more suitable for their studies.Additional file 1. Additional Analyses. Description of data: Results of additional simulations and real-data analyses.


The multifactorial risk prediction model BOADICEA enables identification of women at higher or lower risk of developing breast cancer. BOADICEA models genetic susceptibility in terms of the effects of rare variants in breast cancer susceptibility genes and a polygenic component, decomposed into an unmeasured and a measured component - the polygenic risk score (PRS). The current version was developed using a 313 SNP PRS. Here, we evaluated approaches to incorporating this PRS and alternative PRS in BOADICEA.2) need to be estimated. The mean, SD, and proportion of the overall polygenic component explained by the PRS (αParameters were computed for 11 PRS, including 6 variations of the 313 SNP PRS used in clinical trials and implementation studies. The logistic regression approach underestimates BOADICEA can be readily adapted to different PRS in a manner that maintains consistency of the model.: The methods described facilitate comprehensive breast cancer risk assessment. BOADICEA  is a risThe current version . i. The final term models the effects of other risk factors. The polygenic variance t:Here The parameters The PRS is incorporated into BOADICEA by partitioning the total polygenic component mentclass2pt{minimocument} :www.canrisk.org), where the proportion of the polygenic variance explained by the PRS is denoted as lleagues  equationmentclass2pt{minimGLM. This method was applied to 22,767 controls and 16,151 women diagnosed with invasive breast cancer from the validation and prospective test sets used in Mavaddat and colleagues .The above analyses make the simplifying assumption that the marginal PRS effect size is a good approximation to the effect size conditional on other risk factors. This is likely to be a reasonable assumption for nongenetic risk factors, which have relatively small effects on risk and appear to be independent of the PRS, as shown in recent analyses of the combined effect of breast cancer PRS and individual SNPs with life-style/environmental risk factors including questionnaire-based factors . HoweverBecause the mean PRS varies by country, we first regressed the PRS on country and principal components, adjusted for case–control status, and performed the analyses on the residual PRS. The likelihood was maximized using the optimize function in R. 95% confidence intervals (CI) were obtained using a grid of values for 313 as a standard. From equations (A) and (B) in the methods above it can be seen that, under the rare disease assumption, the marginal HR associated with the PRS should approximate the conditional HR. If differential age effects can also be ignored, As a third approach, we derived an approximate estimate CHEK2*1100delC, and some of the derivative PRS also include these SNPs. This could result in overestimation of risk in CHEK2*1100delC carriers if the PRS is used in conjunction with gene-panel testing, because BOADICEA assumes that the PRS and major gene genotypes are independent in the population. We therefore also considered PRS without these variants. (Note that CHEK2 p.I157T (22_29121087_A_G) is also included in PRS313 but is only weakly correlated with CHEK2*1100delC and does not introduce a bias). The means and SDs of each PRS, and the proportion ]. PRS313 includes two variants (22_29203724_C_T and 22_29551872_ A_G) which are correlated with the protein truncating variant All studies included in this analysis were approved by the relevant local ethical review boards and used appropriate consent procedures. SEARCH was approved by the NRES Committee East of England - Cambridge South.Data were generated by the authors and is available on request.https://bridges-research.eu/). Of 313 variants, 295 could be designed and a further 11 were replaced by surrogate markers (r2 > 0.9 in Europeans). The PERSPECTIVE I&I PRS was designed to facilitate risk stratified screening in the context of population-based mammographic screening in Ontario and Quebec . DBDS299, using data from the Danish Blood Donor Study (https://bmjopen.bmj.com/content/9/6/e028401) is used in a research study to validate BOADICEA in the Danish population. In addition, we included the earlier PRS77 developed using BCAC data and comprising genome-wide significant SNPs, PRS3820 developed by Mavaddat and colleagues  panel test to facilitate clinical translational studies of BOADICEA implemented in the context of genetic testing of women with a family history ( markers . The PRIGLM) were smaller than those generated using the We note that the per 1 SD ; Fig. 1.ocument} ; Fig. 1.GLM) were consistently smaller than those generated using the We evaluated approaches to incorporating alternate breast cancer PRSs into the risk prediction algorithm BOADICEA. The ent bias .We showed further that the CHEK2 protein truncating variants, we recommend using the PRS without these SNPs when gene-panel testing is performed.A striking observation is the very large difference in the means of the different PRS. This reflects the fact that the removal of a few SNPs with important weights can have a substantial effect on the mean. For example, the means for the PRS excluding the chromosome 22 SNPs are higher. While the mean has no intrinsic significance, this emphasizes the importance of correctly normalizing the PRS. In particular, because BOADICEA also incorporates the effects of It is important to note that estimates derived from European ancestry populations may not be applicable to individuals of other ancestries. The effect sizes may differ among populations, for example due to differences in linkage disequilibrium structure. This has been shown for PRS313, for which somewhat smaller effect sizes have been estimated in Asian and African-American populations . In addiThe analyses used here adjusted the PRS for both the country in which the study was conducted and ancestry informative principal components. An adjustment is necessary because the mean PRS varies by country, even among European populations (and this is not reflected in differences in incidence rates). However, it is possible that adjustment for both country and principal components is over-conservative. Further analyses in large population-specific data sets may be able to address this.The approaches described allow BOADICEA to be adapted for use with any PRS in a consistent manner. However, it should be emphasized that the main validations of BOADICEA used PRS313 . For PRSwww.canrisk.org) tool  tool  and is wSupplementary MethodsSupplementary Methods provides details of methodologyClick here for additional data file.Supplementary Table S1Supplementary Table S1 shows studies and samples used in these analysesClick here for additional data file.Supplementary Table S2Supplementary Table S2 show the country in which the studies were conductedClick here for additional data file.Supplementary Table S3Supplementary Table S3 shows SNPs and weights for the BCAC PRS313.Click here for additional data file.Supplementary Table S4Supplementary Table S4shows SNPs and weights used in the alternative PRS.Click here for additional data file.


Pisum sativum) are the second most cultivated pulse crop in the world. They can serve as human food, fodder, and cover crop. The most serious foliar disease of pea cultivars worldwide is Ascochyta blight, which can be caused by several pathogens. Of these, Peyronella pinodes is the most aggressive and prevalent worldwide. Several traits, including resistance to Peyronella pinodes, stem diameter, internode length between nodes 2–3 and 5–6, and area of 7th leaf, were measured in 269 entries of the pea single plant plus collection. The heritability (H2) of the morphological traits was relatively high, while disease resistance had low heritability. Using 53,196 single-nucleotide polymorphism markers to perform a genome-wide association study to identify genomic loci associated with variation in all the traits measured, we identified 27 trait–locus associations, 5 of which were associated with more than 1 trait.Peas ( Pisum sativum L.) are the second most cultivated pulse crop in the world, coming after dry beans . The United States is the 4th largest producer and 3rd-largest exporter globally  has a large, highly repetitive genome, of 4.45 Gb, almost twice as large as maize  and almost 4 times that of soybean . To preserve genetic diversity and aid in genomic-assisted breeding, the USDA assembled the pea single plant plus collection (PSPPC)  mapping studies for resistance to P. pinode as well as several morphological traits.This study describes the first GWAS for resistance to P. sativum lines from the PSPPC collection were planted in a randomized complete block design in a growth chamber at the Phytotron at North Carolina State University, Raleigh, NC, USA. Cultivars Radley and Solara were used as resistant and susceptible checks, respectively. Due to space limitations, only 1 replicate was planted at a time. The experiment was run 7 times, i.e. in 7 replications. For each replication, 3 seeds per entry were planted in a 7.5-cm (225 ml) Styrofoam cup and thinned to 1 plant per plot before inoculation. The substrate used was 50% Sun Gro Propagation Growing Mix , and 50% cement sand. The cups were organized in metal trays holding 5 cups, and 4 metal traits were in each rolling cart. The chamber was kept at 21°C with 12 hr of light, and air humidity fluctuated between 50% and 100%. Plants were not inoculated with rhizobium but were fertilized 3 times a week when watered with a standard nutrient solution in the growth chambers containing nitrogen, phosphorus, potassium, and other micronutrients . As the plants grew, they were trellised using thin bamboo stakes.Two hundred and sixty-nine P. pinodes originating from different countries were obtained from the USDA collection kept in Washington  was calculated for each trait by dividing the variance due to genotype by the total variance for the phenotype, which was the sum of variance due to genotype, variance due to the interaction of genotype by replication, and variance due to error (VG/(VG + VGR + Ve)).The distribution of each replicate for each trait was visualized using the package ggplot2  in R stuA single disease score per plant for each date was calculated as an average of the score of 3 leaves scored. For each replication, the standardized area under disease progress curve (sAUDPC) was calculated by averaging the value of 2 consecutive ratings and multiplying by the number of days between the ratings. This was done for each set of consecutive ratings. Values were then summed over the intervals and divided by the total number of days between the first and last evaluations . This caiy is the disease score on the ith date, it is the number of days since the first disease evaluation, and nt is the total number of days between first and last scoring.where y is the response variable sAUDPC, and the random effects are: iG is the effect of genotype, jR is the effect of replicate, ijGR is the interaction effect of genotype and replicate, ktSR is the effect of side of the chamber nested in replicate, mnjTCR is the effect of the inoculation unit of tray nested in cart and replicate, pjNR is the effect of the number of nodes nested in replicate.Best linear unbiased predictors (BLUPs) were calculated for each entry and trait. The ASReml package  using GAPIT  adjusted P. sativum v1a genome (https://www.pulsedb.org/jbrowses), were considered as candidate genes.Genes within 1 Mb of highly associated SNPs, as identified using the P. pinodes, and for 4 other morphological traits: 7th leaf area, internode length between nodes 2 and 3, internode length between internodes 5 and 6, stem diameter between nodes 5 and 6. The experiment was run in 7 replications in a complete block design, with 1 full replicate per run. sAUDPC correlations between replicates were highly significant and ranged from r = 0.16 to 0.35    and the s <0.01) . Replicas <0.01) . The moss <0.01) . While gr = 0.58 to 0.84  were measured in 5 replicates. Each morphological trait had a higher correlation between replicates than sAUDPC, ranging from e < 0.01 . Since te < 0.01 . The basAfter filtering 319,141 SNPs for MAF (>0.05) and heterozygosity (<20%), 53,196 SNPs were used for GWAS. Q–Q plots for each GWAS suggested that models had a good fit and that reliable SNP–trait associations were identified . In totahttps://www.pulsedb.org/blast) to find the location on the reference genome of previously identified significant markers and proteins. Past studies conducted both in the field and in controlled conditions, found QTL associated with resistance to P. pinodes on multiple chromosomes  is 2 Mb away and it was associated with leaf area and internode 2–3. Eight other SNPs were associated with internode length, illustrating that more than 1 gene is responsible for the trait.The y Mendel . AlthougUsing biparental populations, Marker S5LG3_544595701, which here was significantly associated with leaf area and stem diameter, is 0.23 Mb from marker Chr5LG3_572669963 which was associated with reproductive stem length by Sclerotinia sclerotium in peas in controlled conditions and found the same QTL to be associated with internode length and 2 measures of disease resistance. The allele conferring resistance was also associated with shorter plant stature. We found that SNP S1LG6_369964198 on chr1 (LG6) had the minor allele associated with less disease , smaller leaf area, and thinner stem. This SNP in conjunction with the positive correlation of growth traits and sAUDPC (d sAUDPC  is a strhttps://github.com/selizpowers/GWAS (GBS data aligned with the reference genome is available at G3 online.jkac168_Supplemental_Figure_S1Click here for additional data file.jkac168_Supplemental_Table_1Click here for additional data file.jkac168_Supplemental_Table_2Click here for additional data file.jkac168_Supplemental_Table_3Click here for additional data file.jkac168_Supplemental_Table_4Click here for additional data file.jkac168_Supplemental_Table_5Click here for additional data file.


Utilising a nested association mapping (NAM) population-based GWAS, 98 stable marker-trait associations with 127 alleles unique to the exotic parents were detected for grain yield and related traits in wheat.Grain yield, thousand-grain weight, screenings and hectolitre weight are important wheat yield traits. An understanding of their genetic basis is crucial for improving grain yield in breeding programmes. Nested association mapping (NAM) populations are useful resources for the dissection of the genetic basis of complex traits such as grain yield and related traits in wheat. Coupled with phenotypic data collected from multiple environments, NAM populations have the power to detect quantitative trait loci and their multiple alleles, providing germplasm that can be incorporated into breeding programmes. In this study, we evaluated a large-scale wheat NAM population with two recurrent parents in unbalanced trials in nine diverse Australian field environments over three years. By applying a single-stage factor analytical linear mixed model (FALMM) to the NAM multi-environment trials (MET) data and conducting a genome-wide association study (GWAS), we detected 98 stable marker-trait associations (MTAs) with their multiple alleles. 74 MTAs had 127 alleles that were derived from the exotic parents and were absent in either of the two recurrent parents. The exotic alleles had favourable effects on 46 MTAs of the 74 MTAs, for grain yield, thousand-grain weight, screenings and hectolitre weight. Two NAM RILs with consistently high yield in multiple environments were also identified, highlighting the potential of the NAM population in supporting plant breeding through provision of germplasm that can be readily incorporated into breeding programmes. The identified beneficial exotic alleles introgressed into the NAM population provide potential target alleles for the genetic improvement of wheat and further studies aimed at pinpointing the underlying genes.The online version contains supplementary material available at 10.1007/s00122-022-04230-9. Grain yield (GY) determines the efficiency of wheat production and food security. In wheat breeding, the main focus is to increase GY. Being the result of many processes occurring within the plant and their interaction with the environment, GY is directly and indirectly influenced by other traits. Often traits that directly and indirectly influence GY are used in wheat breeding to improve GY. In Australia, there are three traits which are key targets for wheat breeding: thousand-grain weight (TGW), screenings (SCG), and hectolitre weight (HW). TGW, defined as the weight of one thousand seeds selected at random, is an essential trait that directly influences GY increase under both favourable and stressful environments  whose genomes are mosaics of the parental genomes  to two Australian elite varieties Gladius and Scout  were collected from weather stations less than 5 km from each trial site (Supplementary Table 2). In this study, a trial was defined as a combination of year, site and NAM set evaluated and is synonymous with environment. Each trial was sown in a row-by-range array and was designed as a partially replicated experimental design  was measured as the weight of a sample of one thousand grains, and SCG was measured by collecting and weighing the proportion of material (including wheat grains and chaff) from a test sample that fell through a 2-mm sieve after 40 shakes. SCG was expressed as a percentage of the sample weight. HW was measured by weighing the grain collected from a levelled 500 ml measuring container of a chondrometer  and converting the weight to kilograms per hectolitre (kg/HL). In the 19RSW-NAM_ABC trial, TGW was not measured for the B and C sets and so the TGW of the 19RSW-NAM_ABC trial is comprised of NAM set A only. HW was not measured in the 18RSW-NAM_B trial due to the trial being harvested late and therefore being deemed as unrepresentative.G × E effects to be portioned into additive and non-additive effects. The mathematics, genetic variance and covariance structures of the model including pedigree information are described in Beeck et al. (G × E interactions (GEI). Outliers were detected using standardised conditional residuals and were removed to reduce bias of estimates. Because the trials were unbalanced, best linear unbiased predictions (BLUPs)  with pedigree information  matrix for estimation and correction of population structure was constructed from the SNPLDB markers as described in He et al. . The tope et al. . The marThe BLUPs for GY, TGW, SCG and HW ranged from 0.2 to 5.1 t/ha, 15.8 to 57.2 g, 0 to 25.8%, 67.0 to 85.8 kg/hl, respectively, across all the environments. Figure The performance of the NAM RILs and the check varieties differed with environment. For GY, TGW, SCG and HW, the environments in which the genotypes performed better were 17RSW-NAM_A, 18DND-NAM_A,19RSW-NAM_ABC and 18DND-NAM_A, respectively  by the two factors of the FALMM we used to analyse the MET data. For GY, the %VAF factor_1 for the additive effects ranged from 14.6 to 99.9% meaning that it did not consistently explain the greater amount of variation in all sites. This indicates the presence of GEI. For TGW, the first factor accounted for more than 76% of the additive variance for all the sites indicating low GEI. For SCG, the first factor for additive effects accounted for at least 60% of the variance for all sites except for 17RSW_NAM_A and 18RSW-NAM_A while for HW at least 65% of the variance was explained by the first factor in all sites except for 19DND-NAM_C,19RSW-NAM_ABC and 20MRA-NAM_BC. The FA2 model generally showed a good fit for all the traits in most sites as evidenced by the high total %VAF the additive effects Table .Table 3Prg) between pairs of environments give a measure of the level of GEI between paired environments. Higher rg, indicates similar performance of genotypes between paired environments and hence low GEI. rg between paired environments showed different patterns for different traits  with all the trials except 18RSW-NAM_A and 18RSW-NAM_B. Trial 19DND-NAM had high correlation (rg ≥ 0.66) with all the trials except 18DND-NAM_A. The Roseworthy trials were generally highly correlated (≥ 0.7) with each other. 18RSW-NAM_A and 18DND-NAM_A were also highly correlated. For TGW  with all the trials except 18RSW-NAM_A. 19RSW-NAM_ABC had high correlation (rg ≥ 0.68) with all trials except 17RSW-NAM_A. For HW  with the other trials except with each other and 19DND-NAM_B and 19LKH-NAM_A.The genetic correlations  greater than 2%. In the following, we present the QTL for each trait separately.In this study, we used a GWAS approach to map marker-trait associations (MTAs) for GY, TGW, HW and SCG in the NAM population. We detected a total of 98 significant MTAs for the four traits Tables , 5, 6, 7R2%) of each MTA ranged from 1.0 to 6.1% with a total of 44.9%.  per MTA ranged from 0.7 to 3.9% with a sum total of 41.3% of the phenotypic variation (Table VRN-A1). Two more loci were also located close to QTL detected in previous studies  ranging between 0.6% and 2.9%, with a sum total of 43.5% of the phenotypic variation (Table We identified 33 MTAs for SCG which mapped to 16 chromosomes with a -log P significance level ranging from 3.1 to 9.3 (Table R2%) ranging between 2.4 and 9.3%, with a sum total of 58.1% of the phenotypic variation , 4A (two MTAs), 4B (three MTAs), 4D, 5B, 6A and 6B (two MTAs) with a  −log P significance level ranging from 3.0 to 9.9 Table . One MTAAs global climate changes, the severity and frequency of drought and heat stress on crop production are expected to increase. Drought and heat stress are the major abiotic stresses limiting wheat production globally. Drought is when a plant experiences water stress at levels that are sufficient to affect plant growth rates  of additive genetic variance explained by the two factors individually and in combination . Phenotypic evaluation of genetic material for important traits in METs provides a way of effectively measuring G × E and identifying stable genotypes and environments suited for specific genotypes  previously reported by Yang et al. (QYld.aww-1B.2) previously identified by Tura et al. Supplementary file2 (XLSX 2011 kb)Supplementary file3 (DOCX 130 kb)Below is the link to the electronic supplementary material.


A Mixed Linear Model (MLM) in TASSEL was used for the GWAS and followed by confirmatory analyses using GAPIT FarmCPU. An average linkage disequilibrium (r2) of 0.10 at 5.2 Mb was found across several chromosomes. Seventeen significant (P ≤ 8.17 × 10−5 (–log10 (p) = 4.088)) MTAs of interest, including six that pertained to yield-related traits, were identified using TASSEL MLM. The latter accounted for 5 to 17% of the phenotypic variation expressed. The highly significant association (P ≤ 8.17 × 10−5) between seed length to width ratio and TcSNP 733 on chromosome 5 was verified with FarmCPU (P ≤ 1.12 × 10−8). Fourteen MTAs were common to both the TASSEL and FarmCPU models at P ≤ 0.003. The most significant yield-related MTAs involved seed number and seed length on chromosome 7  and seed number on chromosome 1 (P ≤ 2.38 × 10−05), based on the TASSEL MLM. It was noteworthy that seed length, seed length to width ratio and seed number were associated with markers at different loci, indicating their polygenic nature. Approximately 40 candidate genes that encode embryo and seed development, protein synthesis, carbohydrate transport and lipid biosynthesis and transport were identified in the flanking regions of the significantly associated SNPs and in linkage disequilibrium with them. A significant association of fruit surface anthocyanin intensity co-localised with MYB-related protein 308 on chromosome 4. Testing of a genomic selection approach revealed good predictive value ) for economic traits such as seed number (GEBV = 0.611), seed length (0.6199), seed width (0.5435), seed length to width ratio (0.5503), seed/cotyledon mass (0.6014) and ovule number (0.6325). The findings of this study could facilitate genomic selection and marker-assisted breeding of cacao thereby expediting improvement in the yield potential of cacao planting material.A genome-wide association study (GWAS) was undertaken to unravel marker-trait associations (MTAs) between SNP markers and phenotypic traits. It involved a subset of 421 cacao accessions from the large and diverse collection conserved  Theobroma cacao L., Malvaceae sensu lato  9-desaturase on chromosome 4 was also of relevance. The association revealed by FarmCPU between TcSNP 932, on chromosome 9, with log cotyledon mass, must be considered due to its functional importance. This SNP was co-localised with 1-acyl-sn-glycerol-3-phosphate acyltransferase 1%2C chloroplastic, which is involved in lipid metabolism.At3g26840 is involved in seed storage (globulins) and seed size in Assembly cotton 46 (um spp.) . GossypiAnother putative candidate gene, unravelled during this study, was Tc01v2_g022850, which encodes Bifunctional inhibitor/lipid-transfer protein/seed storage 2S albumin superfamily protein involved in Glycerolipid metabolism  on chromosome 1) . It is mSince several hundred MTAs or QTLs have previously been identified in cacao, an attempt was made to check for congruence between the findings of this study and previous ones with respect to common traits. Highly significant (stable) associations between yield-related traits, seed length and seed length to width ratio, seed number and seed (cotyledon) mass and pod index, and SNPs were found on chromosomes 1, 3, 4, 5, 7 and 9 in this study . The fin1, 2, 5, 6, 9 and 10, MTAs involving dried seed mass (100 seeds) on chromosomes 2, 4, 9 and 10 as well as one marker associated with seed number per fruit on chromosome 5. In their mapping study, dos Santos Fernandes et al. [4, which were associated with pod index, dried individual seed mass, number of fruits harvested and number of healthy fruits harvested. They also identified a significant association between the marker, Tcm002s23708704, and pod index on chromosome 2. dos Santos Fernandes et al. [4 and 2. Nine of these genes are annotated as transmembrane transporters, specializing in sugar transport, two genes are involved in carbohydrate metabolism, one gene codes for lipid metabolism, and one is involved in glucose metabolism [1 and 9 that were significantly associated with seed number. Clément et al. [4.Eight significant associations between SSR markers and yield-related traits were reported by Marcano et al. . These is et al.  identifis et al.  unravelltabolism . Motilaltabolism  identifit et al.  found tw4 . Motilal et al. [1.A highly significant MTA, observed in this study, involved seed number and TcSNP 785 on chromosome l et al.  also rephttp://dx.doi.org/10.13140/RG.2.2.24148.88966).Criollo, Forastero and Trinitario are widely recognised classes of cocoa in the trade. The cacao accessions in this study that displayed favourable yield potential were mainly Trinitario (cultivated germplasm)  and thosT. cacao may thus be facilitated by using TcSNP 953, TcSNP 555 and TcSNP 344 on chromosome 4 and TcSNP 733 on chromosome 5  and trehalose phosphate phosphatase (TPP) genes have also been identified .Arabidopsis was shown to encode an acyl-CoA-dependent DGAT. Jako et al. [Arabidopsis “enhances oil deposition and average seed mass, which are correlated with DGAT transcript levels”, and that “DGAT has an important role in regulating the quantity of seed TAGs, the sink size in developing seeds and thus seed size.” They also demonstrated that “over-expression” of the acyl-CoA-dependent DGAT in a “seed-specific manner in wild-type Arabidopsis plants results in increased oil deposition and average seed mass.”These recommended follow-up studies would entail gene expression analysis, involving transcriptomics as described by Jako et al. . The DGAo et al.  also dem4 upstream of TcSNP555 (P ≤ 4.29 × 10−4). SWEET 2 was also localized downstream of TcSNP1335, which was significantly associated (P≤ 1.15 ×10−14) with seed number as well as with log seed length (P≤ 6.75 × 10−05) on chromosome 7 (TASSEL MLM)  .https://cocoa-genome-hub.southgreen.fr/). This SNP accounted for 7.1% of the phenotypic variation in fruit colour observed (P ≤ 4.78 × 10−06) with TASSEL MLM  was significantly associated with TcSNP 401 on chromosome 4, located at 20,485,872, about 199 Kb upstream of the gene Tc00_t058610 (mRNA). The latter encodes a Putative MYB-related protein 308. TcSNP 401 was also 198 Kb upstream of the Transcription repressor MYB 6  (https:/SSEL MLM . The MTAP ≤ 6.79 × 10−05) for fruit surface anthocyanin intensity was found in this study with TASSEL MLM analysis. It involved TcSNP 1203, on chromosome 3 , which accounted for 5.6% of the phenotypic variation in fruit surface anthocyanin concentration in this cohort of germplasm.Another significant association . All of the MTAs involving anthocyanin concentration in this study suggest multi-gene control of anthocyanin intensity in the mature fruit epidermis and flower filaments of cacao. Furthermore, the differential expression of pigmentation in the seeds and fruits of cacao, observed at the ICGT, as evidenced in the negative correlations in There were also significant associations between filament anthocyanin concentration and SNPs on chromosomes 3 and 8 , based on the MTAs involving anthocyanin intensity warrant further investigation to determine their value for genomic selection due to the significance of this trait in differentiating among certain genotypes of interest, such as CCN-51 and ancient Nacional cacao , 94, theThe results of this study support the observation of Rockman  that mosThe data on gene ontology presented in The observations regarding drought tolerance in this study are of considerable significance since the conditions at the ICGT, where these accessions were conserved, are considered sub-optimal in terms of soil moisture content . TherefoOnce the functional roles of putative genes co-localized with markers with significant associations to traits of interest are elucidated, the effects of relationships of these putative genes with geography and local adaptation must be established, as recommended by McKown et al. . MicheliDespite the fact that yield is a complex trait, our results on potential genomic selection (GS) for yield traits are very promising, given the high predictive values obtained for these traits, generally superior to 0.5. The detection of several markers associated with yield-related traits with good predictive value, in this study, could facilitate GS and marker-assisted selection (MAS) for yield in cacao.http://dx.doi.org/10.13140/RG.2.2.29601.48484), and SNP genotyping data (http://dx.doi.org/10.13140/RG.2.2.25826.61123), generated via transcriptome sequencing, were subjected to GWAS. Twenty-nine cacao accessions represent promising material for breeding in terms of yield potential. The results presented herein indicated oligogenic and polygenic control of yield-related traits in cacao. The stringently significant marker-trait associations related to yield, found in this study, were indications of the presence of QTL on chromosomes such as 3, 4, 5, 7, 8 and 9. Chromosome 4 putatively contains a QTL cluster associated with yield-related traits. Some annotated candidate genes associated with seed development, seed lipid accumulation, metabolism and development and plant stress responses (including to drought and to cadmium) have been identified during this research. The identification of yield-related traits with good predictive value could further facilitate GS for yield potential in cacao. The performance of the non-phenotyped individuals at the Trinidad genebank (ICGT) could also be predicted once they are genotyped. This would be particularly useful for the enhanced genotypes (GEBP progeny) [In this study, carefully collated phenotyping data on traits of economic interest in cacao, such as yield potential (progeny) . IdentifS1 Tablehttp://dx.doi.org/10.13140/RG.2.2.16179.71202.(DOCX)Click here for additional data file.S2 Tablehttp://dx.doi.org/10.13140/RG.2.2.29601.48484.(XLSX)Click here for additional data file.S3 Tablehttp://dx.doi.org/10.13140/RG.2.2.22890.59849.(DOCX)Click here for additional data file.S4 Tablehttp://dx.doi.org/10.13140/RG.2.2.36312.37128.(DOCX)Click here for additional data file.S5 Tablehttp://dx.doi.org/10.13140/RG.2.2.17438.00328.(DOCX)Click here for additional data file.S6 Tablehttp://dx.doi.org/10.13140/RG.2.2.27504.33282.(DOCX)Click here for additional data file.S7 Tablehttp://dx.doi.org/10.13140/RG.2.2.20793.44647.(DOCX)Click here for additional data file.S8 Tablehttp://dx.doi.org/10.13140/RG.2.2.34215.21927.(XLSX)Click here for additional data file.S9 Tablehttp://dx.doi.org/10.13140/RG.2.2.12404.83842.(DOCX)Click here for additional data file.S10 Tablehttp://dx.doi.org/10.13140/RG.2.2.25826.61123.(TXT)Click here for additional data file.S1 Fighttp://dx.doi.org/10.13140/RG.2.2.24148.88966.(TIF)Click here for additional data file.