Horns are the most obvious common feature of Bovidae. The naturally occurring absence of horns in these species, also known as polledness, is of surprisingly heterogeneous nature, although they are Mendelian traits. This review compares in detail the molecular differences among the causes of inherited polledness in the domestic ruminant species of cattle, yak, sheep, and goat based on the causal gene variants that have been discovered in recent years. The genetic causes for the lack of horns in small ruminants seem not only to be more complex, e.g., in sheep, breed-specific characteristics are still unexplained, but in goats, there is also the associated disorder of intersexuality—polled intersex syndrome (PIS). In connection with animal welfare and the associated discussion about a legal ban on the dehorning of all farm animals, naturally hornless animals and the causal genetic variants are of increasing research interest in the age of genome editing. However, the low acceptance of genetic engineering in livestock, especially in European societies, limits its use in food-producing animals. Therefore, genotype-based targeted selection of naturally occurring variants is still a widely used method for spreading this desired trait within and across populations, at least in cattle and sheep. During evolution, pecorans  developed a notable diversity of bony skull attachments called "headgear", which are likely to have an identical genetic origin . RuminanHorns in bovids, the biological family of cloven-hoofed, ruminant mammals, including cattle, goat, and sheep, play a role in social behavior and protection. It is assumed that horns serve as a weapon in self-defense against predators and in ranking fights, for example, by stabilizing the head position during fights, as well as in sexual selection through intramale competition. Even impact absorption by horns is possible ,4,5,6,7.However, naturally occurring, genetically hornless (polled) animals are more or less common in most domesticated Bovidae, including various important livestock species  . In geneHorned animals pose a danger when interacting with humans  and flocThere is a wide variety of naturally occurring forms of horn size, shape, and position, including rare forms of hornlessness (polledness) ,31,32,33In cattle and sheep, another form of horn growth is known: scurs. Scurs are hornlike formations that occur occasionally in a wide variety of sizes and forms as an unexpected phenotype when breeding polled cattle or sheep ,36. ThesThe development of horns involves hundreds of genes . Since pPOLLED locus (P) in cattle is located on bovine chromosome 1 . In. InPOLLEdeletion ,46. Bothmosome 1 . Howeverbud area . Interesbud area ,50.Bos mutus), a third “Mongolian” allele (PM or P219bpID) for polledness, affecting the same genomic locus on chromosome 1, was discovered [In wild yaks . This variant is a 110-kb tandem duplication located in the same genomic region on bovine chromosome 1 , genetically polled individuals are known to occur, but the underlying variant(s) remain unknown (OMIA 000483-89462). Little is known about the genetic background of horns or potential hornlessness in further bovids. For example, in water buffalo  gene on bovine chromosome 4, representing a loss-of-function allele, was found and highlights the genetic complexity of horn-growth phenotypes in cattle [In addition to horned and polled cattle, there are also some with an intermediate phenotype, the so-called scurs or “wiggle horns”. Animals showing scurs are heterozygous for one of the polled alleles . The devn cattle .As in cattle, polledness in goats  follows So far, it is unclear which elements of these complex structural variants contribute to the lack of horn growth in goats. In contrast to what is known from other species, the dominantly inherited polledness in goats is associated with recessive intersexuality  Table 1Table 1. FOXL2, which is also implemented in the polledness of cattle as mentioned above [FOXL2 topologically associating domain (TAD) when compared to the corresponding human genome region, i.e., in the regulatory domain responsible for FOXL2. Duplications of genomic regions are associated with various disorders, but the phenotypes, which are thought to arise from an increase in gene copy number, often cannot be explained by changes in gene dosage [Functionally, the originally reported PIS-causing 11-kb deletion published by Pailhoux et al. (2001) affects elements that regulate the transcription of ed above ,63. Obvied above . The rece dosage . Howevere dosage . TADs arSRY-box transcription factor 9 (SOX9) and potassium inwardly rectifying channel subfamily J member 2 (KCNJ2) that lies within the SOX9 TAD results in sex reversal from female to male. In contrast, an inter-TAD duplication that involves sex reversal duplication and spans into the KCNJ TAD—but without the KCNJ genes included—has no influence on the phenotype [KCNJ gene and parts of the ETS transcription factor ERG (ERG) gene as well as parts of the respective TADs. Therefore, the duplicated segment contains a boundary that separates the TADs and, thus, the regulatory domains from each other. When the duplicated segment is inserted into the breakpoint of the FOXL2 region, it can be assumed that a fusion TAD (neo-TAD) is formed, consisting of one part of the duplication and the remaining of the FOXL2 TAD. Due to the inversion, KCNJ is placed on the other side of the boundary and is, therefore, isolated , while horns are found mainly in autochthonous breeds. Some sheep breeds, such as the Poll Dorset were specifically bred after the model of the horned basic breed (Dorset Horn), only hornless. Besides sheep breeds fixed either for horns or polledness, there are also those in which one or both sexes have a variable horn status, and even those in which rams are always horned and females are always polled . ConsideRXFP2 gene located in this region  to the 3′-end of RXFP2 transcripts, does not segregate perfectly with the polled phenotype in sheep breeds with variable or sex-linked horn status [However, it was recognized that this polledness-associated 1.8-kb insertion variant, which adds a potential antisense RNA sequence of n status . TherefoRXFP2-related 1.8-kb insertion [RXFP2-associated alleles. A visual inspection of the region of the RXFP2 gene using the integrated genome viewer (IGV) [The rams of the African Dorper sheep breed can have normal horns or scurs or be hornless, whereas female Dorper ewes are scurred or polled. Interestingly, this breed is fixed for the nsertion . Publicler (IGV)  revealeder (IGV) .The localization of the homologous sequences of the caprine PIS region ruled out the possibility that intersexuality in sheep has the same or similar underlying genetic causes as in goats . HoweverRXFP2 gene region on chromosome 10 [RXFP2 [RXFP2-related markers that segregate, at least partly, with horn morphology, in terms of length and shape [In addition to the mere presence or absence of horns (including scurs), the expression of horn shape and size in sheep also varies. In studies of the isolated Soay sheep population on St. Kilda, the locus for these continuous traits could also be located in the osome 10 . This su0 [RXFP2 ,31. Wholnd shape .Even though European law strictly limits the use of new genetic engineering since the landmark ruling of July 2018 , the so-Celtic” polled allele (PC) of cattle was independently integrated into the genomes of horned cattle [PC after genome editing expressed the polled trait as expected. At the same time, other intended changes in the genome sequence were not detectable [As one of the first successful applications in domestic animals, the “d cattle ,81,82. Mtectable . Since polledness in cattle is apparently not associated with any harm, it is possible to spread the desired trait through conventional breeding within a few generations in previously horned populations without any negative effects. However, to give genome editing an edge, some argue that the population of hornless breeding bulls with reliable and good breeding values (comparable to horned counterparts) is too small to avoid inbreeding while supporting the introgression of the polled trait. For this reason, it takes much longer to increase the frequency of polledness in a population using conventional breeding strategies than with genetic engineering ,85,86. FPC variant, into the goat genome. So far, no such attempts have been published.These techniques have also been successfully applied to sheep and goats when dealing with issues other than polledness ,89. For However, in addition to strict legislation in Europe, there are also reservations among the public about genetically modified animals for consumption. Both ethical concerns and risk–benefit assessments of genetic engineering in food production explain the critical attitude of consumers. In general, it can be summarized that Europeans are more critical of the topic than American and Asian consumers ,91,92. FAlthough the presence or absence of horns is a trait as old as livestock, it is a topic that never loses its relevance. The study of horn phenotypes in ruminants confirms the still underestimated role of domestic animals as unique models for biomedical research due to their long history (thousands of years) of strong phenotypic selection. The three known hornless loci in cattle, goat, and sheep each affect different genes, although the resulting phenotypes with the absence of horn growth are more or less identical. This confirms the assumed heterogeneity and complexity that determines the development of these organs, which are unique in the animal kingdom. Nevertheless, the underlying genetic mechanisms, especially in sheep, remain largely unknown, highlighting the need for further research in this field. A challenge will be to clarify the implementation of intermediate phenotypes, such as scurs and sex-linked factors. In addition, the underlying mechanisms in all three ruminant species still need to be investigated. There is no doubt that breeding for polledness is a sensible and permanent alternative to surgical dehorning in order to take animal welfare into account and offer an animal-friendly alternative ,84.The new possibilities offered by genome editing techniques could serve as a tool to spread this trait faster than through conventional breeding, especially in cattle. In goats, it may be possible to specifically modify the corresponding genomic regions that are altered in cattle or sheep to avoid the negative association of the naturally occurring hornless variant with intersexuality.


The effect of genetic variation in the male-specific region of the Y chromosome (MSY) on coronary artery disease and cardiovascular risk factors has been disputed. In this study, we systematically assessed the association of MSY genetic variation on these traits using a kin-cohort analysis of family disease history in the largest sample to date.We tested 90 MSY haplogroups against coronary artery disease, hypertension, blood pressure, classical lipid levels, and all-cause mortality in up to 152 186 unrelated, genomically British individuals from UK Biobank. Unlike previous studies, we did not adjust for heritable lifestyle factors (to avoid collider bias) and instead adjusted for geographic variables and socioeconomic deprivation, given the link between MSY haplogroups and geography. For family history traits, subject MSY haplogroups were tested against father and mother disease as validation and negative control, respectively.Our models find little evidence for an effect of any MSY haplogroup on cardiovascular risk in participants. Parental models confirm these findings.Kin-cohort analysis of the Y chromosome uniquely allows for discoveries in subjects to be validated using family history data. Despite our large sample size, improved models, and parental validation, there is little evidence to suggest cardiovascular risk in UK Biobank is influenced by genetic variation in MSY. Genetic variation in the Y chromosome is highly structured across Great Britain, warranting adjustments for population structure and geographic confounding when investigating its link to disease.Putative associations of Y haplogroups with disease in subjects can be validated using kin-cohort analysis, where father disease acts as replication and mother disease acts as negative control.Among 152 186 White British individuals from UK Biobank, none of the tested Y haplogroups are meaningfully associated with coronary artery disease, its risk factors, or all-cause mortality.Statistically suggestive Y haplogroup associations in UK Biobank subjects are not supported by parental data.1 Its sequence can be classified into 2 parts: (1) the pseudoautosomal regions, which constitute around 5% of its sequence, and (2) the male-specific region of the Y chromosome (MSY). Although the pseudoautosomal regions are homologous to the X chromosome, the MSY is unpaired and therefore does not undergo recombination.2 The lack of MSY recombination allows this region to be transmitted largely unchanged from father to son, and as such, it can be used to trace patrilineal ancestry3 and migration.4The Y chromosome is the smallest human chromosome spanning only 57.2 Mb (23 Mb euchromatic) and containing just ≈78 protein-coding genes.5 Since 2000, a series of small-scale studies investigated the link between blood pressure and the HindIII(–) polymorphism—known as the P haplogroup using modern notation6—for individuals from various European ancestries. These studies found the P haplogroup was associated with higher blood pressure5 (N=409), lower blood pressure7 (N=920), or no change in blood pressure10 . Similarly, the P haplogroup has been linked to higher levels of low-density lipoprotein cholesterol and triglycerides in one study11 (N=1288) but showed no such effect in another9 (N=1983). Other polymorphisms, such as those in Y chromosome genes TBL1Y and USP9Y, have been linked to changes in high-density lipoprotein cholesterol and triglyceride levels only in black individuals.12Despite its small size, the Y chromosome has been hypothesized to account for sex differences in coronary artery disease (CAD) susceptibility.13 (2019), who found an association between CAD incidence in UK Biobank  and the I1 haplogroup (I1-M253) after correcting for cardiovascular risk factors and family history of heart disease.13 Despite the increased sample size, 3 methodological concerns draw into question their findings.The largest MSY study to date was performed by Eales et al16 This issue may be especially pertinent for the MSY, which is uniquely sensitive to population structure.18 Eales et al13 only corrected for 5 genetic principal components, which may not be adequate to rule out such confounding. Second, cardiovascular risk factors are heritable and can therefore cause collider bias when used as covariates.19 More precisely, MSY haplogroups with causal effects on risk factors (but not CAD directly) can have biased associations with CAD after adjusting for the risk factor. Figure S1 illustrates this scenario in more detail. Third, correcting for paternal history of heart disease in an MSY study of CAD is inappropriate since fathers and sons share the same MSY. This adjusted estimate captures the excess effect of MSY haplogroups on CAD in the current generation compared to their effect on heart disease in the previous generation.First, recent studies show uncorrected fine-scale population structure in UK Biobank causes geographic and socioeconomic confounding that results in spurious associations with disease traits20 where we test UK Biobank participant MSY haplogroups against paternal traits as validation and maternal traits as negative control. In contrast to previous studies, we conclude there is limited evidence for an association of MSY haplogroups with cardiovascular disease phenotypes and all-cause mortality.Given these conflicting results and methodological concerns, a reexamination of the role of genetic variation in MSY on cardiovascular risk and disease outcomes is warranted. Here, we systematically attempt to validate findings from previous studies using the latest release of medical records from UK Biobank. We mitigate concerns about collider bias and population stratification by replacing heritable risk factors with adequate genetic principal components and geographic covariates. Instead of adjusting for family history traits, we use information on parental disease to perform a special type of kin-cohort analysis,https://www.ukbiobank.ac.uk/).UK Biobank phenotypes and genotypes are available upon application  provided by yhaplo, 232 were genotyped on both UK Biobank arrays. SNPs with a call rate of <95% (n=39) and monomorphic SNPs (n=27) were removed, leaving 166 SNPs for haplogroup inference. Eighty-nine haplogroups were reported directly by yhaplo, and a further 38 were defined through combinations of derived groups to increase statistical power of rarer haplogroups and to detect effects arising from deeper nodes in the Y chromosome genealogy (Table S1). For example, G-M201 was created from all individuals carrying the 9 derived G haplogroups, analogous to combining the effects of these individual haplogroups. In a few cases, these newly defined haplogroups were approximate, as with R1b-S21, where 4 derived subgroups could be recognized from the SNPs available on the array, other members of this group being subsumed into the upstream paragroup (in this case R1b-S128*). Haplogroups were named using up to 5 characters of the hierarchical nomenclature from the International Society of Genetic Genealogy 2019-2020 tree , followed by the SNP name defining the haplogroup . Paraphyletic groups or paragroups (where the group does not include every descendant of the ancestor) are indicated with an asterisk suffix . All the main deep branches of the Y haplogroup tree  were recovered in this way, with a large, but random selection of more recent and rarer haplogroups, the defining SNPs for which happened to be present on the genotyping arrays.Phylogenetic analysis was performed on all male-inferred UK Biobank participants (N=223 513) using yhaplo v1.1.2.13 we discarded SBP and DBP outliers based on a modified Tukey test: values >3 interquartile range below the 25th percentile or >3 interquartile range above the 75th percentile. The interquartile range was recalculated after removing outliers, and the test was repeated until no further values were removed. Mean arterial blood pressure was calculated as DBP+(DBP+SBP)/3. As is standard for genetic analyses of blood pressure,26 individuals who reported taking antihypertensive medications had their SBP increased by 15 mm Hg and their DBP increased by 10 mm Hg. Individuals taking antihypertensive medication or showing either SBP ≥140 or DBP ≥90 were classified as cases of hypertension.At assessment, systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured using an automated OMRON digital sphygmomanometer. If multiple technical replicates were available, the mean value was used. Analogous to Eales et al,27 individuals who reported taking lipid-lowering medication had their low-density lipoprotein cholesterol and total cholesterol measures divided by 0.7 and 0.8, respectively. After log-transformation of triglyceride values, all lipid phenotypes had outliers removed using the modified Tukey test. Again, outlier removal was repeated until no further values were removed.Lipid levels were measured at assessment using a Beckman Coulter AU5800 clinical chemistry analyzer. Total cholesterol and triglycerides were measured by glycerol-3-phosphate peroxidase analysis, whereas high and low-density lipoprotein cholesterol were measured by enzyme immunoinhibition analysis and enzymatic protective selection analysis, respectively. As is standard for genetic analyses of lipid levels,13 which uses participant reports coded by the interviewing nurse or doctor, hospital, and death records coded using the International Classification of Diseases (ICD) chapters and surgical records coded using the OPCS (Office of Population Censuses and Surveys) classification system. In brief, the algorithm defines CAD cases by the presence of self-reported or hospital-recorded incidence of myocardial infarction , percutaneous coronary intervention , or coronary artery bypass graft (OPCS codes K40–46). In addition, individuals with death records showing CAD  as a primary or secondary cause of death were also defined as cases. Controls were defined as all remaining individuals who did not report or had no hospital records of (unstable) angina  and were not taking any medications containing aspirin, glyceryl trinitrate, isosorbide mono- or dinitrate, or nicorandil (see Table S2 for medication codes). Among the unrelated male British UK Biobank participants, the algorithm identified 16 223 cases and 129 533 controls .CAD was defined using the classification algorithm proposed by Eales et al,Father and mother hypertension and CAD were defined using self-reported data from subjects. Specifically, a touchscreen questionnaire asking “Has/did your father ever suffer from? (You can select more than one answer),” allowed subjects to select whether their father suffered from a list of disorders including heart disease and high blood pressure. An analogous question was asked about their mother’s illnesses. Father and mother hypertension cases were defined by the selection of high blood pressure by the participant, whereas CAD cases were defined by the selection of heart disease (the accuracy of heart disease as a proxy for CAD is assessed below). All other selections were denoted as controls unless the participant preferred not to answer. Finally, to gather more data on the association of IJK-S137 with higher blood pressure, we selected subjects with only one male sibling, and created brother hypertension variables from the question “Have any of your brothers or sisters suffered from any of the following diseases? (You can select more than one answer),” analogous to father and mother hypertension definitions.28 North and east coordinates of UK Biobank participant postcodes (rounded to the nearest kilometer) were projected onto the European Petroleum Survey Group Projection 27700 reference and then mapped onto geographic regions  using R packages sp v1.4-5  and rgdal v1.5-23 . This mapping was done for the first instance of (1) home location coordinates at assessment and (2) birthplace coordinates.Spatial coordinates and deprivation indices were retrieved from the UK Office for National Statistics 2011 census.All statistical analyses were performed using R 3.6.1, unless otherwise specified.32). The presence or absence of a Y haplogroup was coded as a binary variable, and each haplogroup with at least 100 samples was tested in a separate model . The following model was used:Associations with blood pressure and lipid traits were tested using a multivariable linear model, implemented in the R package speedglm v0.3-3 . The following model was used:Associations with  CAD and hypertension were tested using a multivariable logistic model, implemented in the R package speedglm v0.3-3 (EneaP(Y) is the probability of participant CAD or hypertension. The other variables are as in Equation 1. Separate models were fit for each haplogroup containing at least 40 cases of CAD , or 100 individuals with complete blood pressure data .where 33). The following model was used:Associations with  all-cause mortality were tested using a Cox proportional hazards model, implemented in the R package survival v3.2-11  is the baseline hazard at age x, and the remaining variables are as described in Equation 1.where h(x) is the hazard at age x, hSurvival models were constructed using participant age at assessment, with death status inferred from the presence of a death record at time of analysis. The censoring date was set to 01 November 2021 to allow for a 2-week delay in the processing of death records . Parental survival models were constructed using the parent age and alive/dead status as reported by the participants at time of assessment. Parents with reported age at death before 40 were excluded to limit deaths due to accident or injury .13 models were adjusted for the continuous variables, age, body mass index, and the first 5 genetic principal components, as well as the categorical variables, genotyping array (2 levels), hypertension (2 levels), number of days performing moderate exercise (8 levels), average household income (5 levels), smoking history (2 levels), completion of further education (2 levels), employment status (6 levels), weekly alcohol intake (6 levels), father CAD (2 levels), and mother CAD (2 levels). Exact definitions of each variable can be found in Eales et al.13 Outliers in the age and body mass index phenotypes were removed based on the modified Tukey test, as described above.The Eales et alSocioeconomically robust models were adjusted for the continuous variables, (parent) age, (parent) age squared, north and east coordinates at assessment and birth, Townsend deprivation index of the Lower Super Output Area at assessment and birth, and the first 40 principal components of ancestry, as well as the categorical variable, genotyping array (2 levels). As survival models already incorporate (parent) age to calculate survival, linear and quadratic age were not used as covariates in the survival model.Supplemental Tables S5 to S9.We include models adjusting only for age (if appropriate), 40 principal components, and genotyping array, without further adjustment for geography or socioeconomic variables as sensitivity analyses in P values were adjusted by multiplying them by the number of independent haplogroups and, if applicable, the number of independent phenotypes.There is substantial correlation between Y haplogroups because of their hierarchical nature; therefore, adjusting for multiple comparisons using Bonferroni correction is too stringent. Principal component analysis of the correlation matrix was used to identify the number of independent components explaining 95% of the variation in Y haplogroups. This identified 22 independent components for the 66 Y haplogroups used in the CAD model, and 38 components for the 90 Y haplogroups used in the other models. Separately, we also estimated the number of components for related blood pressure traits (2 components) and lipid traits (2). For each phenotypic category, 13 algorithm described above. CAD proxy accuracy was calculated as the proportion of CAD cases among fathers reported to have heart disease by their sons (CAD cases=26 and heart disease cases=37). CIs were approximated by bootstrapping all father-son pairs and recalculating this proportion (100 000 iterations).Heart disease describes a variety of conditions affecting the heart, of which CAD is only a subset. The proportion of parental heart disease cases meeting CAD inclusion criteria was calculated using father-son pairs in UK Biobank. As such pairs are not directly reported by UK Biobank, father-son relationships were inferred from autosomal genetic relatedness and subject-reported age of fathers alive at assessment. Specifically, for each unrelated White British subject in UK Biobank, father age (in years) was subtracted from the date of subject assessment, creating a father date of birth interval allowing for 6 months on either side. Pairs of UK Biobank men with a genetic kinship coefficient >0.2 and containing one date of birth covered by the other’s father's date of birth interval were inferred to be father-son pairs (n=301). For each father, CAD was inferred using the Eales et al23 successfully identified 103 unique Y haplogroups for 223 513 individuals. In line with known population genetic structure,24 Y haplogroups differed significantly by self-reported ancestry. For example, the most abundant haplogroups in self-reported British-, Chinese-, and African-heritage individuals were R-P311 , O-M122 , and E-M180 , respectively. We restricted all subsequent analyses to unrelated individuals with genomically similar White British ancestry (n=169 635) to avoid confounding due to the extreme population stratification of Y chromosome variation.17 In addition to the Y haplogroups identified by yhaplo, we further grouped Y haplogroups hierarchically and kept for analysis only Y haplogroups and groupings with at least 40 cases of CAD or 100 individuals with nonmissing blood pressure and lipid trait values, for a total of 90 Y haplogroups (Table S1). As expected, the Y chromosome haplogroups carried by White British UK Biobank individuals show strong geographic structuring across the nations of Great Britain, with certain lineages being more common in England, Wales, or Scotland .34 For example, the I1-M253 and P-M45 haplogroups each show a latitudinal and longitudinal gradient, with the former increasing in prevalence towards England and the latter increasing in prevalence towards Scotland and Wales  using yhaplo13 Using identical definitions of CAD and CAD risk factors, we identify 12 226 cases of CAD with complete covariate information in our UK Biobank sample, representing an increase of 1032 (9.2%) cases since the publication of their study (see Table S3 for sample descriptives). Fitting an identical logistic model to Eales et al,13 we find the I1-M253 haplogroup has a largely attenuated effect on participant CAD in our study  compared with the original estimate . Moreover, increasing the number of genetic principal component covariates from 5 to 40 to account for any residual population stratification further attenuates this effect , with principal components 9 and 14 being significant confounders with moderate effects . Simplification of this model by removing father CAD as a covariate—which is also dependent on the participant haplogroup—further reduces the strength of the association , despite the increased sample size with complete covariates .We first set out to replicate the CAD analysis in Eales et al.35 and added geographic/deprivation variables and 40 principal components to mitigate confounding due to geography and socioeconomic differences.15 Finally, our models included a quadratic age term to account for any nonlinear associations of cardiovascular phenotypes with age.36 This more restricted set of covariates allows the dependent variable in our model to be replaced by reported father disease or death , and mother disease or death (as negative control), assuming geographic/deprivation variables are similar for participants and their parents.Next, we set out to systematically assess all Y haplogroup associations with CAD, hypertension, and mortality using new models. We did not include heritable covariates in our models to avoid collider biasP<0.0023. Kin-cohort validation of Y haplogroups reaching suggestive significance (P<0.10) shows MSY haplogroup effects on CAD suggested by subjects are not directionally consistent with MSY haplogroup effects on father heart disease . Parental validation also does not support the suggestive effect observed for I1-M253. As heart disease is an imperfect proxy for CAD (between 55% and 85% of cases of father heart disease are expected to meet CAD inclusion criteria in our study), power to replicate CAD-specific effects in parents may be reduced . Performing the same analysis on hypertension and separately, all-cause mortality, we again find no robust evidence for an association with any of the haplogroups . Here, haplogroups reaching suggestive significance (P<0.10) have no effect in fathers and/or overlap with the maternal negative control .In our models, we find no evidence for any of the 66 MSY lineages associating with CAD at the Bonferroni-adjusted significance threshold of P=0.0001; Padj=0.009; Figure S5; Table S8). Notably, the IJK-S137 haplogroup is not significantly associated with an increase in participant or father hypertension, although the direction of effect is concordant. Testing the same group against brother hypertension , we again find a directionally concordant effect, but we lack power to confirm the association . In contrast, we find no evidence for an effect of any Y haplogroup on cholesterol levels or triglycerides .When testing 90 Y haplogroups against blood pressure traits in participant models corrected for socioeconomic status, we find only the IJK-S137 haplogroup is significantly associated with increased blood pressure compared to the other haplogroups. The largest effect of this haplogroup is on mean arterial pressure, which is associated with an increase of 0.93 mm Hg  leads us to hypothesize its effect on hypertension risk—if real—is likely clinically insignificant.11 I-M170,37 or I1-M253 haplogroups.13 It is possible findings from these studies were false positives due to their smaller sample sizes , less well-controlled population stratification, collider bias, or geographic confounding. However, differences in haplogroup frequency between studies complicate direct comparisons of haplogroup effect estimates. We can confidently compare our effects with those reported by Eales et al13 as we replicated their UK Biobank sample, but haplogroup frequencies in other European cohorts used in previous studies will be different from genomically British UK Biobank individuals. As such, there could be population-specific effects of MSY haplogroups that are not apparent in UK individuals.Despite our large sample, we did not replicate previously reported associations between cardiovascular phenotypes and the P-M45,38 we saw no need to perform the adjustment as we did not find any significant kin effects to begin with.The MSY haplogroup kin-cohort analysis we present is a useful framework for validating putative associations of phenotypes with genetic variation in the Y chromosome and can be easily applied to any family history phenotype of interest. However, this method relies on several assumptions. First, we made the simplifying assumption that kin effect size estimates are independent. Strictly, phenotypic correlations between family members due to shared environment will result in MSY haplogroup effect size correlations. Although this can be adjusted for by inflating standard errors,39 although it is difficult to disentangle this indirect effect from any shared environment and autosomal genetic confounders. Alternatively, the independence assumption could be violated if genetic variation inherited from the mother  modifies Y haplogroup effects. Such epistatic effects exist between autosomal CAD variants,40 and it remains to be tested whether this phenomenon extends to genetic variation in the Y chromosome. We are underpowered to exclude the presence of indirect or epistatic correlations between MSY haplogroups and maternal phenotypes but expect this correlation to be a fraction of the MSY haplogroup effects on participants themselves.Second, using mothers as negative control assumes the participant Y haplogroup and their mother’s risk of CAD and hypertension are independent. It is conceivable this assumption could be violated if genetic variation in the participant indirectly influences the mother phenotype. For example, mothers are at increased risk of CAD after losing a child ,41 It is likely severe mLOY leads to poor genotype call rates, and as such, individuals with substantial mLOY could have been removed from our analysis, potentially resulting in a sample biased to be healthier than average. However, in an effort to reproduce the work by Eales et al,13 we did not take into account mLOY and used the same quality control criteria as their study, which should have allowed us to detect an effect with our sample if one were there.Third, we did not test whether our results were influenced by mosaic loss of Y chromosome (mLOY). Older men and men with unhealthy lifestyles tend to experience a greater degree of mLOY.42 Rather, we find no evidence of any common or rare MSY haplogroup lineages tested here to significantly influence CAD, hypertension, or all-cause mortality. It remains possible that other types of variation, such as epigenetic or transcriptomic differences, could influence CAD risk. Future work will involve testing the effects of rarer MSY haplogroups on CAD—possible when whole-genome sequences in large population biobanks become available—and testing the effects of MSY nucleotide variation on health and disease beyond cardiovascular phenotypes.Finally, it is important to note our analyses do not rule out all effects of the Y chromosome on human disease. Large structural variations such as Y chromosome deletions are known to have pronounced effects on male fertility and possibly cancer.www.nisra.gov.uk, and OS (Ordnance Survey) data Crown copyright and database right (2022). For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising from this submission. P.R.H.J. Timmers contributed to Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing—Original Draft, Writing—Review & Editing, Visualization. J.F. Wilson contributed to Conceptualization, Methodology, Formal analysis, Investigation, Resources, Writing—Original Draft, Writing—Review & Editing, Supervision, Funding acquisition.The authors thank UK Biobank for making their data available through application 19655. Geographic maps and socioeconomic variables were derived from National Statistics data Crown copyright and database right 2022, NRS  data Crown copyright and database right 2022, source: NISRA (Northern Ireland Statistics and Research Agency); website: The authors acknowledge funding from the Medical Research Council Human Genetics Unit program grant, Quantitative traits in health and disease (U. MC_UU_00007/10).P.R.H.J. Timmers is an employee of BioAge Labs, Inc. The other author reports no conflicts.Figures S1–S6Tables S1–S9


The variants were compared at a founder level with the commonly screened SNPs and small indels. At the F2 level, we carried out an association study using imputed structural variants and tandem repeats with four growth and carcass traits followed by a comparison with a previously conducted SNPs and small indels based association study.Structural variants and tandem repeats are relevant sources of genomic variation that are not routinely analyzed in genome wide association studies mainly due to challenging identification and genotyping. Here, we profiled these variants via state-of-the-art strategies in the founder animals of four Fr from 0.48 to 0.57) level of co-localization between SNPs or small indels and structural variants or tandem repeats. In the association step 56.56% of the significant variants were not in high LD with significantly associated SNPs and small indels identified for the same traits in the earlier study and thus presumably not tagged in case of a standard association study. For the four growth and carcass traits investigated, many of the already proposed candidate genes in our previous studies were confirmed and additional ones were identified. Interestingly, a common pattern on how structural variants or tandem repeats regulate the phenotypic traits emerged. Many of the significant variants were embedded or nearby long non-coding RNAs drawing attention to their functional importance. Through which specific mechanisms the identified long non-coding RNAs and their associated structural variants or tandem repeats contribute to quantitative trait variation will need further investigation.A total of 13,201 high confidence structural variants and 103,730 polymorphic tandem repeats (with a repeat length of 2-20 bp) were profiled in the founders. We observed a moderate to high (The current study provides insights into the characteristics of structural variants and tandem repeats and their role in association studies. A systematic incorporation of these variants into genome wide association studies is advised. While not of immediate interest for genomic prediction purposes, this will be particularly beneficial for elucidating biological mechanisms driving the complex trait variation.The online version contains supplementary material available at 10.1186/s12864-022-08716-0. Genome wide association studies (GWAS) aim to identify associations between genotypes and phenotypes. The term “genotype” commonly refers to SNPs, as most GWAS are performed using data from SNP arrays, primarily due to cost efficiency, high-throughput, and stability of the SNPs. With a steady increase in the volume of whole-genome sequence (WGS) data, within the variant discovery step, alongside SNPs, small insertions and deletions  can be similarly called and incorporated in the GWAS. Although further variation classes exist, such as structural variants (SVs) and tandem repeats (TRs), they are not considered in current association studies because they are not routinely screened. However, in the light of high-depth WGS data, it is now feasible to profile a wider spectrum of variation provided that appropriate algorithmic approaches exist , 54, 62.the standard genetic marker. However, subsequently it was seen as a disadvantage, because compared with the emerging SNPs, TRs were rather unstable and challenging for high-throughput screening. Regardless of their faith over time, the TRs are large contributors to the overall genetic variation. There is a growing body of evidence suggesting that TRs play a critical role in the regulation of gene expression [Structural variants are large genomic alterations, extremely diverse in type and size that can be typically classified as deletions, insertions, duplications, inversions, and translocations and can generally be characterized by various combinations of DNA gains, losses, or rearrangements . To datepression  and splipression , as wellpression . Specifipression , 72.2 pig crosses originating from various breeds . We employed state-of-the-art detection strategies to screen SVs and TRs relying on high coverage WGS data. Further, we examined this variation in contrast with the commonly addressed polymorphisms  in terms of density and genome localization but also functional impact. We imputed the SVs and the TRs information to the F2 generation and together with phenotypic data on average daily gain (ADG), backfat thickness (BFT), meat to fat ratio (MFR), and carcass length (CRCL) we conducted a SVs and TRs based GWAS. Therefore, in a second step, we evaluated the implications that SVs and TRs have for GWAS and devised how this particular type of variation aids to gain deeper insights into the genetic basis of complex traits.In this study, to investigate the genome-wide structural variants and tandem repeats landscape in pigs, we first profiled such variation across the founder generation of four Fn=24, Table SMThe first part of this study focused on providing a sequence-based systematic characterization of the different types of variation  existing in the founder individuals . The tetranucleotide repeats were the most abundant (27.93%), followed by the dinucleotide repeats (16.39%)  was found when comparing the density of the SNPs with the one of the small indels, both types of variation being called via the same pipeline. Moreover, a mild positive correlation between SVs and SNPs (r = 0.48) or small indels (r = 0.49) was detected. When assessing the co-localization of the genotyped TRs with either SNPs, small indels, and SVs we observe a low to moderate negative correlation suggesting that the TRs occupy genomic regions in which other types of features are less prominent. However, when looking specifically at the pTRs, a contrasting scenario reveals a higher level of co-localization with the SNPs and small indels.The distribution of the genomic features was assessed in 500 kb windows and can be visualized in the SM for SNPs, small indels, SVs, TRs, and pTRs Figure SM. AddressThe number of functional annotations was 48,685,675, 13,725,269, 37,278, 492,658 and 324,331 for SNPs, small indels, SVs, TRs and pTRs, respectively. The percentage breakdown of the effects by region and by impact for each feature type is summarized in Fig. n=19,483) and small indels , the GO enrichment analysis revealed that more than 500 GO terms passed the FDR threshold (0.05) and, in both cases, the most significantly enriched term was Localization (GO: 0051179)  revealed ten significantly overrepresented GO terms  yielded 146 enriched GO terms with the top 30 being depicted in Fig. n=445) no significant enrichment was found.The gene enrichment analysis for the SVs gene set  with the previously found significant SNPs and small indels. The number of SVs and TRs that were not tagged by a significant SNP or small indel were 12 (out of 17), 112 (out of 156), 89 (out of 105), and 237 (out of 339) for ADG, BFT, MFR, and CRCL, respectively. From these variants, the top variants per chromosome were retained. The top five genes incorporating or lying in the proximity of these highly significant associations are presented in Table 3/(TTTG)5, SSC7:29,488,854) as a fixed effect in the LOCO mixed linear model, the significant signal dropped greatly for all traits, even below the designated threshold for ADG and MFR , we had access to SNPs, small indels, SVs, and TRs. These variation classes varied in terms of their genomic properties including size, distribution across the genome, abundance as well as functional impact on the nucleotide sequence.In the current study, we investigated structural variants and tandem repeats as a considerably less exploited resource of genomic variants with a special focus on their meaning in the context of genome wide association studies. To address this, we relied on data from four Fsed GWAS . In the SVs occurred at a much lower frequency than any other type of variation, yet, concerning their functional annotation, they could potentially have a pronounced phenotypic impact by disrupting gene function Fig. . These tThe number of TRs profiled was 844,556, from which 103,730 were categorized as polymorphic (pTR). Particular to this study was the augmentation of the usually targeted TRs, the STRs 2-6 bp) , 72, by -6 bp , 7The landscape of genomic features and their co-localization within the founder individuals can provide useful guidelines towards the selection of genetic markers for conducting phenotype-genotype associations. From a 500 kb window-based genome wide comparison, we observe that pTRs are more co-localized with SNPs and small indels as compared to SVs Table , suggest0 to F2). Therefore, variant stability over generations could be a limiting factor in imputing SVs and TRs in general, regardless of the species. Further investigations and validations to assess the behavior of SVs and TRs in imputation procedures need to be undertaken. For elucidating biological mechanisms, it can be beneficial to incorporate SVs and TRs in association studies. With a wider genomic marker spectrum, causative mutations are even more accessible and put into the right context, the cascade of molecular events leading to the variation in complex traits can be reconstructed. Apart from association studies, Chen et al. [In the second part of the study, we performed a GWAS to investigate the associations of SVs and TRs with four growth and carcass traits. Prior to the GWAS, we ran the imputation step following the same strategy as in our past study . To accon et al.  showed t2<0.8) by an earlier associated SNP or small indel. Among the top five genes overlapping or in the proximity of highly significant SVs and TRs (Table BMP2 (bone morphogenetic protein 2) was suggested as a strong candidate in previous studies conducted on these crosses [ENSSSCG00000043546, ENSSSCG00000047884; downstream: ENSSSCG00000044805) could drive the molecular mechanisms involving the BMP2 in carcass length variation in the current F2 crosses. Similarly, the GWAS signal on SSC16 for CRCL was led by a lncRNA (ENSSSCG00000050819) together with ISL1 (ISL LIM homeobox 1) and has not been previously reported in the AnimalQTLdb [ENSSSCG00000044971) and a snRNA (U6) leading the association peak on SSC7 for ADG. Both are located between BMP5 (bone morphogenetic protein 5) and HMGCLL1 (3-Hydroxymethyl-3- Methylglutaryl-CoA Lyase Like 1), genes that are related to lipid metabolism [ENSSSCG00000044971 and U6) were identified for BFT and CRCL . In the crosses  did not malQTLdb . ISL1 ismalQTLdb  and to bmalQTLdb . The samtabolism  and coul SHANK2 (SH3 And Multiple Ankyrin Repeat Domains 2) gene due to several highly significant TRs and to one deletion . A similar significant signal, involving the same variants, was identified for MFR and ADG  on SSC9 that plays a key role in the control of feeding behavior and metabolic rate [For ADG, we have detected a QTL on SSC16 that was not found in the previous GWAS study, yet it has been reported in a microsatellite based linkage analysis using three out of our four crosses . This aslic rate . FurtherVRTN (vertnin) has been shown to affect vertebrae numbers and thus carcass length in pigs due to two likely causative variants: SSC7:97,614,602A>C and SSC7:97,615,879-97,615,880ins [ins/ins, specifically in two Landrace x Large White (sample 693 and 750) and in one Large White (sample 728). The drawback was that the identification of this variant was done by one of the SV callers (i.e. manta) whereas for the imputation step only variants supported by three callers qualified. Furthermore, also on SSC7, the gene COL21A1 (Collagen Type XXI Alpha 1 Chain) harbored the top GWAS signal, driven by an intronic TR ((TTTG)3/(TTTG)5, SSC7:29,488,854) for BFT, MFR and CRCL with a -log10  of 45.40, 12.88 and 44.28, respectively  =7.24) (Table DST (dystonin), ENSSSCG00000044091 (lncRNA), LRFN2 (leucine rich repeat and fibronectin type III domain containing 2) whereas for CRCL we have identified only one peak in the proximity of the intronic TR region corresponding to LRFN2. As DST was already discussed as a strong candidate in our previous GWAS, we want to draw attention on LRFN2 that could be under the influence of the nearby lncRNA (ENSSSCG00000044091). LRFN2 knockout mice exhibited autism-like behavioural abnormalities [SHANK2 and ASB4, could be relevant in terms of pig feeding related behaviors. Nevertheless, due to the biological roles of COL21A1 . Althousample 69 and 750 sample 69 and 750 malities  and, simEven though the integration of SVs and TRs in association studies is still in its infancy, this paper demonstrates the benefits of adding additional dimensions to the panel of commonly used genomic markers . To achieve this, we deployed an efficient strategy to utilize well phenotyped and well investigated pig experimental design established in the past. Briefly, we have identified that, despite physical co-localization, SNPs or small indels do not always capture the effects of SVs and TRs on complex traits. Furthermore, we emphasize on highly significant SVs and TRs embedded or nearby lncRNAs as relevant drivers of phenotypic variation. Overall, this paper can be regarded as a valuable resource for future studies examining SVs or TRs in the context of GWAS and how these types of variation regulate gene expression and ultimately contribute to complex trait variation.2 pig resource populations. The founder data set was comprised of 14 Piétrain , 7 crossbred Landrace x Large White , 1 Large White (sample 728), 1 Wild boar (sample P181) and 1 Meishan individual (sample M199). The F2 designs under investigation were described in detail by Rückert and Bennewitz [Whole-genome sequence data was available for 24 founder individuals from four Fennewitz  and Borcennewitz  providedSVs were called with three independent variant callers: smoove, DELLY, and manta. smoove v0.2.6  was usedTo screen for all tandem repeats with a motif length of 2-20 bp (comprising both STRs and short VNTRs) we employed GangSTR . A pre-r r and the p-value of the test were available.Each autosome was binned into successive 500 kb windows dividing the genome into 4,538 windows. The density of the features  was simply counted window-wise based on the starting position of the feature (e.g. the POS field in the vcf file). To investigate the co-localization of the various types of variation, we performed pairwise correlation using the Pearson’s product-moment correlation . ConseqTo annotate variants and predict the coding effects of genetic variation  on genes, transcripts, protein sequence, and regulatory elements, we used the SnpEff tool . The dat0 reference genotype panel is described in the section “Structural variants and tandem repeats profiling”. The average depth of coverage of SNPs/small indels was 21.08 and the average sequencing depth of pTRs was 27.11. Average sequencing depth for SVs could not be calculated due to the nature of the pipeline, in which SURVIVOR creates a high confidence call set from the output of three variant callers. The resulting VCF file was phased with Beagle 5.2 [1 individuals from the above-mentioned study were imputed with Beagle 4.0 and pedigree information. The resulting VCF was phased with Beagle 5.2 and used a reference panel for imputation of medium density (60k) chip genotyped F2 individuals [For haplotype estimation, SNPs and small indels from our previous study  were meragle 5.2  and low ividuals . All phaividuals  to produy* = a + xb + g- + e, where y* is the adjusted phenotype, a is the mean term, b is the additive effect size (fixed effect) of the candidate SV or TR to be tested for association, x is the SV or TR indicator variable (coded 0/1/2), g- is the polygenic effect (random effect) and in case of the LOCO analysis is the accumulated effect of all SNPs except those on the chromosome where the candidate SV or TR is located. Multiple genomic relationship matrices (GRMs) were created from the F2 60k SNP chip data by excluding each chromosome once. The imputed SVs and TRs with a minor allele frequency (MAF) cutoff of 1 % were used in the model together with an additional cross covariate (4 classes representing each of the 4 crosses). The choice of p-value significance level of marker effects was set up by the corresponding Bonferroni correction of the p-value of 0.05/Number of tests, where the number of tests here was the total number of SVs and TRs , therefore the -log10 threshold in this analysis was 6.03. Furthermore, a conditional association study was carried out. For this, top highly associated variants were included as a fixed effect in the mixed linear model framework. Manhattan plots and other figures were created in R using the package qqman [ggplot2 [GCTA v1.93.2 beta was used for single trait association analyses  for prevge qqman  and ggpl[ggplot2 .Additional file 1.


Arabidopsis phenotypes to validate the new algorithm. Compared to established methods, emBAI is more powerful in terms of prediction accuracy, mean square error (MSE), mean absolute error (MAE), the area under the receiver operating characteristic curve (AUC) and correlation of prediction in simulation studies. In addition, emBAI performs well under the increasing genetic background. The analysis of the Arabidopsis real dataset further illustrates the benefits of emBAI for genomic prediction according to prediction accuracy, MSE, MAE and correlation of prediction. Furthermore, the new method shows the advantages of significant loci detection and effect coefficient estimation, which are confirmed by The Arabidopsis Information Resource (TAIR) gene bank. In conclusion, the emBAI algorithm provides powerful support for GS in high-dimensional genomic datasets.Currently a hot topic, genomic selection (GS) has consistently provided powerful support for breeding studies and achieved more comprehensive and reliable selection in animal and plant breeding. GS estimates the effects of all single nucleotide polymorphisms (SNPs) and thereby predicts the genomic estimation of breeding value (GEBV), accelerating breeding progress and overcoming the limitations of conventional breeding. The successful application of GS primarily depends on the accuracy of the GEBV. Adopting appropriate advanced algorithms to improve the accuracy of the GEBV is time-saving and efficient for breeders, and the available algorithms can be further improved in the big data era. In this study, we develop a new algorithm under the Bayesian Shrinkage Regression  framework, an improved expectation-maximization algorithm for BayesA (emBAI). The emBAI algorithm first corrects the polygenic and environmental noise and then calculates the GEBV by emBayesA. We conduct two simulation experiments and a real dataset analysis for flowering time-related  Genomic selection (GS) is a method for predicting the genetic value of a test population based on the genomic estimated breeding value (GEBV) predicted from high-density molecular markers positioned throughout the genome ,2. UnlikPowerful prediction ability is a prerequisite for the successful application of GS, which is influenced by several factors, including sample size, the genetic structure of traits, marker density, statistical algorithm, genetic background and so on. The advanced statistical algorithm is an intuitive and essential way to improve the prediction accuracy for breeders. In recent decades, a number of statistical methods have been proposed for GS, including best linear unbiased prediction (BLUP), genomic BLUP (GBLUP ), least Since the introduction of the mixed linear model (MLM) approach ,19,20,21In this study, the population structure and polygenic background are considered in the mixed model, which can effectively improve the accuracy of GS. We propose a two-stage flexible approach to GS to estimate the GEBV. In our mixed linear model, all SNP effects are considered random effects. In the first stage, the emBAI algorithm first whitens the covariance matrix of the polygenic and environmental noise. Subsequently, the emBayesA method is applied to implement estimation and prediction. In this study, a series of simulated experiments and real datasets analyses are conducted to illustrate the advantages of this new method. For comparison, the established methods , includin individuals and p genetic markers, p >> n, it can be described as:ith individual in a sample of size n; α is a W is the corresponding designed matrix for Z is an K is a known MVN denotes multivariate normal distribution.For the following mixed linear model, assuming y in the model (1) is:As The emBayesAI algorithm is a two-stage approach for GS, which simultaneously estimates regression effects and calculates the GEBV. We describe the stages as follows:The estimations of two ratios ) by λ^g ,28, avoiAn eigen  decomposition of the positive definite matrix VN0,σ2In ,28.α is a vector of fixed non-genetic effects, including the population mean, and cW is the corresponding corrected design matrix. q × 1 random effect vector, q is the total number of SNP on the whole genome, In the emBayesA method ,15, we fThe log-posterior distribution of Maximizing the log-posterior and making the partial derivative with respect to each parameter equal to 0, the estimations are shown as the following equations:The EM algorithm for BayesA is summarized as follows: 1.E-step: 2.M-step: updated The E-step and M-step are repeated until the convergence criterion is satisfied. In the study, we adopt the maximum iterations equal to 200 for the EM algorithm .Expectation maximization BayesA (emBA  under Hardy–Weinberg equilibrium. The simulated dataset contained 2000 individuals with 10,000 genetic variants generated by MLM. The population mean was set to 10.0, and the residuals were set to 10.0.We conduct two simulations in this study. For the first simulation, only one fixed position QTN placed on the 98th marker with 0.1 heritability was simulated. For the second simulation experiment, we randomly selected 50 QTNs. The MAF of each QTN is larger than 0.3, and the total phenotypic variance of all QTNs was 0.5. Each simulation experiment was repeated 100 times. For each simulation, we further considered nine scenarios of three background noise by three sample size combinations, including two, five and ten times polygenic background and 500, 1000 and 2000 sample sizes.Arabidopsis inbreed lines (http://www.arabidopsis.usc.edu/ (accessed on 5 January 2022)), which contain 216,130 SNPs and 107 traits [http://www.arabidopsis.usc.edu/ (accessed on 22 January 2022).The real dataset included 199 7 traits . Among tr and AUC were selected to evaluate the performance of all methods.In this study, MSE, MAE, Pearson correlation coefficient MSE is defined as:MAE is defined as:MSE and MAE are common indexes to evaluate the prediction accuracy in regression model. Smaller values of MSE and MAE indicate higher accuracy of the results. Assuming that Both ris defined as:The Pearson correlation coefficient AUC is defined as the area under the receiver operating characteristic curve (ROC), which illustrates the prediction ability of model. The closer AUC is to 1, the more powerful of the predictive algorithm is.First, we compare the model performance of emBAI to the established approaches, including emBA, emEN, emRR, emML and emBC, through two Monte Carlo simulation experiments. The simulation datasets contained 2000 individuals with 10,000 genetic variants. For the two simulated experiments, we considered one fixed-position QTN and 50 randomly selected QTNs, respectively.For the first simulation experiment, For simulation experiment 2, there are 50 random QTNs associated with the phenotype. The results are shown in the right panel of In addition, we compared the computing times for the simulation experiments under 100 replications D,I. AmonArabidopsis dataset [To obtain further insights into the accuracy of the GEBV among emBAI and the established methods, emBA, emEN, emRR, emML and emBC, we analyze two traits related to flowering time in  dataset , includiArabidopsis data using the emEN method, which failed to generate any meaningful results . It may owe to the fact that the emEN method is not flexible enough to analyze the “big P, small N” datasets. Therefore, emBAI is more robust than the other methods from multiple perspectives.According to the computing speed of the five different methods, emML is computationally much faster than the other methods. The computing times of emBAI, emBA and emRR are on the same order of magnitude, which requires less than 25 s. The emBC method is much more time-consuming than the other four methods and takes over two times more computing time than the emBAI algorithm. The emBAI method has a more stable computing time in simulation experiments and real data analysis. Overall, the emBAI algorithm is recommended in terms of correlation, prediction accuracy and computing speed across all experiments.https://www.arabidopsis.org/ (accessed on 13 March 2022)). The quantity of confirmed genes is listed in To further validate the emBAI algorithm, the top 20 putative QTNs of different methods are used to mine the candidate genes from TAIR , under the framework of MLM. The emBAI algorithm first assumed the marker effect as the random effect and adopted the model transformation of FASTmrEMMA to whiten the covariance matrix of the polygenic and environmental noise, then employed emBayesA to estimate the GEBV. The results of this study show that population structure and polygenic background control are crucial for emBAI in GS, particularly for the accuracy of estimation and AUC of coefficients under different levels of genetic background. It demonstrates that controlling population structure and kinship indeed improves the ability of GEBV estimation and provides theoretical support for GS. Similarly, this idea of background control was applied to emBC, emRR, emEN and emML. The results showed that all the new methods retain computing efficiency and improve the estimation and prediction from the perspective of correlation, MSE and MAE.In this study, the proposed new method emBAI under the Bayesian framework has the comment merit of a faster computing speed than MCMC-based Bayesian methods. To compare the computing efficiency, all the simulated datasets were reanalyzed by the MCMC-based method BayesA. The results intuitively indicated the computational advantage of the emBAI method. BayesA had a more obvious computational burden as the sample size increased. Taking the second simulation as an example, emBAI took about 2 s for the estimation of 10,000 SNP effects and 500 individuals, and 5 and 10 s for 1000 and 2000 individuals on average, whereas MCMC-based BayesA took more than 20, 40 and 70 s in each dataset of 500, 1000 and 2000 sample sizes, respectively. Moreover, the number of iterations in emBAI was 200, and the iterations ranged from 30 to 120, satisfying the convergence criterion. Compared with the other established EM Bayesian methods, the computing time of emBAI presented an approximately linear increasing trend, while emEN showed an exponential increase. This means that emBAI is more stable and flexible in high-dimensional datasets analysis. In breeding practice, multi-trait GS models have been proposed by utilizing the correlation information among multiple traits, and the advantages of increasing the predictive ability and efficiency are shown in multi-trait with low heritability. The statistical methods of multi-trait GS models will be central to this further work.


Genome-wide variance quantitative trait loci (vQTL) analysis complements genome-wide association study (GWAS) and has the potential to identify novel variants associated with the trait, explain additional trait variance and lead to the identification of factors that modulate the genetic effects. I conducted genome-wide analysis of the UK Biobank data and identified 27 vQTLs associated with systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP). The top single-nucleotide polymorphisms (SNPs) are enriched for expression QTLs (eQTLs) or splicing QTLs (sQTLs) annotated by GTEx, suggesting their regulatory roles in mediating the associations with blood pressure (BP). Of the 27 vQTLs, 14 are known BP-associated QTLs discovered by GWASs. The heteroscedasticity effects of the 13 novel vQTLs are larger than their genetic main effects, which were not detected by existing GWASs. The total R-squared of the 27 top SNPs due to variance heteroscedasticity is 0.28%, compared with 0.50% owing to their main effects. The overall effect size of the variance heteroscedasticity is small in GWAS SNPs compared with their main effects. For the 411, 384 and 285 GWAS SNPs associated with SBP, DBP and PP, respectively, their heteroscedasticity effects were 0.52%, 0.43%, and 0.16%, and their main effects were 5.13%, 5.61%, and 3.75%, respectively. The number and effects of the vQTLs are small, which suggests that the effects of gene–environment and gene–gene interactions are small. The main effects of the SNPs remain the major source of genetic variance for BP, which would probably be true for other complex traits as well. Such variance heteroscedasticity may be induced by gene–environment interactions8, gene–gene interactions4, multiple linked functional variants at the locus10 or scale effects12. Since standard genome-wide association studies (GWASs) focus on testing differences in the means across genotypes, genetic variance attributable to variance heteroscedasticity is missed. Genome-wide vQTL analysis complements GWASs and has the potential to identify novel variants associated with a trait. In addition, the variance heteroscedasticity of a vQTL is genotype-dependent, therefore, could explain additional trait variance that is genetically related. Without the need to measure environmental factors, genome-wide vQTL analysis has been used to screen potential gene–environment interactions and search for factors that modulate the genetic effects8.Variance quantitative trait locus (vQTL) refers to a locus that is associated with the difference in the variance in a quantitative traitFTO gene locus was not only associated with the mean of body mass index (BMI), but also the variance of it13. In the analysis of BMI with UK biobank data, Young et al. identified 48 genome-wide significant loci that demonstrated smaller P values from the test including variance heteroscedasticity effect than from the test of additive effect in trait mean only5. In the analysis of 13 quantitative traits from the UK Biobank, Wang et al. discovered 75 significant vQTLs for 9 traits, 60 of which were for those related to obesity6. Their interaction analysis showed that the vQTLs were enriched with gene-environment interactions. In the analysis of the genetic risk score of BMI with 376 variants, Sulc et al. demonstrated that while the genetic risk score explained 5.2% of BMI variance, its interactions with environmental factors explained an additional 1.9%7. Marderstein et al. showed that the discovery and replication rates of gene-environment interactions for BMI were significantly higher when prioritizing variants in vQTLs compared to when accessing all genetic variants. They also demonstrated strong gene-environment interactions mediated the genetic contribution to body weight and diabetes risk.To date, the identified vQTLs are largely for obesity-related traits. Yang et al. found that the 14. In this work, I propose statistical methods for vQTL analysis at the biobank scale that are based on a linear mixed model and regressions. I conducted genome-wide vQTL analysis of BP data in the UK Biobank to search for novel single-nucleotide polymorphisms (SNPs) associated with BP and evaluated additional BP variance explained by their variance heteroscedasticity.A recent study of blood pressure (BP) shows that some portions of the BP variance could be attributed to gene–environment interactions15. In this work, I employ a linear mixed model and test the variance heteroscedasticity of a vQTL using the maximum likelihood approach, similar to that in 7. Nevertheless, it focuses on testing the variance heteroscedasticity due to the interactions between a polygenic score and unknown environmental factors. The method is statistically powerful when the quantitative trait approximately follows a normal distribution.A variety of heteroscedasticity tests have been suggested for finding vQTLs, which have been reviewed previouslyy is associated with a genetic factor G and n covariates Xi, i = 1,…,n, as follows:i = 1,…,n, are the genetic and covariate effects, respectively, and G is assumed to be additive, which could be the dosage or coded genotype of a SNP. Ei, i = 1,…,m are m environmental factors that modulate the genetic effect, and Ei, i = 1,…,m may include other factors that modulate the genetic effect.Suppose that a quantitative trait Ei, i = 1,…,m are centralized and uncorrelated, conditional on the covariates and genetic factor, trait variance depends on the genetic factor in a quadratic mannerLetting I further assume that 16. To alleviate computational burden, one can also first regress the trait on the covariates and genetic factor and then use the residual I solve the linear mixed model (1) numerically by the maximum likelihood method based on its profiled likelihood function and Newton–Raphson algorithm0: 17. All tests proposed in this paper were implemented in the program “heter”, which is available at https://github.com/eat1000/heter.To test the variance heteroscedasticity, the null hypothesis is H18 into a quadratic form, and the squared residual is regressed on the genetic factor as follows:To solve the linear mixed model (1) or the variance component model (2), it is usually desirable for the starting values of the iterating parameters to be close to their estimates. I extend the Breusch–Pagan test for heteroscedasticitySince the squared residual Notably, 19. Deep phenotyping, genomic, and health-related data have been collected and are available for research investigating a wide range of diseases caused by a combination of genes, lifestyles, and environmental factors20. The UK Biobank has obtained informed consent from all participants and has obtained Research Tissue Bank approval from its ethics committee. This research has been approved by the UK Biobank under application number 44080 and I have complied with all relevant ethical regulations in this work.The UK Biobank is a large propective study in the United Kingdom with more than 500,000 participants aged between 40 and 69 years at the time of recruitment. The study design was described previously20. I conducted genome-wide vQTL analysis of BP data in the UK Biobank using imputed genotype data. I restricted the analysis to autosomal SNPs that have minor allele frequencies higher than 0.01 and information scores larger than 0.9, obtaining 9,117,915 SNPs that passed the filters. I excluded quality control outliers for heterozygosity or missingness and samples with sex discordance between the self-reported and genetically inferred sex according to the sample quality control files provided by the UK Biobank. I also excluded non-European samples, samples from pregnant women, one sample in each related pair up to second-degree relatives, and samples from participants who had withdrawn consent.Genome-wide genotype data of 487,422 individuals were imputed with the Haplotype Reference Consortium (HRC) reference panel by the UK Biobank, resulting in 93,095,623 autosomal SNPs, short indels and large structural variants21 to SBP and DBP, respectively. Pulse pressure (PP) was computed as the difference between SBP and DBP and then logarithmically transformed. Covariates in the analysis included sex, age, age squared, BMI and the top 10 principal components (PCs). Samples with BP or BMI values that were 5 or more standard deviations outside the respective means and those with missing BP values or covariates were also excluded. The total sample sizes in the analyses were 396,077, 396,079 and 396,077 for SBP, DBP and PP, respectively.Systolic blood pressure (SBP) and diastolic blood pressure (DBP) in the genome-wide vQTL analysis were averaged over multiple measurements assessed at baseline, which were further adjusted for antihypertensive medication use by adding 15 and 10 mm HgI conducted simulation studies to examine the distributions of the four test statistics under the null hypothesis and their statistical power to detect a vQTL with different heteroscedasticity effects. Covariates and genotype data in the UK Biobank were used for the simulation studies.22, and I simulated an SBP trait under the null hypothesis as follows:22, and −823 were considered to be significant, and a portion of the significant results among the 1000 replicates was the empirical power.To evaluate the statistical power of the vQTL tests, I assumed that the effect of one SBP-associated SNP, rs880315, was random, following a normal distribution 10 scale, and the horizontal coordinates are their expected values from a uniform distribution between 0 and 1. As can be seen, the empirical distributions and their expected distributions align well for the four tests. The QQ plots for the linear mixed model (1) and variance component model (2) are almost identical. I compared the likelihood ratio statistics of the two tests, the test statistics based on the linear mixed model (1) and the variance component model (2) are almost identical as well. This is probably because the maximum likelihood estimates of fixed effects in linear mixed models are robust to the misspecification of their covariance structure24. Even though the estimates of the fixed effects are updated in each iteration when solving the linear mixed model (1), the changes are minuscule. Without the loss of much precision, the fixed effects can be estimated and kept unchanged, and the parameters of the random effects are estimated iteratively, which is equivalent to solving the variance component model (2). Similarly, the QQ plots for linear regression (3) and Chi-square regression (4) are very close, which is possibly due to the robustness of the linear regression.Under the null hypothesis, quantile–quantile (QQ) plots of the four vQTL tests are presented in Fig. The statistical power of the four tests is displayed in Fig. −8). The top SNPs that have the lowest P values at the loci are shown in Table R2 values of the heteroscedasticity effects were computed as the likelihood-ratio based pseudo-R-squared25.I conducted genome-wide vQTL analysis of BP data in the UK Biobank, and the results of the variance component model are displayed as Manhattan plots in Figs. −8). rs1275984, rs11977526 and rs569550 are known BP-associated SNPs that are included in the GWAS catalog26. rs2029827 and rs12368847 are in linkage disequilibrium (LD R2 ≥ 0.8) with rs820430 and rs11609905, respectively, which are associated with BPs. rs7607074, rs115525024, rs10262140, rs376861852 and rs8032315 are in intermediate LD (0.1 ≤ LD R2 < 0.8) with the BP-associated SNPs rs16849211, rs7812039, rs7812039, rs3934939, and rs17514846, respectively, and their LD R2 ranges from 0.19 to 0.45.The 27 significant vQTLs included the 23 top SNPs from 22 genomic loci, of which 10 SNPs from 9 loci showed significant SNP main effects , DBP (EFO_0006336) and PP (EFO_0005763), respectively, of which 411, 384 and 285 SNPs were present in the analysis. The sums of the SNP main effects were 5.13%, 5.61%, and 3.75%, and the heteroscedasticity effects were 0.52%, 0.43%, and 0.16% for SBP, DBP and PP, respectively. The contributions of the heteroscedasticity relative to the genetic main effects are approximately 0.1 for SBP and DBP and 0.04 for PP. Similarly, the relative contributions of the heteroscedasticity would be underestimated compared with that of all BP SNPs since the GWAS SNPs were largely detected by their genetic main effects. PP has the smallest relative contribution from the heteroscedasticity effects, possibly because PP is a derived trait and its random variation is larger than that in SBP and DBP.The SNP main effects of the 27 vQTLs explain 0.50% of the BP variances collectively, and their heteroscedasticity accounts for an additional 0.28%, which is approximately half the variance attributed to the main effects. Because the vQTLs were discovered by the heteroscedasticity test, the relative contribution of the variance heteroscedasticity would be larger than that of all BP QTLs. I queried the GWAS catalog−7 to 0.91 in this study and were not previously reported to be associated with BPs. The sizes of their SNP main effects, in terms of explained BP variance, are smaller than those of their heteroscedasticity effects. The total R2 values of the SNP main effects and heteroscedasticity effects are 0.017% and 0.11%, respectively. Clearly, it would be difficult to identify this type of locus in standard GWASs that focus on testing SNP main effects only.Out of the 27 significant vQTLs, 13 are novel and are highlighted in Table 27, 12 of the 23 top SNPs are eQTLs, and 8 top SNPs are both eQTLs and sQTLs. All of the eQTLs and sQTLs had target genes in cis. As many GWAS loci are associated with complex traits, the vQTLs likely play regulatory roles that mediate the BP associations as well. Interestingly, 8 of the 12 eQTLs were annotated as being both eQTLs and sQTLs, while cis-eQTLs have only a 12% overlap with cis-sQTLs in GTEx27. This agreed with the finding that sQTLs in GWAS results display stronger associations with complex traits than variants that exclusively affect gene expression29.The 27 vQTLs are enriched for genetic loci that affect the expression (eQTL) or splicing (sQTL) of protein-coding genes. Querying the GTEx data version 831 on the 27 vQTLs identified by the variance component test. SNP genotypes were called those with the largest probabilities and residuals after adjusting for covariate effects, and the SNP main effect was used to test the equality of variance in the three genotype groups. The P values of Levene’s tests and the variance component test are compared in Fig. Nonparametric methods are widely available for heteroscedasticity testing and have been used in vQTL analysis. They are not based on particular assumptions about trait distributions and are applicable for analyzing a wide range of quantitative traits. Of the BPs in this work, SBP and DBP approximately follow normal distributions. PP is highly skewed with a long and fat right tail; hence, I used logarithmic transformation. After adjusting for the covariate effects, residuals of SBP, DBP and the transformed PP have skewness values of 0.55, 0.36, 0.03 and kurtosis values of 0.61, 0.25, 0.23, respectively, although their normality tests were rejected given the large sample size of this study. I further conducted Levene’s (Brown-Forsythe) testI carried out genome-wide vQTL analysis by testing the allelic substitution effects on trait variance and identified 27 vQTLs associated with BPs. Such heteroscedasticity effects explained additional BP variance that was missed by GWASs. The heteroscedasticity effects of the 13 novel vQTLs were larger than their genetic main effects and were not detected by existing GWASs. In addition, 9 novel vQTLs demonstrated almost no genetic main effects, and their P values for testing SNP main effects were larger than 0.05 in this biobank-scale analysis. Complementary to GWAS, vQTL analysis has the potential to discover more variants associated with other complex traits.33, and detecting gene–environment interactions usually requires much larger sample sizes34. Considering the large sample size used in this study, the number and effect sizes of the vQTLs are small. Hence, the gene–environment and gene–gene interactions are unlikely to explain a major part of the “missing heritability”35 of BPs.On the other hand, if the heteroscedasticity effects harbor some gene–environment or gene–gene interactions, the overall interaction effects appear to be small compared with the additive main effects of GWAS SNPs. The heteroscedasticity effects of the 411 SBP-associated SNPs that were discovered by GWASs added up to 0.52%, compared with the 5.13% attributable to the SNP main effects. For the 20 vQTLs that were associated with SBP in the analysis, the total heteroscedasticity effects and genetic main effects were 0.20% and 0.30%, respectively. This is consistent with that additive main effects are the major sources of genetic variance36, the reported effect sizes of the vQTLs in the discovery samples tend to be overestimated. Replication in external datasets would require much larger sample sizes, which implies that meta-analysis is necessary. To the best of my knowledge, methods and software that facilitate meta-analysis of results from variance component analysis are presently lacking. Alternatively, regression analysis of the vQTLs can be performed in the replication samples. Estimated 37. In this way, the meta-analysis of vQTL is methodologically equivalent to the meta-analysis that jointly tests SNP main effects and interaction effects39.While I provide statistical evidence supporting the vQTLs of BP traits, the results should be considered preliminary. In particular, I lack independent replication. Because of the so-called “winner’s curse”40, analyzing samples of a single ancestry not only limits the transferability of results across populations, but results in ascertainment bias and missing the vQTLs that differ among diverse populations. My study is also limited by the geographical and environmental diversity. Complex traits are known to have a strong geographical component involving genetic predisposition and environmental exposure41. Effect sizes of the gene-environment interactions may be smaller in the study samples than in geographically and environmentally more diversified samples. Hence, many potential vQTLs could be missed in this study.My discovery of the vQTLs is limited by the diversity of population. Samples in the vQTL analysis were restricted to individuals of European ancestry. Since the causal alleles that by chance have reached higher frequencies are more likely to be identified8, vQTL analysis can be used as a screening tool for prioritizing variants that may harbor interaction effects. Factors modulating the genetic effects can be hypothesized and tested thereafter, which is beyond the scope of this paper. Any interactions that can be detected and confirmed will positively contribute to the understanding of complex traits or diseases.In this work, I focused on genome-wide vQTL analysis of BP data in the UK Biobank. The reported vQTLs may include some interaction effects that were not previously identified. As suggested in the literature