By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact—thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search-based methods. Our proposed mvMAPIT builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate mvMAPIT as a multivariate linear mixed model and develop a multitrait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. With simulations, we illustrate the benefits of mvMAPIT over univariate (or single-trait) epistatic mapping strategies. We also apply mvMAPIT framework to protein sequence data from two broadly neutralizing anti-influenza antibodies and approximately 2,000 heterogeneous stock of mice from the Wellcome Trust Centre for Human Genetics. The mvMAPIT R package can be downloaded at https://github.com/lcrawlab/mvMAPIT.Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this study, we present the “multivariate MArginal ePIstasis Test” (mvMAPIT)—a multioutcome generalization of a recently proposed epistatic detection method which seeks to detect  Genome-wide association (GWA) studies have contributed substantially in the discovery of genetic markers associated with the architecture of disease phenotypes . EpistasJ variants in a study), explicit search-based methods often suffer from heavy computational burden. Even with various efficient computational improvements   , pathway (MAPIT) . HoweverP-value computation in the multitrait setting  encoded as j) and consider the following linear model:The original motivation behind the original “MArginal ePIstasis Test” (MAPIT) was to identify variants that are involved in epistasis while avoiding the need to explicitly conduct an exhaustive search over all possible pairwise interactions  and αl∼jth and jth variant and all other variants. Under the variance component formulation in equation   is a multioutcome extension of the statistical framework MAPIT which aims to identify variants that are involved in epistatic interactions by leveraging the covariance structure of nonadditive genetic variation that is shared between multiple traits. Once again, consider a GWA study with  (mvLMM) D-dimensional vectors of additive effects for the jth and lth genotypic vectors; D-dimensional vector of coefficients for the interaction effects between the jth and lth SNPs spanning all traits; and D traits, and independent within row covariance among the N individuals in the study.where, in addition to previous notation, jth variant of interest. Here, we will assume that these Similar to the univariate setting, we need to make additional probabilistic assumptions to ensure model identifiability when equation  is an unequation  as the fD traits and jth SNP across the D traits. Here, the term where D traits. If one were to also consider higher order interactions, an additional resource burden would come from requiring additional covariance matrices to be stored as well as projecting these covariance matrices onto a space that is orthogonal to the variant of interest and the population intercept. In the multivariate setting, the time complexity of the projection step scales on the order of N being the number of samples in the data.As we will show below, mvMAPIT works by analyzing pairs of traits at a time; this allows the framework to be applied to any number of measured phenotypes. Analyzing more traits simply requires more computational resources both in terms of wall-clock time and computer memory. For each point estimate, mvMAPIT performs matrix operations that scale quadratically with sample size. The software also needs to store covariance matrices corresponding to the number of random effects in the model. Both these added costs scale as jth SNP would now increase both with the number of individuals (N) and with the number of phenotypes (D). This could make model fitting infeasible for large biobanks even with only two traits. As an alternative, we present a combinatorial approach which first fits univariate MAPIT models and then combines the resulting P-values with those stemming from a “covariance statistic” which looks for shared marginal epistatic effects between all pairwise combinations of the D traits. Importantly, our combinatorial approach does not make assumptions about the covariance structure between traits, which would need to be known (or assumed) in the Kronecker formulation.The goal of the mvMAPIT framework still comes down to assessing the null hypothesis that tests for nonzero marginal epistatic effects. However, parameter estimation in mvLMMs can present substantial computational challenges. For example, one common way in the literature to rewrite the model specified in equation  is to vequations –8) to f to fy=veTo implement the multivariate marginal epistasis test, we follow a similar strategy used in the univariate MAPIT model and right multiply equation  by a varcth and dth rows of the adjusted phenotypic matrix where, in addition to previous notation, equation  which iswhere equation  and the equation  takes onximation :Once again, notice that when equation  is equalequation .The combinatorial hypothesis testing procedure that is used in mvMAPIT occurs in three key steps:D traits of interests  from fromD trquations  and 13)D traits P-values for the covariance components P-values P-values from the first two steps into an overall marginal epistatic P-value. Assume that we have P-values . The mvMAPIT software carries out the P-value combining procedure in three different ways. The first assumes that each of the P-values into a single chi-square test statistic using the formula P-value from the tth test. In Fisher’s method, the P-values tend to be small (i.e. when the null hypothesis is not true for every test). The second approach assumes an unknown dependency structure between each of the T tests and computes a harmonic mean 13) highlx2∘x3)α2 a, while x3∘x4)α2 b. The kescenario a but notscenario b.To test the utility of the mvMAPIT framework, we modified a frequently used simulation scheme  for each parameter combination across 100 simulated replicates.http://mtweb.cs.ucl.ac.uk/mus/www/mouse/index.shtml). The genotypes from this study were downloaded directly using the BGLR-R package (http://mtweb.cs.ucl.ac.uk/mus/www/mouse/HS/index.shtml and https://github.com/lcrawlab/mvMAPIT). In the analyses below, we focused on 15 hematological phenotypes including: atypical lymphocytes , basophils , hematocrit , hemoglobin , large immature cells , lymphocytes , mean corpuscular hemoglobin , mean corpuscular volume , monocytes , mean platelet volume , neutrophils , plateletcrit , platelets , red blood cell count , red cell distribution width , and white blood cell count . All phenotypes were previously corrected for sex, age, body weight, season, year, and cage effects  the P-values resulting from the univariate test on each trait, (2) the P-values derived from the covariance test, and (3) the final overall P-value that is computed by combining the first two sets of P-values via Fisher’s method, the harmonic mean, or the Cauchy combination procedure. Note that we expect the P-values from the first univariate test to be well-calibrated since it is equivalent to the MAPIT model. P-values combined using Fisher’s method, while In this section, we make use of a previously described simulation scheme . Once again, we generate synthetic phenotypes using real genotypes from the 22nd chromosome of the control samples in the WTCCC 1 study  and precision-recall curves  the power of running the univariate MAPIT model on each trait individually, (ii) the marginal epistatic effects detected by the covariance test, (iii) the power from the overall association identified by mvMAPIT, and (iv) a baseline meta-analytic approach which combines only the l curves . There aP-values in the final step of mvMAPIT. This becomes increasingly relevant in the low heritability cases. When there is no correlation of interaction effects shared between pairs of traits, combining only the univariate P-values in the meta-MAPIT model performs equally well as mvMAPIT which includes contributions from the covariance statistic. P-values. In Intuitively, the joint modeling approach of mvMAPIT provides a viable strategy for identifying SNPs contributing to nonadditive variation that would have otherwise gone undetected by univariate methods. The scenario where mvMAPIT shows significant gains over the univariate MAPIT modeling approach is when there is nonzero correlation between the effects of the epistatic interactions shared between traits , the covariance test, and the mvMAPIT approach. Here, green colored dots are positions that have significant marginal epistatic effects beyond a Bonferroni corrected threshold for multiple testing , and each mouse has been genotyped at In this section, we apply mvMAPIT to individual-level genotypes and 15 hematology traits in a heterogeneous stock of mice data set from the Wellcome Trust Centre for Human Genetics  hematocrit (HCT) and hemoglobin (HGB) and (ii) mean corpuscular hemoglobin (MCH) and mean corpuscular volume (MCV)  on chromosome 16 which has been shown to play a role in differentiation of erythrocytes (Cacna2d3) on chromosome 14, which has been associated with decreased circulating glucose levels  more than half of phenotypic variation has been attributed to genetic effects  set of variants that are worth further exploration, and thus represents an important first step towards identifying and understanding detailed epistatic associations. In previous studies , two-steocedures . ExploriIn the future, we plan to expand the mvMAPIT framework to also identify individual variants contributing other sources of nonadditive genetic variation such as gene-by-environment (G×E) or gene-by-sex (G×Sex) interactions. We can do this by manipulating the marginal epistatic covariance matrix in equation  to encodjkad118_Supplementary_DataClick here for additional data file.


Whether human sexuality is the result of nature or nurture (or their complex interplay) represents a hot, often ideologically driven, and highly polarized debate with political and social ramifications, and with varying, conflicting findings reported in the literature. A number of heritability and behavioral genetics studies, including pedigree-based investigations, have hypothesized inheritance patterns of human sexual behaviors. On the other hand, in most twin, adoption, and nuclear family studies, it was not possible to disentangle between underlying genetic and shared environmental sources. Furthermore, these studies were not able to estimate the precise extent of genetic loading and to shed light both on the number and nature of the putative inherited factors, which remained largely unknown. Molecular genetic studies offer an unprecedented opportunity to overcome these drawbacks, by dissecting the molecular basis of human sexuality and allowing a better understanding of its biological roots if any. However, there exists no systematic review of the molecular genetics of human sexuality. Therefore, we undertook this critical systematic review and appraisal of the literature, with the ambitious aims of filling in these gaps of knowledge, especially from the methodological standpoint, and providing guidance to future studies. Sixteen studies were finally retained and overviewed in the present systematic review study. Seven studies were linkage studies, four studies utilized the candidate gene approach, and five studies were GWAS investigations. Limitations of these studies and implications for further research are discussed. Whether human sexuality is the result of nature or nurture  represents a hot, often ideologically driven, highly polarized debate with political and social ramifications , and witSexuality is a multi-dimensional construct that, according to the “World Health Organization” (WHO), can be defined as “a central aspect of being human throughout life”, encompassing “sex, gender identities and roles, sexual orientation, eroticism, pleasure, intimacy, and reproduction. Sexuality is experienced and expressed in thoughts, fantasies, desires, beliefs, attitudes, values, behaviors, practices, roles, and relationships. While sexuality can include all of these dimensions, not all of them are always experienced or expressed. Sexuality is influenced by the interaction of biological, psychological, social, economic, political, cultural, legal, historical, religious, and spiritual factors”. Of note, sexuality includes several aspects, like sex at birth, gender identity, and expression, as well as sexual identity, sexual orientation, sexual activity/sexual behavior, and sexual role, but these, if known/disclosed, cannot be used as proxies to infer the other dimensions, if unknown.An increasing number of genetic-epidemiological surveys, including heritability and behavioral genetics studies, such as family and pedigree-based investigations , have hyOn the other hand, some investigations reported contrasting findings, with similar rates of sexual behavior in biological and adoptive siblings of male homosexual index subjects . FurtherMolecular genetics and genomic studies offer an unprecedented opportunity to overcome these drawbacks, by dissecting the molecular basis of human sexuality and allowing a better understanding of its biological roots if any . Howevera priori study protocol was written after preliminary familiarization with the scholarly literature, and can be requested by contacting the corresponding author.An The research aims were to better understand the genetic basis of human sexuality and sexual orientation, also appraising the methodological quality of published research. The research questions were as follows: “Which are the genetics/genomics basis of human sexuality? To which extent do they contribute to shaping human sexuality? How replicable are these putative genetic/genomic factors?”in vitro”). This search string was elaborated based on three major pre-identified domains: namely, 1) human sexuality, 2) genetic/genomic aspects, and 3) human subjects/populations. The search string was developed with the help of a librarian and was revised and refined after an initial familiarization with the literature.We utilized the following search string:  AND  NOT , which focus their analysis on the relation between a specific allele and the disease/trait within population, genome-wide association meta-analyses (GWAMA), and transcriptome-wide association studies (TWAS), attempting to isolate and identify chromosomal and genetic regions and their transcripts relevant to sexual orientation.MEDLINE, a major electronic, scholarly database, consisting of more than 35 million records from the biomedical literature, was mined via its freely available interface (PubMed) from its inception, without time or language filters. Inclusion/exclusion criteria were devised according to the “Sample, Phenomenon of Interest, Design, Evaluation, Research type” (SPIDER) acronym . SpecifiBesides genetic linkage and association studies in families/populations, candidate gene studies were also considered. We discarded heritability and behavioral genetics studies; in that they are unable 1) to disentangle between biological (genetic) and environmental/socio-cultural factors and 2) estimate the contribution of every single variable and their interactions to shaping complex behaviors and traits.in vitro studies were not retained. They were considered only if conducted as follow-up studies of genetic-epidemiological investigations, to provide more biological insights into the genetic loci identified/discovered.Studies focusing on human populations and investigating members of the two spirit-lesbian-gay-bisexual-transsexual/transgender-queer-intersex-asexual-polysexual (2SLGBTQIAP+) community were included with the exception of those focusing on the disorders of sex development (DSD), whilst bioinformatic analyses, animal models, or Any study design/publication type  was deemed eligible for inclusion, whilst editorials, letters to the editor without original data, commentaries, or technical notes merely focusing on genetic/genomic methodologies were excluded. Extant review studies, if any, were scanned to ensure that relevant articles were not missed but were not retained in the present systematic review of the literature.Extensive cross-referencing was applied, to ensure broad and relevant coverage of the topic. Target journals, like the “American Journal of Human Genetics,” “Archives of Sexual Behavior,” “Hormones and Behavior,” “Journal of Andrology,” “Journal of Sexual Medicine,” “Journal of Sex Research,” and “Sexual Medicine Reviews,” were hand-searched. Finally, findings were reported according to the “Preferred Reporting Items for Systematic reviews and Meta-Analyses” (PRISMA) guidelines .The initial literature search yielded a pool of 32,404 items. After removing irrelevant items, using an Artificial intelligence-enhanced tool based on a text mining algorithm, twenty-six studies were assessed in full-text. Out of these 26 studies, the following ten studies were excluded with reason: the studies by Sixteen studies were finally retained and overviewed in the present systematic review study. Seven studies  were linlocus (or loci) related to sexual orientation lies within approximately 4 million base pairs of DNA on the tip of the long arm of the X chromosome”. The authors were able to exploit the development of chromosomal genetic maps densely populated with highly polymorphic markers, which enabled the application of the standard techniques of modem human genetics  to the study of complex phenotypes and traits, including human sexuality. In this study, homosexuality was operationalized as scoring 5 or 6 on the “Heterosexual-Homosexual Rating Scale”, commonly referred to as the “Kinsey Scale”, homosexual individuals were also individuals acknowledged as such to the proband or another family member, whilst in all the other cases individuals were categorized as non-homosexual . Seventy-six gay male index subjects were recruited through outpatient “Human Immunodeficiency Virus” (HIV) clinics and homophilic organizations: these constituted the randomly ascertained pool of the study. One or more relatives from 26 of these families were also included, whilst the sample for the sib-pair pedigree study comprised 38 pairs of homosexual brothers, along with their parents or other relatives, recruited through advertisements in homophilic publications. Finally, two additional families from the randomly ascertained pool were retained in the DNA linkage study. In total, the investigators analyzed 114 families of homosexual men , finding increased rates of same-sex orientation in their maternal uncles and male cousins through maternal aunts, but not in their fathers or paternal relatives. As such, the authors hypothesized a sex-linked transmission of homosexuality at least in a portion of the population: then, the analysis was restricted to a selected group of 40 families with two gay brothers and without non-maternal transmission, leading to the discovery of statistically significant correlations between homosexuality and the inheritance of polymorphic markers located on the X chromosome in about 64% of the sib-pairs tested. The genetic linkage to markers on Xq28, the subtelomeric region of the long arm of the sex chromosome, yielded a multipoint LOD score of 4.0 , indicating, with a high statistical confidence level (>99%), that, at least, one (sub-) type of male sexual orientation can be inherited and is genetically influenced. Of note, the population studied was relatively young and exhibited bimodal distributions of Kinsey scores. This study was criticized  and health-related outcomes. Furthermore, the Hamer’s report would suffer from type 1  error, making it, as such, irreproducible. Finally, scoring of allele sharing was not performed in a blinded way and different laboratory steps  were conducted by the same person(s).In 1993, Hamer and colleagues  were abliticized  for sevelocus that contributes to shaping individual variations in sexual orientation in men but not in women.In a follow-up study conducted in 1995 , the autHowever, these findings  were chap = 0.0001), which is statistically significant and corresponds to a λs value of 1.4, the ratio for male homosexuality in the brothers of a gay index subject, with respect to the population frequency attributable to a gene in this region. All this seems to suggest a modest level of influence, which can be expected for a single locus when the trait of interest is complex, confirming the Xq28 hypothesis.Furthermore, Hamer  replied loci. As such, leveraging genomewide linkage scanning technique could help identify genes involved in human sexuality , without having to use paternal transmission pattern and, as such, exclude some families from the enrollment.However, even though suggestive, the Xq28 hypothesis can account for, at most, only a small portion of the overall heritability of male homosexual orientation as shown by family and twin studies, which showed evidence of non-maternal transmission and, therefore, of the impact of other contributory Subsequently, two major genetic/genomic datasets have been released, which account for the majority (>90%) of the published GWLS concordant sibling pairs on the male homosexual trait: namely, 1) the “Molecular Genetic Study of Sexual Orientation” (MGSOSO), which consists of 409 concordant sibling pairs, 908 analyzed individuals in 384 independent multiplex families , from several primarily English-speaking countries, such as the United States (98.2%), Canada (1.6%), and the United Kingdom (0.2%), published by Sanders and colleagues in 2015 , and 1) loci contributing to the male homosexual trait.The former study  utilizedThe latter study  recruite2 > 0.1) did not significantly modify the magnitude of LOD scores previously obtained (maximum LOD = 2.47). The empirical p-value for the linkage peak, computed by analyzing 1,000 simulated datasets of pedigree genotypes generated by MERLIN, was 0.256, showing proof of “failed to reach genome-wide significance”.Moreover, versus SNPs), then, finding the genetic positions of the respective markers in the Rutgers map and using nonparametric S-pairs and grid 1 cM options to perform multipoint linkage on both datasets, followed by combining LOD scores at each grid position across the marker sets. The authors as well incorporated a GWLS dataset on 55 families by using meta-analytic approaches on published summary statistics. The approach adopted by Sanders and colleagues has enabled to maximize the positional information from GWLSs of currently available family resources, helping prioritize findings from GWAS and studies leveraging other approaches. In conclusion, genetic components of human sexuality and, in particular, sexual orientation have been identified and highlighted, even though current understanding in terms of contributory genetic loci remains limited, consistent with the observation that genes involved in these highly complex traits may exert more modest effects than those detectable by linkage studies, warranting large-scale GWAS.To overcome these contrasting results, recently, in 2021, Sanders and colleagues  conductea priori knowledge of the gene’s biological function and its impact on the trait or disease in question. We were able to identify only four candidate gene studies, carried out by Candidate gene studies are studies that rely on the candidate gene approach to conduct genetic association studies focusing on associations between genetic variation within pre-specified genes of interest, and complex traits, phenotypes, or disease states. Candidate genes are selected for study based on The former study  failed iversus the control group; and, 3) coding region sequence variation was not commonly found within the androgen receptor gene of homosexual individuals. Finally, the DGGE-based screening allowed the identification of two rare amino acid substitutions, namely, ser205-to-arg and glu793-to-asp, the biological significance of which is still not fully understood.The second study  formulatversus 387 heterosexual control Chinese men, by using a PCR-based assay and genotyping analysis. Statistically significant differences, both in genotype and alleles, between male homosexual individuals and controls could be found, suggesting a recessive model of the impact of the COMT gene SNP on male sexual orientation.The third study  assessed2 tests and logistic regression models, investigating the interplay of sociodemographic characteristics, childhood abuse experiences, and polymorphisms of COMT at rs4680, rs4818, and rs6267, and methylenetetrahydrofolate reductase (MTHFR) at rs1801133 on male homosexuality. The authors found a statistically significant association between a more frequent occurrence of physical (adjusted odds ratio [aOR] of 1.78), emotional (aOR of 2.07), and sexual (aOR of 2.53) abuse during childhood and male homosexuality. The polymorphisms of MTHFR at rs1801133 and COMT at rs4818 were as well associated with male homosexuality in the homozygote comparisons . Finally, the authors could compute statistically significant interaction effects between childhood abuse experiences and the COMT and MTHFR genetic variants on male homosexuality.The latter study  was desip-value of 1.41 × 10−8) as a genetic factor potentially mediating between prenatal sex hormones and digit ratio, which both have been extensively studied and implicated in homosexuality. The digit ratio is generally used as a biomarker for prenatal testosterone exposure, and, besides sexuality and sexual orientation, it has been correlated with a wide range of complex traits and disease conditions including prostate cancer, overweight and obesity, autism, and “Attention-Deficit/Hyperactivity Disorder” (ADHD). SMOC1 is a gene coding for the “SPARC Related Modular Calcium Binding 1” protein, which is instrumental in limb formation and development as well as in the sexually dimorphic development of the gonads and is mostly expressed in prostate tissues, in a sex hormone-dependent fashion. In a meta-analytical follow-up of this finding and an independent study, a probability of p-value of 1.5 × 10−11 could be computed, confirming the potential role of SMOC1 in shaping male sexuality.While, as expected most of candidate gene studies failed in finding a genetic association with a given gene and human sexual orientation, being relatively effective at identifying relative risk genes in Mendelian diseases, but inadequate when studying complex traits, subsequent GWAS found different putatively genetic factors involved in the inheritability of human sexuality. For instance, the 2013 GWAS investigation by The investigation by p-value less than 10–5, in the 10−5–10–7p-value range, as confirmed by a close visual inspection of the Manhattan plot. Each region with 9–10 SNPs exhibited p < 10–5 and various SNPs had 10–7<p < 10–6 and, with the most suggestive and “prominents” SNPs on chromosome 13  and chromosome 14 . The genes nearest to these peaks have biological functions plausibly relevant to the development of sexual orientation. On chromosome 13, the SNP is located between SLITRK6  and SLITRK5 (∼1.8 Mb telomeric), with SLITRK1 located ∼2.0 Mb centromeric, SLITRK6 is a gene coding for a protein, which is involved in some neurodevelopmental pathways and is mostly expressed in the diencephalon. This neuroanatomic structure contains a region that has been previously linked to male homosexuality  for an SNP association previously documented , with the combined analysis employing a meta-analytic statistic that did not need direction of effect, namely, the Fisher’s combined probability test, and yielding a p-value of 6.7 × 10−9, which represents a genome-wide statistically significant association, reaching the threshold of genome-wide significance . rs77013977 is an intronic SNP in NKAIN3, a gene codifying for the protein “Sodium/Potassium-Transporting ATPase Subunit Beta-1-Interacting Protein 3”, which potentially plays a key role for neuronal function. In conclusion, the authors found the strongest linkage support at pericentromeric chromosome 8, as well as at chromosomes 13, 14, and X, even though indirectly and more on a speculative basis. However, the study suffers from several limitations: among the most important shortcomings, the investigation mainly relies on two datasets, with relatively limited sample sizes. Also, it is exclusively focused on male homosexuals and mostly samples from European ancestry.In 2017, exuality . SLITRK1xual men . On periloci  significantly associated with same-gender sexual behavior . These loci were linked with the reproductive genital apparatus and the endocrine and olfactory systems. They withstood replication in independent samples consisting of 15,142 individuals from the United States and Sweden, meeting with stringent testing for individual statistical significance: overall, the authors were able to estimate that genetic influence accounted for 8%–25% of the variance in sexuality-related outcomes, even though the magnitude of measurable genetic influences was too small to yield reliable predictions of any individual’s same-gender sexual behavior based on their genome. Moreover, despite being large-scale, this study suffers from several shortcomings, including self-selection bias, as less than 6% and 2% of UK Biobank and 23andMe members, respectively, gave their consent to participate in the study. Also, the findings are not generalizable, since the authors, as per protocol, excluded subjects of non-European ancestry. Even if this is methodologically correct and sound, since GWAS should not be conducted on heterogeneous samples, this significantly constrains the degree to which the results of the study can be applied to other contexts and non-European populations. Moreover, of note, the birth years of the UK Biobank population ranged from 1937 to 1940 (from 40 to 69 years): homosexuality would have still been considered a mental disorder until 1973 when the American Psychological Association (APA) decided to remove it from its “Diagnostic and Statistical Manual of Mental Disorders” (DSM). In the UK, homosexuality would have been decriminalized only in 1967. Probably, the major shortcoming of the investigation is that, by operationalizing same-gender sexual behavior as ever-engaging in same-gender sexual contact, it conflates “constitutional/stable” and “facultative/unstable” same-gender sexuality, failing to capture the continuum spectrum of human sexuality  strongly and consistently associated with male sexual orientation. FMR1NB encodes the FMR1 Neighbor protein, which is mostly expressed at the levels of spermatogonia, spermatocytes, and spermatids, whilst ZNF536 encodes the Zinc Finger Protein 536, mainly expressed in the brain. In the second part of the investigation, the authors conducted a fixed-effect meta-analysis including individuals of Han Chinese (n = 4791) and European ancestries . The meta-analytical follow-up was able to reveal three genome-wide significant loci of same-sex sexual behavior . Moreover, to provide biological insights into the identified genetic loci, the authors supplemented the GWAS investigation with a post-mortem study and an animal study. More in detail, they defined the average ZNF536-immunoreactivity (ZNF536-ir) concentration in the suprachiasmatic nucleus (SCN) as lower in homosexual individuals than in their heterosexual counterparts  in the post-mortem study. Also, the percentage of ZNF536 stained area in the SCN was significantly smaller in the homosexuals, compared with heterosexuals . Finally, more homosexual preference could be reported in a murine model (FMR1NB-knockout mice), along with statistically significant differences in the expression of serotonin, dopamine, and inflammation pathways that were reported to be related to sexual orientation when comparing CRISPR-mediated FMR1NB knockout mice to matched wild-type target C57 male mice.Finally, in 2021, the study by loci for gender nonconformity, the authors carried out SNP genotyping analyses. The strongest linkage peaks, each with significant or suggestive two-point LOD scores and multipoint LOD score support, were on chromosomes 5q31 (maximum two-point LOD of 4.45), 6q12 (maximum two-point LOD of 3.64), 7q33 (maximum two-point LOD of 3.09), and 8q24 (maximum two-point LOD of 3.67). Of note, the latter did not overlap with the previously reported strongest linkage region for male sexual orientation on pericentromeric chromosome 8. Family-based association analyses were used to identify associated variants in the linkage regions, with a cluster of SNPs  found at the 5q31 linkage peak. Genome-wide, clusters of multiple SNPs in the 10–6 to 10–8p-value range were found at chromosomes 5p13, 5q31, 7q32, 8p22, and 10q23, highlighting glutamate-related genes and the potential role of the glutamatergic pathways on gender identity. However, this is the first reported GWL/GWAS investigation on gender nonconformity/gender variance and, as of today, the only study ever conducted. Further studies should be carried out to increase our genetic knowledge about gender nonconformity/gender variance and its relationships to male sexual orientation, to help advance our currently limited understanding of the biology of these associated traits.Gender nonconformity, or more contemporarily gender variance, has been found to be one of the strongest correlates of homosexuality, displaying a high amount of familiarity. We were able to find only one linkage/GWL/GWAS investigation, carried out by Sanders et al.  in 2021.a priori hypothesis and can therefore detect any statistically significant genetic variation. However, while increasing the sample size may facilitate the identification of elusive genetic variations, this method still requires a significant investment of time and money, as well as addressing various statistical issues such as multiple comparisons, due to the need to analyze a large number of samples and data. It is worth noting, however, that predicting an individual’s sexuality accurately based on these SNPs is not possible since no single gene has a significant impact on sexuality.Sexuality is a complex and multifaceted aspect of human experience that can encompass a wide range of factors, including biological, psychological, cultural, and social influences. Sexuality is an integral part of human identity and can play a significant role in relationships, self-expression, and overall wellbeing. According to previous research, we can cautiously conclude that there exists a substantial number of genes that impact human sexuality, a considerable proportion of which remain undiscovered. Recent advancements in human genome analysis have made it feasible to dissect the molecular basis of complex traits, such as sexual orientation, even if these traits are probably influenced by multiple genes and/or shaped by environmental or experiential factors, or, more probably, by some complex, non-linear combinations of these . For insAccording to McGuire , geneticloci, either X-linked or autosomal, have been reported - even though inconsistently and with a varying degree of replicability and reproducibility—to be associated with some human sexuality-related phenotypes and traits  sexual identity subtypes, 2) discrepancies between self-identification  and behaviors , 3) longitudinal changes in sexual identity, 4) complexity, nuances, and fluidity of gender, 5) divergences among settings , and 6) the impact of interpersonal and situational  factors on shaping human sexuality.On the other hand, besides suggesting that human sexuality is polygenic, studies like those by continuum. In other words, the two constructs of heterosexuality and homosexuality “are put in opposition to each other to yield a single number, such that a higher score on homosexual interest necessarily equates to a lower score on heterosexual interest” . As such, the forced dichotomization operated by the Kinsey scales does not allow to appreciate the nuances of being bisexual/ambisexual. Moreover, the Kinsey scales do not incorporate or reflect the experiences of transsexual, intersex, transgender, and nonbinary individuals, whose lack of representation in the arena of sex research remains critical.For instance, the Kinsey scales have conflated distinct constructs, including the “degree of sexual attraction/behavior toward opposite-sex others and degree of sexual attraction/behavior toward same-sex others” , operatinterest” . This “fnterest”  has beenversus “never”) engaging in same-gender sexual behavior and the ratio of same-gender to other-gender sexual contact . The authors showed a lack of genetic correlation between these two variable traits: 0.03 for men and −0.3 for women. The genetic correlation between the “exclusively same-gender” group and the “over two-thirds same-gender,” the “between one-third and two-third same-gender,” and the “less than one-third same-gender” groups was 0.95, 0.80, and 0.13, respectively. In other words, the genetic correlation between the “exclusively same-gender” group and its phenotypically closest and most distinct categories differed by seven times in magnitude, excluding from the “Kinsey continuum” several subtypes of sexual identity, like “mild bisexuality” and “mostly heterosexuality” (versus other-gender) sexual behavior” does not allow to guess the intensity of “same-gender (versus other-gender) interest”; and 2) the knowledge about the “proportion of same-gender sexual behavior” does not allow to infer the “proportion of other-gender sexual behavior” , since the “same-gender”- and “other-gender”-related parameters are not at the opposite ends of a single continuum, but seem to be orthogonal dimensions. Also, the findings of the study by xuality” . Of noteversus homosexuals), whilst female sexuality exhibits a more continuous distribution with lower rates of homosexuality, and higher rates of bisexuality and “sexual openness”  in the study by 2 and D4, DRD2 and DRD4, dopamine transporter, DAT1, serotonin transporter, 5-HTTLPR, brain-derived neurotrophic factor, BDNF, FK506 binding protein 5, FKBP5, and COMT), to shape trait expression and moderate the detrimental impacts of postnatal family adversity on child externalizing behaviors, such as aggression and conduct disorder   and the brain  . Childhoetworks) . These eetworks) . These cetworks) , even thAs interestingly noted by A consensus on what is meant by “human sexuality” should be reached, also engaging the 2SLGBTQIAP + community, as recommended by several institutions, such as the “International Gender Diversity Genomics Consortium”. Of note, only a few studies applied participatory research and public involvement approaches. Adopting such approaches is paramount, especially when dealing with “sensitive” topics like human sexuality, that has important implications in terms of public understanding of science and public trust towards science, as well as impact on decision- and policymakers, given that, as of December 2020, 69 United Nations (UN) member States still continue to criminalize consensual same-sex sexual activity.A more nuanced and “consilient” approach  should be adopted when dealing with human sexuality, carefully (re-) thinking of the epistemological, ethical, and philosophical basis and socio-cultural and anthropological implications of behavioral genomics .loci is still limited, probably consistent with the complexity of the trait. Further increasing genetic knowledge about human sexual orientation, especially via large-scale GWAS studies and in female and non-European/non-North American populations, could help advance our understanding of the biology of this important trait, really and fully embracing its pluralism, and diversity, potentially providing new biological insights into the genetic basis of human sexuality from a wider population scope. To achieve this ambitious goal, consortia of cooperating multidisciplinary/interdisciplinary research teams will be necessary to collect sufficiently large samples and have adequate statistical power to detect also tiny effects of putative sexuality/sexual orientation-related genes, along with exploring new research areas and foci such as behavioral epigenetics/epigenomics, given the documented parent-of-origin effects, especially in male homosexuality.In conclusion, even though an accumulating body of scholarly evidence seems to highlight genetic contributions to human sexual orientation, especially in males and in European/North American populations, our current understanding of contributory


A bidirectional association between atopic dermatitis and chronic kidney disease (CKD) has been revealed in observational studies, whereas the causality of this association was unclear. We conducted a Mendelian randomization study to determine the bidirectional causal association between atopic dermatitis and CKD.Independent genetic instruments associated with atopic dermatitis and CKD at the genome-wide significance level were chosen from corresponding meta-analyses of genome-wide association studies. Summary-level data for atopic dermatitis were obtained from the EAGLE Eczema consortium  and FinnGen consortium . Summary-level data for CKD were derived from CKDGen consortium  and FinnGen consortium . The inverse-variance weighted method was used in the main analysis and supplemented with three sensitivity analyses.Genetic predisposition to atopic dermatitis was associated with an increased risk of CKD. For a one-unit increase in the prevalence of atopic dermatitis, the odds ratio of CKD was 1.07 . In the reverse Mendelian randomization analysis, the odds ratio of atopic dermatitis was 1.14  for a one-unit increase in the prevalence of CKD. The associations persisted in sensitivity analyses and no pleiotropy was detected.This Mendelian randomization study suggests a bidirectional positive association between atopic dermatitis and CKD. Chronic kidney disease (CKD) is a common health issue affecting 7–12% of adults worldwide . In a caUtilizing genetic variants as instrumental variables for exposure , Mendelian randomization (MR) analysis can strengthen causal inference in an exposure-outcome association by minimizing residual confounding and reverse causality . Since gHere, we conducted an MR study to examine the impact of atopic dermatitis on CKD risk. Given that pruritus caused by skin diseases is a common symptom in CKD patients , we perfThe MR study was based on publicly available summary-level data from corresponding consortia . The stup < 5 × 10−8) were identified from a meta-analysis of 21 genome-wide association studies (GWASs) including up to 21,000 cases and 95,000 controls of European ancestry  and used as instrumental variables for atopic dermatitis. Detailed information on genetic instruments is displayed in Twenty-one single nucleotide polymorphisms (SNPs) associated with atopic dermatitis at the genome-wide significance level (p < 5 × 10−8) were selected from CKD Genetics consortium including 64,164 cases and 625,219 controls of multi-ancestries  were identified as instrumental variables for CKD. Detailed information on genetic instruments is displayed in SNPs associated with CKD at the genome-wide significance level p < 5 × −8 were scestries . Thirty-2. The R5 release of genome-wide analysis on CKD in FinnGen consortium was used, which includes 3,902 cases and 212,841 controls of European ancestry  Eczema consortium and FinnGen consortium. Data from 23andMe were excluded from the EAGLE consortium due to ethical issues, leaving 30,047 cases and 40,835 controls of European ancestry . Atopic p value for the intercept in MR-Egger as an indication of pleiotropy . Assuming that half of the instrument weights are derived from valid instrumental variables, the weighted median method can provide consistent estimates . The MR-eiotropy . The MR-eiotropy . All stasoftware , 20. MR software , ‘Mendelsoftware , and ‘MRsoftware  packagesgr = −1.65; p = 0.099). One SNP was unavailable in both directional MR analyses in FinnGen and replaced by a proxy SNP (r2 > 0.8). Genetic predisposition to atopic dermatitis was associated with an increased risk of CKD in the analysis based on CKDGen consortium and in the meta-analysis of CKDGen and FinnGen data .Genetic liability to CKD showed a borderline association with atopic dermatitis risk in both analyses based on data from the EAGLE and FinnGen consortium . The assEven though atopic dermatitis might be genetically inversely associated with CKD, the MR analysis found a bidirectional positive association between atopic dermatitis and CKD. To the best of our knowledge, this is the first MR study that examined the mutual impacts between atopic dermatitis and CKD.Studies focusing on the influence of atopic dermatitis on CKD are limited. A matched case–control study of Danish adults found that mortality due to urogenital diseases is more common among individuals with atopic dermatitis compared to those without . AnotherUnderlying mechanisms linking atopic dermatitis to CKD are uncovered, but chronic inflammation appears to be a compelling explanation. Inflammation in patients with atopic dermatitis is thought to be caused by disruption of the epidermal barrier and activation of epidermal inflammatory dendritic and innate lymphoid cells, that attracts and interacts with invading Th2 cells . BesidesSkin disease, including atopic dermatitis, is a common comorbidity in patients with CKD . SeveralThere are several strengths of the present MR study. The major merit is the MR design, which strengthened the causal inference in the bidirectional association between atopic dermatitis and CKD. This study used data from consortia with large sample sizes. Thus, our study might have adequate power to detect weak associations. In addition, we replicated the associations in two sources with independent populations and performed several sensitivity analyses. The high consistency of estimates in the two data and sensitivity analyses ensured the robustness of our findings.Several limitations need consideration when interpreting our findings. We used data from genome-wide association analysis on CKD based on participants of multi-ancestries to enlarge statistical power. Thus, the population structure bias was likely to be introduced and bias the observed associations even though the proportion of non-European population is small, and the associations persisted in the analysis confined to individuals of the FinnGen consortium. Besides, only using the GFR to identify CKD cases in the CKDGen consortium may underestimate CKD since not all patients would develop every form of the disease or progress, and dysfunction of the glomerular barrier  and reduced kidney function (represented by eGFR) can develop independently . Also, sIn conclusion, this MR study suggests a bidirectional association between atopic dermatitis and CKD. Our findings may shed a light on CKD prevention in patients with atopic dermatitis as well as dermatological issue management in CKD patients.The original contributions presented in the study are included in the article/All studies included in cited genome-wide association studies had been approved by a relevant review board, and participants gave informed consent.YW, XL, and LH contributed to the study conception and design. HZ, SY, YL, and DL performed material preparation, data collection, and analysis. HZ and SY wrote the first draft of the manuscript. HZ, SY, YL, DL, ZY, LH, XL, YW, and SL commented on previous versions of the manuscript. All authors contributed to the article and approved the submitted version.The study is supported by research grants from the Swedish Research Council for Health, Working Life and Welfare  and the Swedish Research Council .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Pork quality can directly affect customer purchase tendency and meat quality traits have become valuable in modern pork production. However, genetic improvement has been slow due to high phenotyping costs. In this study, whole genome sequence (WGS) data was used to evaluate the prediction accuracy of genomic best linear unbiased prediction (GBLUP) for meat quality in large-scale crossbred commercial pigs.L* (lightness), a* (redness), and b* (yellowness) of genomic prediction. The prediction accuracy was defined as the Pearson correlation coefficient between adjusted phenotypes and genomic estimated breeding values in the validation population. Using different marker density panels derived from WGS data, accuracy differed substantially among meat quality traits, varied from 0.08 to 0.47. Results showed that MultiBLUP outperform GBLUP and yielded accuracy increases ranging from 17.39% to 75%. We optimized the marker density and found medium- and high-density marker panels are beneficial for the estimation of heritability for meat quality. Moreover, we conducted genotype imputation from 50K chip to WGS level in the same population and found average concordance rate to exceed 95% and r2 = 0.81.We produced WGS data  from 1,469 sequenced Duroc × (Landrace × Yorkshire) pigs and developed a reference panel for meat quality including meat color score, marbling score, Overall, estimation of heritability for meat quality traits can benefit from the use of WGS data. This study showed the superiority of using WGS data to genetically improve pork quality in genomic prediction.The online version contains supplementary material available at 10.1186/s40104-023-00863-y. Duroc × (Landrace × Yorkshire) (DLY) commercial pigs contribute to humans with a substantial fraction of meat supply to meet the consumer’s growing demands for animal protein. Pork meat quality can directly affect customer purchase tendency and consequently attract more attention in the pork production . It is ePrediction accuracy of GP in animals has subject to many factors including but not limited to heritability of the analyzed traits, reference sample sizes, and marker density , 9. In pL*: lightness, a*: redness, and b*: yellowness) in 1,469 crossbred commercial DLY pigs using WGS data and optimized the marker density in GP scheme; (ii) capture missing heritability of meat quality traits using INDEL variants from WGS data. Genotype imputation, leveraging LD to infer genotypes at undetected polymorphic loci [The aim of this study was to (i) evaluate the prediction accuracy of genomic best linear unbiased prediction (GBLUP) and MultiBLUP  for meathic loci , has beeThe procedure for collecting tissue samples from pigs was performed with the approval of the ethics committee of South China Agricultural University  under 2018F098.longissimus thoracis (LT) muscle was removed from the left side of each carcass and samples of LT were kept at 4 ℃ inside the refrigerator until the marbling score, meat color score, L*, a*, and b* were measured at 12 h post mortem. Marbling score and meat color traits  measurements were performed on LT muscle as described previously [L*, a*, and b*) were measured on the exposed cut surface after blooming for 30 min of the LT at 12 h post mortem using a CM-2600d/2500d Minolta Chromameter  with an 8-mm measuring port, D65 illuminant, and one trained observer. Meat color score  and marbling score , which also refers to intramuscular fat content (IMF), were rated by one trained personnel on the same cut surface of LT muscle following the U.S. National Pork Producer Council guidelines [In the current study, 1,469 crossbred commercial DLY pigs (born from 2018 to 2019) that raised on four farms of Wen’s Foodstuffs Group Co., Ltd.  were used to collect phenotypes and genotypes as described previously . Brieflyeviously . Brieflyidelines .Yc) for meat quality traits using PREDICTF90 module in BLUPF90 software [Yc was used for subsequent analyses.A single-trait animal model was used to calculate corrected phenotypic values  on Illumina Hiseq platforms provided by the Novogene Biotech Co., Ltd.  with 150 bp paired-end reads. Firstly, the FASTQ format sequence reads were aligned to the pig reference genome assembly M-0.7.12  with defM-0.7.12 . Then, tM-0.7.12  were useM-0.7.12  with “VaM-0.7.12 .swimgeno.org) and estimated the accuracy of imputation from commercial medium-density marker panel (50K) to WGS level data [Sscrofa11.1 after genotyping. Then, the PLINK ped/map formatted SNP array genotypes were submitted to SWIM web server (swimgeno.org) to conduct genotype imputation procedure. By doing genotype imputation, 30,489,782 autosomal SNPs and 4,125,579 autosomal INDELs were acquired. Quality control (QC) were conducted using PLINK 1.9 [It is expected to use imputed-based WGS data to improve the power and prediction accuracy of GP for many studies that lack of WGS level data. To this end, we performed genotype imputation using a public web server (vel data . FirstlyLINK 1.9  followinr2) between imputed and observed genotypes. We measured CR and r2 on a per SNP basis and averaged them over SNPs in MAF bins (0.01 in size) or across the whole genome.To validate the performance of pig haplotype reference panel (termed SWIM), we randomly selected 294  sequenced pigs to calculate its imputation accuracy for DLY pigs. Herein, we defined the imputation accuracy using two values including the overall concordance rate (CR) between imputed and observed genotypes, which refers to the percentage of genotypes imputed correctly divided by total imputed genotypes, and the squared Pearson correlation coefficient (r2 < 0.6) to remain 3,216,966 variants. Principal component analysis (PCA) was conducted on the pruned SNPs dataset using GCTA 1.93.2 [Genetic distances between individuals of the 1,469 DLY pigs were calculated using an identity-by-state (IBS) similarity kinship matrix by PLINK 1.9. In an aim to analyze the population structure of the DLY pigs in this study, we pruned SNPs with LD using PLINK 1.9 with the parameters “-indep-pairwise 50 10 0.6”  using common WGS data to construct LD-based SNPs panels. Finally, a total of 28 panels (the complete WGS SNP data was included) that represent different marker density, categories of variants were generated. The GBLUP and MultiBLUP models were used for genomic prediction to estimate the GEBV of meat quality traits with these different variant panels.From the 21,708,028 SNPs and 3,000,017 INDELs of WGS data and the 19,348,898 SNPs and 2,725,851 INDELs of imputed-based WGS data after QC, respectively, common SNPs  and INDELs  that were present on the two sequence datasets were selected as final subsets of WGS data and were used for subsequent analyses. These different variant panels also can be used to access the prediction accuracy of GP when using variants from imputed-based WGS data. In an attempt to optimize the marker density of variants in GP scheme and compare the prediction accuracy when different variant types (SNP and INDEL) were incorporated into model, we constructed three levels density of marker subsets, including low-density variant panels , medium-density variant panels , and high-density variant panels  using PLINK 1.9 with parameter “--thin” . Accordingly, we make a combination of the SNPs and INDELs in the sequenced datasets selected above with the same marker density into one model. Besides, it has been shown that the degree of LD of WGS data had an effect on prediction accuracy of GP [Yc were used as response variable in the model. For partitioning contributions to heritability by different types and different marker densities of variants, we estimated the genetic relationship matrix (GRM) between pairs of animals from a set of SNPs and INDELs, respectively. In this step, we set the parameter with “--make-grm-alg 1” to calculate GRM using the equation as describe in [The proportion of phenotypic variance explained by preselected SNP and INDEL panels with different marker density were calculated using GCTA 1.93.2 software with “GREML” function . The prey is an N × 1 vector of Yc, 1 is the N × 1 vector of ones, N with five repetitions. Briefly, for each repetition, the 1,469 individuals were randomly split into five subgroups. In each round of five-fold CV, the five subgroups were successively treated as a validation population, and the remaining four subgroups were treated as the reference population. The slope of the regression of Yc on GEBV for animals in the validation population was calculate to measure the degree of inflation or deflation of GP. Average prediction accuracy and the bias values for the 25 CV round per trait were reported.In this study, prediction accuracy was defined as the Pearson correlation coefficient between To better elucidate the population structure and genetic distance among the studied populations, we showed the two-dimensional principal components plot of the three DLY subpopulations and the relationship between individuals for all pairs. As shown in Fig. r2 of 0.81, as shown in Fig. A total of 294 pigs were extracted as target population to evaluate the imputation accuracy. We achieved an average CR across all variants in excess of 95.28% and a* was 0.28 (1K), however, the value was 0.61 estimated by medium and high-density variants panels. In addition, we observed no increase in heritability when the marker density of WGS data was reached to more than 5 million . These results demonstrated that medium and high-density marker panels and variant category are beneficial for the estimation of heritability for meat quality traits.In the current study, common SNPs 18,695,907) and INDELs  that were present on the WGS and imputed-based WGS datasets were selected as final subsets of variants datasets for subsequent genomics analyses. The number of variants of the totally 28 panels that represent different marker density, categories of variants are listed in Table 8,695,907L* trait, GBLUP using low-density SNP panels and INDEL panels resulted in low prediction accuracy, ranged from 0.12 to 0.16 and 0.13 to 0.16, respectively. The results of accuracy of GBLUP using medium-density and high-density SNP panels and INDEL panels were same, reached to 0.16. By using SNPs with high-density panels, the best accuracy values were represented by increases of 33.33% for GBLUP and MultiBLUP, compared to GBLUP with 1K panel. For a* trait, the results showed that the accuracy of GBLUP using low-density SNP panels and INDEL panels ranged from 0.38 to 0.46 and 0.36 to 0.44, respectively. The values of accuracy of GBLUP using medium-density and high-density SNP panels and INDEL both ranged from 0.46 to 0.47. Compared with the accuracy of GBLUP using 1K variant panels, the best accuracy values of GBLUP and MultiBLUP with 1,000K SNPs (INDELs) were represented by increases of 23.68% in a*. For b* trait, the best accuracy value was 0.14 for GBLUP using 30K (10K) SNP panel and 100K  INDEL panel and for MultiBLUP using low-density panels (except for 1K panel). Compared to GBLUP with 1K SNP panel, the best accuracy value of GBLUP and MultiBLUP were represented by increases of 75% in b* trait. In general, compared with the GBLUP using complete WGS data, we observed no increases in accuracy for all meat quality traits, except for the b* trait, when the density of WGS data was reached to more than 500,000 (500K). The results of accuracy of GBLUP and MultiBLUP were increased with the increase of heritability of meat quality traits  were increased by 6.25%, compared with GBLUP using complete WGS data. In contrast, the prediction accuracy of LD pruned SNPs (r2 < 0.6 and 0.8) decreased in comparison with that using complete WGS data for MC trait. For all meat quality traits, the prediction accuracy of LD pruned SNPs (r2 < 0.3 and 0.2) could reach the highest level of prediction ability of GBLUP, implying that LD pruned SNPs can substantially improve computing efficiency in constructing G matrix, as the similar pattern as we observed in studying the impact of marker density on accuracy of genomic prediction.In the current study, the impact of LD-based marker pruning of WGS data on prediction accuracy for meat quality traits were investigated using GBLUP model. Four different L*, a*, and b*) consisting of 1,469 whole genome sequenced pigs to implement GP. The genetic structure of this reference population consisted of three admixed genomic relationship subpopulations, however, population structure did not have substantial influence on meat quality traits  than for the low heritability traits . The highest increase of accuracy in the analyzed meat quality traits was the up to 75% increase for b*. Furthermore, the power of genomic prediction can probably be improved by incorporating INDELs from WGS data. Our results also demonstrated that medium and high-density marker panels and variant category are beneficial for the estimation of heritability for meat quality traits.Improving the meat quality is of importance for satisfying the needs of high-quality products of consumers and consequently affecting the purchase decision . Despitex traits . Our resx traits . Herein,b* trait. Furthermore, MultiBLUP and GBLUP models have equivalent predictive ability of meat quality traits in pigs when using medium-density and high-density SNP panels. The possible reason is that the modified genomic relationship matrix derived by the two random variance components were allowed to add higher weights to exhibit the kinship between pairs of animals [Arabidopsis thaliana) [Drosophila when using preselected SNPs from eQTL (expression QTL) mapping. In this study, the MultiBLUP outperform GBLUP, at least not loss power in compared with GBLUP, for estimating the GEBV of meat quality traits. The lack of improvement in prediction accuracy of MultiBLUP using medium- and high-density variant panels may be owing to only INDELs from WGS data was used, the assignment of different weights to the In this study, we investigated the superiority of MultiBLUP model that considered different genomic information from WGS data as two random variance components over GBLUP model. Owing to the difficulty in obtaining a large-scale pig WGS dataset, the prediction accuracy of MultiBLUP incorporating INDELs from WGS data has rarely been explored. We compared the increase in prediction accuracy that can be improved by including INDELs from WGS data as additionally random variance component. The results of accuracy of MultiBLUP were increased with the increase of heritability of meat quality traits in pigs. Our results show that MultiBLUP model outperform GBLUP for all the analyzed meat quality traits when using low-density variant panels and yield a higher prediction accuracy improvement of the most 75% in  animals , 39, esp animals , milk fa animals , body we animals , disease animals , and alshaliana) . Howeverhaliana) . The acchaliana)  found thr2 = 0.81 whereas the accuracy of genotype imputation of the used haplotypes panel is CR = 95.84% and r2 = 0.89. We observed that the average CR increased along with the number of markers in MAF bins increased. The average CR is sensitive to MAF maybe due to the count of variants with MAF higher than 0.4 in the reference panel was also small [Genotype imputation is used to predict or impute the genotypes at the SNPs that are not directly genotyped in the study sample using a reference panel of haplotypes with high-density SNPs . It has so small . Anotherso small , 54. Ourso small , but it In many cases, sequencing all animals in the reference population is not realistic owing to cost-expensive collecting of phenotypes and genotypes. However, GP of economically important traits in livestock benefits a lot from genomic data and accurately measurement of phenotypic records of animals, especially in pigs . The genr2 = 0.81.In this study, we produced WGS data from 1,469 sequenced pigs to call variants and developed a reference panel for meat quality of GP in pigs. Results from this study showed that the accuracy of MultiBLUP model outperform GBLUP model for meat quality when using low-density variant panels. We optimized the marker density and found medium and high-density marker panels and variant category are beneficial for the estimation of heritability for meat quality traits. We conducted genotype imputation from commercial SNP panel (50K) to WGS level in the same population and found average concordance rate to exceed 95% and Additional file 1: Table S1. Summary statistics of meat quality traits in pigs.Additional file 2: Fig. S1. The relationship between the Euclidean distance calculated with meat quality phenotype values and kinship.Additional file 3: Table S2. The mean accuracy and bias values of GBLUP for meat quality traits using LD pruned SNPs.Additional file 4: Table S3. The estimated heritability for meat quality traits using LD pruned SNPs.


Ginger has been an important cash crop with numerous applications since ancient times. As the demand for ginger is ever-growing and being a seasonal crop, a high-yielding variety of ginger would be economically profitable.In this study, 150 germplasm were collected from different regions of NE India and evaluated for three years in CRBD design with three replications. The present study thus focused on the variability, association, and diversity studies for the first time on 150 ginger germplasm from across North East India. The genotypic and phenotypic coefficient of variation, heritability, correlation, and path analysis were evaluated for the germplasm.2 and Tocher methods grouped the 150 ginger germplasm into ten clusters. Based on the results of the path coefficient analysis determined for essential oil yield and rhizome yield per plant, it can be concluded that the characters’ initial rhizome weight, the weight of mother rhizome, and weight of secondary rhizome were the most important and appeared promising in improving the overall yield potential of ginger rhizome and essential oil yield. Thus, selection based on the identified traits would lead to an effective ginger breeding program for higher rhizome and essential oil yield.Analysis of variance (ANOVA) revealed considerable differences among the studied germplasm for studied characters, revealing sufficient variability in the materials. The Mahalanobis D Zingiber officinale Rosc. or ‘Ginger’ in English belongs to the Zingiberaceae family and is a herbaceous perennial plant commonly diploid with a chromosome number of 2n = 22. Ginger is one of the oldest spices known and used by mankind and it is mostly used as fresh  and the heritability estimate give a fair estimate for the expected amount of advance from the selection. The amount of genetic variability is the determining factor for the genetic advance for selection. The various agronomic traits are interlinked with other agronomic traits. Thus, the study of the correlation of the traits is of significance. In the crop improvement program, yield is a prime objective in the breeding program. However, yield is complex and is regulated by the combination of many other characteristics. Therefore, insight into the direct and indirect effects of the various attributes on crop yield is of paramount importance. In this regard, path analysis is essential to analyze the multiple attributes. In plant breeding programs, the correlation study and path analysis gives improved information into the relationship of cause and effect among the agronomical traits.Although very little work on the morphological diversity of ginger germplasm has been done, still various studies have been conducted on ginger cultivars of different regions. Being a biodiversity hotspot, Northeast India is a rich hub of ginger diversity. The biodiversity of ginger from the entire northeast region has not been exploited so far, which could lead to promising lines of ginger for high rhizome and essential oil yields. Since the demand for ginger is ever-growing and is a seasonal crop, a good variety of ginger with a better shelf-life would be economically more profitable. Given the previous reports, only limited germplasm has been studied, which do not account for stable, reliable data. Hence, the present study focuses on studying variability parameters for the first time on 150 ginger germplasm across North east India. The study would be of great benefit to the ginger breeding program.In total, 150 ginger germplasm were collected across Northeast India. The collection sites were Assam, Meghalaya, Arunachal Pradesh, Mizoram, Manipur, Nagaland, and Sikkim. All the collected germplasm was identified by the plant breeder of CSIR-NEIST, Jorhat and maintained at the experimental farm of CSIR-NEIST, Jorhat, Assam. The herbarium specimen was submitted to the herbarium record of the department.The 150 ginger germplasm were planted in a 2 × 2 m plot size with triplicates in RBD (Randomized Block Design) at the experimental farm of CSIR-NEIST, Jorhat. The plant to plant and row to row distance of 35 × 35 cm was maintained. The experiment was conducted for three years , respectively.The detailed data recording was carried out by considering sixteen agronomical traits, including plant height (PH) (cm), number of tillers per plant (NTP), number of leaves per plant (NLP), leaf length (LL) (cm), leaf width (LW) (cm), number of mother rhizome (NMR), number of primary rhizomes (NPR), number of secondary rhizomes (NSR), initial rhizome weight (IRW) (g), the weight of mother rhizome (WMR) (g), the weight of primary rhizome (WPR) (g), the weight of secondary rhizome (WSR) (g), the diameter of mother rhizome (DMR) (cm), diameter of primary rhizome (DPR) (cm), diameter of secondary rhizome (DSR) (cm) and rhizome yield per plant (RYP) (t/ha). The morphological data were recorded from five randomly selected plants from each replication for each germplasm. In addition to that, the essential oil yield (EOY) (%) was also recorded. As for the essential oil yield, 300 g of shade-dried rhizome were hydrodistilled using Clevenger apparatus for 8 2 analysis was performed using Indostat software  for the genetic diversity study. The Tocher method was used for the cluster analysis and the inter and intra distance of the clusters were found using Mahalanobis Euclidean Distances. The Analysis of variance (ANOVA), genetic advance (GA), genetic gain (GG)  , and herain (GG)  were anaain (GG)  for the ain (GG)  were perThe studied 150 germplasm of ginger were from across seven states of Northeast India. The morphological data for the 150 germplasm of ginger were recorded for three consecutive seasons spring 2018, 2019 and 2020. Pooled data from three years was used to estimate variability parameters, correlations, path, and morphological diversity.The ANOVA was analyzed for three-year pooled data . ANOVA rThe highest difference between PCV and GCV was observed for NTP , followed by IRW  and WSR , indicating the influence of environmental effects. While the lowest difference between GCV and PCV was observed in EOY  followed by LL , RYP , DMR , PH  and DPR  . High he2 method was used to analyze the genetic divergence based on their morphological data. The ginger germplasm was grouped into ten (10) clusters based on the traits under study. The maximum intra-cluster distance as per Mahalanobis Euclidean Distance was 46.48 and the minimum was 0  was observed for EOY and RYP, while moderate heritability (>50%) was recorded for WMR, LL and NTP. This indicated a high transmission index for the characters. It has been reported that GCV, together with heritability would provide a clear idea of the efficiency of selection as GCV depicts the amount of genetic variation, while the proportion of transmittance of the variability of a character to its progenies is estimated by the heritability . However2 method based on their morphological data, which grouped the germplasm into ten (10) clusters. According to Mahalanobis Euclidean Distance, 46.48 and 0 were reported as the maximum and minimum intra cluster distances observed. Cluster 7 exhibited the maximum intra-cluster distance while cluster 8, cluster 9 and cluster 10 exhibited the minimum intra-cluster distance (0). The genotypes of cluster 8 and 6 exhibited maximum divergence, and clusters 1 and 3 revealed minimum divergence among them. As per the Tocher method, the ginger germplasm were grouped into 10 clusters among which cluster 3 has the highest of 61 germplasm while clusters 8, 9, 10 had a single cluster indicating unique accession.A previous study on 13 ginger germplasm for two years reported that based on cluster analysis, was grouped into three clusters. However, the germplasm assignment into the clusters differed for both years. During the first-year cluster analysis, cluster I was grouped based on the genotypes possessing high mean values for the studied traits, while a similar observation was revealed for cluster II during the second year. The clustering pattern was not based on collection sources but was instead found to be based on quantitative characters . The genThe information on the genetic correlation of RYP and EOY is necessary; their components and various quality characteristics are of paramount importance in a breeding program that aims at combining desirable quality and agronomic parameters with high yield potential. Therefore, the association study would provide in-depth data on the nature, direction, and extent of selection. The rhizome yield was positively and significantly correlated with NSR, IRW, WPR, DMR, and DSR, and selection based on these traits would be more rewarding. The EOY was reported to be correlated positively and significantly with IRW and WSR. However, a previous study reported that plant height, leaves per tiller and tiller thickness seemed significant as these traits were found to directly influence the yield, which differs from our report . A previThe results of the correlation study do not clarify the contribution factor of each character. Moreover, since association studies include more variables, revelation on the direct association becomes significant and complex. For finding the associated contributing factors of a trait, the path coefficient analysis is of great aid in classifying the indirect and direct causes of association or correlation. It provides an insight into the traits contributing to producing a given correlation . The patThe matrix of path coefficient analysis for RYP revealed that the IRW exhibited maximum direct effect followed by WMR and WSR. The IRW was correlated significantly and positively with RYP mainly due to its direct and indirect effects via EOY and WSR. The WMR was found to have a significant and positive correlation on EOY mainly due to its indirect and direct effects via IRW. The WSR was found to have a significant and positive correlation with EOY mainly due to its direct effect and indirect effect via NTP, WMR and DSR. Previous report on ginger revealed that for improving the high yield trait selection should be done based on rhizome, thickness of secondary rhizome, and leaflet number . PreviouBased on the results of the path coefficient analysis determined for EOY and RYP, it can be concluded that the characters IRW, WMR, and WSR were the most important and could be used for making effective selection program for high rhizome and essential oil yield of ginger.Ginger is an important cash crop among all the spices. The extensive use of ginger includes both fresh and dried forms, in candied form, and as an important raw material for various pharmaceutical applications. As ginger is a vegetatively propagated crop, the scope of variability creation ceases. Hence, the proper evaluation of the available genetic baseline of ginger is of utmost importance for selecting and identifying the elite germplasm of ginger. Northeast India has a rich biodiversity, a powerhouse of such wide variability available in nature. In this regard, the detailed evaluation of ginger germplasm across Northeast India could be a high-potential region for selecting ginger germplasm with elite traits. In view of the above reason, the present study was undertaken to assess the morphological diversity of ginger germplasm across Northeast India. The analysis of variance revealed that phenotypic variance is more compared to the genotypic variance, which indicated the influence of environmental impact on ginger germplasm. High heritability coupled with high GA was observed for EOY, followed by RYP. Moderate heritability with moderate GA was observed for LL, NTP, PH, and DMR. Therefore, these characters might be exhibiting additive gene effect predominance. Hence, selecting these traits would be effective for the genetic improvement of RYP and EOY in ginger. Based on the path coefficient analysis results determined for EOY and RYP, it can be concluded that the characters IRW, WMR, and WSR were the most important and appeared promising in improving the overall yield potential of ginger rhizome and essential oil yield.10.7717/peerj.15966/supp-1Supplemental Information 1Click here for additional data file.


When polygenic risk score (PRS) is derived from summary statistics, independence between discovery and test sets cannot be monitored. We compared two types of PRS studies derived from raw genetic data (denoted as rPRS) and the summary statistics for IGAP (sPRS).APOE is derived from International Genomics of Alzheimer’s Project (IGAP), which records ΔAUC and ΔR2 of 0.051 ± 0.013 and 0.063 ± 0.015 for Alzheimer’s Disease Sequencing Project (ADSP) and 0.060 and 0.086 for Accelerating Medicine Partnership - Alzheimer’s Disease (AMP-AD). On UK Biobank, rPRS performances for hypertension assuming a similar size of discovery and test sets are 0.0036 ± 0.0027 (ΔAUC) and 0.0032 ± 0.0028 (ΔR2). For height, ΔR2 is 0.029 ± 0.0037.Two variables with the high heritability in UK Biobank, hypertension, and height, are used to derive an exemplary scale effect of PRS. sPRS without Considering the high heritability of hypertension and height of UK Biobank and sample size of UK Biobank, sPRS results from AD databases are inflated. Independence between discovery and test sets is a well-known basic requirement for PRS studies. However, a lot of PRS studies cannot follow such requirements because of impossible direct comparisons when using summary statistics. Thus, for sPRS, potential duplications should be carefully considered within the same ethnic group.The online version contains supplementary material available at 10.1186/s12863-023-01151-4. Recently, genetic studies involving a polygenic risk score (PRS) have dramatically grown, and sophisticated tools and methodologies are being developed for its use –3. AlongAn underlying assumption of PRS models is that the subjects from the discovery set do not overlap with those of the test set , which aAmong prior studies, we identify multiple signs of potential inflation in PRS performance attributable to overlapping subjects. First, if the independence between discovery and test sets is clearly stated in the paper, the PRS performance of binary traits not statistically significant, while if not, results were highly variable or inflated –16. For The clues mentioned above, albeit circumstantial, led us to a systematic investigation for detecting and quantifying the overestimation bias in sPRS due to overlapping subjects. On AD prediction, we first prove that PRS models overfit to overlapping subjects, resulting in overestimated prediction performances on the test set. Then we extend our experiments using UK Biobank data to derive the scale effect of the inflation and brief guidelines for detecting the bias in sPRS.Figure To adjust for the scale effect, we leverage a large number of samples in UK Biobank in terms of two phenotypes with higher heritabilities than AD –21, nameP = 1.51 × 10–10). After removing the overlapping individuals from AMP-AD, the ΔAUC decreases to 0.0017. Notably, ΔAUC loses its statistical significance (P = 0.57). ΔR2 shows a similar level of deflation, which drops from 0.11 to 0.0041 by removing the identical subjects  of ADSP and AMP-AD data are  and . P-values are not significant (P > 0.05). In other words, when data independence is guaranteed, PRS for AD displays unexpectedly low performance. In sPRS, a respective set  of ADSP and AMP-AD are as high as  and . The results of sPRS are significantly inflated in comparison to those of rPRS.In rPRS, a respective set  monotonically increases in a growing portion of subject overlap. sPRS performances remained relatively unchanged while maintaining the inflated values greater than those in independent rPRS  are used to derive an rPRS model, which subsequently is evaluated on a mixture of AMP-AD  and ADSP data randomly selected from test splits of ten-fold cross-validation , for which the portion of the latter increases from 0 to 100% via an increment of 10%. sPRS is derived from the first stage data of the IGAP study and evaluated in the same way  is . When the size is 300k,  is , which is still significantly smaller than the sPRS of ADSP and AMP-AD , and roughly equal to the size of ADSP, IGAP, and UK Biobank, respectively . Lassosum [To obtain statistical significance for ΔAUC, both the discovery and test sets require a substantially large number of subjects Fig. . For insLassosum , which uThe results can vary based on the case:control ratio of the discovery set. The IGAP study consisted of 17,008 cases and 37,154 controls, yielding a case:control ratio of 1:2.18, while the UK Biobank study had a case:control ratio of 1:3.14 Table . Thus, t2 for binary traits [2 for non-binary traits, which are the respective upper limits drawn by hypertension and height from UK Biobank.This study illuminates the overestimation bias in PRS studies. In AD prediction, we prove the presence of latently overlapping subjects for sPRS and demonstrate performance inflation. Developing sPRS without knowledge of the overlapping individuals raises concerns over overestimation and the model’s generalizability. We argue that overestimation needs to be suspected when the contribution of sPRS derived from discovery datasets with much smaller sample sizes than nation-wide biobank, by the referenced PRS methods , exceedsy traits  and 7–8%As evidenced by the three separate AD studies sharing identical subjects, subject overlap may be prevalent in other cohort studies and large-scale meta-analyses. In our study, ADSP and AMP-AD data include 432 identical subjects, corresponding to 38.12% of AMP-AD data. Also, ADSP and ADNI have 21 overlapping subjects  SNPs in core genes curated from multiple GWA studies [P-value thresholding step required in conventional PRS studies for selecting SNPs. Also, GWA SNPs tend to show highly significant P-values and therefore are regarded as reliable. Moreover, GWA SNPs information can be conveniently accessible via reviewing prior works, even if the authors do not release summary statistics. However, GWA SNPs are frequently found in the uninterpretable non-coding regions [PRS also could be developed using GWA , Alzheimer’s Disease Sequencing Project (ADSP), and AMP-AD–are used to demonstrate overestimation bias in PRS –43. Non-We perform logistic regressions for binary traits and linear regressions for a continuous phenotype using PLINK (v1.9) . Three dWhere APOE ε4 status, and the sequencing centers. PCs are computed using the principal component analysis function of PLINK (v1.9) [2 are used to assess model performances, which are calculated using “pROC” and “fsmb” packages of R (v4.0.3), respectively [2 of Model II from those of Model III, which we label as ΔAUC and ΔR2. The statistical significance of ΔAUC is examined using DeLong’s test [2 via “lm” in the R program. PRS contributions are determined by comparing Model II and Model III with the extra sum of squares test.For AD datasets, common covariates include sex, K (v1.9) . For UK K (v1.9) . For hypectively , 48. Perg’s test . For heiFor ten-fold cross-validation tests, we balance the number of samples between the discovery and test splits based on each covariate in the statistical analyses using “StratifiedKFold” function from Python’s (v3.8) “scikit-learn” (v0.24.1) package . When tehttps://www.niagads.org/datasets/ng00036). After selecting SNPs with P < 0.5 in the association tests using the discovery dataset, we perform clumping with the window of ± 1Mbp and r2 < 0.1. Clumping is performed using PLINK (v1.9) [APOE (apolipoprotein E) region. When analyzing the effect of the number of SNPs on the results, the SNPs are selected in the order from the lowest P-value. We construct PRS with PRSice (v2.3) and Lassosum (v0.4.5) [For computing PRS, we select common (MAF ≥ 1%) SNPs and use summary statistics from discovery sets, followed by measuring PRS in test datasets. For rPRS, we calculated summary statistics by logistic regression. For sPRS, we downloaded summary statistics from IGAP web page (K (v1.9) . For AD (v0.4.5) .Below is the link to the electronic supplementary material.Additional file 1: Table S1. Demographic characteristicsAdditional file 2: Table S2. PRS performance after excluding genetically close individuals from the test setAdditional file 3: Table S3. rPRS and sPRS results on AD Additional file 4: Table S4. PRS performance comparisons for hypertension in UK BiobankAdditional file 5: Table S5. PRS performance comparisons for height in UK Biobank Additional file 6: Table S6. Performance comparisons between PRS and LassosumAdditional file 7: Fig. S1. The number of test set subjects required to gain statistical significance (P < 0.01) for hypertension using UK BiobankAdditional file 8: Fig. S2. Comparisons of PRS performance across different case:control ratios of discovery sets using hypertension phenotype of UK biobankAdditional file 9: Supporting material: material, methods, and additional references (docx)Supplementary Material 10


Dairy cows contribute to greenhouse gas emissions from livestock, and reducing methane emissions is vital for the long-term sustainability of the dairy industry. Genetics and breeding strategies can be used to bring about permanent and long-term enteric methane emission reduction from dairy cattle. Here, we assess three definitions for methane emission traits, investigate their genetic parameters, and compare potential implications of including them in a genetic selection program. All three commonly used methane traits  were heritable, and are potential candidates for a selection program. Additionally, all traits were highly correlated with each other, indicating that selection on one trait would lead to an indirect response on the other methane traits. By exploring trait definitions for including methane in selection strategies, this work contributes to potential mitigation strategies for reducing greenhouse gas emissions in dairy cattle using genetics.Genetic selection can be a feasible method to help mitigate enteric methane emissions from dairy cattle, as methane emission-related traits are heritable and genetic gains are persistent and cumulative over time. The objective of this study was to estimate heritability of methane emission phenotypes and the genetic and phenotypic correlations between them in Holstein cattle. We used 1765 individual records of methane emission obtained from 330 Holstein cattle from two Canadian herds. Methane emissions were measured using the GreenFeed system, and three methane traits were analyzed: the amount of daily methane produced (g/d), methane yield (g methane/kg dry matter intake), and methane intensity (g methane/kg milk). Genetic parameters were estimated using univariate and bivariate repeatability animal models. Heritability estimates (±SE) of 0.16 (±0.10), 0.27 (±0.12), and 0.21 (±0.14) were obtained for daily methane production, methane yield, and methane intensity, respectively. A high genetic correlation (rg = 0.94 ± 0.23) between daily methane production and methane intensity indicates that selecting for daily methane production would result in lower methane per unit of milk produced. This study provides preliminary estimates of genetic parameters for methane emission traits, suggesting that there is potential to mitigate methane emission in Holstein cattle through genetic selection. The global population is rapidly increasing, and environmentally sustainable food production is becoming a research priority. The agricultural industry is often targeted for its contribution to climate change due to livestock production. Although dairy cattle represent only a fraction of total greenhouse gas (GHG) emissions, increased awareness of their contribution to the problem has resulted in consumer pressure on the dairy industry to improve its efficiency and sustainability and to investigate ways to mitigate its environmental footprint. Methane is the primary GHG emitted by ruminant livestock, and is generated by the normal feed digestion process; considering that methane is considered to be one of the more potent GHGs, its mitigation is of high priority to the dairy cattle industry.Several systems for measuring direct methane production have become available, including respiration chambers, the automated head-chamber system , and the sulfur hexafluoride SF6) tracer gas system . Studie tracer gPrevious studies have shown that methane emissions from dairy cattle have a heritable component that ranges from 0.12 to 0.45 depending on the trait analyzed and the stage of lactation  ,11,12. AAnimal Care Committee approval was obtained from the University of Guelph  and from the University of Alberta .https://genomedairy.ualberta.ca (accessed on 7 April 2023)), a global initiative investigating the use of selective breeding and novel genomics technologies to improve feed efficiency and reduce methane emissions in dairy cattle. Data from two Canadian research stations located in Alberta and Ontario were used in the analyses. The data collection in these two stations are described below.The data used in this study were collected through the Efficient Dairy Genome Project  at the University of Guelph’s dairy research facility, first lactation cows between 120 and 150 days in milk (DIM) were moved in groups of two to four into a tie-stall area of the barn for methane emission testing. During their time in the tie-stall, animals were fed a total mixed ration (TMR) ad libitum delivered daily at approximately 11:00 h and had ad libitum access to water. The average ingredient composition of the feed was corn silage (30%), haylage (28%), high-moisture corn 26%), supplement (12%), and chopped straw (2%). A detailed composition analysis is available in Seymour et al. . At each%, supple4 emission measurement period, CO2 recovery tests were performed at the start of each group and gas calibration was performed every week during the experimental period. The details of the methane emission measurement methods have been described in our previous study [The Dairy Research and Technology Center (DRTC) is part of the University of Alberta infrastructure. Mixed parity cows between 30 DIM and 250 DIM were housed in a ventilated tie-stall barn with ad libitum access to water and TMR, which was delivered daily at approximately 08:00 h. The average ingredient composition of the feed was barley silage (58%), alfalfa hay (10%), and supplement (31%). Cows were milked twice per day at 03:00 h and 15:00 h. Animals had access to an exercise area (an open dry lot) for 3 h every second day. Methane measurements were collected using the GreenFeed system from 2016 to 2020. Measurements were collected on groups of 10–15 mixed parity lactating cows. Groups were measured over twelve consecutive days at 12-h intervals, providing two measurements per day. The first day measurement was conducted at 01:00 h and 13:00 h, then shifted by one hour every day to cover all 24 h throughout the twelve days of recording. In 2019, this method was adjusted to correspond more closely to the ODRC herd, where measurements were made at three time points per day  for five consecutive days. The GreenFeed system was adjusted so that each cow could receive drops (approximately every 40 s) of the barley grain used to bait the cow to the head compartment. During the entire CHt-test, which assumes unequal variances between the groups, were conducted to test the differences in variances and means between the different measurement time periods, respectively. This was done to evaluate whether there were specific time points at which methane emission peaked or variation in methane outputs corresponding to the time of measurement. Differences were considered significant at 5% probability.Potential differences in methane emission among recording times  were tested for data collected at ODRC using R Version 3.5.0 software [A total of 2469 methane emission records were collected on 422 Holstein cows from August 2016 to March 2020 from the two research herds. Only first lactation cows were analyzed. Records ranged from 26 to 257 DIM from cows who calved between 22 and 33 months of age. Possible outliers were removed using a 1.5 × interquartile range. Cows were required to have at least three methane measurements (days) for analysis, as methane measurements from both herds were collected more than once per cow to assess variability. The pedigree file used for all methane traits in the analysis included 1434 animals, consisting of 892 dams and 253 sires over 25 generations.Various methane emission traits are used in livestock species ,18. Thisμ is the intercept; ith age of cow at calving class ; jth herd-year-season of recording , where season is split in two classes (March–August and September–February) or of the jth herd-year of recording (six classes: MeY); kth days in milk class (three classes); lth animal, distributed as mth animal with records, distributed as nth observation.Variance components were estimated using the Average Information Restricted Maximum Likelihood (AIREML) methodology and a repeatability animal model for MeP, MeY, and MeI. All analyses were performed using the ASReml software Version 4.1 . ConnectA better and less restrictive model for repeated records, such as a random regression model, was not used because of the limitations imposed by the sample size.j and trait k and j and trait k, respectively. Similarly, genetic correlations were estimated as j and trait k and j and trait k, respectively. Lastly, permanent environmental correlations were estimated as j and trait k and j and trait k, respectively.Bivariate models were used to estimate genetic, permanent environment, and phenotypic correlations using the same effects as in the univariate model. Phenotypic correlations were estimated as h2) and repeatability (r) from the univariate models were calculated as follows:The estimates of heritability (Heritability of the average of a different number of methane emission records (from 2 to 5) was obtained following Falconer and MacKay :hn2=h2×nn = 103) with records for the methane traits. The Spearman rank correlations were obtained using the COR.TEST function in R Version 3.5.0 software [Estimated breeding value (EBV) rank correlations between methane traits were used to assess potential re-ranking, considering either all animals or only sires with daughters  for the ODRC herd. Differences in the means between 12:00 h and 16:00 h and between 20:00 h and 8:00 h were not significant . All other differences in means and variances were significant (p < 0.05). The results showed that the highest amount of methane is emitted after feeding, increasing by an average of 35 g/cow from 480 g/cow (8:00 h) to 515 g/cow (12:00 h); fresh feed is delivered approximately an hour before the 12:00 h testing. These results agree with the literature. Hristov et al. [4 measurements were to be reduced due to the amount of effort to make all four measurements in a day, the night measurement at 20:00 would be a good candidate for elimination, as they have the same mean as the 8:00 measurements and the 8:00 measurements, while the 20:00 measurements have the same variance as the measurements at 16:00.F-tests and Welch two-sample v et al. , who usev et al.  observedEstimated variance components, heritability , 0.49 (0.03), and 0.74 (0.02) for MeP, MeY, and MeI, respectively . These rCurrently, the ideal number of days an animal’s methane emission should be measured in order to better capture its genetic merit is unknown , althougHeritability estimates for dry matter intake have been shown to vary throughout lactation ,40,41. TPhenotypic and genetic correlations are presented in Previous reports in the literature concerning correlations between methane traits in dairy cattle are limited. However, Kandel et al.  reportedn = 103) for each methane trait  with phenotyped daughters were much lower, the highest being between MeP and MeI at 0.67. The rank correlations between MeP and MeY and between MeY and MeI were 0.65 and 0.64, respectively, and a large re-ranking of sires was observed. This indicates that sires who may be best for one specific methane trait may not necessarily be the best for the other methane traits. That said, a moderate to high genetic correlation between the methane traits exists, allowing for indirect response to selection.Spearman rank-order correlation coefficients were estimated to determine the degree of re-ranking that may occur due to the use of the ratio traits MeY and MeI, accounting for dry matter intake or milk yield, respectively, or using MeP . ConsideMethane emission is a moderately heritable trait regardless of the expression considered, i.e., methane production, yield, or intensity. The overall rank correlations between estimated breeding values for methane emission traits in this study were high. However, rank correlations for sires with phenotyped daughters were only moderate, indicating substantial sire re-ranking and impact on selection decisions. The methane emission trait of choice depends on the breeding goals of a particular evaluation program. A larger dataset is necessary in order to increase the accuracy of the estimated breeding values for methane emission traits.