Streptococcus iniae is a problematic gram-positive bacterium negatively affecting Nile tilapia (Oreochromis niloticus), one of the main aquacultural species produced worldwide. The aim of this study was to identify the genetic architecture of survival to S. iniae and identify single nucleotide polymorphism (SNPs) linked to quantitative trait loci (QTL) related to survival to S. iniae challenge. With this purpose, Nile tilapia from the Spring Genetics breeding program were sent to a controlled S. iniae challenge test where phenotypes were scored as dead for fish that died during challenge test and survivors for the fish alive at the termination of the test. Additionally, fin-clip samples from all fish in the test were collected for DNA extraction. Out of 1904 fish in the challenge test, tissue samples of 321 fish were sent for genotyping using double digest restriction site associated DNA sequencing (ddRADseq). After quality control and filtering, 9,085 SNPs were used to perform a genome-wide association study (GWAS). A significant signal in LG8 was observed indicating association with survival to S. iniae challenge, with SNPs explaining from 12% to 26% of the genetic variance. To demonstrate the usefulness of marker assisted selection (MAS) to selectively breed fish for survival to S. iniae, offspring of breeding candidates classified as “resistant” and “susceptible” based on haplotypes of the four most significant markers were sent to a controlled S. iniae challenge test. At the end of the test, the differences in mortality between the two groups were strikingly different with a final cumulative percent mortality of less than 1% and 73% for offspring from “resistant” and “susceptible” parents, respectively. These results demonstrate that MAS for improved resistance to S. iniae is feasible. Oreochromis niloticus) is among the three most important cultured aquatic species with more than four and a half million tons produced worldwide in 2020  to the global tilapia industry (S. iniae in the United States was estimated at US$ 10 million (S. iniae (O. niloticus) populations . Results and details of these two challenge tests have been previously published by S. iniae, fish from all families were challenged intraperitoneally with diluted S. iniae culture (see details from Fish from the fourth generation (G4) of the spring Genetics Nile tilapia breeding program were used for the GWAS. A total of 144 full sib families were produced by natural mating in single pair breeding units using a nested mating design (144 dam with 72 sires) and reared separately until tagging as previously described . At taggS. iniae challenge test. Families were grouped according to their genetic distances estimated from pedigree. From resulting groups, half-sib families that maximized phenotypic variation were selected and within each family individuals were randomly sampled. After this process, 321 individuals representing 39 families were selected to create the training population for S. iniae  for DNA extraction, library preparation and sequencing. In brief, DNA was extracted from fish fin clips (16–25 mm2) using sbeadex livestock kit . 100–200 ng of genomic DNA were digested with MsII (NEB) in NEB4 buffer. After inactivation of restriction enzyme, 10 µL of each restriction digest were transferred to a new 96 well PCR plate with one of 96 inline barcoded forward blunt adaptors, and ligation master mix. After ligation, all reactions were purified using Agencourt AMPure XP beads  and libraries eluted in Tris buffer. Ten µl of each of the 96 Libraries were separately amplified in 20 µL PCR reactions using MyTaq  and standard Illumina TrueSeq amplification primers  limiting the cycle number to 14 cycles. Five µl from each of the 96 amplified libraries were pooled, PCR primer and small amplicons were removed using Agencourt AMPure XP beads  and PCR enzyme removed by an additional purification on Qiagen MinElute Columns . Pooled libraries were eluted in a final volume of 20 µL Tris Buffer (5 mM Tris/HCl pH:9). Normalisation was done using Evrogen Trimmer Kit . Normalized library pools were re-amplified in 100 µL PCR reactions using MyTaq  and limiting cycle number to 14 cycles. For each pool a different i5-adaptor primer was used to include i5-indices into the libraries. Libraries were size selected on Blue Pippin and LMP-agarose gel, keeping fragments between two hundred and four hundred base pairs.Tissue samples of 321 Nile tilapia challenged with Sequencing was done on an Illumina NextSeq 500 V2 platform , resulting in Oreochromis niloticus reference genome from NCBI accession number GCA_001858045.1 ASM185804v2 , μ was the overall mean, b was the vector of unknown additive effect of the evaluated SNP, a was the vector of additive polygenic effects with distribution e is a vector of residual effects X and Z are design matrices to the respective vectors b and a. Statistical significance threshold for p-values was estimated using Bonferroni correction method for multiple testing with p and q are allele frequencies and a is the estimated allele substitution effect.GWAS was performed using the linear mixed animal model implemented in GCTA software  with theocus i  and e S. agalactiae and from 2020 to Francisella oreintalis; thus, male breeding candidates are routinely genotyped using a custom BMK Genetics 50K SNP array developed in collaboration with Neogen  which includes the ten most significant markers for survival to S. iniae. Additionally, Spring Genetics in collaboration with Neogen, designed a panel with 222 SNPs for parental assignment and traceability including the ten most significant markers for survival to S. iniae and is routinely used to perform MAS on females to S. iniae. Thus, on generation seven (G7), genotypes of breeding candidates from the Spring Genetics population were available. In total 768 candidate males were genotyped with the 50K SNP array, and 800 candidate females were genotyped with the parental assignment panel. Five months after the production of generation eight (G8) of the Spring Genetics’ breeding nucleus, experimental groups were produced to probe the effectiveness of MAS for selection to survival to S. iniae. For this purpose, using available genotypes of breeders that remained alive, fish were classified into “resistant” and “susceptible” groups, based on haplotypes of the four most significant markers. Individual pairs of breeders were subsequently stocked in separate hapas according to their group. Five “resistant” hapas and five “susceptible” hapas were stocked, allowing single pair mating. Due to low percentage of spawning caused by cold water temperatures during this period, fry from one “resistant” female and two “susceptible” females were collected . The survival during trial  was used as the phenotype. All challenge procedures utilizing fish were approved by the USDA-ARS, AAHRU Institutional Animal Care and Use Committee.From 2019 onwards, Spring Genetics has implemented genomic selection for resistance to ollected . OffspriTissue samples from all challenged fish were sent for genotyping using the custom SNP panel for parental assignment; offspring were assigned to their parents using hsphase package  in R R .S. iniae, and after day 5 daily mortalities were below 1%. Cumulative mortality reached 46% after 21 days. Brain tissue samples were collected from 265 fish that died during the trial and 99% of these samples yielded pure cultures of S. iniae confirming the cause of death. Heritability reported was high (h2 = 0.52 ± 0.12)  .S. iniae challenge test  differences with a final CPM of 73% for the “susceptible” group and less than 1% for the “resistant” group. Survival curves (time to death) between the two groups were also significantly different (Log-Rank tests p < 0.001) (S. iniae was reisolated from 100% (44/44) of the sampled fish brain tissue.The overall cumulative percent mortality at the end of the  was 39% . Mortali was 39% . Analysi< 0.001) . S. iniaS. iniae resistance (NC_031973.1_9485417) was removed as it did not show polymorphism. S. iniae accurately.After genotyping, parental assignment showed fish from “susceptible” group came from a single paternal half-sib family , and fish from the “resistant” group came from a single mating . After QS. iniae suggest the presence of a novel QTL area on linkage group 8 (LG8) explaining from 12% to 26% of the genetic variance. Estimates of percentage of genetic variance explained by the SNPs may be underestimated because of the dichotomous nature of the trait  is located about 100 kb downstream the first significant SNP, which has known chemotactic activity, and changes in expression of genes from the same family have been reported as part of the acute response of infection of S. iniae in Nile tilapia (exoc7 (exocyst complex component 7) was located about 20 kb downstream the ninth most significant SNP; exocyst complex is known to regulate entry of bacterium into host cells , including the third most significant SNP. Gene grid1b is known to play an important role in excitatory synaptic transmission in the central nervous system    which arS. iniae and harvest weight (S. iniae will likely not impact selection for harvest weight, probably the main trait under selection in Nile tilapia breeding programs.No significant correlation has been found between survival to t weight , meaningS. iniae in Nile tilapia and demonstrates that MAS is an appropriate tool for improving resistance to S. iniae in this population to reduce incidence of this disease in the farms.This study confirms the presence of a QTL with a considerable effect on survival to


Background: Suicidal behaviour (SB) refers to behaviours, ranging from non-fatal suicidal behaviour, such as suicidal ideation and attempt, to completed suicide. Despite recent advancements in genomic technology and statistical methods, it is unclear to what extent the spectrum of suicidal behaviour is explained by shared genetic aetiology.Methods: We identified nine genome-wide association statistics of suicidal behaviour , ten psychiatric traits  and collectively, nine summary datasets of anthropometric, behavioural and socioeconomic-related traits . We calculated the genetic correlation among these traits and modelled this using genomic structural equation modelling, identified shared biological processes and pathways between suicidal behaviour and psychiatric disorders and evaluated potential causal associations using Mendelian randomisation.Results: Among populations of European ancestry, we observed strong positive genetic correlations between suicide ideation, attempt and self-harm  and moderate to strong genetic correlations between suicidal behaviour traits and a range of psychiatric disorders, most notably, major depression disorder . Multivariate analysis revealed a common factor structure for suicidal behaviour traits, major depression, attention deficit hyperactivity disorder (ADHD) and alcohol use disorder. The derived common factor explained 38.7% of the shared variance across the traits. We identified 2,951 genes and 98 sub-network hub genes associated with the common factor, including pathways associated with developmental biology, signal transduction and RNA degradation. We found suggestive evidence for the protective effects of higher household income level on suicide attempt  and while further investigation is needed, a nominal significant effect of smoking on suicide attempt .Conclusion: Our findings provide evidence of shared aetiology between suicidal behaviour and psychiatric disorders and indicate potential common molecular mechanisms contributing to the overlapping pathophysiology. These findings provide a better understanding of the complex genetic architecture of suicidal behaviour and have implications for the prevention and treatment of suicidal behaviour. Suicidal behaviour is a major public health concern. It is estimated that approximately 700,000 individuals die by suicide every year; with a global suicide rate of 9.0 per 100,000 population . AccordiSuicidal behaviour is a broad and complex term used to describe suicidal thoughts and a range of self-injurious behaviour involving intent to die  . Attemptp factor,” may explain why so many psychiatric disorders are comorbid  psychiatric disorders. This framework was further supported by Allegrini and others who reported that the p factor remained stable across childhood and adolescence over a life course, suggesting that the shared genetic influences of psychiatric disorders in childhood is also linked to the development of adult psychiatric disorders  have continued to explain only a small proportion of the heritability of suicidal behaviour, the increase in the availability of data from studies with larger sample sizes over the last few years, has expanded the scope of available statistical methods to improve the understanding of suicide burden . One sucn = 4), suicide attempt (n = 2), and self-harm (n = 2), and summary statistics of completed suicide in an East Asian population.This study was conducted using 28 publicly available genome-wide association studies (GWAS) summary data generated by previous studies. We identified nine SB traits, ten psychiatric traits, and five behavioural and two anthropometric and socioeconomic-related variables, respectively  and attempted self-harm and needed hospital treatment . Attempted suicide datasets were obtained from the United Kingdom Biobank study, a self-report measure indicating having ever attempted suicide  and attempted suicide cases  and controls  from a GWAS study from the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) .Briefly, the self-report measures of suicide ideation and self-harm were derived from GWAS studies of the United Kingdom Biobank (UKB) population  and accessed from the Neale lab  and East Asian populations . Behavioural traits included the average number of drinks per week and smoking habits among individuals of European and East Asian ancestry. Drinks per week (DPW), defined as the average number of drinks a participant reported drinking each week, aggregated across all types of alcohol, was examined in a combined approach with the GSCAN consortium and United Kingdom Biobank (UKB)  , while cigarettes per day were defined as the average number of cigarettes smoked per day, either as a current or former smoker  . Summary-level data was obtained for socioeconomic-related traits, i.e., household monthly income from United Kingdom Biobank . GWAS summary statistics were filtered for SNPs included in HapMap3 to reduce the likelihood of bias induced by poor imputation quality. SNPs were excluded if minor allele frequency (MAF) < 1% and information (INFO) scores <0.9 or if they were located in the human major histocompatibility complex (MHC) region. Datasets without a marker name (rsID) were annotated using ANNOVAR software, with avsnp142, an abbreviated version of dbSNP 142 with left-normalization, on human genome build hg19 (Datasets were formatted according to requirements for linkage disequilibrium score regression (LDSC) and genomic structural equation modelling (SEM) . We obtaild hg19 .https://github.com/bulik/ldsc) (2) divided by the expected median of the chi-squared distribution and was used to assess systematic bias or genomic inflation present in the GWAS summary data due to population stratification. A λGC estimate of around 1, indicates no systematic bias. An LDSC intercept near one indicates little or no confounding and larger than 1.3 indicates that the results might be affected by confounding bias (http://www.bioinformatics.com.cn/srplot). We retained four  of the nine suicidal behaviour GWAS summary data with genetic correlation estimates with heritability z-scores above 4, as scores below 4 do not produce reliable estimates  . SNP-basing bias . SNP intstimates .https://groups.google.com/g/ldsc_users/c/3jtyM4mmTGs). Genetic correlations were corrected for multiple testing based on the total number of correlations by applying a Bonferroni corrected threshold of p < 0.05/52, corrected for four suicidal behaviour traits x 13 psychiatric, sociodemographic and behavioural traits, 9.615 × 10−4 for GWAS studies of European ancestry and seven East Asian studies of completed suicide and psychiatric disorders (p < 0.05/7 = 0.007).We calculated pairwise genetic correlation, i.e., the standardised proportion of the variance shared by the phenotypes that can be attributed to genetic factors, using LDSC, a method that is not biased by sample overlap. Correlations are reported as the coefficient ±standard error. To note, the LDSC estimator is unbounded and can produce genetic correlation estimates outside of −1 to 1 due to sampling variation.  . This men = 6,870,289) were extracted from all studies and mapped to genes located within or less than 20 kb distance up/downstream of the protein-coding gene using FUMA  network from the IntAct database (IntAct release 239)  (www.dgidb.org). DGIdb collects data on drug-gene interaction and druggable genes from 30 different sources and 22 databases (https://www.whocc.no/atc_ddd_index/). We visualised the interaction between the genes significantly associated with the common factor and each therapeutic subgroup using the R package circlize v0.4.15  . We perfase 239) , plug-inase 239) . GO inclIdb 4.0) , an openatabases . We dete v0.4.15 .p-value > the corrected threshold of 9.615 × 10−4. For instrument variables (IV), we used the GWAS for the behavioural and socioeconomic-related traits listed in p > 0.05, indicates no evidence of directional pleiotropy. We used heterogeneity markers (Cochran Q-derived p < 0.05) from the IVW approach to represent potential horizontal pleiotropy. We applied RadialMR to detect potential outliers and removed the outliers to re-estimate the exposure-SB relationship. Genome-wide significant SNPs were selected at p < 5 × 10−8 significance and were clumped to ensure independence at linkage disequilibrium (LD) r2 = 0.001 and distance of 10,000 kb. If an SNP from the instrument was unavailable in the outcome, an attempt to find proxies was made with a minimum LD r2 = 0.8 and palindromic SNPs were aligned with minor allele frequency <0.3. Additional sensitivity analyses were performed using the constrained maximum likelihood and model averaging and Bayesian Information Criterion (cML-MA-BIC) method  method was applied. In addition to assessing horizontal pleitropy, the MR-APSS approach accounts for sample structure simultaneously and allows the inclusion of more genetic variants with moderate effects as instrument variables to improve statistical power without inflating type I errors .We performed Mendelian randomisation to determine if the genetic correlations between the modifiable risk factors and suicidal behaviour arise from genes with pleiotropic effects and biological influences across the traits, or if the effects are causal. Mendelian randomisation uses genetic variants as a proxy for modifiable risk factors (an exposure) to estimate the causal effect on the outcome . The pri) method  and the ) method . The cMLI errors . For MR-We found significant SNP-based heritability estimates of SB traits among European populations ranged from 0.0129 ± 0.0038 (1.3%) for Self-harm needing hospital treatment to 0.1461 ± 0.0892 (14.6%) for Ever attempted suicide (ESH), and 0.078 ± 0.0303 (7.8%) for completed suicide for East Asian populations .p = 1.049 × 10−15] and between SA and ESH  and slightly lower between SA and TLNWL . As expected, suicidal ideation phenotypes (TLNWL and ECSH) were highly correlated .We used cross-trait LD Score regression (LDSC) to estimate genetic correlations among suicidal behaviour (SB), psychiatric disorders and socioeconomic-related traits among populations of European ancestry. We observed strong positive and significant correlations within the SB traits , . This mep = 0.05/52 = 0.000962), five psychiatric disorders, smoking and drinking habits, and education and monthly income were significantly genetically correlated with four SB traits among European populations (p = 1.62 × 10−36). Moderate positive and significant genetic correlations were observed between schizophrenia and ECSH . Similarly, moderate positive genetic correlations were observed for bipolar disorder and ESH , ADHD and attempted suicide (SA) , and AUD and SA .After multiple testing correction (ulations . The strp = 8.27 × 10−7), and alcohol drinking habits and SA . In contrast, education achievement  and monthly income  were negatively associated with attempted suicide, meaning that educational achievement and household monthly income were protective against suicide attempt.Among the behavioural traits, the strongest genetic correlations were observed for smoking habits and SA . We observed no significant associations between completed suicide and MDD, drinking and smoking habits and BMI.Among East Asian populations , complet2(35) = 794.66, AIC = 834.66, CFI = 0.753, SRMR = 0.127). Standardised loadings indicated that MDD and SA loaded most strongly onto the common factor, while anorexia nervosa and insomnia loaded the weakest. We then assessed the fit of a correlated two-factor model where three suicidal behaviour traits were loaded onto the latent suicidal behaviour factor and seven psychiatric traits loaded onto a psychiatric latent factor (2(34) = 747.07, AIC = 789.07, CFI = 0.768, SRMR = 0.125].First, we tested a model in which three SB traits  and seven psychiatric traits  loaded onto a single common latent factor . Model ft factor . We obse2(7) = 51.31, AIC = 75.15, CFI = 0.954 and SRMR = 0.083], suggesting that this model may represent a common or shared genetic pathway/s to suicidal behaviour across MDD, ADHD and AUD.Next, we conducted an exploratory factor analysis and examined different factor structures that would fit the data best . While bWe extended genomic SEM to determine the genetic correlations between the revised common factor model [that represents SB  and psychiatric disorders ] and selected socioeconomic and behavioural traits. The revised common factor had a moderate positive correlation with smoking (rg = 0.47 ± 0.03) and inverse correlations with monthly income (rg = −0.52 ± 0.04) and education achievement (rg = −0.37 ± 0.02). Weak positive correlations were observed between the common factor and drinks per week (rg = 0.18 ± 0.02) and BMI (rg = 0.19 ± 0.02). In other words, the genetic factors that increase smoking and drinking habits, i.e., the number of cigarettes per day and drinks per week also increase SB/psychiatric disorders. In contrast, the genetic factors that influence an increase in education years and monthly income also decrease SB/psychiatric disorders; meaning higher education and household monthly income, a proxy for socioeconomic status is protective for non-fatal SB, MDD, ADHD and AUD.p < 3.02 × 10−6) with the common factor at gene-level  associated with the common factor was GDNF Family Receptor Alpha 3 (GFRA3), located on chromosome 5 and is involved in RAF/MAP kinase cascade pathway and nervous system development (DPPA4) located on chromosome 3, ankyrin repeat domain 46 (ANKRD46) located on chromosome 8, KH domain containing 3 Like (KHDC3L) located on chromosome 6 and neuronal olfactomedin related ER localized protein 2 (OLFM2), located on chromosome 19.ancMETA was used to perform gene and pathway-specific meta-analysis and estimate the aggregated genetic effects and the level of significance of the derived common factor  on 16,530 genes. This technique identified 2,951 genes that were associated (ne-level  and 98 gelopment . Genes wp = 3.15 × 10−27) gene at sub-network level was transducer of ERBB2, 1 (TOB1), located on chromosome 17. In addition, the top sub-network genes were RAN binding protein 9 (RANBP9), located on chromosome 6, involved in developmental biology, signalling pathways and nervous system development; Serine and Arginine Rich Splicing Factor 3 (SRSF3), located on chromosome 6, Heat shock protein Family B  member 3 (HSPB3) located on chromosome 5, and Serine/Threonine Kinase 24 (STK24) on chromosome 13.The most significant (SMAD3 and SMAD4 (SMAD family member 3 and 4) appeared in most enrichment pathways (−14) and nuclear lumen (FDR = 1.69 × 10−9). Moreover, sub-network genes were enriched in molecular functions relating to protein binding (FDR = 1.37 × 10−10) and enzyme binding (FDR = 5.23 × 10−10). Likewise, cellular component organisation or biogenesis (FDR = 2.63 × 10−6) was identified as the most significant biological process. Most of the subnetwork genes were highly expressed in the central nervous system (FDR = 1.27 × 10−7), nervous system (FDR = 1.27 × 10−7), and the brain  with the common factor, for known drug interactions in the Drug Gene Interaction Database v4.0 (DGIdb 4.0) , NOTCH1  and APEX1 . Additional interactions were observed between APEX1 and N04, anti-Parkinson drugs and between AGT (Angiotensinogen) and C09, drugs acting on the renin-angiotensin system.Drug-gene interactions: We explored potential drug-target genes among the 98 sub-network genes significantly associated (Idb 4.0) . A totalIdb 4.0) . AnatomiIdb 4.0) . The grep < 0.05 threshold of the potential effect of smoking on the risk of a suicide attempt , and suggested no causative relationship between smoking and suicidal ideation  or self-harm  (p = 0.053), and there was evidence of substantial heterogeneity (Cochran’s Q statistic p = 3.53 × 10−3). High levels of heterogeneity in the estimated effects from each SNP are an indication of potential pleiotropic effects of some of the SNPs associated with smoking and suicide attempt. We conducted a sensitivity analysis, using a radial regression framework, and identified a variant (rs34406232) on the EGLN2 gene, as an outlier potentially introducing bias to IVW and MR Egger estimates  and the Cochran’s Q statistic for heterogeneity was 32.61 (p = 0.001), indicating that removing the SNP made no substantive difference to the results. In addition, the cML-MA-BIC-DP results  and the MR-APSS method  were consistent with IVW method, with significant associations observed at the p-value threshold of 0.0083  . The intstimates . After rf 0.0083 , suggestIVW 0.55, 95% CI 0.44–0.70, p = 1.29 × 10−5) (p = 0.106) suggests directional pleiotropy was not biasing the estimate, while the Cochran’s Q statistic (p = 8.35 × 10−3) showed high levels of heterogeneity, indicating that some SNPs are pleiotropic but the average pleiotropic effect is close to zero and therefore balanced. We conducted a sensitivity analysis removing two variants (rs11665242 on the DCC gene and rs589914 on the RP11-734C14.2 gene) and the effects remained constant . Additional sensitivity analyses showed that the cML-MA-BIC-DP method  yielded a similar result to IVW and weighted median methods (p = 0.038)  potentially leading to a 45% decrease in the probability of attempting suicide (OR × 10−5) . Similar methods , while t= 0.038) . These fp = 0.349), however, the Cochran’s Q statistic (p = 0.0001) showed heterogeneity between individual SNP estimates at the global level (p = 0.0001), suggesting that some SNPs are pleiotropic but the average pleiotropic effect is close to zero .Our findings did not suggest a causal relationship association between suicide attempt and alcohol drinks per week or educational achievement (school years). There was no indication of directional pleiotropy .The observed strong genetic correlations within the non-fatal suicidal behaviour traits suggest that suicidal ideation, self-harm and attempted suicide have a shared genetic component and provides support for the possibility that suicidal behaviour may exist on a spectrum of behaviours from thinking of suicide to acting on these thoughts . HoweverWe identified significant positive correlations between non-fatal suicidal behaviour and psychiatric disorders, with the strongest correlation observed for major depressive disorders in individuals of European ancestry. Genetic factors play an important role in the aetiology of psychiatric disorders, with heritability estimates from twin and family studies ranging from 32% to 79% for major depression , 77%–88%TOB1, RANBP9, SRSF3, HSPB3 and STK24 were among the most significant. Findings from enrichment analysis suggest that the hub genes were mainly involved in developmental biology, signal transduction, gene transcription and RNA degradation pathways. SMAD3 and SMAD4 genes, observed in most enrichment pathways are members of the SMAD family, and code for intracellular signal transducer proteins involved in transforming growth factor-beta (TGF-ß) signalling. The TGF-beta/SMAD signalling pathway plays an important role in neurogenesis in the hippocampus and has been implicated in the development of mood disorders and the manifestation of depression and anxiety disorders  genes and key pathways associated with the common factor. Among the hub genes, isorders . Interesehaviour .TOB1, RANBP9, HSPB3, and SRSF3 were also linked to neurodegenerative disorders, such as Alzheimer’s disease, Parkinson’s, and amyotrophic lateral sclerosis, through various pathways. The RNA degradation pathway, linked to TOB1 as indicated by the KEGG enrichment analysis, is a critical step in the control of various biological pathways. In neurons, the non-sense-mediated RNA decay (NMD) pathway serves as a regulatory mechanism to control mRNA, and mutations in the NMD genes have been associated with neurodevelopmental disorders, such as schizophrenia and neurodegenerative disorders, such as amyotrophic lateral sclerosis  member 3], is involved in the inhibition of the apoptosis pathway and regulates cell death by inhibiting actin polymerization. HSPB3 has previously been linked to alcohol dependence (SRSF3 (serine and arginine-rich splicing factor 3) plays a key role in the metabolism of RNA/gene transcription (SRSF3 can lead to aberrant gene splicing and the development of neurodegenerative disorders (STK24 (sterine/threonine kinase 24), promotes apoptosis in response to stress stimuli and caspase activation and can act as a regulator of axon regeneration in optic and radial nerves and is involved in programmed cell death  and N04 drug classification, which comprises of anti-Parkinson drugs, and includes anticholinergic and dopaminergic agents.Our study findings suggest that individuals with major depression, ADHD or alcohol use disorder are at increased risk of suicidal behaviour. Understanding the shared biological mechanisms and pathways that may account for the similarities between suicidal behaviour and psychiatric disorders at the epidemiological, neuropathological, and molecular levels could provide potential avenues to treatment and prevention strategies. We found a number of interactions between the hub genes and the ATC therapeutic sub-groups. These exploratory findings, to be interpreted with caution, suggest that pharmaceutical treatments that are currently available may target the genetic component of the common factor. The most notable drug-gene interactions were observed between drugs grouped in the L01 drug classification, which comprises antineoplastic and immune-modulating agents, and Our study has limitations. First, we planned to examine the full spectrum of fatal and non-fatal suicidal behaviour in the exploratory factor analysis. However, only one of the nine publicly available genome-wide summary datasets consisted of individuals who completed suicide and the population was of East Asian ancestry. Owing to the confounding effects of ancestral variation in LD score regression, our factor analysis included only non-fatal suicidal behaviour data of individuals of European ancestry. Therefore, the findings from the genetic factor analyses relate only to non-fatal suicidal behaviour and do not include completed suicide. Second, the modest SNP-based heritability (z-scores <4) of completed suicides and psychiatric traits of East Asian populations meant that we could not explore the factor structure of these traits independently for individuals of East Asian ancestry. As most suicides in the world occur in low- and middle-income countries , the curIn conclusion, our study results support previous findings of genetic overlap between suicidal behaviour and psychiatric disorders. This highlights the importance of further investigation into the overlapping influences of these phenotypes with larger sample sizes and diverse ancestry. Understanding the biology reflected by the shared genes and related pathways could provide new directions in revealing shared etiologies that could help prioritise targets for suicidal behaviour for early intervention.


Partners are often similar in terms of their physical and behavioural traits, such as their education, political affiliation and height. However, it is currently unclear what exactly causes this similarity—partner choice, partner influence increasing similarity over time or confounding factors such as shared environment or indirect assortment. Here, we applied Mendelian randomization to the data of 51,664 couples in the UK Biobank and investigated partner similarity in 118 traits. We found evidence of partner choice for 64 traits, 40 of which had larger phenotypic correlation than causal effect. This suggests that confounders contribute to trait similarity, among which household income, overall health rating and education accounted for 29.8, 14.1 and 11.6% of correlations between partners, respectively. Finally, mediation analysis revealed that most causal associations between different traits in the two partners are indirect. In summary, our results show the mechanisms through which indirect assortment increases the observed partner similarity. Partners are often similar in traits, such as their education and political views, but it is unclear what exactly causes this similarity. Using Mendelian randomization and data on 50,000 partner pairs, the authors show that similarity in different traits can be explained by partner choice, confounding factors and convergence over time. Partners tend to be similar with respect to traits such as various anthropometric measures (body mass index (BMI) and height), socioeconomic factors, behaviours . Second, phenotypic similarity can reflect trait convergence during the partnership. In this case, traits become more similar over time because partners share a household or they influence each other’s behaviour15. Third, partner similarity may be caused by confounders at the time of partner choice (or later), such as shared sociocultural environment and geographical barriers18  models25. Moreover, partner similarity can introduce collider bias in within-spouse association models26.The causes of partner similarity matter for the fields such as behavioural science, population genetics and public health. For instance, high phenotypic similarity could imply genetic similarity. In this case, otherwise independent gene variants would become correlated and would ultimately increase genotype homozygocity27, avoiding reverse causality and confounding.Similar to classical epidemiological studies where it is difficult to discern causal factors from confounders, mere phenotypic similarity within couples poses interpretational challenges. Mendelian randomization (MR) is a special case of instrumental variable (IV) analysis, whereby genetic markers are used as instruments to infer causal relationship. Because of the random allocation of genetic variants at birth, MR can infer causality between an exposure and an outcome28. The authors of ref. 29 used an approach similar to ours to study partner similarity in alcohol use. They showed that phenotypic correlation in couples does not increase with age and there was a difference between correlation and the direct causal effect29.In this work, we used MR to study causality in couples where the exposure and outcome traits belong to different people Fig. . This coHere, we applied MR to estimate the causal effects between partners for 118 traits. We studied direct effects on trait similarity, the impact of time couples have lived together and the role of confounders. Finally, we explored how cross-trait partner similarity emerges by dissecting them to direct and indirect parts.This study analysed data from the UK Biobank (UKBB) cohort, a prospective population-based study with over 500,000 adult participants. Among them, 51,664 couples were identified and selected according to a procedure described in the P < 0.05/66) causal effects in partners after adjusting for the effective number of tests (66) . We assessed the 64 significant results for sex differences but did not identify any after adjusting for the effective number of tests among the remaining traits based on their pairwise correlation matrix (P < 0.05/29).Among the 118 phenotypes tested, we identified 64 significant (P < 0.05/66). We also examined the Pearson phenotypic correlation within the different bins and assessed for the presence of a trend using linear models (phenotypic correlation versus median bin) for all 118 phenotypes. Two traits showed a significant (P < 0.05/66) trend across the bins according to time spent together, namely body fat percentage  and forced expiratory volume in 1 second . In both cases, the correlation decreased as time spent together increased. We found two other traits that showed a significant trend across the bins by median age, namely previous smoking status  and aspirin use . In this case, for both phenotypes, the slope increased as median age increased .To identify if partner traits converge over time, we explored the impact of age and time spent together (proxied by the amount of time at the same address) among the 64 significant traits in both men and women separately and both sexes combined. Using linear regression of MR estimates versus the median of the five age and/or time spent together bins, we detected no significant results in the sex-combined results after adjustment for the number of effective tests , we compared (standardized) causal MR effects to the raw phenotypic correlation among couples to identify any traits where the correlation was different than the MR estimate. After adjusting for the effective number of tested traits (P < 0.05/66), we identified 43 traits which showed different phenotypic correlation compared to MR estimate , systolic blood pressure (Pdiff = 4.89 × 10−9), height (Pdiff = 6.63 × 10−6), forced vital capacity (Pdiff = 5.62 × 10−16), basal metabolic rate (Pdiff = 9.78 × 10−7) and basophil count (Pdiff = 1.76 × 10−38)  and observational correlations (𝑟̂). Using MR, the causal effects between partners (within couples) were estimated for 118 phenotypes. These traits were selected based on their elevated correlation between partners and sufficient (more than five) valid IVs, making them suitable for MR analysis. Using a two-tailed 38) Fig. .Fig. 4PhSignificant differences could be indicative of the presence of confounders (either negative or positive) driving the observed phenotypic correlation. Thus, for traits where couple correlation was significantly different than MR causal estimates, we sought to identify potential confounders which may, in part, explain the discrepant estimates. For the three traits where correlation was less than the MR estimate, we searched for negative confounders  as described above  can be split up into three components: (1) AM through X  causal effect between X and Y in the index individual  any remaining effect of We sought to identify the mechanisms underlying partner similarity by comparing three estimated paths from a phenotype in the index case (2]) among couples, which were reduced to 1,088 pairs after removing pairs with phenotypic correlation >0.8 (a summary of a set of pruned traits can be found in Supplementary Table ρ (AM through the outcome) and others dominated by γ (AM through the exposure). Specifically, we found 326 relationships which were significantly different between ρ and γ, of which 89 (27.3%) showed larger effects through ρ and the other 237 (72.7%) showed larger effects through γ. For instance, we found causal relationships between partners for leg fat percentage on the time spent watching television and BMI on overall health rating, all dominated by ρ.We computed within-couple cross-trait causal effect estimates γ (AM through the exposure), including comparative height at age 10 , forced vital capacity and standing height on hand grip strength. Finally, we found other pairs where neither ρ nor γ captured the relationship  is stronger (P = 1.1 × 10−5) in general compared to Y). When we summed up the effects of Finally, we estimated the contribution of the first two components  Fig. .Fig. 7MoX-associated (confounding) factors for which direct assortment may occur, so that Overall, when direct environmental or genetic assortment is moderate, parental effects lead to negligible bias in the causal effect estimate between In this article, we studied causal relationships behind trait similarity within couples by applying MR to the UKBB data. We analysed 118 traits, representing a wide range of anthropometric-, behavioural- and disease-related traits. Among the 118 phenotypes tested, we found widespread evidence of causal effects among partners. In particular, we identified 64 same-trait causal effects within partners (of 118 traits) and no evidence of heterogeneity among same-trait couple MR estimates , but their correlation decreases with time; the longer partners stay together, the less important it gets to remain similar. On the other hand, we found that couples become more similar with respect to smoking cessation and aspirin use as their age increases. As age and time spent together are highly correlated, it is difficult to distinguish whether this is an effect of convergence or of age-dependent partner choice. We did not find any significant trends of causal MR effects on time spent together or by age. While this could be because of limitations such as statistical power, this is consistent with previous reports which suggest that initial partner choice is more important than convergenceDeriving an analytical formula for the bias in the cross-partner MR estimation allowed a detailed analysis of model parameters that contribute most to such potential bias. Our analysis revealed that the largest contribution to the bias is direct environmental assortment combined with strong parental effects. This conclusion prompted us to explore potential confounders for each examined trait.y is measured with a noise with variance s2, the causal effect estimates do not change noticeably because the exposure and outcome effects are equally diluted. This could lead to an underestimation of confounding effects in our results and may explain why for three traits we observed larger causal effect than correlation.When investigating the impact of common confounders on our entire panel of phenotypes , we found that household income, age completed education and participant of a sport club or gym are important confounders, explaining on average 29.8, 11.6 and 17.1% of the phenotypic couple correlations among traits tested, respectively. These results also suggest that phenotypic correlations in couples are significantly confounded and point to a relatively few key traits which are driving observed partner similarity. These confounder traits are strongly intertwined and hence correlated, therefore elucidating that the key driver is not feasible with the data at hand. Overall, we noticed that the tested confounders are not sufficient to account for the gap between couple correlations and causal effects or the latter being incorrectly estimated. Of note, phenotypic correlations in couples are impacted differently by measurement noise than causal effect estimates. While the former estimates are attenuated by a factor of X  appear much later than mate choice.Our findings investigating cross-trait assortment suggest that causal effects from ω), which can be summarized by three categories: (1) driven by assortment on the exposure (ω being greater than both ρ and γ). Of note, there were fewer cases in category 3, where the causal effect from γ or ρ, suggestive of either a direct effect ρ, whereby height  increases educational attainment (found previously33) within a single individual and AM subsequently occurs via education level. Finally, as an example for category 3, we found a negative causal effect of never having smoked on leucocyte count within partners, such that leucocyte count was higher among individuals with partners who smoked. While we also identified a significant effect through γ (AM through smoking), the effect was much stronger through ω. These findings suggest that there could be a direct effect from index partner by way of secondhand smoke. These results are consistent with previous work showing higher white blood cell count in smokers34, which might already be achieved by secondhand smoking.We found 1,088 significant cross-trait causal effects within couples  to answer this question, but these are poor proxies of the relevant traits. To properly disentangle the relationship between AM and convergence, additional longitudinal data including phenotypes at the time of partner selection would be necessary. A complementary approach could be to contrast genetic and phenotypic correlations; however, it is hard to tell whether differences reflect postmating effects or different  correlation to traits under primary assortmentZ) measured in either individual. Given the limited evidence of direct cross-trait, cross-partner effects, the most likely such path would be X and Z , but their product is expected to negligible35. Also, while assortment/convergence through the exposure (γ) and the outcome (ρ) represents independent paths from Xi to Yp, our results suggest that the computed effects using MR estimates are not perfectly independent. This could potentially be because of the overlap in genetic instruments or bidirectional causal effect between them or because both estimates depend on the causal effect from X to Y. To the best of our ability, we tried to mitigate this bias by (1) using a multivariable Mendelian randomization (MVMR) approach to remove effects of X on Y in the calculation of ρ and (2) first residualizing γ for effects of ρ to ensure independence before summation of the effects. Finally, we were limited to the available traits and white British samples in the UKBB. AM and partner selection are often population specific36. Therefore, our findings may not generalize to other populations, and more diverse biobanks are needed to systematically explore the heterogeneity in assortative behaviour.Third, we have not explored indirect causal paths from In summary, we have surveyed 118 complex traits with significant couple correlation in the UKBB and explored the major contributors to the observed couple similarity: partner selection, couple convergence and confounding. We found that cross-trait assortment can largely be explained by single-trait assortments between either trait and substantial causal effects between these traits. Our findings provide insights into possible mechanisms underlying observed partner similarity patterns at an unprecedented scale and resolution.All analyses were run using the R software (v.3.6.3).This study used the UKBB cohort, a prospective population-based study with over 500,000 adult participants. UKBB has approval from the North West Multi-Centre Research Ethics Committee as a Research Tissue Bank approval. This approval means that researchers do not require separate ethical clearance and can operate under the Research Tissue Bank approval. UKBB also possesses a Human Tissue Authority licence, so a separate Human Tissue Authority licence is not required by researchers who receive samples from the resource.22 additionally requested couples to have been recruited in the same centre, living in the same location, for the same amount of time, living with the same number of people with the same household income, same Townsend deprivation, same number of smokers and so on. Howe et al.29 used a very similar definition to ours (resulting in 10% fewer couples than us); however, they restricted their analyses to only couples who reported to live at the same address for the same amount of time. We believe that discrepancies in self-reported data can occur frequently  and hence, decided to use a more liberal couple definition to increase sample size at the cost of minor misclassification.Couples were identified and selected according to the following procedure. The initial UKBB sample comprised 502,616 individuals. First, participants were filtered to only genotyped, white, unrelated individuals according to the genetic quality control (QC) file. Redacted samples and participants who removed consent were also excluded. After filtering, 337,138 participants remained. Within this sample, we retained individuals coming from households with exactly two unrelated, opposite sex individuals, leaving 108,898 participants. Finally, using the data at data field 6,141, ‘How are people in household related to participant’ pairs were filtered to only include couples who had both responded ‘Husband, wife, or partner’, leaving 103,328 participants or 51,664 couples for downstream analyses . Second, IVs must not be associated with any confounder in the exposure–outcome relationship (the exchangeability assumption). Third, IVs must not affect the outcome except through the exposure (the exclusion restriction assumption). There are several methods to estimate the causal effect using MR, the simplest being the Wald method, whereby a ratio is taken between the variant–outcome association and the variant–risk factor association. A natural extension of this approach, known as the inverse-variance weighted (IVW) method, combines multiple IVs, applied in this reporthttp://www.nealelab.is/uk-biobank/). This list was intersected with our internal database (application number 16389), leaving 1,278 phenotypes available for analysis. Phenotypes were processed in the filtered QC dataset  according to a slightly modified version of the PHEnome Scan ANalysis Tool (PHESANT) pipeline to accommodate the phenotypes that we had available in our database38. Continuous variables were transformed to a normal distribution using a rank-preserving inverse normal quantile transformation, while ordinal and binary traits were recategorized according to PHESANT documentation . We then filtered these phenotypes as follows. First, to focus on traits with some indication of assortment, we computed the raw phenotypic correlation  among couples and removed phenotypes with a Pearson correlation of <0.1. To ensure that inverse normal quantile transformation was not significantly impacting the correlations of each trait, we also calculated the correlation between partners for each trait using the nonparametric Spearman correlation and found consistent estimates . Third, using the sex-specific summary statistics, the IV heterogeneity between sexes was calculated. IVs that showed (Bonferroni-corrected) significant evidence of heterogeneity between sexes were excluded (P < 0.05/[number of IVs]). After this procedure, phenotypes were again filtered to those with at least five valid IVs remaining. Fourth, dietary phenotypes were removed due to high correlation among these phenotypes (due to the shared household), insufficient power, problems with reverse causation and difficult interpretation40. Finally, we manually removed several duplicated and redundant phenotypes. Specifically, (1) left-side body traits (highly correlated with right side) were removed; (2) we retained only one of the duplicated phenotypes for BMI and weight ; and (3) all ‘qualifications’ data were removed  due to the availability of finer-scale correlated variables, such as ‘age completed full-time education’ (data field 845). This ad hoc procedure was meant to capture only major redundancies. After this process, 118 phenotypes remained for analysis  and pruned for independence. Next, we estimated the effects of single-nucleotide polymorphisms (SNPs) on the outcomes of interest by testing the association between each genetic instrument measured in the index individual with the phenotype measured in the partner using the UKBB partner dataset described above. In other words, for each phenotype, the corresponding genetic data for the IVs were obtained from the index case, while the phenotypes (dependent variable) were taken from the corresponding partner. All SNP trait estimates were estimated in men and women separately , adjusting for age and the first 40 genetic principal components of both the index and partner. To mimic the Neale models, we performed linear regression of SNP effects on phenotypes, regardless of data type (including binary). Continuous phenotypes were scaled to have mean of zero and s.d. of one before regression, while ordinal and binary phenotypes were left as processed by PHESANT.To investigate the causal effect of a trait in one individual on the same trait of their partner, we performed couple-specific MR analyses. Specifically, the trait in the index case was used as the exposure and the same trait in the partner was used as the outcome trait. The effect of genetic variants on the exposure was obtained from the Neale summary statistics using the full UKBB sample. Instruments for each trait were selected as described above: that is, being both genome-wide significant  to minimize weak instrument bias41. Effects of the genetic estimates on both the exposure and outcome were first standardized (such that the squared effect size represents the explained variance) to allow for seamless comparison across traits and to the raw phenotype correlation. Significance was determined by adjusting for the number of effective tests based on the correlation matrix of phenotypes tested42, resulting in 66 independent tests. The significance threshold was adapted accordingly as P < 0.05/66.To estimate the causal effect of a trait from an index case to a partner  using MR (P < 0.001 (Steiger filter)43, whereby SNPs were removed if the standardized SNP effect on the outcome was stronger than the effect on the exposure based on a one-tailed t test at a significance level of P < 0.001. SNP effects were standardized before calculating MR effects.After estimating single-trait causal effects in couples, we used a two-tailed X (P < 0.05/66). As the confounding impact of each P < 0.05/).Using the same process as in the AM analysis involving a single trait, we also sought to investigate causal effects within couples involving two traits (X in an index case (Xi) could causally impact a phenotype Y in the partner (Yp) as illustrated in Fig. Xi is unlikely to have a direct effect on another Yp. Alternatively, Xi might indirectly impact Yp by inducing changes in Xp, which in turn, impacts Yp. For instance, increased BMI in an index case is not expected to directly increase cardiovascular disease risk in their partner but rather, to modify the partner’s risk through first increasing their BMI. To explore this intuition, we dissected the causal effect from Xi to Yp (ω) into three possible (nonindependent) mechanisms. First, Xi could exert a causal effect on Xp, followed by Xp having a causal effect on Yp in the partner alone (γ). Second, the reverse could occur, whereby Yi in the index alone, followed by a causal effect of Yi case on Yp (ρ). Third, there could be other mechanisms, either acting directly or through other unmeasured/considered variables. These three scenarios could also act in some combination. In this way, the ω estimate would capture the paths of γ, ρ and other mechanisms combined.There are several independent paths through which a trait in an index case could exert a causal effect on another trait in the partner. We wanted to explore if one path was more dominant, in general, and if there was evidence for the presence of direct effects . Restricting to Bonferroni-significant trait pairs (with phenotypic correlation of <0.8) from the couple MR, we sought to explore the various paths through which a phenotype γ and ρ representing the various paths from Xi to Yp and contrasted them to the total cross-trait cross-partner effect ω. To quantify γ, the single-trait couple causal effect estimate . We took the coefficient of Yi as the direct causal effect from Yi to Yp  . Such violation would most likely happen if X and Y. The formula assumes that the effect of Y) is expected to be its genetic effect on the primary trait (X) multiplied by their genetic correlation.Violation 1 (Violation 2 (Violation 3 (Y). Hence, its typical size is X), whose genetic component is strongly associated with intelligence, for which additional assortment occurs.Violation 4 X being BMI and E representing ‘going to the gym’. In such case, beyond AM for BMI, people are more likely to choose their partners from the gym ; hence; the environment has an additional direct effect of the partner’s BMI.Violation 5 , MR estimates , differences between phenotypic estimates and MR estimates , sex-specific MR estimates and differences , and model estimates to assess the trend across bins by both age and time spent together for (1) MR estimates (weighted by SE), (2) Pearson correlations and (3) Spearman correlations . Note that n_pairs refers to the number of couples who each have data for the specified phenotype to compute couple correlations , n_exposure refers to the number of participants in the Neale models (sample size summed across men and women models) and n_outcome refers to the number of participants in the models estimating the effect of SNPs on the outcome of interest. Specifically, the model estimated the association between each genetic instrument measured in the index individual with the phenotype measured in the partner using the UKBB partner data set described. As these models were performed in males and females separately and do not require phenotype information for each partner, the result n_outcome is larger than n_pairs. All Supplementary Table 2C statistics were summed (C-sum).Summary of the identified potential confounders. Of the 40 traits showing phenotypic correlations significantly larger than MR estimates, we were able to identify at least one potential confounder for all but one trait. Potential confounders were pruned to only those that had correlation with any other potential confounder .Summary of the 1,088 significant two-trait MR associations. Results are pruned such that pair A–B and C–D and if maxcorr,corr × corr) > 0.8, then one pair is dropped, prioritized by lower Supplementary Table 5P values come from a normal linear regression of this model of the form V ≈ ω + 0.Summary of the linear regression results comparing different mediated and total effects between traits across partners.


To estimate variance components and predict random effects of the model efficiently with limited computational resources has always been of primary concern, especially when it involves increasing the genotype data scale in the current genomic era. Here, we thoroughly reviewed the development history of statistical algorithms used in genetic evaluation and theoretically compared their computational complexity and applicability for different data scenarios. Most importantly, we presented a computationally efficient, functionally enriched, multi-platform and user-friendly software package named ‘HIBLUP’ to address the challenges that are faced currently using big genomic data. Powered by advanced algorithms, elaborate design and efficient programming, HIBLUP computed fastest while using the lowest memory in analyses, and the greater the number of individuals that are genotyped, the greater the computational benefits from HIBLUP. We also demonstrated that HIBLUP is the only tool which can accomplish the analyses for a UK Biobank-scale dataset within 1 h using the proposed efficient ‘HE + PCG’ strategy. It is foreseeable that HIBLUP will facilitate genetic research for human, plants and animals. The HIBLUP software and user manual can be accessed freely at   As the most direct and effective measure to improve the individual performance on agricultural traits and to maximize economic benefits for breeding, genetic evaluation has experienced several major revolutions during the last half-century. Before the theory of genetics was proposed, breeders simply selected outstanding candidates based on their empirical experience by observing phenotypes. However, the phenotypic records of traits were the results of the combined influence of genetic and environmental factors; therefore, genetic progress for breeding was extremely slow due to the unquantified environmental contributions to phenotypic observations. In the mid-20th century, the BLUP (best linear unbiased prediction) model , which ehttps://queries.uscdcb.com/Genotype/cur_freq.html). Using these large-scale datasets becomes more problematic for genetic evaluation, especially when the objective is to estimate variance components. This is because the algorithm implemented in most software tools [e.g. DMU variance components of traits are known, HIBLUP can obtain all the random effects quickly, which include breeding values, without the need to compute the inverse of any big matrices directly. Moreover, we compared it with other software tools or packages that are used widely regarding the computational efficiency and memory cost to demonstrate its huge application potential in genetic evaluation using big genomic data.We consider the single trait linear mixed modelwhere The corresponding full rank MME for model  can be eThis can be simplified to the form To obtain the solution for Equation , the firet al.  ,27 was d Solver)  and a su Solver) ,30. YAMSHowever, calculating the inverse of the genomic relationship matrix to construct a coefficient matrix is becoming computationally challenging due to the increasing number of genotyped individuals. The cost of dense matrix operations with GRM using YAMS is quadratic for memory and cubic for operations, which limits the computations to ∼50 000 animals. Subsequently, the Algorithm for Proven and Young (APY) strategy was presented ,31, whicbeginner . On the beginner .Although the optimizations of the MME-based algorithm have been in progress for several decades, the inverse computations of relationship matrices and of the coefficient matrix required to estimate variance components still face big challenges with the continuously growing size of genotyped individuals in plant and livestock breeding.In our developed software HIBLUP, we implemented another version of AIREML that could be more competent in breeding evaluation on big genomic data. By assuming multivariate normality of the phenotypic records mentclass2pt{minimThe corresponding logarithm of the restricted likelihood, which is different from Equation , can be where The iterative process for V-based AIREML is the same as Equation , but theand the first derivatives of the log likelihood function with respect to each variance component areFrom the above descriptions, the most time-consuming part for V-based AIREML is to calculate the inverse of the Equation , sparsifEquation  could beIn addition to REML analysis, a simple and approximate algorithm named HE (Haseman–Elston) regression, which estimates heritability directly by regressing pairwise similarities of phenotypic observations on pairwise similarities of genotypes, is an alternative . We havewhere mentclasspt{minimamentclasspt{minimaIt should be noted that previous studies indicated that the stability and power of HE regression varied with different genetic architectures of traits . In HIBLIn practical breeding evaluations, estimating variance components does not need to run frequently. Estimation is generally updated quarterly or annually, and the most common computation is to solve the MME to obtain the estimated breeding values (EBVs) for all individuals with prior calculated variance components. For the MME-based algorithm, all random effects including EBVs can be obtained easily from Equation , and theThe matrix entclass1pt{minimaFor the V-based algorithm, EBVs can be obtained by the following equation:The component Computing The Note that computing the inverse of ). All the parallelizable loops (e.g. matrix filling) have been accelerated by OpenMP technology; some basic statistics, for example, genotype counting and allele frequency calculations, are accomplished by bit operation and a population count algorithm. We also designed and optimized the whole computational process to avoid repeat matrix operations across different functions. Here in HIBLUP, a look-up table (LUT) strategy was implemented for genotype numeric coding, and the accessed binary byte containing four individuals will be converted into an index to extract values from a prior designed array of 256 combinations . This strategy virtually digitalizes four individuals at a time by the trivial hash searching, rather than looping individuals one by one, and this improves efficiency significantly. We have developed a new function to compute a GRM in a very efficient manner with a very low cost of memory using block-wise operations.HIBLUP was developed in C++, and dense and sparse matrix implementations are conducted by Armadillo ,41. The In HIBLUP, we only store the lower triangle elements of a relationship matrix in a binary format file to save space in the local disk. To load the binary files of a relationship matrix and a Plink format genotype into memory efficiently, we use the memory-mapping technology with an integration of OpenMP, which can realize the fast access of any of the desired elements from a local disk rather than loading them all at one time with an expensive memory cost. We flexibly employ a mixture of float and double precision for matrix storage and operations in programming to reduce the memory usage for analysis.In addition to implementing general genetic evaluation, including estimation of variance components, solving MMEs to obtain individual breeding values, HIBLUP has been enriched with many functionalities that are involved in genomic breeding. For example, these include statistical significance tests for fixed effects and covariates, fitting interactions among multiple effects , the construction of different relationship matrices, the derivation of inbreeding coefficients by using either or both pedigree and genotypic information, the calculation of genotype and allele frequency, the computation of single nucleotide polymorphism (SNP) effects, principal components analysis (PCA), genomic mating, and so on. We will continue to update HIBLUP with more features based on the users’ feedback, which should allow HIBLUP to always be fresh for general users and academic researchers.), which ensures that HIBLUP has wider application scenarios.HIBLUP is a command-line program that relies on text inputs to perform the analysis, and the names of parameters and options in HIBLUP are quite consistent with those widely used in software tools (e.g. Plink and GCTA) so as to satisfy the programming habits of users. Users can achieve simple linear models, linear mixed models, pedigree-based BLUPs, GBLUPs or SSGBLUPs, and specify fixed or environmental random effects with just a few commands. Moreover, we have released multiple versions of executable files for the platform of Linux, MacOS, Windows and particularly Kunpeng . Finally, a historical population of 10 000 individuals with detailed pedigree and genotypic information was generated for the downstream evaluation. Although the simulation scheme was not fully consistent with practical breeding, the simulated data structure was sufficient to evaluate the computational efficiency of the algorithm implemented in different software tools. We only simulated 10 000 individuals because the publicly available version of BLUPF90 used for computational comparison has a limitation for non-commercial analysis. Since GCTA only supports two traits at a time for a multiple traits model, two correlated traits that were controlled by 600 randomly selected SNPs with effect size normally distributed at zero mean and a variance of one were simulated at a heritability of 0.1 and 0.3, respectively. The genetic correlation was set to 0.3. Additionally, two environmental random effects and two fixed effects were added to the final phenotypic records for each trait. The computational comparisons were tested on a Linux server, the detailed system information of which had been described in the notes of each table, and the time and memory costs were recorded by running the systematic function ‘https://mran.microsoft.com/) to achieve parallel computing. When evaluating all the software tools or packages, we first computed the relationship matrix or its inverse, then used it directly for the downstream estimation of variance components and derivation of breeding values. When fitting a single trait model, two fixed effects and two environmental random effects were added in the analysis. Because parts of software tools (e.g. GCTA and LDAK) have no corresponding options to process environmental random effects, we prepared the relationship files manually in the required format by using HIBLUP. For a multiple traits model, we did not add any fixed effects and environmental random effects in the analyses because GCTA had no corresponding function; only one additive genetic random effect was fitted in the model for estimating genetic correlations. For BLUPF90, since it has no relevant function for fitting the GBLUP model, we ran the SSGBLUP model instead; the option ‘use_yams’ for YAMS was opened for a fast computing block dense matrix, but the APY strategy was turned off to ensure the same results from different software tools. For ASReml, we ran ASREML-SA instead of ASReml-R for parallel computing, and its required inverse of the relationship matrix was constructed by the R package ASRgenomics on the platform of MRO.We first compared the computational efficiency for estimation of variance components for the most widely used parallelizable software tools or packages in the domain of genomic breeding evaluation by running the GBLUP model, which only requires genotypic information for prediction and is considered to be the most computationally expensive model in genetic evaluation. The software tools of choice were BLUPF90 , ASReml G−1’ in the table represents those software tools that were compiled into the MME-based strategy, and the others implemented the V-based strategy. For GRM construction, HIBLUP computed the fastest, and it was accomplished within 15 s by using 32 threads for a population at a scale of 10 000 individuals and 100 000 SNPs. Because HIBLUP can also output the inverse of GRM directly, we also demonstrated the efficiency of HIBLUP for calculating the inverse matrix, it was amazing that HIBLUP only spent 3 s to compute the inverse of a matrix with a dimension of 10 000 × 10 000 while the memory cost remained unchanged . Table Finally, we compared the efficiency of solving an MME to obtain EBVs using the prior estimated variance components. Note that the default method for a linear solver in BLUPF90 was PCG, but it was Cholesky or LU decomposition in HIBLUP. From Table Although the observed computational benefits from HIBLUP over other software tools had been outstanding for the one or two traits models with only 10 000 individuals, we further simulated a UK Biobank scale dataset consisting of 500 000 individuals and 1 million markers, then used it to evaluate the computational efficiency of HIBLUP and GCTA  or there is a very large pedigree in which only a very small proportion of individuals are genotyped, the software that implements the MME-based algorithm should be the first choice to handle this kind of data because the inverse of the relationship matrix is pretty sparse, and it can be obtained extremely quickly. On the contrary, the relationship matrix required for the V-based algorithm is generally dense in this situation, and factorization and inversion of a dense matrix could be much more time and memory expensive than on a very sparse matrix. However, on the one hand, if the pedigree-based relationship between the predicted generation and their ancestors beyond 3–5 generations has become very weak, the prediction accuracy of the predicted generation would not be improved much when the depth of pedigree records exceeds five generations ,46, and Nowadays, general genetic evaluation only accounts for the additive genetic effect in a model because the additive genetic effect is the only one considered to be heritable across generations. However, non-additive genetic effects may have an important contribution to the total genetic variation of complex traits, especially for a hybrid or crossbred population. Published papers have indicated that the model that includes additive and non-additive genetic effects can be beneficial for both genomic prediction  and genoWe conclude that the V-based algorithm is more computationally advantageous compared with the MME-based algorithm for genomic breeding; the highly efficient ‘HE + PCG’ strategy implemented in HIBLUP can avoid computing the inverse of any matrix, and no doubt it should be the most appropriate solution for genetic evaluation using large genomic data. However, the real problem is that all these kinds of algorithms require the construction of a relationship matrix or its inverse matrix. However, with the rapidly increasing number of individuals that are genotyped, the size of the relationship matrix exhibits square growth. Leaving aside the time used in computing, the memory needed for storing such a large matrix may be up to several terabytes. To address this issue, we could make a dense matrix more sparse manually; however, this may generate biased estimation of variance components for an MME-based algorithm  or only In conclusion, to improve the efficiency of genetic evaluation in the genomic era, we presented a computationally efficient, functionally enriched and user-friendly software called ‘HIBLUP’. Powered by the new advanced algorithms, the elaborate computational design and the efficient software programming, HIBLUP computed the fastest, required the least memory for analysis and the computational benefits from HIBLUP over other software tend to be greater with an increasing number of genotyped individuals. Owing to the user-friendly software design, abundant functional modules, comprehensive logging information and clear reminder of events, HIBLUP is simple to start, easy to learn and flexible to use.https://www.hiblup.com.The HIBLUP software and user manual can be accessed freely at https://CRAN.R-project.org/package=simer; ASRgenomics, https://asreml.kb.vsni.co.uk/asrgenomics-download-success; ASReml, https://vsni.co.uk/software/asreml; BLUPF90, http://nce.ads.uga.edu/html/projects/programs/; GCTA, https://yanglab.westlake.edu.cn/software/gcta; LDAK, https://dougspeed.com/ldak/; MTG2, https://sites.google.com/site/honglee0707/mtg2; and Sommer, https://CRAN.R-project.org/package=sommer.The software packages and tools used were Simer, gkad074_Supplemental_FilesClick here for additional data file.


Introduction: Genome-wide association studies (GWAS) have identified genetic markers for cattle production and reproduction traits. Several publications have reported Single Nucleotide Polymorphisms (SNPs) for carcass-related traits in cattle, but these studies were rarely conducted in pasture-finished beef cattle. Hawai’i, however, has a diverse climate, and 100% of its beef cattle are pasture-fed.Methods: Blood samples were collected from 400 cattle raised in Hawai’i islands at the commercial harvest facility. Genomic DNA was isolated, and 352 high-quality samples were genotyped using the Neogen GGP Bovine 100 K BeadChip. SNPs that did not meet the quality control standards were removed using PLINK 1.9, and 85 k high-quality SNPs from 351 cattle were used for association mapping with carcass weight using GAPIT (Version 3.0) in R 4.2. Four models were used for the GWAS analysis: General Linear Model (GLM), the Mixed Linear Model (MLM), the Fixed and Random Model Circulating Probability Unification (FarmCPU), the Bayesian-Information and Linkage-Disequilibrium Iteratively Nested Keyway (BLINK).Results and Discussion: Our results indicated that the two multi-locus models, FarmCPU and BLINK, outperformed single-locus models, GLM and MLM, in beef herds in this study. Specifically, five significant SNPs were identified using FarmCPU, while BLINK and GLM each identified the other three. Also, three of these eleven SNPs  were shared by multiple models. The significant SNPs were mapped to genes such as EIF5, RGS20, TCEA1, LYPLA1, and MRPL15, which were previously reported to be associated with carcass-related traits, growth, and feed intake in several tropical cattle breeds. This confirms that the genes identified in this study could be candidate genes for carcass weight in pasture-fed beef cattle and can be selected for further breeding programs to improve the carcass yield and productivity of pasture-finished beef cattle in Hawai’i and beyond. In Hawai’i, there is a considerable amount of land classified as pasture, much of which is suitable grazing land . Year-roCarcass weight is a critical factor that affects beef cattle production and its economic returns S.-H. . This trGenetic interventions hold the potential to significantly boost net carcass production and expand the local supply without additional retention of cattle heads for finishing. With the development of genetic testing technologies and the reduction in the cost of genotyping, significant advances in research have been made over the past two decades to improve the breeding and genetics of domestic cattle . AffordaOne of the main challenges for the GWAS analysis is managing false positives and false negatives that may occur due to population structure and familial relationships. To address this issue, mixed linear models (MLMs) are commonly used, incorporating covariates for structure and kinship to control for false positives. LD is the non-random association of SNPs markers at different chromosome loci and is mainly determined by the physical distance between the markers, which can influence false positive and false negative rates. Several factors, such as population stratification, migration, recombination, mutation, and selection, affect the pattern of LD in a population . In thisTherefore, this study aims to test several statistical models and determine the most appropriate model for association mapping in Hawai’i beef cattle by using carcass weight as a phenotype. The main objective of this paper is to identify SNP markers and candidate genes related to carcass weight for pasture-finished beef cattle. The goal of this research is to advance the genetic enhancement of beef cattle by incorporating the identified candidate genes in breeding programs in Hawai’i.p-value less than 10–6, and missingness per individual greater than 10%. SNPs on mitochondrial DNA and sex chromosomes were removed using PLINK 1.9. Therefore, 85 K SNPs in 351 cattle after the quality control were used in further analysis to identify the genetic markers associated with carcass weight in Hawai’i beef cattle herds. All animal experiments were conducted in accordance with NIH guidelines for housing and care of laboratory animals and under the University of Hawaii (UH) regulations UH IACUC Policy 18.0 after review and approval by the UH Animal Welfare and Biosafety Programs Committee (Assurance number A3423-01).Samples were collected from 400 beef cattle at the commercial harvest facility in Kapolei, on the Island of Oahu. The sampling was random and included representatives from the major eleven ranches on four Islands in Hawaii: the Island of Hawaii (Big Island), Maui, Oahu, and Kauai. Both male and female cattle were included, and the age of the cattle ranged from younger than 30 months to older than 30 months to ensure that the samples were truly representative of the Hawai’i cattle population. The original source farms for the samples are mapped in the base map of Hawaii. However, to maintain the confidentiality of the commercial farms, we assigned them a nickname with Alphabet A to K, such as Hawaii Ranch A (HWRA) and Hawaii Ranch B (HWRB), as shown in post hoc Fisher’s least significant difference (LSD) test using the Agricolae package (https://www.R-project.org/). Statistical significance was evaluated using an alpha value of 0.05, and any relationships falling below this value were considered significant. The log-transformed phenotypic data were eventually used in the GWAS analysis. GAPIT (version 3.0) R package (https://zzlab.net/GAPIT/gapit_help_document.pdf) that includes fundamental codes and pipelines with a brief explanation of the models and algorithms used for association mapping. The pipeline was followed in our study, and the LD decay plot was displayed over distance from the LD results from TASSEL 5 using R software. To check our population stratification, the genomic inflation factor, Lambda (λgc), was estimated, which is determined by comparing the median of the chi-squared test statistics obtained from a GWAS to the anticipated median of the chi-squared distribution. The median value of a chi-squared distribution with one degree of freedom is 0.4549364. The approach used to calculate lambda can differ based on the association analysis output, such as z-scores, chi-square statistics, or p-values  and computed λgc using the qchisq function in R Studio 4.2. To correct for population structure, the first PC was fitted in the model, and the kinship matrix was added to account for the confounding effect of ancestry. Marker-trait associations were considered significant if they met an exploratory threshold of p < 10–6 (-log10p > 6), which were displayed in Manhattan plots. The validity of associations was verified using the quantile-quantile (QQ) plot to distinguish between true and spurious results, such as false positives and false negatives , and any additional SNPs were listed in To test for normality, the Shapiro test was performed on the phenotypic data, specifically the carcass weight. Since the original data are not normally distributed, a log transformation was performed to normalize it. The normalized data on carcass weight were then subjected to a one-way analysis of variance (ANOVA), followed by  package  in R 4.2 package  was used package , ii) Mix package , iii) Fi package , and iv) package . Carcassp-values . In thisegatives . AdditiobosTau9 or ARS-UCD 1.2 genome assembly) (https://genome.ucsc.edu/), NCBI (https://www.ncbi.nlm.nih.gov/), and Ensembl (https://uswest.ensembl.org/index.html), were used to annotate the significant SNPs identified for carcass weight in Hawai’i cattle population. The function of the candidate genes, their homologs, and their roles in carcass-related traits and other mammals were explored through GeneCards (http://www.genecards.org/) and UniProt/Swiss-Prot browser (http://uniprot.org).The result of linkage disequilibrium decay (LD) was used as a sliding window to find the genes within a certain distance from the identified SNP markers using the latest reference genome assembly for cows (ssembly) . CandidaN = 351 cattle heads) from eleven diversified farms located across the Islands of Hawaii (Big Island), Maui, Oahu, and Kauai , indicating a deviation from a normal distribution. The results of the raw data revealed a right-skewed distribution with a few extreme values (p > 0.05), with the majority of the data concentrated around the central value (p < 0.05) in carcass yield among the islands. The pair-wise multiple comparisons mean tests revealed that cattle from Maui Island had significantly lower carcass weight (p < 0.05) than those from other neighboring islands  was used to evaluate the LD (linkage disequilibrium) estimate. When LD reaches an r2 value below 0.2, it is typically expected to decay by half  to investigate the genetic structure of 351 Hawai’i cattle, utilizing quality-controlled single nucleotide polymorphism (SNP) data. The analysis revealed that there were no distinct genetic clusters among the sampled accessions, as shown in  by half . In the y 100 kb . Thus, tp-values, with λgc values close to 1, indicating effective control of spurious results and a high likelihood of true associations. Among the four models used in the association study, three models  identified significant SNPs associated with carcass weight. Five significant SNPs were identified with the FarmCPU model while the other three significant SNPs were identified with the model BLINK (p < 10−6)  resulted in the identification of a greater number of significant SNPs. Specifically, 58 significant SNPs were identified after FDR adjustment, and these are listed in In this study, four different models were used to compare their strengths in controlling false positives and false negatives in the population. Among them, two multi-locus models, BLINK and FarmCPU, coincided with the expected straight line diagonally with a sharp deviation at the tail, indicating true association by controlling both false positives and false negative markers. The genomic inflation factors (λgc) were calculated for four models to check our population stratification, where GLM had a λgc of 1.55, MLM had a λgc of 1.002, FarmCPU had a λgc of 1.03, and BLINK had a λgc of 1.11 . The qua10−6) , 6. Two le herds . Additios weight , 6. FalsZMAT3, CERS6, PLEKHA5, MYCT1, RGS20, TCEA1, LYPLA1, MRPL15, EIF5, CKB, and MARK3) were identified to be associated with carcass weight in Hawai’i cattle. Also, eight of these genes  overlapped with significant SNPs identified by at least two models   have been previously identified in several studies to be associated with carcass traits and growth traits in cattle . The specific roles of these genes in cattle have not been defined yet and may serve as supporting and maintenance functions.All the significant SNPs identified in our population were explored for their biological function annotation. Genes within 100 kb upstream and downstream relative to the identified SNPs were scanned, and eleven genes (and GLM) . Detailsn cattle . ZMAT3 hs cattle , and CKBy cattle . PLEKHA5Out of the four models used in this study, BLINK, FarmCPU, and GLM performed well in predicting significant SNP markers for carcass weight, while MLM failed to identify the markers with the same trait. The GLM commonly produced false positives and the MLM commonly produced false negatives. These results were consistent with other published results . Based oIn this study, FarmCPU identified five significant markers, which are more than BLINK identified; two of those significant SNP markers are common in both models. Researchers are increasingly using multi-locus models in association mapping, more exclusively in plants and some in animal studies. In a similar study in plants, researchers compared several qualitative traits in soybean and maize flowers using eight popular models where FarmCPU performed better for most of the traits than other models, including GLM and MLM . FarmCPURGS20, TCEA1, LYPLA1, and MRPL15 on BTA14 have been associated with the back fat thickness (BFT) and Intra Muscular Fat (IMF) in a composite beef cattle breed  has been widely explored for quantitative trait loci (QTL) and genes related to feed intake, weight gain, and carcass traits in dairy and beef cattle . The genle breed . RGS20 he breeds . TCEA1 ae breeds . Furthers breeds  and avers breeds . EIF5 gee cattle . Express Indicus . Candidand sheep . FurtherCERS6 and MYCT1 genes have an association with carcass-related traits in other animals  identified in our study were reported in dairy cattle  are mostly related to muscles, subcutaneous fat, and obesity in humans, mice, sheep, and pig’s meat, placing them in the list of possible candidate genes for carcass-related traits in beef cattle. CKB (Creatine Kinase B) is conserved in 315 mammals, including humans, mice, monkeys, and cattle. A previous study revealed that the CKB gene located in BTA 21 governs the fertility of cattle  regarding their association with carcass-related traits in beef cattle, and further research on their functional validations is required to confirm whether these genes are indeed associated with carcass weight and meat quality traits in beef cattle.Previous work identified  animals  but not y cattle . Very liin sheep . Anotherted diet . CERS6 e obesity . MYCT1 ggyu pigs . These gf cattle . The CKBterility . MARK3 iand mice . The pron cattle . CKB andn cattle . TherefoEIF5 gene on chromosome 21 and four other genes in the BTA14 region  were found to be associated with carcass weight in Hawai’i beef cattle, and these results align with the previous findings showing their correlation with carcass weight and related traits in other cattle breeds. Future work incorporating selection pressures using these genes  will facilitate genetic improvement in Hawaiian beef cattle, enhancing productivity while utilizing limited resources without harming the delicate ecosystem of the island.Multi-locus models such as FarmCPU and BLINK were found to be superior to single-locus models (GLM and MLM) in identifying SNP markers and minimizing false positives. Two SNP markers were identified using both multi-locus models. Three other markers were identified using GLM and FarmCPU models, strengthening the correlation of these SNPs with carcass weight in beef cattle. The