Observational studies suggest that inflammatory markers may increase the risk of idiopathic sudden sensorineural hearing loss (ISSHL). However, the causal relationship between the two has not been established. We sought to assess the possible causal effect between several genetically predicted inflammatory markers and ISSHL by Mendelian random (MR) analysis.n = 196,592). Effect estimates were assessed using inverse variance weighting (IVW) as the primary method. Sensitivity analyses were performed using weighted median, MR-Egger, and MR-PRESSO to evaluate heterogeneity and pleiotropy.We extracted single nucleotide polymorphisms (SNPs) associated with C-reactive protein (CRP), Tumor necrosis factor-α (TNF-α), and fibrinogen from abstract data from the European Individual Large genome-wide association studies (GWAS). Genetic data for ISSHL were obtained from the FinnGen study (P = 0.03). In contrast, genetic TNF-α and fibrinogen were not risked factors for ISSHL . All the above results were consistent after validation by different Mendelian randomization methods and sensitivity analyses.In the random-effects IVW approach, there was a significant causal relationship between genetic susceptibility to CRP levels and ISSHL (OR = 1.23, 95% CI = 1.02–1.49, This Mendelian randomization study provides causal evidence that CRP is a risk factor for ISSHL, while TNF-α and fibrinogen do not increase the risk for ISSHL Introduction. Idiopathic sudden sensorineural hearing loss (ISSHL) is a typical otolaryngological emergency of unknown etiology, defined as a sensorineural hearing loss of at least 30 dB at least three consecutive frequencies for at least 72 h, which may be accompanied by tinnitus, a sensation of ear congestion and vertigo . AccordiPrevious studies have shown that inflammatory factors may play a crucial role in developing ISSHL by increasing the risk of microvascular injury and ischemia during these processes . PreviouMendelian randomization (MR) is an emerging genetic epidemiological approach based on the principle of using genetic variation as an instrumental variable (IV) for risk factors, thereby fully elucidating the causal effect of exposure on outcome . Since gWe investigated the causal relationship between inflammatory markers and ISSHL using a two-sample MR design. A convincing MR design should be based on three key assumptions: (1) genetic variation is directly and strongly associated with exposure (inflammatory markers); (2) genetic variation is not associated with potential confounders, and (3) genetic variation affects outcome (ISSHL) only through exposure and not through other pathways . Figure The IV for CRP was derived from the most extensive available meta-analysis of European individual genome-wide association studies (GWAS). The cohort study drew on 88 previous pooled statistics comprising 204,402 individuals . Ligtharhttps://gwas.mrcieu.ac.uk/). This study's most recently published GWAS included 196,592 participants .Genetic information related to ISSHL was obtained from publicly available GWAS abstract summary data, which are accessible through the GWAS catalog (p < 5 × 10−8) were chosen as instrumental variables to achieve genome-wide significance. However, given the limited sample size and number of SNPs, we loosened the association threshold with p < 5 × 10−6 when selecting SNPs associated with TNF-α and fibrinogen. To avoid the effects of linkage disequilibrium, r2 < 0.1 at a distance of 10,000 kb (p < 1 × 10−5) were also removed in each analysis. Finally, we calculated the overall F-statistic: F = F > 10 to ensure that each SNP had sufficient strength for the analysis. Summary data on single nucleotide polymorphisms associated with exposure and their association with ISSHL can be found in A criterion for genetic instrument selection was developed to select scientifically valid SNPs. When CRP was used as the exposure, SNPs  on the outcome (genetic variance in susceptibility to ISSHL), with each conflict representing one data point, and analyzed the causal relationship between exposure and ISSHL using an inverse variance-weighted random effects (IVW) method with a  this MR .p < 0.05 for the intercept  as a complement to IVW to identify any bias in evaluating MR hypotheses. MR-Egger analyses allow for pleiotropy for all genetic variants, but the magnitude of pleiotropy  should be separate from the main effect (from genetic variation to exposure) extent. Apart from this, we also applied the MR-Egger intercept test to detect unbalanced horizontal pleiotropy. If pleiotropy was present, then the analysis yielded a ntercept . The weintercept . The MR-ntercept . For thestimates . In the stimates .All MR analyses in this study were performed using R software (version 4.2.1) and the “TwoSampleMR” package (version 0.5.6).P = 0.03). These results remained broadly consistent in the supplementary weighted median analysis . Scatter and forest plots also showed a statistically significant association between genetic susceptibility to CRP levels and ISSHL , and the funnel plot visualization is shown in P = 0.29). This strongly suggests that our analysis is robust.This study investigated the causal effect of CRP levels and ISSHL using MR with two samples. The detailed results can be found in nd ISSHL . There wP = 0.74) and fibrinogen  were not directionally pleiotropic with ISSHL. Cochrane Q-statistics for TNF-α (P = 0.50) and fibrinogen (P = 0.13) also showed no heterogeneity. None of the MR-PRESSO methods identified potentially aberrant SNPs. A leave-one-out analysis showed that a single SNP did not influence the estimates of TNF-α and fibrinogen with ISSHL (Regarding TNF-α and fibrinogen, no evidence supported the hypothesis that they are associated with the ISSHL results of the main IVW methods. Furthermore, the weighted median, MR-PRESSO, and MR-Egger methods in sensitivity analyses support the above statement, as shown in th ISSHL .Based on large-scale GWAS data from the current European population, the two-sample MR study suggests a vital role in a positive association between CRP levels and the risk of ISSHL, while our results also indicate that TNF-α and fibrinogen are not significantly associated with the development of ISSHL. These results remain robust when validated by multiple sensitivity analyses, supporting our conclusions.via the classical pathway, and there is increasing evidence that it is a risk factor for atherosclerosis (Chronic inflammation not only contributes to the development of the disease but also influences its prognosis. As the disease progresses, it increases the likelihood of microvascular damage and ischemia, promoting atherosclerosis . The cocclerosis . TNF-α hclerosis . In addiclerosis . As an iclerosis . The resclerosis . Anotherclerosis . Howeverclerosis . Anotherclerosis . HoweverIt is worth noting that this is the first two-sample MR study to investigate the causal relationship between CRP, TNF-α, fibrinogen, and ISSHL from a genetic perspective. The strength of this study is that the correlation between these three factors and ISSHL risk was analyzed using large-scale GWAS data from European populations, reducing the bias due to population stratification and making our statistical power very strong and convincing. A two-sample MR design was also used to provide causal evidence that CRP is a risk factor for ISSHL, while the association of fibrinogen and TNF-α with ISSHL was not significant, mainly eliminating the impact of limitations of conventional studies.However, some limitations in this study need to be taken into account. First, the participants of GWAS involved in the study were limited to Europe with big data, and it remains highly questionable whether the same results can be extrapolated to other ethnic groups. Secondly, the lack of stratified data such as gender and age in the existing aggregated statistical sets prevents us from conducting a comprehensive and more granular analysis. If more detailed, publicly available stratification data were available in the future, this would allow further specification of the new MR analysis. Finally, the relatively small sample size of the GWAS for TNF-α and fibrinogen may lead to some bias in the results. Therefore, we should be cautious in interpreting the negative results of the effects of TNF-α and fibrinogen on the risk of ISSHL.In conclusion, this MR study provided preliminary genetic evidence for a causal relationship between CRP levels and increased ISSHL risk but no proof of a causal relationship between TNF-α and fibrinogen and ISSHL. These findings may provide new ideas for understanding the relationship between inflammatory markers and ISSHL, and additional studies are urgently needed to validate our findings further.The original contributions presented in the study are included in the article/TZ: study conception and design. MC and ZY: data analyses. TZ and ZX: draft preparation. SZ and HC: prepared figures. RL and ZZ: supervision of the study. All authors reviewed the manuscript. All authors contributed to the article and approved the submitted version.


Bambara groundnut (BG) (Vigna subterranea [L.] Verdc) is an indigenous, resilient, but underutilized leguminous crop that occurs mostly as genetically heterogeneous landraces with limited information on the drought tolerant attributes. This study elucidates the associations between sequencing-based diversity array technology (DArTseq) and phenotypic character as well as differing indices related to drought tolerance in one hundred accessions of Bambara groundnut.The field experiments were conducted at IITA research stations in Kano and Ibadan between 2016 and 2018 planting seasons. The experiments were arranged in randomised complete block design with three replications, under the different water regimes. The phenotypic traits evaluated was further to construct the dendrogram. Genome-wide association mapping was conducted based on 5927 DArTs loci with < 20% missing data.The genome wide association study predicted drought tolerance in Bambara accessions for geometric mean productivity (GMP) and stress tolerance index (STI). TVSu-423 had the highest GMP and STI values , while TVSu-2017 had the lowest at GMP (1.74) and STI (0.01) respectively. The relative water content (%) was significantly higher for accessions; TVSu-266 , TVSu-2 , and TVSu-411  in 2016/2017 and 2017/2018, respectively. The phenotypic characters studied delineated the accessions into two major clusters and five distinct sub-clusters, indicating variations across all the geographical locations. The 5,927 DArTseq genomic markers in association with STI further grouped the 100 accessions into two main clusters. TVSu-1897 from Botswana (Southern Africa) was in the first cluster, while the remaining 99 accessions from Western, Central, and Eastern Africa made up the second cluster. The eight significant Quantitative Trait Loci (QTLs)  identified with Bonferroni threshold was in association with STI, indicative of variations under the drought-stressed condition. The observation of consistent SNPs in the 2016 and 2017 planting seasons, as well as in combination with the 2016 and 2017 planting seasons, led to the designation of these QTLs as significant. The drought selected accessions could form basis for hybridization breeding. The identified quantitative trait loci could be useful in marker-assisted selection in drought molecular breeding programs. Vigna subterranea) belongs to the group of underutilized crops presently being promoted as climate-smart crops   is the commonest approach to understanding the association between the complexities controlling most agronomic traits of interest to their genetic bases. It relies on the association of several representative markers and a huge genetically diverse population of organisms. It finds the linkage disequilibrium between the phenotypic trait and the genetic markers  without The basic DNA-based marker systems used in mapping the most complex traits in different crop species include simple sequence repeat (SSR) and Amplified fragment length polymorphism (AFLP), which are mostly gel-based markers and are limited in their ability to perform rapid analysis on a large number of marker loci. Single Nucleotide Polymorphism (SNP) and the Diversity Arrays Technology (DArT) marker system are the most recent and better mapping tools, and they become more robust when used together . DiversiThe specific objectives of the present study are:(ii) To evaluate the performance of DarTseq method-derived markers in Bambara groundnut,(ii) Map QTLs/for drought tolerance in Bambara groundnut.For the study, 100 accessions of Bambara groundnut seeds from 12 African countries were collected from the International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria. The 100 accessions Table 1 The 100 accessions of Bambara groundnut, plus two as control, were raised in controlled environments at the IITA research field and leaf tissues were sampled for DNA extraction at 4 weeks after planting. Young leaf tissue weighing one gram was harvested, promptly frozen in liquid nitrogen, and then kept at 80°C. Using a typical cetyltrimethylammonium bromide (CTAB), chloroform, and isoamyl alcohol technique with a little modification by the addition of 20% Sodium Dodecyl Sulphate (SDS), genomic DNA was recovered from the frozen leaves . The quawww.diversityarrays.com) for individual genotyping with the HiSeq 2000 (Illumina) next-generation sequencer. After rigorous quality control and filtering with a call rate of 80%, marker reproducibility of 95%, and missing data of 20%, 5,927 SNPs were found to be polymorphic out of a total of 11,821 DarTseq markers generated for further analysis. Phenotypic data were subjected to analysis of variance (ANOVA) using the Statistical Analysis System (SAS 9.0) while the treatment means were all compared using the Duncan Multiple range test at a probability level of 0.05. To ascertain the association significance using the established threshold P<1.68× 10−4  in the performances of the 100 Bambara groundnut accessions with regards to location, year water regime, and their interaction effects for all the studied traits. High and positive correlations were observed for all traits, however, no significant difference was recorded for the year effect on the 50% flowering rate while the second-order interactive effect of water and location on chlorophyll was also not significant. Analysis of variance further indicated significant differences in the chlorophyll content index among the accessions with respect to location, water treatment condition, and all other studied interactions except for water treatment/location combination Table 2.A total number of 11,821 DarTseq markers were discovered and from these, 5,927 SNPs showed polymorphism and were subsequently retained for further analysis after thorough quality control and filtering were ascertained. Quality control was ensured at a consistent call rate ≥ 80%, while marker reproducibility was at ≥ 95%, and missing data ≤ 20%. The most frequently occurring polymorphism type was a cytosine to thymine combination , followed by guanine to adenine , with the least being observed with guanine to thymine at 4% and a proportion of 255 out of the whole 5927 SNPs Table 3.Neighbor-Joining Phylogeny was generated with the DArT markers, and these produced several sub-clusters of related accessions. Clustering was mostly based on agro-climatic areas and a possible similar genetic background. This was evident with an observed delineation into two main clusters, with the first cluster harboring just one accession sampled from South Africa. The remaining 99 accessions were within three sub-clusters, having accessions from West, Central, and East Africa all clustering together. Within the 44 West Africa accessions, there were approximately seven Central Africa accessions clustered within. The uniqueness of these seven accessions was in the fact that they all originated from Cameroon. While 28 selections were from Central Africa, the remaining 27 accessions were from East Africa. Accession TVSu-362 from West Africa was found among the bulk of the Central Africa accessions, while four different accessions from East Africa and one accession from West Africa were observed to have clustered among the 27 Central Africa accessions Figure 3The principal coordinate analysis (PCA) was exploited to measure the observed variations based on the DArTseq markers. The PCA is explained by fifteen components with wide-ranging variations, as the first two principal coordinates explain the largest proportion of the genetic diversity. Of the total variations, 34 (34%) were accounted for by the PC1 while the remaining variations observed by the rest of the components were accounted for at just 2.82%.  on the 102 lines, there was a yield of 5927 SNPs. Of the 5927 SNPs, 4095 sequences could not be definitively linked to any of the referred mung bean genomes, while the remaining 1595 SNPs were evenly scattered across the whole “Mung bean chromosome”. Chromosomes 5 and 7 contained the most SNPs (203 SNPs), followed by Chromosome 8 (199 SNPs), Chromosome 1 (158 SNPs), Chromosome 6 (148 SNPs), Chromosome 11 (134 SNPs), Chromosome 10 (132 SNPs), Chromosome 3 (120 SNPs), Chromosome 2 (119 SNPs), and Chromosome 9 (98 SNPs). Approximately 5% of markers were mapped to scaffolds that are still unrelated to a chromosome, making up a tiny portion of the total. The  threshold was taken as the cutoff point for significant detection of the marker-trait relationship, as shown in the first Quantile-Quantile figure (QQ plot) Figure 6Pisum fulvum  platform. The application of the NGS platform has been described to give in-depth knowledge of an organism’s genome compared to the regular markers  application. A wide range of performances within and across environments for most of the studied drought-related traits observed with high estimates of values as well as significant and positive correlations among the 100 accessions of Bambara groundnut was obtained. For an effective association between the phenotype and genotype traits of interest through the GWAS approach, the population size, trait of interest, marker density, and phenotypic evaluation were all considered to be some of the key factors to variations. The high population size gave allowance for the production of a higher number of markers and, thus, a strong verdict of the power of the QTLs. This study used 100 accessions and this agrees with what was observed on GWAS by m fulvum , as wellm fulvum  amongst To determine the most appropriate stress tolerance criteria, the overall grain yield was subjected to a correlation coefficient between yield in irrigated (Yp) and yield in stress (Ys) using the 11 quantitative indices of stress tolerance. This research identified STI and GMP as significant among the screened indices to be used in screening Bambara groundnut, as a positive response was observed with both indices. In addition to other indices, such as GMP and MP that were collectively interpreted as being suggestive of greater drought stress tolerance, Given that Bambara is extremely cleistogamic, the examined accessions demonstrated significantly greater observed heterozygosity (Ho = 0.5) than what was expected (Ho = 0.01). This high heterogeneity observed among a few of the accessions (up to 20%) may serve as a breeding source for future purposes. Contrary to this, The cluster analysis revealed a distinct pattern of relationships among the 100 accessions included in this study. This was brought about by the mutually exclusive grouping of similar descriptions into the same cluster to authenticate the similar pedigree based on common generated data. The SNPs presented thousands of markers that differentiated the accessions on the basis of geographical origin as it had usually been known with Bambara accessions. The South African accessions stood out distinctively from other accessions from other regions while the West African accessions were found clustering with Central African accessions as earlier evidenced by The statistical method was another key determinant for the success of the GWAS . The MLMGenome-wide association study (GWAS) methodologies applied in this study were able to identify relationships between the SNP markers and stress tolerance index as the phenotypes of interest for the association. The complexity of the trait of interest, known to be controlled by multiple genes, and the critical nature of GWAS facilitated effective and repeated field experiments for different years and different locations. Genome-wide association studies (GWAS) are, thus, valuable tools needed for identifying candidate genes, as well as the genetic loci that are responsible for the variations observed in a targeted quantitative trait.In order to feed the continent’s ever-growing population, underutilized crops, such as Bambara groundnut, need an urgent revolution to increase their productivity by utilizing modern technologies. Marker Trait Association is, therefore, a profound key in locating genomic areas linked to these phenotypic traits of breeding importance and, by doing so, can alleviate most of the production constraints. The genome-wide association study on Bambara groundnut was able to establish a strong meeting point between the phenotypic and genotypic traits. With this, it will make it easier to take more steps toward the future exploitation of the identified quantitative loci for marker-assisted selection, marker-assisted backcrossing, and other genomic and breeding initiatives. Future prospects can be improved by conducting an omics study on the identified drought-tolerant accessions to gain a thorough understanding of the genes that can be up- or down-regulated, providing clues as to potential genes that could be introduced for advantageous plant breeding processes for drought stress tolerance.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.KO, OAO, and MA conceived and designed the experiments; KO and RP performed the statistical analysis; KO wrote the manuscript and prepared the references; KO, OAO, MA, RP, and OJO, revised the manuscript. All authors contributed to the article and approved the submitted version.


Fst values and principal component analysis (PCA). The existence of associations was investigated between the mean genome‐wide heterozygosity of each full‐sib family (year class 2017) and the corresponding inbreeding coefficient or survival to the eyed stage. A moderate correlation (−0.33) was estimated between the mean observed heterozygosity of each full‐sib family and the corresponding inbreeding coefficient, while no linear association was obtained with the survival to the eyed stage. GWAS did not detect loci with major effect for any of the studied traits. However, genomic regions explaining more than 1% of the additive genetic variance for either studied traits were suggested across 14 different chromosomes. Overall, key insights valuable for future selection decisions of Arctic charr have been obtained, suggesting ddRAD as an attractive genotyping platform for obtaining genome‐wide information in a cost‐effective manner.Routine implementation of genomic information for guiding selection decisions is not yet common in the majority of aquaculture species. Reduced representation sequencing approaches offer a cost‐effective solution for obtaining genome‐wide information in species with a limited availability of genomic resources. In the current study, we implemented double‐digest restriction site‐associated DNA sequencing (ddRAD‐seq) on an Arctic charr strain with the longest known history of selection (approximately 40 years) aiming to improve selection decisions. In total, 1730 animals reared at four different farms in Sweden and spanning from year classes 2013–2017 were genotyped using ddRAD‐seq. Approximately 5000 single nucleotide polymorphisms (SNPs) were identified, genetic diversity‐related metrics were estimated, and genome‐wide association studies (GWAS) for body length at different time points and age of sexual maturation were conducted. Low genetic differentiation amongst animals from the different farms was observed based on both the results from pairwise  Thereafter, genome‐wide association studies were performed aiming to identify genomic regions associated with growth  and age of sexual maturation.22.1All experiments of the current study were performed in accordance with the Swedish legislation described in the Animal Welfare Act 2018:1192 (ethics permit: 5.2.18 – 09859/2019).2.2n = 48), Lockne (n = 16) and Timrå (n = 16) originated from the Arctic charr breeding programme . According to information provided by the corresponding fish farm personnel, all tested animals originated from the national breeding programme of Arctic charr which is located at the facilities of Aquaculture Center North (ACN) in Kälarne, Sweden. In addition, no information regarding the year class of the above animals was available. Furthermore, apart from the breeding nucleus site (Kälarne), the other three fish farms use in addition various other strains of Arctic charr, while at the same time no pedigree recordings are being kept. As such, the obtained genotypic data were used to assess whether the sampled animals from Arjeplog (mme Data .n = 70), year class 2013 (n = 243) and year class 2017 (n = 1337). Animals from year classes 2013 and 2017 were PIT‐tagged and therefore had pedigree data available, while the animals from year classes 2014–2016 did not have accompanying pedigree data. In terms of samples from the 2014–2016 year classes, no information was available regarding their exact year class as those fish were communally reared in a single tank. Regarding the 2017 year class, the sampled animals consisted of 44 full‐sib families with family sizes ranging between 27 and 63 animals. Moreover, available recordings in terms of growth, sexual maturation and survival to the eyed stage were available for the aforementioned full‐sib families (n = 1469). In total, fin‐clips for DNA extraction and genotyping were obtained from 1730 animals.In terms of the sampling conducted at the breeding nucleus site, animals from a range of year classes were used. More specifically, we sampled animals from year classes 2014–2016  using a lysis solution (200 μl SSTNE containing 10% SDS and 100 μg proteinase K). Thereafter, 5 μl RNaseA (2 mg/ml) were added to degrade RNA residues with the samples incubated at 37°C for 60 min. Proteins were precipitated by the addition of 0.7 volume of 5 M NaCl. Genomic DNA was precipitated from the isolated supernatant by addition of 0.7 volume of isopropanol and pelleted by centrifugation. Following overnight incubation in 75% ethanol, the DNA pellet was dissolved in c. 30 μl of 5 mM Tris (pH 8.0). DNA content and quality (260 nm/280 nm and 260 nm/230 nm ratios) was primarily determined by spectrometry . Thereafter, DNA concentration was measured using the Qubit dsDNA Broad Range Assay Kit. Finally, the samples were diluted to a concentration of 15 ng/ml using 5 mM Tris (pH 8.0) and stored at 4°C before further processing.2.4In total, 18 ddRAD libraries comprised of 96–98 individuals each were prepared according to Peterson et al. , with mi2.5Q < 30) and missing the expected restriction sites were discarded. The retained reads were aligned to the Salvelinus sp. reference genome assembly [GenBank accession number GCF_002910315] using bowtie2 . In the case where a single ddRAD locus had multiple SNPs, only the first encountered SNP was used for downstream analysis (‐‐write‐single‐snp). SNPs with minor allele frequency (MAF) <0.05, found in less than 30% of the animals in each population and with mean heterozygosity >0.7 across the tested samples, were discarded using the populations module of Stacks v2.5. Finally, SNPs deviating from Hardy–Weinberg equilibrium (HWE) were discarded using the preGSf90 v1.21 software from the BLUPF90 suite  and expected (He) heterozygosity per population were estimated using the Stacks software v2.5  values between all tested populations were estimated using the R package StAMPP v.1.6.1  or inbreeding coefficient.The range of 2.8+ year of age animals assigning them to males, females or immature‐sterile. In total, recordings from 1469 animals were obtained.Growth measurements, including body weight and total length, were taken on three occasions from each individual fish during 2018 and 2019. In particular, measurements were taken in January 2018, September 2018 and September 2019 Data . On all 2.9h2 ranged between 0.27 and 0.33 for the different time point measurements in the case of body length. In the current study, variance components were estimated for sexual maturation using THRGIBBS1F90 ; X is the incidence matrix relating phenotypes with the fixed effects; Z is the incidence matrix relating phenotypes with the random animal effects; u is the vector of random animal effects ~N where G is the genomic relationship matrix  and σe2 is the residual variance.Heritability estimates related to growth have been recently estimated in our previous study  using Gibbs sampling . Heritability was estimated using the following formula:The probit link function was used to connect the observed binary phenotype  with the underlying liability scale. Residual variance on the underlying scale is not identifiable in threshold models  analysis was performed  or maturation status at 2D = I and t = 1, where I the identity matrix and t is the iteration number.Initialization of G.Calculation of Estimation of genomic breeding values (GEBVs).α^=qDZ′G∗u^, where α^ the vector of SNP effects and u^ the vector of GEBVs.Estimation of SNP effects from GEBVs: dii(t+1)=1.125a^isd(a^)‐2, where a^i the estimated SNP effect using the nonlinearA approach .Iteration from step (3) until convergence (10iw refers to the estimated SNP weight amongst iterations.Convergence of SNP weights was tested using the following criterioniα) the additive genetic variance of the tested window of adjacent SNPs, σg2 the total additive genetic variance, iz the genotype of each of the 10 individual SNPs located in the tested window and iα the corresponding SNP effect.Finally, the percentage of explained additive genetic variance was estimated by nonoverlapping windows of 10 adjacent SNPs as follows:33.1In total, 1730 Arctic charr individuals were genotyped using ddRAD. Approximately 5.4 billion 150 bp long paired‐end reads were obtained. Overall, approximately 74% of the obtained reads (~4 billion reads) passed all quality control filtering steps and were retained. Sequencing of 12 samples failed with 1718 animals being retained. The number of reads passing quality filters per sample ranged between ~200 thousand and 11 million with a mean and standard deviation of 2.4 million and 1.2 million reads, respectively Figure .Salvelinus sp. reference genome, while the remaining 1063 were found in unplaced contigs . In total, 5072 SNPs fulfilled the QC criteria  and were kept for downstream analysis. From the identified SNPs, 4009 were located in the 39 assembled chromosomes of the 9×. In to3.2n = 12), while on the other hand, Kälarne was the population with the highest number of animals (n = 277). The mean observed heterozygosity (Ho) for the genotyped Arctic charr samples was 0.34, while the mean expected heterozygosity (He) was 0.37. The Timrå population had the highest levels of Ho (0.39), while the other three populations had a Ho of 0.33–0.37 , phenotypic sex and sexual maturation were available from 1469 Arctic charr individuals. Total length in the three different time point recordings ranged between 120 and 259, 215 and 395 and 305 and 560 mm, respectively. The correlation between the three recordings ranged between 0.45 and 0.74  on the underlying scale was obtained. Out of 1469 animals with available phenotypic recordings , 1320 had corresponding genotypic data derived from ddRAD. As such, WssGBLUP was preferred in order to use all the available information. Due to the low phenotypic correlation between length at the first and last time points, we conducted WssGBLUP only on the last two time points due to their higher relevance with actual harvest sizes. The convergence of the WssGBLUP algorithm was achieved after two iterations using the nonlinearA approach to estimate SNP effects. Genomic regions explaining more than 1% of the additive genetic variance for the tested traits were detected in 14 different chromosomes out of which three  included all three tested traits. The region with the highest percentage of explained additive genetic variance was found in chromosome 21, where all three tested traits explained more than 3% of the additive genetic variance  and rainbow trout (Oncorhynchus mykiss), genome‐wide related resources for Arctic charr are starting to appear only in the last couple of years  limiting our ability to derive conclusions regarding amongst others the genetic diversity status of the breeding nucleus. Furthermore, all the above animals were sampled only from the breeding nucleus. The identified SNPs (~5000) of our study that were detected in a large number of animals spanning through different year classes (2013–2017) provide a valuable resource for all sort of future genomic studies on the selected strain of Arctic charr.In terms of prior genomic studies on the Swedish selectively bred Arctic charr strain, limited research has been conducted so far and as such available genomic resources are scarce. It has to be stressed that the aforementioned strain has the longest selection history amongst all known selected populations of Arctic charr worldwide. Therefore, most interesting opportunities exist for studying critical evolutionary mechanisms like selection signatures along the genome as in other fish species with long history of selection  both PCA and the derived Fst values indicated a minor genetic differentiation amongst the sampled populations confirming our prior information that all sampled animals originated from the Arctic charr breeding programme. Even though no substitute exists for the ideal scenario where the genetic differentiation analysis is based on large sample sizes for all the tested populations, the Fst index is relatively resilient to the effects of sample size  individuals reported Fst values amongst the studied populations that ranged between 0.03 and 0.53 (median = 0.28).Over the years, germplasm from the Arctic charr breeding programme has been disseminated to farms across the country. Nevertheless, since other strains of Arctic charr are also reared in Sweden, it is not always clear whether admixture has taken place with the selectively bred animals. Since tracking individual fish through PIT‐tagging is not usually performed in commercial farms, the usage of genetic markers appears to be the only alternative for deciphering the genetic diversity status of the reared stocks. Samples from three other commercial Arctic charr farms were collected in addition to the sampling that was performed in the breeding nucleus site. Even though, in our analysis we were able to sample only a small number of animals from two of the four sampled farms . Moreover, using pedigree data we were able to investigate for potential associations between heterozygosity estimates and the corresponding pedigree derived inbreeding coefficients. Overall, the range of observed heterozygosity amongst the 44 full‐sib families of the 2017 year class was in accordance with previous reduced representation sequencing studies on fish  was observed in the case of the 2017 year class which at first sight might appear counterintuitive for the case of a closed breeding nucleus. However, a similar phenomenon with similar heterozygosity estimates was observed in selectively bred rainbow trout with a possible explanation being a recent bottleneck (<100 generations ago) reflecting the relatively recent domestication and selection process . Even though the above is to be expected for growth‐related traits, major loci associated with sexual maturation have been previously documented. More specifically, the vgll3 gene has been found to control the age of sexual maturation in anadromous Atlantic salmon males  than the corresponding genotypic information (n = 1320), a WssGBLUP approach (Zhang et al., As the number of available phenotypic recordings for the 2017 year class was higher (5Genome‐wide information was obtained in the current study aiming to investigate the genetic diversity status of selectively bred Arctic charr from different year classes. Even though no signs of inbreeding depression were observed, continuously monitoring the genetic diversity of the selected population using genomic information is expected to benefit the breeding programme. A genome‐wide association approach with WssGBLUP did not unveil loci with major effect for the tested traits. Nevertheless, genomic regions with potential interest for the studied traits were detected. Overall, the routine implementation of reduced representation genotyping by sequencing platforms like ddRAD can benefit the selection decisions by offering a high resolution in terms of individual genetic diversity levels of each breeding candidate and provide insights about the underlying genetic architecture for traits of interest.The authors declare no conflict of interest.Figure S1Click here for additional data file.Data S1Click here for additional data file.Data S2Click here for additional data file.Data S3Click here for additional data file.


Basmati is a speciality segment in the rice genepool characterised by explicit grain quality. For the want of suitable populations, genome-wide association study (GWAS) in Basmati rice has not been attempted.To address this gap, we have performed a GWAS on a panel of 172 elite Basmati multiparent population comprising of potential restorers and maintainers. Phenotypic data was generated for various agronomic and grain quality traits across seven different environments during two consecutive crop seasons. Based on the observed phenotypic variation, three agronomic traits namely, days to fifty per cent flowering, plant height and panicle length, and three grain quality traits namely, kernel length before cooking, length breadth ratio and kernel length after cooking were subjected to GWAS. Genotyped with 80K SNP array, the population was subjected to principal component analysis to stratify the underlying substructure and subjected to the association analysis using Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) model.We identified 32 unique MTAs including 11 robust MTAs for the agronomic traits and 25 unique MTAs including two robust MTAs for the grain quality traits. Six out of 13 robust MTAs were novel. By genome annotation, six candidate genes associated with the robust MTAs were identified. Further analysis of the allelic combinations of the robust MTAs enabled the identification of superior allelic combinations in the population. This information was utilized in selecting 77 elite Basmati rice genotypes from the panel.This is the first ever GWAS study in Basmati rice which could generate valuable information usable for further breeding through marker assisted selection, including enhancing of heterosis. Basmati rice is a prominent subgroup of rice, indigenous to the Indian subcontinent, which is endowed with a unique combination of aroma, grain characteristics and palatability, due to which it is a premium price in both domestic and international markets  is a very popular quantitative genomics tool, which has gained prominence in mapping quantitative traits with the advent of high-density genotyping platforms, like SNP arrays. GWAS has played a pivotal role in identifying many important genes which govern various complex agronomic traits such as flowering time, plant height and panicle length in rice , particuAmong the agronomic traits in rice, grain quality is of paramount importance in defining consumer acceptance and marketability. In premium quality rice such as Basmati, where quality enjoys equal, sometimes even more weightage than grain yield, traits such as kernel length before cooking, kernel length after cooking, and length-breadth ratio are considered major physical quality traits that largely determine consumer preference. Several studies have reported the allelic variations for kernel length before cooking, and the length-breadth ratio in rice through GWAS . EmployiSeveral statistical models such as general linear model (GLM) and mixed linear model (MLM), have been developed for computational analysis in GWAS. Multiple loci models such as multi-locus mixed model (MLMM), Fixed and random model circulating probability unification (FarmCPU), and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) are considered superior over single-locus models such as GLM and MLM , as theyLimited efforts have been made towards improvement of Basmati rice parental lines. A set of 172 elite Basmati rice breeding lines have been developed through systematic intermating within restorers and maintainers. The utility of these elite breeding lines requires the characterisation of accumulated genomic regions associated with target traits. To address this, GWAS was performed on a collection of 172 elite Basmati breeding lines, which were bred for developing diverse parental lines for Basmati hybrid breeding. Mapping was carried out on six agronomic and grain quality-related traits by evaluating them across seven different environments, to explore the allelic status of these traits and to identify breeding lines with superior allelic combinations in the population.22.1Rf3 and Rf4, the panel accommodated 119 putative restorers and 53 putative maintainers. The complete set of genotypes was evaluated at three Basmati growing locations in India during the Kharif season of 2019, viz. ICAR-IARI Research Station, Karnal, Haryana located at 29.50°N, 77.10°E, 243m (KNL19), IARI-CORC, Rakhra, Punjab, located at 30.36°N, 76.98°E, 252m (RKR19), and Basmati Export and Development Foundation, Modipuram, Uttar Pradesh located at 29.04°N, 77.42°E, 230m (MDP19) and at four locations during Kharif 2020 i.e., ICAR-IARI New Delhi located at 28.08°N, 77.12°E, 228.6m (DEL20) in addition to the previous season’s three locations . The seeds were sown in nursery beds and 25-30 days old seedlings were transplanted manually in puddled experimental plots. The experimental design followed was augmented RCB with 6 blocks, in all the locations. Genotypes were planted in 1 m2 plots each with 20 cm spacing between rows and 15 cm between plants. Except for time of sowing, all the environments received similar and recommended agronomic practices for irrigated transplanted rice.An association mapping panel comprising of 172 diverse Basmati breeding lines developed and maintained at the Division of Genetics, ICAR-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India was constituted for this study. These breeding lines were derived from multi-parent crosses involving 15 Basmati rice and 9 non-Basmati rice genotypes as founder parents (2.2The pre-harvest observations included days to 50% flowering (DFF), plant height (PH), and panicle length (PL). DFF was recorded on single plot basis, while for the remaining traits five randomly selected plants per genotype were tagged after flowering and data were recorded at physiological maturity. Data from all the seven environments were used for the statistical analyses. Being Basmati lines, the genotypes were also assessed for grain and cooking quality parameters. For this, harvested grains were left for ageing for up to 3 months, and about 200g per genotype was dehusked using SATAKE™ testing husker  and later milled through SATAKE™ testing mill . The observations were recorded on kernel length before cooking (KLBC) and length-breadth ratio (LBR) was calculated by dividing KLBC by kernel breadth. The milled samples of each entry were then cooked for estimating kernel length after cooking (KLAC). A total of ten whole milled grains were soaked in a test tube containing 10ml of distilled water for 30 minutes and then placed in a water bath containing boiling water for 8 to 10 minutes .2.3We have used the data from the four environments  for the GWAS. The data from individual environments were analyzed separately by adopting a linear mixed model approach. In each environment, block effects were considered random, while the genotype effects were considered fixed. Best linear unbiased estimates (BLUEs) were calculated for the augmented complete block design (ACBD) using PBTools v.1.3  softwareij denotes the phenotypic value of the ith genotype at the jth environment, µ is the mean effect, Genj stands for the fixed effect of unreplicated genotypes, Blocki is representing the fixed effect of the ith block, and Checkj denotes the effect of the checks that within each block, and the factor eij is representing the error component.Where YBest linear unbiased estimates (BLUE) of genotypes were extracted from the mixed model which was used for further downstream analyses.2.4Genomic DNA was isolated from the young leaves at the seedling stage using CTAB method . The qua2.5SNPRelate package was used under R statistical environment with a 100Kb window size and r2 threshold of 0.4 (ad hoc statistic (ΔK) against the natural logarithms of probability data [LnP(K)].For the final analysis, the SNPs from the pseudochromosomes were removed, leaving 60026 SNPs corresponding to the 12 Nipponbare chromosomes. The data were further filtered using TASSEL 5.2.81 software  through d of 0.4 . Resultid of 0.4 . The optd of 0.4  by plott2 values in TASSEL 5.2.81 software. The r2 values of intrachromosomal marker pairs were then filtered out using criteria, p< 0.05 for the LD decay analysis. Marker pairs within 5Kb distances are then clustered together in individual bins. LD decay curve was created by plotting the average r2 values of bins against the distance. The corresponding distance at which the average r2 value dropped to half of its average maximum value was considered the rate of LD decay  and QTL annotation rice online database , coupled with a thorough literature search. Putative candidate genes in the vicinity of MTAs were browsed in the database of Rice Annotation Project Database .The MTAs which were detected in more than one environment were considered for assessing their novelty. Physical positions of such stable MTAs were compared with previously reported quantitative trait loci (QTL) and candidate genes for the concerned trait. The QTLs in the vicinity of stable MTAs were searched using the Gramene QTL database  whilst the highest value was recorded for GPM100 (34.91 cm) in MDP20. The pattern of variation for the grain quality traits, was more or less uniform across all the environments for all three traits  of individual environments revealed significant variability for all the traits among the genotypes, except for PL Table 1.3.22 values of bins were plotted against the physical distance between the markers, resulted in a near flat curve indicating relatively less LD decay in the population. The maximum r2 value (rmax) obtained was ~0.7 within a genomic span of less than 5Kb region and the rmax was reduced to half at a distance of ~1.8 Mb  was detected in four environments and hence regarded as the most stable MTA for PHT. Other stable MTAs for PHT were qPHT1.2, qPHT12.1. For PL, 18 MTAs were detected spanning across eight different chromosomes and six environments, of which 13 were unique. Chromosomes 5, 10, 11 and 12 carried one MTA each, while chromosomes 3 and 6 each carried two unique MTAs. However, there were five MTAs located on chromosome 2. The SNP, AX182222834 (qPL11.1) was detected across four environments and hence was considered the most stable MTA for PL. Besides, two more MTAs, qPL2.4 and qPL6.1 were also identified as stable.Association analysis using the BLINK model, used a threshold estimate Figure 5qKLBC7.1 was identified in more than one location (DEL20 and KNL20). Similarly, nine significant MTAs were found for LBR of which five QTNs were unique in their occurrence across locations. The QTN, qLBR11.1 identified in three environments was recognised as the most stable QTN for LBR. In the case of KLAC, 10 MTAs were detected and all the detected significant QTNs were identified only in individual environments. Among these, one QTN was each found on chromosomes 4, 10 and 12, while, two each were located on chromosomes 2 and 7 and there were three QTNs on chromosome 1.For the grain quality parameters, the MTAs detected are summarized in 3.43.4.1qDFF6.1 hastened the flowering of genotypes by about 15 days as against its alternate allele ‘G’ (105.02 days)  of ectively Table 5.qPHT1.1 displayed an average plant height of 104.36 cm, and the effect of allele ‘G’ was estimated at 121.76 cm, with a difference of 17.4 cm. Similarly, the genotypes with allele ‘T’ of qPHT1.2 showed a height of 105.37 cm, while those with allele ‘C’ registered 122.86 cm. Thus, the allelic contribution of qPHT1.2 was 17.49 cm. The allele ‘T’ of the third MTA, qPHT12.1 registered an average plant height of 87.92 cm and allele ‘C’ showed 101.87 cm , while timely sowing was adopted in Kharif 2020 (first week of June). The effect of sowing and transplanting time on the agronomic performance of aromatic rice, especially in Basmati types is well documented  through iterative procedures built around various regression models. This is particularly important with complex traits governed by a multitude of genomic regions, where the expression can be largely influenced by allelic effects, interactions as well as environments. Further, GWAS uses filters such as population structure and kinship (co-ancestry) to nullify spurious associations. Mapping of MTAs through GWAS also permits the identification of favourable allelic combinations vis-à-vis trait expression. This is critical for a breeding programme aimed to integrate favourable alleles during crop improvement . Such ancumented .The panel showed ample genetic variation, with 52.8% genome-wide divergence. Furthermore, the study is unique in several ways – it is one of the first studies that utilized the 80K SNP array as well as an improved multiparent population composed of elite Basmati breeding lines as an association mapping panel. The population structure analysis revealed around 45% of admixtures, which is expected due to fewer recombination events during population development from the founder parents with one cycle as compared to germplasm which accumulates a large number of recombinations due to long history of evolution. Furthermore, the Basmati lineage in the rice gene pool has a restricted genetic variability, when compared to other cultivated rice lineages, which also can lead to larger haplotypes within the population. This was apparent from the LD decay within the panel which approximately elbowed at 1.8Mb, the value ordinarily larger than the LD decay reported in rice germplasms . BesidesqDFF6.1 was detected as the most reliable QTN for DFF, since its presence was evident in most of the locations and lies within the RFT1 gene (Os06g0157500), which is directly involved in controlling flowering time in rice  that governs flowering time in rice , the green revolution gene , which plays a significant role in panicle development (OsSK1 in the production of shikimate 3-phosphate and thereby influencing panicle development is well illustrated by qPL6.1 and qPL11.1), and therefore, are considered unreported. The allelic combination study indicated that all three MTAs were having a minor but cumulative effect on panicle length, hence selection for the best allelic combination of the three MTAs can reward higher panicle length.Plant height is another important factor in hybrid seed production as optimum plant height combination in the male and female parents can facilitate better pollen flow . The MTAion gene . qPHT1.1D1 locus . The all qPHT1-1 . However hybrids . Out of elopment . The rolqKLBC7.1 was found to co-localize with the QTL for grain length, qGL7.2 – Centre for Advanced Agricultural Science and Technology (CAAST) project (Code: 12-115) on “Genomics Assisted Breeding for Crop Improvement,” of the World Bank and the Indian Council of Agricultural Research, New Delhi.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.


Our results demonstrate substantial confounding bias in standard GWASs for some phenotypes.Effects estimated by genome-wide association studies (GWASs) include effects of alleles in an individual on that individual (direct genetic effects), indirect genetic effects  and bias from confounding. Within-family genetic variation is random, enabling unbiased estimation of direct genetic effects when parents are genotyped. However, parental genotypes are often missing. We introduce a method that imputes missing parental genotypes and estimates direct genetic effects. Our method, implemented in the software package snipar (single-nucleotide imputation of parents), gives more precise estimates of direct genetic effects than existing approaches. Using 39,614 individuals from the UK Biobank with at least one genotyped sibling/parent, we estimate the correlation between direct genetic effects and effects from standard GWASs for nine phenotypes, including educational attainment ( snipar is a software package for imputing missing parental genotypes and estimating direct genetic effects. Application to UK Biobank data shows that effects estimated by standard genome-wide association study designs have confounding bias for some phenotypes. GWASs typically estimate the linear association between a phenotype and a single-nucleotide polymorphism (SNP) by regression of individuals’ phenotypes onto the number of copies of an allele (genotype) that they carry.GWASs have found thousands of associations between genetic variants and human phenotypes3, referred to here as ‘population effects’, as they reflect the genotype–phenotype association in the population, including the causal effects of alleles carried by an individual on that individual, called direct genetic effects; effects of alleles in relative(s) through the environment, called indirect genetic effects (IGEs) or genetic nurture4; and confounding due to population stratification and assortative mating (AM), which lead to correlations between the SNP and other genetic and environmental factors. Although methods such as principal-component (PC) analysis and linear mixed models (LMMs) adjust for population stratification6, residual confounding often persists in GWAS summary statistics8. By modeling the effects of many genome-wide SNPs, LMMs can reduce confounding due to AM9. However, unless all of the heritability is captured, some confounding due to AM will remain. Depending on the regression design , the amount of confounding due to population stratification and AM can differ9.Multiple phenomena contribute to the associations found by GWASs10, and residual stratification in GWASs of height resulted in inflated signals of selection11. These problems and others can be remedied with stratification-free estimates of direct genetic effects.Decomposing the population effects estimated by GWASs into the different components is important for interpreting and applying GWAS results. For example, IGEs and AM can lead to spurious inference of disease causes in Mendelian randomization15. However, parental genotypes are often missing. In the absence of parental genotypes, genetic differences between siblings can be used to estimate direct genetic effects, at the cost of lower power and potential bias due to IGEs from siblings17. Here, we show that by treating parental genotypes as missing data and imputing them based on Mendelian laws, we can perform a unified analysis of different data types, including sibling pairs and parent–offspring pairs. Compared to existing approaches18, our method increases power and identifiability while retaining unbiased estimates of parameters and sampling variance.Offspring genotype varies around the expectation given the genotype of the mother and father due to random segregation of genetic material in the parents during meiosis. Thus, analysis of parent–offspring trios can be used to obtain unbiased estimates of direct genetic effects21, which are typically designed for breeding applications in large, complex pedigrees. In contrast, our method is designed for estimation of direct effects from sets of nuclear families that are approximately unrelated to one another, the kind of data typical in human genetics. We provide the methods for imputing missing parental genotypes in a software package, snipar (single-nucleotide imputation of parents), that also infers identity-by-descent (IBD) segments shared between siblings and performs genome-wide association and PGI analyses.Our imputation approach is similar to methods used in animal breeding to impute ungenotyped members of a pedigreeWe apply our methods to UK Biobank (UKB) data. Among our findings are results showing that, for educational attainment (EA) and cognitive ability, effects estimated from standard GWASs provide inaccurate estimates of direct genetic effects.Yij be the phenotype of sibling j in family i. Thengij is the genotype of sibling j in family i, δ is the direct effect of the SNP and gp(i) and gm(i) are the genotypes of the father and mother in family i. SNPs are assumed to be biallelic with alleles ‘0’ and ‘1’, and genotypes are counts of the allele ‘1’ with frequency f. Sibling genotypes are conditionally independent of environmental effects given parental genotypes. Therefore, estimates of direct effects from fitting model (1) are unbiased12. We refer to αp and αm as ‘nontransmitted coefficients’ (NTCs), as they are the expected coefficients on the alleles not transmitted to the proband in a regression of proband phenotype on proband genotype and nontransmitted alleles4. The NTCs capture IGEs from relatives, in addition to confounding due to population stratification and AM4. The residual ϵij is uncorrelated with gij, gp(i), and gm(i), but ϵi1 and ϵi2 may be correlated. Note that standard GWAS methods that regress proband phenotype onto proband genotype estimate the ‘population effect’, β = δ + (αp + αm)/2, which is the direct effect plus the average NTC, α = (αp + αm)/2.We consider a model for the effect of a SNP on the phenotypes of two siblings.  Let ηs is the IGE from the sibling. Because both proband and sibling genotypes are conditionally independent of environment given parental genotype, estimates of δ and ηs from fitting model (2) are unbiased4. Because a parental allele has a 1/2 chance of being passed onto a sibling, the NTCs include one half of the indirect sibling effect, but this is removed from the coefficients on the parental genotypes in model (2) due to inclusion of the sibling genotype.We also consider a model that adds IGEs from siblings:4 − 1 = 15 complete-missing data patterns  that are unobserved are treated as missing data and imputed based on Mendelian laws. Imputations that are linear functions of observed genotypes do not add information for estimation of the parameters of models 1) and (2). However, there are seven cases out of the 2 and (2).rns Fig. , genotyprns Fig.  and genorns Fig. . When twrns Fig. . We provrns Fig. . In certrns Fig. , althougWe replace unobserved parental genotypes with their imputed values to estimate direct effects and NTCs. We estimate the direct effect and NTCs of each SNP as fixed effects in an LMM, which includes a family-level random effect, thereby accounting for phenotypic correlations between siblings .In Supplementary Note Section Consider a sample of families where the genotype of the proband and its mother have been observed but the father’s genotype is unobserved Fig. . If we igi1,gmi] , then ouδ, αp, and αm. If no imputation is performed, then it is impossible to obtain unbiased estimates of δ without making an additional assumption, such as αp = αm. When using phased data, the effective sample size for estimation of direct genetic effects relative to complete observation of parental genotypes , this corresponds to:Many previous analyses regressed phenotypic differences between siblings onto genotypic differencesδ − ηs.This method yields unbiased estimates of gpar(i). By imputing gpar(i) as the conditional expectation given gi1 and gi2 and the IBD state of the siblings  by regression of phenotype jointly onto proband, sibling and imputed parental genotype. Note that by performing the imputation, we are able to separately estimate δ and ηs, whereas the sibling difference approach can only estimate δ − ηs. This shows how Mendelian imputation enables identification of more complex models than approaches that do not perform imputation.When genotypes are imputed from sibling data Fig. , we havengs Fig. , we can ηs = 0, at the cost of some bias if ηs ≠ 0. Letting r be the correlation of the siblings’ residuals , then the bias in estimates of δ is −[(1 + 2r)/(2 + r)]ηS , with examples in Table δ, ηs, αp, αm), they can contribute to an overall estimate of  when the combination of data types enables identifiability. If genotypes are observed or imputed as outlined above, then a single regression that combines all the data types together gives consistent estimates of the full parameter vector provided that the resulting regression design matrix is not collinear  affects the phenotype of the offspring through the environment), vertical transmission and AM25 and population stratification. We did not detect any bias in estimates of direct effects across the simulated traits  of the EA PGI . Assuming ηs = 0, we obtained more precise estimates of direct effects and average NTCs  were obtained for all phenotypes other than ever-smoked and neuroticism. Estimates of maternal minus paternal NTCs were not statistically significant for any of the phenotypes , although power for this analysis was limited.Statistically significant estimates of direct effects were obtained for all phenotypes; and statistically significant average NTCs . The fraction of variance explained by the direct effect is the square of this ratio, implying that only 25% of the variance explained by the EA PGI is due to its direct effect alone (compared to 49% in Iceland).Across the phenotypes, the direct/(direct + average NTC) ratio was similar to a previous analysis of an EA PGI in Icelandic dataηs = 0.We estimated the direct effects, NTCs and population effects of 1,586,010 SNPs with MAF > 1% on nine phenotypes . Phenotyr , the genome-wide correlation between direct effects and average NTCs. To estimate the correlations, we used a moment-based estimator that adjusts for the known sampling variance-covariance matrix of the estimates  . This mer and r for the phenotypes simulated from genetic data from the UKB ‘White British’ subsample  is the correlation between direct effects and average parental IGEs, which our simulation results confirmed.We estimated 24. We simulated a phenotype affected by vertical transmission for 20 generations of random mating, reaching an approximate equilibrium. For this phenotype, r = 0.953 (s.e. = 0.009).A plausible model for IGEs is vertical transmissionr = 0.883 (s.e. = 0.009). For a phenotype affected by direct effects and a random environmental component , r = 0.949 (s.e. = 0.008) after 20 generations of AM. We also simulated a phenotype affected by direct effects and population stratification, for which r = 0.917 (s.e. = 0.007). These results show that population stratification, AM, and vertical transmission, along with their interactions, can lead to r substantially below 1.When there is population stratification or AM, average NTCs (and therefore population effects) capture effects due to other genetic and environmental factors with which the SNP is correlated due to nonrandom mating, in addition to IGEs from relatives. We simulated 20 generations of AM for the same vertical transmission phenotype model, reaching an approximate equilibrium. For this phenotype, r was not statistically distinguishable from 1  < 1) except for EA  = 0.739, s.e. = 0.086, P = 1.2 × 10−3) and cognitive ability  = 0.490, s.e. = 0.086, P = 1.6 × 10−9)   = −0.588, s.e. = 0.094, P = 3.1 × 10−10, two-sided Z-test for r ≠ 0) and neuroticism  = −0.421, s.e. = 0.190, P = 0.027), and a positive correlation for height  = 0.666, s.e. = 0.270, P = 0.014). For height, the results are similar to simulation results for a phenotype affected by direct effects and AM  = 0.791 (s.e. = 0.066), an increase of 0.053 ; and increased the estimated correlation for cognitive ability to r = 0.568 (s.e. = 0.088), an increase of 0.078 .To investigate whether residual population stratification that persists after adjustment for PCsn to PCs . This in8, which we used to adjust the phenotypes in our primary analysis. To better adjust for recent structure, we adjusted EA and cognitive ability for the top 40 PCs of the IBD relatedness matrix in addition to the 40 common variant PCs used originally  = 0.785 (s.e. = 0.076), an increase of 0.048 ; and increased the estimated correlation for cognitive ability to r = 0.621 (s.e. = 0.058), an increase of 0.131 .PCs based on rare variants or IBD sharing capture recent population structure better than PCs based on common variantsiginally . This in27. Estimated correlations between birth coordinates and direct effects were attenuated toward zero relative to correlations between birth coordinates and average NTCs and population effects. Furthermore, correlations between birth coordinates and average NTCs and population effects tended to line up along a south-east to north-west axis  and direct effects, average NTCs, and population effects . The corβBOLT for an unspecified phenotype and βBOLT:EA to refer to a specific phenotype (in this case EA). This enabled us to estimate, without needing to adjust for sampling correlations, correlations between direct effects on EA and cognitive ability (δEA and δcog) from our primary analysis and population effects on nine phenotypes and north and east birth coordinates from the unrelated sample (βBOLT), which we refer to as r and r. We compared these to correlations between population effects on EA and cognitive ability (βEA and βcog) from our primary analysis and population effects on nine phenotypes and north and east birth coordinates from the unrelated sample (βBOLT), which we refer to as r and r  to the sample used in our primary analysis. We used BOLT-LMM to estimate population effects on the same nine phenotypes as in our primary analysis as well as north and east birth coordinates . We refer − r = 0.319 (s.e. = 0.073), suggesting either shared confounding in population effects on EA and cognitive ability, or shared IGEs that are not highly correlated with direct effects on cognitive ability, or both.Across the phenotypes, population effects were more strongly correlated with population effects on EA and cognitive ability than with direct effects on EA and cognitive ability Fig. , support29, familial imputations31 and methods in animal breeding19. However, our approach focuses on imputing missing parental genotypes in a nuclear family rather than in the large pedigrees typical in animal breeding. We found that our imputation method improves on a recent method developed for imputing missing genotypes in a nuclear family, AlphaFamImpute21, both in terms of bias and R2 between imputed and actual parental genotypes  for EA and cognitive ability. We found evidence that this is in part due to recent structure in the population that is captured by PCs of the IBD relatedness matrix, but not by PCs computed from common variants33 could push the correlation estimate in the negative direction and reduce correlations between direct and population effects. Analysis of simulated phenotypes under ascertainment supports this hypothesis, where strong ascertainment reduced r to −0.264 (s.e. = 0.091) for a phenotype with uncorrelated direct effects and parental IGEs  = 0), which may explain why we observed negative r for cognitive ability and neuroticism, as observations for these phenotypes are ascertained for higher education and lower neuroticism35.Another phenomenon that may contribute is ascertainment: If direct effects and NTCs are not very strongly correlated, but both are also correlated with ascertainment, then collider bias37  and could introduce confounding related biases. Confounding related biases can also lead to spurious inferences in mendelian randomization10 and studies of selection11. If NTCs are substantial and imperfectly correlated with direct genetic effects, then prediction accuracy could be increased by including predictors based on NTCs of parental genotypes in addition to predictors based on proband genotypes.If population effects are not highly correlated with direct effects, then this has implications for genetic prediction methods. For example, for prediction of differences between embryos, only direct effects are relevant, so selecting embryos using population effects would perform poorly compared to using direct effects38.We found evidence that correlations between GWAS summary statistics on many of the nine phenotypes examined here and EA and cognitive ability are inflated by factors other than direct effects. Application of the methods developed here to larger sample sizes will enable us to estimate the relative contribution of direct effects, IGEs, and confounding factors to estimates of genetic correlations4. This implies that the combined influence of IGEs, population stratification, and AM is stronger in the UK than in Iceland. The PGI was constructed from standard GWAS summary statistics, so the average NTCs of the PGI could reflect bias and/or IGEs in the original GWAS summary statistics. Future studies could examine prediction using PGIs constructed from direct effect estimates, which do not have these biases.By analyzing an EA PGI, we observed a lower direct/(direct + average NTC) ratio for EA than was observed in IcelandCollection of genetic data on close relatives is inevitable as sample sizes grow larger. However, samples of close relatives will remain much smaller than samples of distantly related individuals. We see data on unrelated individuals as one possible pattern of missing data in a framework for human genetic analysis that treats the nuclear family as the fundamental unit of analysis rather than the individual. By combining information from different missing data patterns, we will be able to construct a more accurate picture of the role of genetics in human phenotype variation.Given the genotypes of a sibling pair Fig.  and the gp(i)) in family i, and let gi1 and gi2 be the genotypes of the two siblings. We compute i is the IBD state of the two siblings. Because all four alleles are observed in IBD state 0, we have thatBecause parent-of-origin cannot be determined from sibling data, we impute the sum of maternal and paternal genotypes Fig. . Let \dof. Therefore,When we do not observe a parental allele, we impute it using the population frequency of allele 1, When the siblings share one allele IBD, let then the squared correlation between imputed and observed parental genotype is ¾. This is because we observe two parental alleles in IBD2, with probability ¼; three parental alleles in IBD1, with probability 1/2; and four parental alleles in IBD0, with probability ¼; giving an average of 3 observed parental alleles. The squared correlation is higher than based on best linear unbiased imputation, 2/3 , the alleles not shared are known unless both siblings are heterozygous. When both are heterozygous, information from neighboring phased SNPs can be used to resolve the uncertainty Fig. . Howeverni siblings have been observed in family i, on average we observe We generalize the above approach to imputing from genotype observations on three or more siblings  genotypes; θ is the corresponding vector of parameters; μi is the mean in family i, which we model as a mean-zero normally distributed random effect with variance ϵij is the residual for individual j in family i, independent for each individual. This implies that, conditional on X, the phenotypic correlation of siblings is Phenotype observations of siblings are correlated through both shared genetic factors and shared environmental factors. To obtain efficient estimates of SNP effects, the phenotypic correlations between siblings should be modeled. We implemented an LMM in snipar that achieves this by modeling the mean phenotype within each family as a random effect. Let θ depend upon the data type and model being estimated (Table The columns of X and ed Table . The defn) computations  computations.For estimation of the effects of genome-wide SNPs, snipar first infers the variance components st. Bias is introduced into estimates of direct effects when data types with different numbers of observed parental alleles are mixed together. For imputation from sibling pairs, the number of observed parental alleles differs with the IBD state of the siblings, introducing a bias into estimates of δ that is approximately equal to st is small. For relatively homogeneous samples, any such bias is therefore likely to be negligible at the individual SNP level. Further, SNPs with large values of Fst will tend to be filtered out during quality control because they violate Hardy–Weinberg Equilibrium.In the results above and the main text, we have assumed random mating. When population structure is present, this leads to bias in the imputed parental genotypes. We analyze the consequences of this in Supplementary Note Section δ that splits the regression by the number of observed parental alleles, and we prove that this estimator is not biased by population structure. Although this estimator is more robust, it is less precise than the estimator described above, which performs a single regression using all individuals, irrespective of the number of parental alleles observed. However, the alternative estimator for δ is still more precise than the estimator based on genetic differences between siblings, having an effective sample size r =0.In Supplementary Note Section L SNPs for the father in family i:wl is the weight of SNP l, and l. If the father is not genotyped, then the imputed PGI is:L SNPs are in linkage equilibrium, then theoretical results for single SNP analyses carry over.Consider a PGI composed of 41. Because each SNP is imputed individually without conditioning on other SNPs that contribute to the PGI, the imputed PGIs are not exactly the conditional expectations given the observed PGIs. Consider a model for the association between a phenotype and a PGI:PGIij is the PGI of sibling j in family i, and PGIm(i) is the PGI of the mother in family i. We show in the Supplementary Note Section δ, even when AM is present, when the number of SNPs, L, is large. However, a slight bias in estimates of NTCs is introduced. For example, if using parental genotypes imputed from sibling pairs with phased data, the estimate of α would be inflated by a factor of ram is the equilibrium correlation between maternal and paternal PGI. We note that, even with fully observed genotypes, AM implies that α captures confounding due to correlation between the parental PGI and the genetic component of the phenotype that would be uncorrelated with the PGI under random mating, as described previously42.In practice, linkage disequilibrium (LD) between some of the SNPs is expected. However, if many SNPs from across the genome contribute to the PGI, only a small fraction of the pairs of SNPs will have non-negligible correlations due to local LD, and the effect on the imputations and estimates would be negligible. However, for a phenotype with AM, contributing SNPs can become correlated regardless of their physical positions22. We filtered out individuals identified by the UKB as having excess relatives, excess heterozygosity, or sex chromosome aneuploidy. We used the kinship coefficients computed by the UKB to identify individuals with a first-degree relative, where a first-degree relation is defined as a kinship coefficient of 0.177 and above23. We used KING23 with the ‘–related –degree 1’ options to infer the sibling and parent–offspring relations within that set of individuals (Table We used the UKB sample that had been identified by UKB to have predominantly ‘White British’ ancestry22. Phasing was performed using SHAPEIT3 (ref. 43) and the 1000 Genomes Phase 3 dataset44 as a reference panel. This resulted in phased haplotypes for a set of 658,720 autosomal SNPs with an estimated switch error rate of 0.229%22.Haplotypes for the SNPs that were present on both the UKB Axiom and the UK BiLEVE genotyping arrays and that passed quality control were provided by the UKBIn addition, we used SHAPEIT2 with the –duohmm option (with -W 5 parameter) to phase 1.1 million HapMap3 SNPs with MAF > 1% from the imputed genotype data provided by the UKB. The ‘duohmm’ option takes advantage of parent–offspring relations to improve phasing. We merged the haplotypes provided by UKB with the haplotypes for HapMap3 SNPs using QCTOOL, giving haplotypes for 1,652,145 unique SNPs, 1,586,010 of which had MAF > 1%.23 with the –ibdseg option to infer IBD segments between all pairs in the 39,619 individuals from the ‘White British’ subsample of the UKB for which parental genotypes were observed or could be imputed, along with their first-degree relatives, giving a total sample of 44,553. The relatedness between two individuals based on IBD sharing was calculated as (1/2) × P(IBD1) + P(IBD2), where P(IBD1) and P(IBD2) are the fractions of the autosome shared in IBD1 and IBD2 segments respectively. We extracted the eigenvectors with the 40 largest eigenvalues from the resulting relatedness matrix.To compute the PCs of the IBD relatedness matrix, we used KING45. For all phenotypes, we regressed out age, age2, age3, sex and interactions between sex and age, age2, and age3, along with the 40 genetic PCs provided by the UKB. For quantitative phenotypes, we normalized the phenotypes to have variance 1 separately in males and females.We analyzed EA; standing height (Data Field 50); body mass index (Data Field 21001); neuroticism score (Data Field 20127); whether an individual answered that they had ever smoked or not (Data Field 20160), encoded as a binary variable; cognitive ability, derived from a test of ‘fluid intelligence’ (Data Field 20016); high-density lipoprotein cholesterol (Data Field 30760); forced expiratory volume in one second (Data Field 3063); age at first live birth (in women) (Data Field 2754); and north (Data Field 129) and east (Data Field 130) birth coordinates. For EA, we converted the answers to the qualifications question (Data Field 6138) to years of education according to the method used in the most recent GWAS of EATo further investigate the impact of residual population stratification on EA and cognitive ability, we adjusted EA and cognitive ability for birth coordinates and the center where they were assessed (Data Field 54), in addition to the covariates listed above. To do this, we regressed the phenotype onto the covariates listed above and linear and nonlinear functions of north and east birth coordinates, assessment center coded as a categorical variable and interactions between assessment center and north and east birth coordinates and the squares and cubes of north and east birth coordinates. For the nonlinear functions of north and east birth coordinates, we used north coordinate, its square and cube; east coordinate, its square and cube; and all pairwise products between north coordinate and its square and cube, and east coordinate and its square and cube.46 software with a 1 centimorgan (cM) window. The probability of transitioning from one IBD state to another is inferred from the genetic distance between the SNPs. We account for genotyping errors, which requires a parameter γ for the probability of a genotyping error. We smooth the IBD segments inferred by the HMM to remove short segments that are improbable based on their length in cM and whose neighboring segments have the same IBD state. This requires a parameter, m, the minimum allowed length (in cM) of an IBD segment that differs from its adjacent segments.We developed a hidden Markov model (HMM), implemented in snipar, to infer IBD segments shared between siblings  =  gave the highest probability of inferring the true IBD state, 99.65%. We give the proportions of SNPs with inferred IBD states 0, 1 and 2 as a function of the true IBD state in Supplementary Table We optimized the parameters We compared this to IBD segments inferred by KING using the –ibdsegs option, which had an overall probability of inferring the true IBD state of 98.5%. Our method therefore gave around a fourfold reduction in IBD errors compared to KING. Furthermore, we found that the distribution of IBD states inferred by KING diverged substantially from the theoretical expectation near the ends of chromosomes and centromeres, whereas the distribution of IBD states inferred by snipar was close to theoretical expectations from end to end , we imputed missing parental genotypes from phased haplotypes for 1,586,010 SNPs, the union of the genotyping array SNPs and the HapMap3 SNPs with MAF > 1%. We examined the bias in the imputed parental genotypes by performing the imputation for families with both parental genotypes as if one or both parental genotypes were missing. If the imputation is unbiased, then the regression coefficient of the observed parental genotypes onto the imputed parental genotypes should be 1. This is because the covariance between the imputed parental genotypes and the observed parental genotypes should be equal to the variance of the imputed parental genotypes  from the sample of individuals with observed and/or imputed parental genotypes, who all have at least one first-degree relative also genotyped in the UKB. As in the related sample, we filtered out individuals identified by the UKB as having excess relatives, excess heterozygosity or sex chromosome aneuploidy, giving a sample size of 276,419. We applied BOLT-LMM to the 658,720 SNPs present on the UKB genotyping array.We conducted GWASs using BOLT-LMMIn addition to the nine phenotypes used in the related sample, we also analyzed north and east birth coordinates. We adjusted the phenotypes for the same set of covariates as in our analysis of the related sample, including 40 genetic PCs. We estimated correlations between the summary statistics in the related sample and the summary statistics in the unrelated sample using the moment-based estimator . Supplementary tables are provided as separate file (sib_imputation_supplementary_tables_March_18_22).Reporting SummaryPeer Review FileSupplementary tableLegends/captions are contained within the XLSX file.


Runs of homozygosity (ROH) are widely used to estimate genomic inbreeding, which is linked to inbreeding depression on phenotypes. However, the adverse effects of specific homozygous regions on phenotypic characteristics are rarely studied in livestock. In this study, the 50 K SNP data of 3,770 S21 Duroc (American origin) and 2,096 S22 Duroc (Canadian origin) pigs were used to investigate the harmful ROH regions on five economic traits. The results showed that the two Duroc lines had different numbers and distributions of unfavorable ROHs, which may be related to the different selection directions and intensities between the two lines. A total of 114 and 58 ROH segments were found with significant adverse effects on the economic traits of S21 and S22 pigs, respectively. Serval pleiotropic ROHs were detected to reduce two or multiple phenotypic performances in two Duroc populations. Candidate genes in these shared regions were mainly related to growth, fertility, immunity, and fat deposition. We also observed that some ROH genotypes may cause opposite effects on different traits. This study not only enhances our understanding of the adverse effects of ROH on phenotypes, but also indicates that ROH information could be incorporated into breeding programs to estimate and control the detrimental effects of homozygous regions. Haplofinder) that can detect unfavorable ROH genotypes associated with phenotypes. Then, Martikainen et al.  are a continuous segment of homozygous genotype in the genome of diploid organisms, which arise when two copies of the same ancestral haplotype are gathered together in one individual . Long han et al.  used Hapa et al.  used samDuroc pig is one of the most popular commercial breeds worldwide. Recently, intensive selection in the Duroc population has resulted in significant genetic gains in multiple economic traits of interest. Duroc pigs have become the basis for many mixed-breed commercial lean boars due to their excellent characteristics in terms of growth, feed conversion efficiency, physique, carcass, and meat quality . For exaThe experimental procedures used in this study met the guidelines of the Animal Care and Use Committee of the South China Agricultural University (SCAU) . The Animal Care and Use Committee of the SCAU approved all animal experiments described in this study.In this study, a total of 5,866 pigs were sampled from the Wen's Foodstuff Group Co., Ltd. . To minimize the impact of nongenetic factors, all pigs were subjected to the same growth and feeding conditions during the fattening period from 30- to 100-kg live weight. Of the 5,866 pigs, 3,770 S21 pigs were born between 2013 and 2017, and 2,096 S22 pigs were born between 2016 and 2017. A total of five economic traits were recorded including average daily gain (ADG) at 100 kg, backfat thickness (BFT), loin muscle area (LMA), lean meat percentage at 100 kg (LMP), and total teat number (TTN) . The twop > 10−6 for Hardy–Weinberg equilibrium test. SNPs without position information and those on the sex chromosomes and duplicates were also eliminated. Finally, 39,416 informative SNPs of 3,770 individuals in S21 population and 35,850 informative SNPs of 2,096 individuals in S22 population were retained for subsequent analyses.Genomic DNA was isolated from ear samples using the standard phenol-chloroform method. Genotyping was performed using the GeneSeek Porcine 50 K SNP Chip containing 50,703 SNP markers . PLINK vdetectRUNS package v0.9.6 , b is a vector of fixed effects , a means a random additive genetic effects, e is a vector of random residuals, and X, Z, and W are incidence matrices that relate b, a, and pe to y, respectively. The random additive genetic effect is assumed to be normally distributed with N, and gene annotation for these shared candidate ROH fragments was analyzed using R package GALLO  with five economic traits in two Duroc lines  and S22  pigs. We also found that several pairs of traits had significant correlations in two Duroc lines, indicating that these traits possibly harbored similar or opposite genetic basics.Pearson correlation test was used to infer the relationships among total ROH length and five economic traits. According to the classification of correlation coefficients , the resoc lines . This re2, 0.29%, 0.3 less ADG, LMA, LMP, and TTN and 0.51 mm more BFT, respectively, than the animals without these genotypes  were related to the immune response; three genes  were associated with growth and skeletal development; two genes were involved in gametogenesis (ICA1L) and embryonic development (BMPR2); and one gene (GKN1) was relevant to fat deposition. In the common ROH region between ADG and TTN, gene DGRX3 was related to mammary gland development. In the overlapping ROH segment between LMA and LMP, genes SLC2A5, H6PD, TARDBP, and MTOR were associated with nutrient absorption, muscle development, fat deposition, and embryonic development, respectively. GPPR55 gene involved in adipogenesis was detected in the identical ROH genotype of LMA and TTN. We also observed that three genes related to embryonic development, growth, and skeletal development were located on the overlapping ROH region among ADG, LMA, and LMP. In addition, CPVL and CREB5 genes associated with immunity and adipocyte differentiation, respectively, were detected in the overlapping ROH among BFT, LMA, LMP, and TTN traits. For S22 population, in five overlapping ROHs between BFT and LMP, three genes were related to lipid metabolism (CRH), fatty acid composition (CYP7B1), and obesity (TLR4); two genes (PITX1 and ARPP21) were associated with muscle development; and four genes were involved in spermatogenesis  and embryonic development (SOX11). Moreover, we detected that two overlapping ROHs significantly affected the ADG and BFT traits in two Duroc lines, respectively. CARMIL1 gene related to growth was located on the unfavorable ROH for ADG. JAZF1 and eight homeobox genes  associated with lipid accumulation were located on the adverse ROH for BFT.To reveal the genetic basis of unfavorable ROHs on the important economic traits of Duroc pigs, we annotated candidate genes within significantly adverse ROHs and analyzed the biological functions of these genes . For S21HaploFinder software to identify unfavorable ROH genotypes within traits and across multiple traits.In this work, we used whole-genome 50 K SNP data from two Duroc pig lines to investigate the effects of ROH fragments on five important economic traits. Intensive selection possibly increases the unfavorable alleles due to genetic hitchhiking  and thenLROH = 100/(2 g*cM), LROH is the length of ROH, g is the generation age, and 1 cM ≈ 1 Mb  and four traits  in S21 pigs. A total of three functional genes were located on three-trait pleiotropic ROH, such as elopment ; BMPER nand mice , 50; Los in pigs . The homimmunity ; CREB5 gntiation .CARMIL1 gene. CARMIL1 was a candidate gene associated with ACTH concentration, which was positively correlated with body weight, cannon circumference, and hip width in cattle . JAZF1 plays an important role in regulating lipid homeostasis , containing the n cattle . We alsoeostasis . Numeroueostasis –60. Preveostasis  using ROy traits –64. Jinhy traits  and spery traits  through via individual selection. Currently, commercial pig breeding has entered the era of genomic selection, which has greatly increased the production performances. However, genomic selection focuses more on stacking beneficial genes and alleles, while ignoring the influence of unfavorable loci on phenotype. Hence, we suggest that the information of unfavorable ROHs can, and should, be included and utilized in the breeding programs, which may play an assisting role in the genetic improvement of pigs. Previous studies (We observed that the average frequencies of significantly unfavorable ROH were low (5.80 and 6.82%) in two Duroc pigs. This is possibly because these two populations were bred in modern commercial farms with excellent feeding and inbreeding control managements, which may reduce the accumulation of adverse ROHs in two populations to some extent. The results were similar to previous study  that spe studies –23 have  studies . Therefo studies . Due to In summary, this work investigated the association between ROH and five economic traits in two Duroc pig lines. The results revealed that the number of unfavorable ROH genotypes had significant difference between two Duroc lines, which may be related to the different selection directions and intensities in two populations. We observed that many pleiotropic ROHs had consistent adverse effects across multiple traits, and candidate genes were mainly related to growth, fertility, and immunity. Hence, these harmful ROH segments can be used as the indicators to detect and manage inbreeding depression in populations. We highlighted that several unfavorable ROHs possibly had opposite effects in different traits. Therefore, correctly understanding and balancing the roles of unfavorable ROHs among different phenotypes may provide new insights into the genetic improvement of pigs.https://doi.org/10.6084/m9.figshare.8019551.v1.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The animal study was reviewed and approved by Animal Care and Use Committee of the South China Agricultural University.ZW, JY, and XW: proposed the idea of study and directed the analyses and revised the manuscript. SW: performed the analysis and wrote the manuscript. GL, RD, ZZ, DR, JW, HY, EZ, and GC: collected samples and performed the experiments. ZW and JY: contributed the materials. All authors reviewed and approved the manuscript. All authors contributed to the article and approved the submitted version.This research was funded by the Project of Swine Innovation Team in Guangdong Modern Agricultural Research System (2021KJ126), the Local Innovative and Research Teams Project of Guangdong Province (2019BT02N630), and the National High-quality lean-type Pig Breeding United Research Program of China.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.