C-reactive protein is involved in a plethora of pathophysiological conditions. Many genetic loci associated with C-reactive protein are annotated to lipid and glucose metabolism genes supporting common biological pathways between inflammation and metabolic traits. To identify novel pleiotropic loci, we perform multi-trait analysis of genome-wide association studies on C-reactive protein levels along with cardiometabolic traits, followed by a series of in silico analyses including colocalization, phenome-wide association studies and Mendelian randomization. We find 41 novel loci and 19 gene sets associated with C-reactive protein with various pleiotropic effects. Additionally, 41 variants colocalize between C-reactive protein and cardiometabolic risk factors and 12 of them display unexpected discordant effects between the shared traits which are translated into discordant associations with clinical outcomes in subsequent phenome-wide association studies. Our findings provide insights into shared mechanisms underlying inflammation and lipid metabolism, representing potential preventive and therapeutic targets. Chronic inflammation and lipometabolism share many causal genes and possibly pathways. Here, the authors use a multi-trait GWAS approach to study shared genetic determinants of low-grade inflammation, measured by C-reactive protein (CRP), and closely linked lipid and metabolic pathways. As a result, metabolic disturbances are commonly associated with immune responses in relation to cardiovascular outcomes. For example, high low-density lipoprotein (LDL) cholesterol levels and their subsequent modification are involved in the chronic inflammatory response of the vascular wall2. C-reactive protein (CRP), a marker of systemic inflammation, is an acute phase reactant secreted mainly by the liver and released in high concentrations in blood as a protective response to harmful irritants such as pathogens or damaged tissue. Genetic and environmental determinants of CRP levels have been widely studied as its levels have been associated with a plethora of cardiometabolic phenotypes and its causal role has been under investigation for years3.Metabolic and immune are evolutionarily conserved and functionally entangled systems whose interplay is believed to play a central role in the pathophysiology of a wide spectrum of cardiovascular diseases4 supporting a polygenic model for this trait. Several of the identified CRP loci are annotated not only to inflammation-related genes but also to lipid and glucose metabolism-related genes whereas several have been identified as pleiotropic affecting both phenotypes6 providing further evidence towards common biological pathways between inflammation and cardiometabolic traits. A deeper understanding of the interplay between inflammation and cardiovascular risk factors is likely to highlight important disease pathways and interventions with added clinical benefit.Genome-wide association studies (GWAS) on CRP levels have identified numerous, robustly associated, genetic loci associated with CRP levelsHere, we investigate further the pleiotropic nature of the genetic architecture of CRP in relation to cardiometabolic traits. We performed a Multi-Trait Analysis of GWAS (MTAG) on CRP levels with established cardiometabolic risk factors followed by a series of in silico analyses to identify novel pleiotropic genes, their tissue site of action, and evidence for causal associations with a range of disease outcomes. Ultimately, the study provides additional biological insights into low-grade inflammation and highlights biological pathways that are likely to link inflammation to different cardiometabolic diseases.P < 5 × 10−8) [Supplementary Data P < 5 × 10−8), and 8 of them (8 loci) with at least 4 of the 5 examined traits [Supplementary Data A schematic overview of our study is shown in Fig. LPL gene  that encodes lipoprotein lipase which is expressed in heart, muscle, and adipose tissue7.The bivariate MTAG between CRP and each of the examined risk factors identified 41 additional loci for CRP 9 through the Functional Mapping and Annotation of GWAS (FUMA)10 pipeline to functionally annotate and biologically interpret the MTAG results. In total, ANNOVAR and MAGMA highlighted 1816 genes associated with CRP levels (Supplementary Data We used ANNOtate VARiation (ANNOVAR)P < 3.2 × 10−6) with various pleiotropic effects (Supplementary Data P = 2.7 × 10−4), and pituitary (P = 1.3 × 10−3) (Supplementary Data P = 2.7 × 10−5) and brain cerebellar hemisphere (P = 1.1 × 10−4)  explaining 100% of the PP of the shared association. Also, an intergenic variant upstream (9 kb) from the CRP gene (rs2211320) was found to colocalize with CRP, HDL, LDL, TG, and CPD (PP = 0.97) with 99.95% of the shared association explained by this SNP. Novel CRP-associated variants rs34811474 (ANAPC4), rs10968576 (LINGO2), and rs879620 (ADCY9), colocalized with HDL, BMI, and CPD, respectively. Of the 41 colocalized variants, 12 had a discordant direction of effects between the examined traits (MTAG P < 0.05) (Supplementary Data 11 in UK Biobank (UKB) to investigate possible discordant effects in clinical outcomes as well (Supplementary Data GCKR) which was associated with higher CRP and LDL levels, but lower BMI in MTAG, was also associated with 28 diseases in PheWAS including direct associations with disorders of lipid metabolism (P = 2 × 10−22), gout (P = 3.9 × 10−18), and angina pectoris (P = 3.9 × 10−4) and inverse associations with type 2 diabetes (P = 3.6 × 10−13), cholelithiasis (P = 9.5 × 10−12), alcoholism (P = 1.8 × 10−4), and fasciitis (P = 6.7 × 10−4). Another variant, rs1421085 (FTO) was associated with 27 diseases including direct associations with obesity (P = 5.6 × 10−39), type 2 diabetes (P = 8.2 × 10−27), and hypertension (P = 6.4 × 10−9) and inverse with breast cancer (P = 1 × 10−11), and fasciitis (P = 3.1 × 10−4).We used HyPrColoc to investigate colocalization between the MTAG-reported 324 CRP loci and the five other traits examined in this study. We found 102 colocalized loci between CRP and at least one other trait with a posterior probability (PP) > 0.8 , we extended the MR analysis to each one of them.We performed two-sample Mendelian randomization (MR) analysis to investigate the causality between CRP levels and a variety of prespecified outcomes with potential biological connection with CRPGenetically predicted higher CRP levels were associated with a lower risk of schizophrenia with consistent results across sensitivity analyses  to increase the number of identified loci and the informativeness of the bioinformatics analysis. Our findings support the close link between CRP and lipid metabolism. Many of the novel CRP loci were well-known lipid and BMI loci such as variants mapped at HIVEP1) gene was found to colocalize between CRP and BMI and was highlighted as a novel locus for CRP from MTAG. HIVEP1 regulates the transcription of inflammatory target genes such as those belonging to the interleukin signaling pathway and HIVEP1 deficiency has been shown to exacerbate inflammation in sepsis14. The colocalized exonic variant provides a possible mechanism for the interaction between infection, inflammation, and adiposity.An exonic variant (rs2228213) within the human immunodeficiency virus type I enhancer-binding protein 1 (ANAPC4) gene was identified as the causal variant for multiple traits including CRP, HDL, BMI, and CPD. Whereas high pleiotropy at the locus and gene level is common across the genome, wide pleiotropic effects at the SNP level are much less abundant15. The same variant has been previously associated with cognitive performance and educational attainment16, lung function17, and osteoathritis18. The biological function of ANAPC4 is largely unknown but it’s wide pleiotropic effects and its low tissue specificity indicate a possible involvement in general biological functions.A non-synonymous exon variant (rs34811474) within the anaphase-promoting complex subunit 4 (DHX36 gene (rs171390) was found to colocalize between CRP and ΒΜΙ and was highlighted as a novel locus for CRP from MTAG. DHX36, a highly conserved member of the DExD/H box helicase family, binds with and unfolds G-Quadruplex (G4) DNA, thereby changing how G4 structures influence DNA- and RNA-dependent processes19. G4 structures and DHX36 interactivity has been studied in relation to cancer and tumorigenesis, neurodegenerative diseases and aging mechanisms including cellular senescence19. Our findings support a possible role of this gene in these traits through inflammation pathways and adiposity.The GCKR). The common non-synonymous mutation of rs1260326 close to the GCKR gene is widely reported to have pleiotropic effects on cardiometabolic traits and has been associated with increased triglyceride levels and decreased glucose levels20. Our study supported these observations. In particular, a discordant direction of effect across CRP, lipids, and BMI was observed which was also translated into a discordant direction of effects with 28 different disease phenotypes.Other highlighted variants which colocalized across multiple traits include variants within well-studied genes such as the glucokinase regulatory protein , and situations of vertical pleiotropy where statistical associations to multiple traits are induced via causal effects of one trait on another or via a third common factor15. Although detecting shared genetic associations between two or more traits suggest that possibly there are pleiotropic genes involved in the biological pathways of all the associated traits, still, this does not necessarily mean that the traits share biological pathways, as the pleiotropic genes could affect the traits independently via different pathways, or they could even be expressed in different tissues in response to different signaling22. The extensive pleiotropy at the CPR loci is likely to bias the MR assumption of no pleiotropy. However, the analyses, limiting genetically predicted CRP to variants that were not also associated with lipids or BMI resulted in a small number of CRP variants and suffering from small power. Furthermore, our GWAS introduced several novel genes for CRP, but high polygenicity and pleiotropy of the trait pose a challenge to uncovering biological mechanisms, especially as many novel variants each have a small effect on CRP. Finally, larger GWAS on lipids or CRP could increase the number of pleiotropic loci in the future and further expand our knowledge of the link between mechanisms of inflammation and cardiometabolism.Our study had several strengths. First, we used large-scale data with substantial sample sizes of over 500,000 participants each resulting in high statistical power. The statistical power of our study was boosted even higher considering that we analyzed the data by performing suitable multivariate methods. Second, a particular strength of our study is that it combines advanced methods of genetic epidemiology such as multivariate analysis, colocalization, PheWAS, and Mendelian randomization. Third, we investigated possible associations between CRP and many risk factors for various complex diseases including CVDs, mental disorders, and neoplasms as well as other diseases. However, our study also had some limitations. Our analysis was restricted to individuals of European ancestry only and results may not be generalizable in other ancestries. Also, we excluded all rare variants (MAF < 1%) from our MTAG analysis and consequently from all following analyses. Therefore, we may not be able to identify rare variants with large effects, However, we should consider that several of these associations may stem from false-positive findings and biased results. Moreover, MTAG was conducted using summary statistics data and hence, it was impossible to adjust for other confounders. We should also pinpoint, there was sample overlap between the studies in the multi-trait analysis. However, the sample overlap was addressed within each GWA study by using bivariate linkage disequilibrium (LD) score regressionIn conclusion, we performed a comprehensive multi-trait analysis on CRP and cardiometabolic traits discovering 41 novels of CRP genetic loci. Our subsequent colocalization analysis further highlighted 41 shared causal variants between inflammation and cardiometabolism implying that a perturbation in these loci might be expected to affect several of these traits. Our comprehensive analysis of pleiotropic effects, therefore, offers a promising path forward for novel preventive and therapeutic targets and for the study of anticipated side effects across several traits. Functional work to elucidate causal variants and mechanisms at these loci may provide further insights into the etiological pathways for this collection of traits.12; HDL, LDL, and TG from the respective GWAS in UKB 23; BMI from the GWAS meta-analysis of UKB and GIANT24; and CPD from a meta-analysis of over 30 GWAS including UKB25. Although all the above GWAS have been described elsewhere, a brief presentation of each GWAS characteristic can be found in Supplementary Data The summary statistics from six GWAS were used in this study: CRP, HDL, LDL, TG, BMI, and CPD. As a rule, the largest GWAS of European ancestry for the respective trait was used. All GWAS used in this study has exclusively or partially been conducted in UK Biobank (UKB). More specifically, the CRP summary statistics were obtained from the GWAS meta-analysis of UKB and CHARGE-1000 GenomesThe initial datasets contained 11,140,987; 13,791,468; 13,791,468; 13,791,468; 27,381,303, and 12,003,614 genotyped and imputed SNPs in CRP, lipids , BMI and CPD, respectively. All insertion and deletion polymorphisms, rare variants (MAF < 0.01), variants with a sample size <2/3 of the 90th percentile and palindromic SNPs were excluded from the analysis. Furthermore, all non-overlapping SNPs that were not present in at least one dataset were further excluded from the analysis leaving 6,206,408 SNPs for analysis.13 was performed to jointly analyze the summary statistics of CRP, HDL, LDL, TG, BMI, and CPD. The genetic correlation between the traits was calculated and further corrected for sample overlap using bivariate LD score regression which estimated the correlation in GWAS estimation error as implemented in MTAG. Distinct trait-specific effect estimates were generated for each SNP resulting in six summary statistic datasets in total, one for each trait. Thus, a distinct P-value per SNP was generated for each trait which can be interpreted like those from a univariate single-trait GWAS.A multi-trait analysis of GWAS (MTAG)As well as analyzing the six traits jointly, three additional bivariate MTAG analyses were performed: CRP with lipids; CRP with BMI; and CRP with CPD, using the same methodology as described above.10 in a total of 14 distinct functional analyses . In each one of them, a similar process was implemented which is described as follows. All trait-specific genome-wide significant SNPs at a predefined threshold of P = 5 × 10−8 were identified and clumped two times at a different r2 threshold each time. The first clumping at r2 < 0.6 was used to determine the coordinates of the genomic risk loci. All the SNPs that survived the first clumping were included for further annotation and gene prioritization. A second clumping was performed afterward at r2 < 0.1 to define independent signals. SNPs in LD with each other at 0.1 ≤ r2 < 0.6 were assigned to the same LD block. The LD blocks were merged into one locus if there were SNPs from different LD blocks closer than 500 kb. The SNPs that survived the second clumping were defined as independent SNPs. Independent SNPs with the smallest P-value in each region were defined as the top lead SNPs representing the corresponding genomic risk loci. The European sample of 1000 Genome Project Phase 3 was used as a reference panel to calculate pairwise LD between SNPs using PLINK v1.926. SNPs were positionally mapped to their nearest protein-coding genes (Ensembl build v92) at a maximum distance of 10 kb using ANNOVAR8.The generated summary results from all the multivariate GWAS were further functionally analyzed using the platform functional mapping and annotation of GWAS (FUMA v.1.3.6a)r2 > 0.1), we compared our top CRP independent signals with those from the UKB-CHARGE GWAS meta-analysis on CRP12. The top CRP signals from our analyses in absolute distance >500 kb and r2 < 0.1 from all previously reported independent CRP signals were considered novel signals and their region’s novel loci.As CRP was analyzed in four distinct MTAG analyses (1 multi-trait and 3 bivariate), we gathered all CRP-independent signals from all four distinct CRP-generated summaries and after excluding the same signals or proxies .We used Multi-marker Analysis of GenoMic Annotation software (MAGMA v1.08)The gene-property analysis was performed to test possible associations between tissue-specific gene expression profiles and the genes found to be associated with the trait of interest. The gene expression data sets were obtained from the Genotype-Tissue Expression version 8 (GTEx v8) testing a positive relationship at a Bonferroni significance level between genetic associations and gene expression in 30 general and 53 more specific tissue types, respectively.28 using HyPrColoc v.1.0.0R package29 which is a Bayesian divisive clustering algorithm for identifying shared genetic associations between traits in a genomic region using GWAS summary statistics. More specifically, this method was performed to identify colocalized loci between CRP and any combination of the other examined traits and candidate distinct causal variants explaining the shared association in genomic regions associated with CRP.Multi-trait colocalization analysis was performed in R v.4.1.0P = 1 × 10−4 and a conditional prior probability that a variant is associated with an additional trait given that it is already associated with another trait at Pc = 0.02. A PP higher than 0.8 was considered strong evidence that the traits colocalize in the region. The variant with the highest proportion of PP explained was considered the candidate causal variant for the shared association if that proportion was at least 80%.We performed a distinct colocalization analysis for each CRP-associated top signal from MTAG in a region ±200 kb from the top SNP using the MTAG summary statistics. We considered variant-specific priors for our analyses, which assumes that the probability of a variant being colocalized with a set of traits decreases as the number of the set of traits increases. The variant-specific priors model requires the specification of two priors. We specified the prior probability that a variant is associated with a single trait only at (P < 0.05).Variants found to colocalize with CRP and any combination of the other examined traits were further investigated focusing on the direction of their genetic effect across traits. For each distinct colocalized SNP, we compared the direction of its genetic effect on all examined traits using the summaries from the multivariate MTAG. Hence, we were able to highlight colocalized SNPs with a discordant direction of effect between CRP and any of the other traits. Opposite directions of effect between CRP and any of LDL, TG, BMI, and CPD or the same directions between CRP and HDL were considered discordant if the association with the discordant trait was statistically significant Afterward, to investigate the impact of the colocalized genetic loci with the discordant direction of effect on the phenome, we performed a distinct PheWAS analysis in UKB for each one of those SNPs. The PheWAS analysis was restricted to participants of European ancestry and to ensure the independence of participants, we randomly excluded one participant from each pair of relatives based on their kinship coefficient > 0.0884 as provided by UKB. After the quality control process, we ended up with 424,439 individuals available for the analysis.30. A series of case-control groups were generated for each phecode, with controls identified as individuals with no record of the respective outcome and its related phecodes.We used the inpatient Hospital Episode Statistics (HES) records, cancer, and death registries, which follow the WHO’s International Classification of Diseases coding system, 9th Revision (ICD-9) or 10th Revision (ICD-10). We used both ICD-9 and ICD-10 to define cases and controls after translating them to the phecode grouping system as implemented in PheWAS R package31. We used logistic regression models adjusting for age, sex, and the first 15 genetic principal components. To reduce false-positive signals due to multiple testing, we implemented the false discovery rate (FDR) method32.To ensure adequate statistical power in these analyses, we tested only phecodes with a sample size ≥200 cases as previously proposed from simulation studiesr2 < 0.1, P < 5 × 10−8) of the respective trait from multivariate MTAG.We used MR analysis to investigate causality between CRP and prespecified outcomes which have previously shown associations with CRP and have been extensively studied. We performed multiple MR analyses, one for each combination between 6 exposures  and 14 outcomes including diseases of the circulatory system, neoplasms, mental disorders and metabolomic traits  the weighted median (WM) method to check if at least 50% of our SNPs were valid instruments34 and (2) the MR-Egger method to test and correct for possible directional pleiotropy35.In all MR, our main analysis was the inverse-variance weighted (IVW) method assuming a random-effects modelP-value > 0.01) for all the other examined traits.We performed a subgroup MR analysis on SNPs associated with CRP levels based on the existence or absence of evidence of pleiotropy. Two groups of SNPs were created; one with the colocalized CRP SNPs only and the other with non-colocalized SNPs that were non-significant (MTAG Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Datasets 1-16Reporting Summary


Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography  and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes  were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects. Within-sibship genome-wide association analyses using data from 178,076 siblings illustrate differences between population-based and within-sibship GWAS estimates for phenotypes influenced by demographic and indirect genetic effects. GWAS associations can be interpreted as estimates of direct individual genetic effects, that is, the effect of inheriting a genetic variant (or a correlated variant) on a phenotype6. However, there is growing evidence that GWAS associations for some phenotypes estimated from samples of unrelated individuals also capture effects of demography8 (assortative mating11 and population stratification12) and indirect genetic effects of relatives19 25 and polygenic adaptation tests29.GWAS have identified thousands of genetic variants associated with complex phenotypes34. GWAS using siblings (within-sibship GWAS)  Fig.  have bee40. We found that within-sibship meta-analysis GWAS estimates are smaller than population estimates for seven phenotypes . We show that these differences in GWAS estimates, which are likely to partially reflect demographic and indirect genetic effects, can affect downstream analyses such as estimates of heritability, genetic correlations and MR. However, we find that genetic associations with most clinical phenotypes, such as lipids, are less strongly affected. We found strong evidence of polygenic adaption on taller human height using within-sibship data. Our study illustrates the importance of collecting genome-wide data from families to understand the effects of inherited genetic variation on phenotypes that are affected by assortative mating, population stratification and indirect genetic effects.Here, we report findings from a within-sibship GWAS of 25 phenotypes using data from 178,076 siblings from 19 studies, the largest GWAS conducted within sibships to date Fig. . Our res14. For comparison, we also applied a standard population GWAS model; a covariate-adjusted linear regression of the outcome on raw genotype, which does not account for the mean sibship genotype. Standard errors were clustered by sibship. Age, sex and principal components were included as covariates in both models. All GWAS analyses were performed in individual cohort studies separately using R (v.3.5.1) and meta-analyses were conducted across these using summary data. Amongst the phenotypes analyzed, the largest available sample sizes in a meta-analysis of European cohorts were for height , body mass index (BMI) , educational attainment , ever smoking  and systolic blood pressure (SBP)   from 77,832 sibships in 19 studies. Sample sizes for individual phenotypes ranged from 13,375 to 163,748 . More information on sample sizes from individual cohorts and for each phenotype is contained in Supplementary Table 40. We aimed to investigate whether similar shrinkage in association estimates is observed for other phenotypes by comparing within-sibship and population genetic association estimates for 25 phenotypes that were widely available in family-based studies. We observed the largest within-sibship shrinkage (% decrease in association estimates from population to within-sibship models) for genetic variants associated with number of children  4%, 130%), age at first birth , depressive symptoms  and educational attainment . We also found evidence of shrinkage for cognitive ability , ever smoking  and height . In contrast, within-sibship association estimates for C-reactive protein (CRP) were larger than population estimates . We found limited evidence of within-sibship differences for the remaining 17 phenotypes, including BMI and SBP  and educational attainment (P = 0.40) across the European-ancestry cohorts  but some evidence of shrinkage on ever smoking   are used to account for differences in power between the models (Linkage disequilibrium (LD) score regression (LDSC) can use GWAS data to estimate SNP heritability, the proportion of phenotypic variation explained by common SNPse models .h2: 0.13; within-sibship h2: 0.04; difference P = 5.3 × 10−26), with attenuations also observed for cognitive ability , ever smoking  and height . The observed attenuations were consistent with theoretical expectation  provided consistent evidence for within-sibship attenuations in SNP heritability for height, educational attainment and cognitive ability  between educational attainment and 20 phenotypes with sufficient heritability (population/within-sibship h2 point estimate > 0) and statistical power. To determine the effects of demographic and indirect genetic effects on rg, we compared estimates of rg using population and within-sibship estimates.We used LDSCrg = −0.32; −0.37, −0.26), ever smoking  and CRP . However, these correlations attenuated towards zero when using within-sibship estimates: BMI , ever smoking  and CRP , with some evidence at nominal significance for differences between population and within-sibship rg estimates . These attenuations indicate that genetic correlations between educational attainment and these phenotypes from population estimates may be inflated by demographic and indirect genetic effects . We also observed similar attenuation from population MR and WS-MR estimates for BMI on age at first birth (difference P = 2.3 × 10−3) and cognitive ability (difference P = 0.020); phenotypes highly correlated with education. These differences illustrate instances where population-based MR estimates might be distorted by demographic and indirect genetic effects or other factors  and BMI . However, in agreement with the genetic correlation analyses, we observed differences between population MR and WS-MR estimates of height and BMI on educational attainment. Population MR estimates provided strong evidence that taller height and lower BMI increase educational attainment . In contrast, WS-MR estimates for these relationships were greatly attenuated , with GWAS P values. However, this approach is sensitive to population stratification as illustrated by recent work using UK Biobank data which showed that population stratification in GWAS data likely confounded previous estimates of polygenic adaptation on height27. Within-sibship GWAS data are particularly useful in this context as they are robust against population stratification29. Here, we recalculated Spearman’s rank correlation (r) between tSDS  and our population/within-sibship GWAS P values for 25 phenotypes, with standard errors estimated using jackknifing over blocks of genetic variants.Polygenic adaptation is a process via which phenotypic changes in a population over time are induced by small shifts in allele frequencies across thousands of variants. One method of testing for polygenic adaptation is to compare Singleton Density Scores (SDS), measures of natural selection over the previous 2,000 years  and within-sibship GWAS estimates   amongst 310 putative height loci from the within-sibship meta-analysis results  and within-sibship  estimates  and lower high-density lipoprotein (HDL)-cholesterol (P = 0.024)  as well as some anthropometric phenotypes  are affected by demographic and indirect genetic effects. However, we found that most analyses involving more molecular phenotypes, such as lipids, were not strongly affected. This suggests that the best strategy for gene discovery and polygenic prediction for these phenotypes remains to maximize sample sizes using unrelated individuals. For phenotypes sensitive to demographic and indirect genetic effects, such as educational attainment, family-based estimates are likely to provide less biased estimates of direct genetic effects.Here, we report results from the largest within-sibship GWAS to date which included 25 phenotypes and combined data from 178,076 siblings. Consistent with previous studies42 or the extent to which health outcomes are mediated by family environments18. Future family-based GWAS could also provide further estimates of indirect genetic effects43.A key aim of GWAS is to estimate direct genetic effects on phenotypes, but other sources of genetic associations can be extremely informative. For example, knowledge of indirect genetic effects can be used to elucidate maternal effectsP < 1 × 10−5) with height and educational attainment, although power was limited by available samples. The limited detectable heterogeneity could indicate that the observed shrinkage is largely driven by assortative mating or indirect genetic effects. Both of these tend to influence associations proportional to the direct effect, whereas population stratification is likely to have larger effects on ancestrally informative markers. Notably, twin studies have indicated effects of the common environment on many of the phenotypes for which we observed shrinkage, such as educational attainment44, cognitive ability45 and smoking46, potentially consistent with indirect genetic effects of parents. In contrast, twin studies do not find strong evidence for common environmental effects on height, where shrinkage is more likely to be a consequence of assortative mating47.We found little evidence of heterogeneity in shrinkage estimates at genetic variants strongly associated  that the association between BMI genetic variants and educational attainment does attenuate. These results indicate cross-trait shrinkage in association estimates for BMI genetic variants even in the absence of same-trait shrinkage.25.Within-sibship GWAS data can be useful for validating results from larger samples of unrelated individuals. Here, we showed that population MR and WS-MR estimates of the effects of height and BMI were generally consistent for 23 outcome phenotypes. However, we observed differences between within-sibship and population MR estimates of height  and BMI . This suggests the MR assumptions do not hold for these relationships in samples of unrelated individuals. In subsequent studies, WS-MR could be used as a sensitivity analysis when including phenotypes likely to be affected by demographic or indirect genetic effectsWe used non-European data from the China Kadoorie Biobank to evaluate whether demographic and indirect genetic effects influence GWAS analyses conducted in the Chinese population. In this sample, we found minimal evidence of shrinkage for height genetic variants but—consistent with the European meta-analysis—suggestive evidence of shrinkage for variants associated with smoking initiation. The absence of shrinkage for height suggests that demographic effects such as assortative mating may differ between populations. Larger within-family studies in non-European populations could be used to evaluate population differences in demographic and indirect effects.28. Previous evidence for adaptation on height using siblings in UK Biobank was suggestive of some adaptation, but statistically inconclusive26. Here, using within-sibship GWAS estimates from a larger (~4-fold) sample of siblings, we found strong evidence of polygenic adaptation on increased height and some evidence of adaptation on number of children and HDL-cholesterol. We anticipate that future studies on human evolution will benefit from using large within-family datasets such as our resource.We also used the within-sibship GWAS data to evaluate evidence for recent selection. A previous study reporting polygenic adaptation on height in the UK population was found to be biased by population stratification in the Genetic Investigation of ANthropometric Traits (GIANT) consortium48 but could have impacted our GWAS estimates. Another limitation is that while assortative mating is unlikely to affect within-sibship GWAS estimates, it can bias within-sibship estimates of heritability downwards49 and so may have affected our LDSC SNP heritability and genetic correlation estimates. However, the within-sibship shrinkage in GWAS estimates and LDSC heritability estimates were largely consistent, suggesting any such bias is unlikely to have impacted our conclusions. Our findings are also limited to adult phenotypes. Within-family GWAS  could use data from children to evaluate if childhood phenotypes are more strongly affected by indirect genetic effects.Within-family GWAS are limited by both available family data and statistical inefficiency (homozygosity within families). To help address this issue, future population-based biobanks could recruit the partners, siblings and offspring of study participants. In contrast, conventional population GWAS designs sampling unrelated individuals are likely to be the optimal approach to maximize statistical power for discovery GWAS for genetic associations. Indeed, we found that many genotype–phenotype associations from population GWAS models were also observed in within-sibship GWASs, albeit sometimes attenuated towards zero. A notable limitation of within-sibship models is that they do not control for indirect genetic effects of siblings, that is, effects of sibling genotypes on the shared environment. Sibling effects have been estimated to be modest compared with parental effectsNineteen cohorts contributed data to the overall study (Supplementary Table https://github.com/LaurenceHowe/SiblingGWAS). Scripts checked strand alignment, imputation scores and allele frequencies for the genetic data as well as missingness for covariates and phenotypes. Scripts also summarized covariates and phenotypes and set phenotypes to missing for sibships if only one individual in the sibship had nonmissing phenotype data. To harmonize variants for meta-analysis, genetic variants were renamed in a format including information on chromosome, base pair and polymorphism type (SNP or INDEL: insertion or deletion). The automated pipeline restricted analyses to common genetic variants  > 0.01) and removed poorly imputed variants (INFO: information score < 0.3). Analyses were restricted to include individuals in a sibship, that is, a group of two or more full siblings in the study. Monozygotic twins were included if they had an additional sibling in the study.GWAS analyses were performed uniformly across individual studies using automated scripts and a preregistered analysis plan  were included as covariates in both models. The pipeline used imputed ‘best guess’ genotype calls rather than dosage data.GWAS analyses involved fitting both population and within-sibship models to the same samples. The population model is synonymous with a conventional principal component adjusted model, and was fit using linear regression in R (v.3.5.1). The within-sibship model is an extension of the population model including the mean sibship genotype (the mean genotype of siblings in each sibship) as a covariate to account for family structure, with each individual’s genotype centered around the mean sibship genotypej in sibship i with ni > 2 siblings:For individual Population model:Within-sibship model:ij, genotype of sibling j in sibship i; i over n siblings; j in sibship i centered around GStandard errors from both estimators were clustered over families at the sibling level to account for nonrandom clustering of siblings within families. Note that this clustering accounts for sibling relationships but does not account for further relatedness present in each sample. For example, a sibling pair could be related to another sibling pair . We performed simulations, described below, confirming that such relatedness can lead to underestimating standard errors in the population model and has no effect on the standard errors of the within-sibship model.50 with population and within-sibship data meta-analyzed separately. We performed meta-analyses using only samples of European ancestry. We used data from 13,856 individuals from the China Kadoorie Biobank separately in downstream analyses. Information on sample sizes for individual phenotypes is contained in Supplementary Table GWAS models were performed in individual studies, harmonized and then meta-analyzed for each phenotype using a fixed-effects model in METAL50.Phenotypes were harmonized between studies using phenotypic summary data on means and standard deviations. GWAS of study-specific phenotypes that did not conform to analysis plan definitions  were excluded from meta-analyses. GWAS presented in different continuous units  were transformed before meta-analysis by dividing association estimates and standard errors by the standard deviation of the phenotype as measured in the cohort. Meta-analyses for 25 phenotypes were performed using a fixed-effects model in METALWe hypothesized that the within-sibship estimates would differ compared with population-based estimates due to the exclusion of effects from demographic and familial pathways. In general, these effects have been shown to inflate (rather than shrink) population-based estimates, so we estimated within-sibship shrinkage (the % difference from population to within-sibship estimates). To estimate this shrinkage, we required estimates of the associations with a phenotype from each within-sibship and population-based analysis that was not affected by winner’s curse. Hence, we adopted a strategy where we used an independent reference dataset to select the variants associated with a phenotype. Using the meta-analysis results to obtain association estimates for these variants, we generated summary-based weighted scores of those association estimates in the within-sibship and population-based analyses and estimated the ratio of those scores. We used the UK Biobank dataset excluding sibling data as the independent reference dataset.51. We started with a sample of 463,006 individuals of ‘European’ ancestry derived using in-house k-means cluster analysis performed using the first four principal components provided by UK Biobank with standard exclusions also removed52. To remove sample overlap, we then excluded the sibling sample , resulting in a final sample of 422,730 individuals. To model population structure in the sample, we used 143,006 directly genotyped SNPs, obtained after filtering on MAF > 0.01; genotyping rate > 0.015; Hardy–Weinberg equilibrium P < 0.0001; and LD pruning to an r2 threshold of 0.1 using PLINK v.2.0 (ref. 53). Age and sex were included in the model as covariates.We performed GWAS in an independent sample of UK Biobank (excluding siblings) for each phenotype using a linear mixed model as implemented in BOLT-LMM53)  using the 1000 Genomes Phase 3 EUR reference panel54 to generate independent variants associated with each phenotype at genome-wide significance (P < 5 × 10−8) and at a more liberal threshold (P < 1 × 10−5).All 25 phenotypes (conforming to our phenotype definition) were available in UK Biobank data except for a continuous measure of depressive symptoms. For depressive symptoms, we performed a GWAS of binary depression which was excluded from the meta-analysis . Using the BOLT-LMM UK Biobank GWAS summary data, we performed strict LD clumping in PLINK v.2.0 (ref. 56:For a particular phenotype the sets of independent variants obtained from the independent UK Biobank GWAS were used to generate a summary-based weighted score using an inverse variance weighting (IVW) approachS represents the weighted average of the association estimates of the M variants on a phenotype, where β and σ represent the beta coefficients and standard errors from the within-sibship (W) or population-based (P) meta-analysis results. The discovery association estimates from the UK Biobank GWAS were used as weights (w). The set of M variants were determined using either the genome-wide significance (G) or the more liberal threshold (L). Hence, depending on which model is used to determine the association estimates and which set of SNPs are used, four scores can be calculated for each phenotype—SP,G, SP,L, SW,G and SW,L.Here, the score P values from their Z scores. The SW,L scores were nominally associated at P < 0.05 for 24 of 25 (exception: number of children) of the phenotypes, with the SP,L scores associated with all 25 phenotypes at this threshold  of genetic variant–phenotype associationsWe used the within-sibship and population-based scores to calculate the average shrinkage :The standard errors of M variants, we removed each variant in turn and repeated IVW and shrinkage analyses as above, extracting the shrinkage point estimate in each of the M iterations. We then calculated However, we do not have an estimate of this covariance term because the two GWAS were fit in separate regression models. We therefore used the jackknife to estimate As a sensitivity analysis, we investigated the effects of positive covariance between the population and within-sibship models on the shrinkage standard errors using individual-level participant data from UK Biobank. Analyzing shrinkage on height, we used seemingly unrelated regression to estimate the covariance term between the population and within-sibship estimators. We found that standard errors for shrinkage estimates decreased by around 15% when the covariance was modeled (Supplementary Table P < 1 × 10−5), with results using the genome-wide threshold (P < 5 × 10−8) reported as a sensitivity analysis. In the main text, we report the shrinkage estimates that reach nominal significance (P < 0.05). We presented shrinkage estimates in terms of % (multiplying by 100).As the primary analysis, we reported shrinkage results using the liberal threshold  and BMI , while the estimate for educational attainment was imprecise 35%; 12%, 57%). Across all phenotypes in the within-sibship data, the median confounding estimate was 21% , but stronger conclusions are limited by imprecise estimates , where LD mismatch leading to elevated LDSC intercept/ratios has been previously discussed20. We investigated this possibility using empirical and simulated data.A large proportion of samples in the meta-analysis were from UK-based studies such as UK Biobank and Generation Scotland, for which the LD scores, generated using 1000 Genomes project (phase 3) European samples , have been shown to fit reasonably wellN > 5,000): UK Biobank, HUNT, the China Kadoorie Biobank (using default East Asian LD scores), Generation Scotland, DiscovEHR, Queensland Institute of Medical Research (QIMR) study and FinnTwin. We found some evidence of heterogeneity between studies: ratio estimates were higher in Scandinavian studies compared with UK-based studies  meta-analysis summary data. The summary data were harmonized using the LDSC munge_sumstats.py function, and we used the precomputed European LD scores from 1000 Genomes Phase 3.N to estimate SNP heritability. For this parameter, we used the effective sample size for each meta-analysis phenotype, equivalent to the number of independent observations. This was estimated as follows using GWAS standard errors, minor allele frequencies and the phenotype standard deviations (after adjusting for covariates).LDSC requires a sample size parameter s.e., GWAS model standard error; MAF, MAF of the variant; s.d._Resid, standard deviation of the regression residual.Effective sample size was estimated for each individual study GWAS and each model . To reduce noise from low-frequency variants, we restricted to variants with MAF between 0.1 and 0.4 (from 1000 Genomes EUR). At the meta-analysis stage, the effective sample size for each variant was calculated as the sum of sample sizes of studies in which the variant was present. Simulations evaluating the use of effective sample sizes are detailed in the 57 as below.In empirical analyses, we decided to focus on the differences between the population model .We used the difference h2) are derived from the formulation below20:χ2 is the square of the GWAS Z score, N is the sample size, M is number of variants such that lj is the LD score of variant j and a is the effect of confounding biases.We calculated the expected effect of shrinkage on LDSC SNP heritability estimates. LDSC heritability estimates (Z scores being multiplied by a factor (1 − k), where k is the shrinkage coefficient, and χ2 statistics being multiplied by (1 − k)2. As above, we have used effective sample size to account for differences in N between the population and within-sibship models. Therefore, assuming all other coefficients remain consistent, the expectation of k and Uniform shrinkage across the genome would lead to GWAS 21, which allows the use of different heritability models with regard to how local LD and allele frequencies affect the heritability contributions of individual SNPs. In SumHer analyses, we followed the same procedure as above for LDSC using effective sample sizes and estimating SNP heritability for all 25 phenotypes. We used the LDAK-Thin model with the precomputed tagging file over the BLD-LDAK model because of the limited power of our datasets .To evaluate the sensitivity of our results to assumptions of heritability models, we also estimated SNP heritability using SumHerrg between educational attainment and other phenotypes using both population and within-sibship data. LDSC requires nonzero heritability to generate meaningful rg estimates, so we restricted analyses to the 22 phenotypes with SNP heritability point estimates greater than zero in both population and within-sibship models /forced vital capacity (FEV1FVC)). We estimated only pairwise genetic correlations between educational attainment and all other phenotypes because of previous evidence that educational attainment is influenced by demographic and indirect genetic effects and, given the limited statistical power, to reduce the multiple testing burden. Estimates failed to converge for genetic correlation analyses involving age at first birth and age at menopause, so these phenotypes were not analyzed here. We estimated the difference between the population  and within-sibship  estimates  using a difference-of-two-means test57.We used LDSC to estimate rg estimate in the kth iteration and μ is the mean rg estimate across all 100 iterations.We used the jackknife to estimate the standard error of the difference, Z score .We used the difference r2 < 0.001, physical distance threshold = 10,000 kb, P < 5 × 10−8) using the 1000 Genomes Phase 3 EUR reference panel54. We then performed an MR-IVW analysis using the within-sibship meta-analysis data to estimate the effect of the exposure on the outcome asβExp is the association estimate from exposure GWAS, βOut is the association estimate from outcome GWAS and σOut is the standard error from outcome GWAS.We performed MR analyses using the within-sibship meta-analysis GWAS data to estimate the effect of two exposures (height and BMI) on 23 outcome phenotypes. For the exposure instruments, we used 803 and 418 independent genetic variants for height and BMI, respectively. These variants were identified by LD clumping in PLINK . With n genetic instruments, we removed each variant from the analysis in turn and then computed βMR,Diff, storing the estimate from the n iterations. We then calculated s.e. as follows:n is the number of genetic variants used as instruments, βkMR, Diff,  is the βMR, Diff estimate in the kth iteration and μ is the mean βMR, Diff estimate across all n iterations.We used the jackknife to estimate the standard error of the difference, s.e..We used the difference 28. Precomputed SDS were downloaded for UK10K data from https://web.stanford.edu/group/pritchardlab/. Genomic regions under strong recent selection  were removed and SDS were normalized within each 1% allele frequency bin.Polygenic adaptation was estimated using similar methods to a previous publicationSDS were merged with GWAS meta-analysis data for 25 phenotypes. Variants with low effective sample sizes (<50% of maximum) were removed for each phenotype. SDS were transformed to tSDS such that the reference allele was the phenotype-increasing allele.Z scores from the population and within-sibship models. Standard errors were estimated using the jackknife. The genome was ordered by chromosome and base pair and divided into 100 blocks. Correlations were estimated 100 times with each kth block removed in turn. The standard error of the correlation estimate, s.e.(Cor), was calculated as follows:Spearman’s rank test was used to estimate the correlation between tSDS and the absolute value of GWAS k is Spearman’s rank correlation estimate in the kth iteration and μ is the mean correlation estimate across the 100 iterations.Cor27, we performed several sensitivity analyses for the height analysis detailed in the Given previous concernsFurther information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-022-01062-7.Supplementary InformationSupplementary information—consortia, funding, cohort descriptions, phenotype definitions and supplementary methods.Reporting SummaryPeer Review FileSupplementary Table 1Excel file with all supplementary tables in.


Black pigs are the most competitive hogs in Taiwan as they have a higher price than commercial non-black hogs due to their desirable meat quality for locals, and they have accounted for 15% of Taiwan’s hog production since 2011. However, the litter sizes before the weaning of local black sows are much smaller than those of maternal breeds such as Landrace and Yorkshire, which are commonly used in Taiwan. Herd productivity is highly influenced by dam litter size and piglet weight before weaning. The KHAPS Black Pig was registered as a new breed in Taiwan in 2010 and was designed as a maternal line for black hog production. The direct–maternal genetic effects on litter size and piglet weight were tested for selection efficiency. The direct–maternal genetic correlation had a great impact on the prediction of maternal breeding value. Therefore, it is crucial to estimate the direct–maternal genetic parameters of litter size and piglet weight to assist future selection decisions for the genetic progress of this new breed.2d), maternal (h2m), realized (h2r), and total (h2T) heritability, as well as correlations  within and between traits. The analyses were performed using single- and three-trait animal models with and without maternal genetic effects. In the three-trait model with maternal genetic effect, all estimates of h2d and h2m were significantly different from zero except the h2d of mean birth weight. Positive values of rd and rm between traits were observed as expected in the range of 0.322–1.000. Negative values of rdm were found within and between traits and were less associated with mean piglet weight traits than litter size traits. Estimates of h2T were consistently larger than those of h2r in both the single- and three-trait model analyses. In addition, the three-trait model can take into account the association between the traits, so the estimates are more accurate with smaller SEs. In conclusion, maternal genetic effects were not negligible in this study, and thus, a multiple-trait animal model with maternal genetic effects and full pedigree is recommended to assist future pig breeding decisions in this new breed.The objective of this study was to estimate the genetic parameters of litter size and piglet weight from farrowing to weaning in KHAPS Black sows. The genetic parameters investigated were the direct (h Black pigs are the most competitive hogs in Taiwan due to their high meat quality, and they have accounted for 15% of Taiwan’s hog production since 2011. The KHAPS Black Pig breed was developed via Meishan–Duroc crossbreeding with a Meishan maternal lineage in a breeding program focused on sow litter performance that was conducted at the Kaohsiung Animal Propagation Station of Taiwan’s Livestock Research Institute. The KHAPS Black Pig population was created after a 12-year (1997–2009) developmental research program and was admitted to registration by the Council of Agriculture, Taiwan in 2010 and then released to the public as a maternal line for local black hog production. Although the carcass characteristics of this new black lop-eared breed are more desirable than those of Duroc, Hampshire, and other local black breeds, its littering performance still has room for improvement when compared to those of maternal breeds in Taiwan such as Landrace and Yorkshire.Sow productivity is the foundation of commercial pork production. Piglets weaned per sow per year (PSY) is one of key factors for evaluating sow productivity and thus the efficiency of pig farming. The littering performance of sows, including litter size and piglet weight at weaning, are features that have a significant impact on the fertility, prolificacy, and reproductive longevity of females. Generally, maternal effects occur when the phenotype is influenced by its dam’s phenotype in addition to the genes it inherits, and thus they have a particular impact on the offspring when it comes to maternal-influenced traits, e.g., litter size and weight from birth until weaned . Ignorinσ2TBV) can generate a selection response. The objective of this study was to investigate the importance of maternal effects and to evaluate the effect of the direct–maternal genetic parameters on the maternal-influenced traits of females, including litter size and weight of piglet from birth to weaned, in KHAPS Black Pigs.In addition to maternal genetic variance, covariance between direct genetic effects and maternal genetic effects may also exist with a magnitude that varies by species ,3,5. In All individuals were from the swine experimental farm of the Kaohsiung Animal Propagation Station, Taiwan. A total of 7388 piglets from 688 litters farrowed by 268 sows from 2013 to 2019 were recorded and analyzed based on sow level. There were no littermates found in the 268 sows due to only one gilt being selected per litter for mating, and thus the common-litter effect was not applicable. Moreover, an optimum estimate of permanent environmental variance was not obtained, and this parameter was therefore excluded from the statistical model. A total of 5381 and 5305 piglets were weighed at 3 and 4 weeks (weaned) of age, respectively. Two datasets, each with three traits, were then analyzed. The three traits were the total number of piglets born (LS0) and the number of piglets at 3 and 4 weeks (weaning) of age (LS3 and LS4). Piglet weights were pre-calculated as within-litter mean piglet weight for birth weight (MW0) and adjusted for 21 and 28 days of age before the within-litter mean piglet weight calculation for 3 and 4 weeks of age, MW3 and MW4. Statistical analysis was carried out at the female breeding stock level. Descriptive statistics were obtained using the PROC UNIVARIATE procedure in SAS .Single-trait and three-trait animal models with or without maternal genetic effects were employed within the datasets. The derivative-free restricted maximum likelihood procedure described by Groeneveld et al.  was usedy, β, d, m, and e are vectors of the observations of traits, fixed effects ), random direct genetic effects, random maternal genetic effects, and random residuals, respectively; βX, dZ, and mZ are incidence matrices relating vectors β, d, and m with y. All random effects are distributed as centered normal distributions with the variance–covariance matrices being equal to:A is the additive genetic relationship matrix , and dG, mG, dmG, and R represent the (co)variance matrices between the traits for direct genetic effects, maternal genetic effects, associations between direct and maternal genetic effects, and residual effects, respectively. I is an identity matrix of the appropriate size. Other models, such as single-trait models with or without maternal genetic effects, were simplified from the full model above.Before finalizing the model, the PROC GLM feature of SAS was used to test the significance of the fixed effects (birth year and parity) and animal effects (using RANDOM statement). The results showed that a significant (α) level were <0.0001 for birth year and animals, and <0.05 for parity. Therefore, the multiple-trait linear model with maternal genetic effects was defined as the full model and formed as follows:σ2d), maternal genetic effects (σ2m), the covariance between direct and maternal genetic effects (σdm), and residuals (σ2e) were used to calculate the phenotypic variance following Dickerson [σ2y = σ2d + σ2m +σdm + σ2e, which was based on the model of iP = id + jm + iε for the phenotype of individual i with dam j, where id and jm are the direct and maternal breeding values for i and j, respectively, and iε is the nonheritable effects of i. Furthermore, the total breeding value (TBV), according to Eaglen and Bijma [i for genetic improvement, and thus iTBV = id + im. Therefore, the heritability was computed based on estimates of variance and covariance components as σ2d/σ2y, σ2m/σ2y, (σ2d + 0.5σ2m + 1.5σdm)/σ2y, and (σ2d + σ2m + 2σdm)/σ2y for direct (h2d), maternal (h2m), realized (h2r) [2T) [2r as total heritability (h2t), although Willham [2r as the fraction of the realized selection differential for mass selection.The variance components for direct genetic effects  ,13, and 2r) [2T)  heritabi2r and h2T represent the covariance between the TBVs and phenotypes of individuals and the total heritable variance, σ2TBV, respectively. Moreover, the direct and maternal genetic correlation between the ith and jth traits were estimated as rd i = j) and between (I ≠ j) trait evaluations.The numerators of hA descriptive summary of the littering performances evaluated is presented in p < 0.05), as is shown in the diagonals of The heritability estimates obtained using single- and three-trait animal models without maternal genetic effects were significantly different from zero (p < 0.001) for litter size and from 0.426 to 0.976 (p < 0.01) for piglet weight until weaning. Strong positive genetic correlations between females’ litter size from farrowing to weaning were found. The result might imply the existence of pleiotropy in the traits evaluated, which is consistent with the findings of Zhang et al. [LS0,LS3 = 0.797 > rMW0,MW3 = 0.567. The same situation is also observed in terms of the genetic correlation between traits.Without maternal genetic effects included in the model, significant and strong positive genetic correlations between the traits were observed, which ranged from 0.790 to 0.998  and the weaker association within piglet weight traits (ranged from −0.745 to −0.022), which might be supported by other studies [2r and h2T also appeared in the results of three-trait model analysis ; 0.065–0.140 (piglet weight traits) vs. 0.038–0.066 (litter size traits) in h2r and 0.116–0.195 (piglet weight traits) vs. 0.076–0.114 (litter size traits) in h2T.In  studies ,16. Dong studies  reported2r and h2T estimates, and the influence depends on the direction and strength of the association (rdm) as well as the magnitude of the maternal genetic variance, σ2m. In other words, a high negative direct–maternal genetic correlation might result in low estimates of h2r and h2T. The estimates of maternal genetic variance found in our study were larger than the absolute value of the within-trait direct–maternal genetic covariance estimate, and thus the h2r estimated from the single- and three-trait models were 40–70% and 50–80% of h2T, respectively.As expected from the formulae, the direct–maternal genetic correlation could affect h2d, h2m, rd, rm, and rdm for litter size traits differed from zero at the 5% significance level. Ranges of h2d and h2m estimates for litter size traits were similar in the univariate and multivariate animal models (0.108–0.181 vs. 0.139–0.170 for h2d and 0.222–0.237 vs. 0.216–0.244 for h2m). They did not substantially differ between the two models. However, estimates of h2m for litter size traits decreased with age in the univariate analysis , but this decreasing trend was not shown in the multivariate analysis.dr between the litter size traits were positively strong (0.924–0.999) and were slightly higher than those of gr obtained from the model in the absence of maternal genetic effects . However, the estimates of the h2d and h2m for piglet weight traits in general were lower when compared to those for litter size traits except at birth weight  within the piglet weight traits (ranging from −0.660 to −0.084) were weaker than the estimates within the litter size traits (ranging from −0.830 to −0.696). In addition, the maternal genetic influence on piglet weight was observed to diminish with age, which appeared to be consistent with maternal genetic effects decreasing with animal age. Therefore, the direct–maternal genetic association within the trait also diminished with age; rdm = −0.660, −0.431, and −0.084 at birth at 3 and 4 weeks of age, respectively.The estimates of the direct–maternal genetic correlations  and piglet weight at 3 weeks of age (h2MW3 = 0.26 ± 0.06) reported by Banville et al. [2 estimates of the prolificacy traits in the literature for pigs in general are about 0.10 and vary by population, as is to be expected [A three-trait analysis can take into account the correlation between traits and thus would be expected to estimate the genetic parameters, htability ,6. In th46–0.253 , which ie et al. . Howeverexpected ,23,24,25g) between the traits within the model were positively favorable and high, as is shown in P) are the sum of the genetic (G) and environmental (E) components, and causal correlations are weighted by the relative importance of heritable (h) and nonheritable effects (e). We can therefore express the rP of traits X and Y as rP = X hY rGh + X eY rEe, where h and e represent the square root of heritability (h2) and the square root of the proportion of phenotypic variance due to environmental factors and r is a correlation with the subscripts P, G, and E repsenting the phenotypic, genetic, and environmental corrleations, repectively, for traits X and Y [g = 0.79, combined with the weak environmental correlation between these two traits resulted in an overall phenotypic correlation that is positive but moderate, rp = 0.64. Similar results were found in the case of the relationships between litter size traits and piglet weight traits presented in this study. Moreover, both the genetic and phenotypic correlations between traits decreased as the corresponding interval between the two traits recorded increased, i.e., rLS3,LS4 > rLS0,LS3 > rLS0,LS4, and rMW3,MW4 > rMW0,MW3 > rMW0,MW4. In the absence of maternal genetic effects, the estimates of the genetic correlations between the traits in this study were in agreement with reports from the literature that show a moderate to strong positive correlation [The genetic correlation estimates  and 0.1 (0.078–0.085) in terms of the litter size and piglet weight traits, respectively; these values are within the ranges of other studies [2d estimate, 0.36, was reported by Roehe et al. [In the presence of maternal genetic effects in a univariate animal model, much lower h studies ,28,29,30e et al.  for birt2d estimate than the h2m estimate shown in this study comport with the results reported in other studies [2d estimates and a relatively large proportion of maternal genetic effects of birth weight and natal litter size in the Soay sheep breed, but the opposite result was also shown for birth weight and average daily gain from birth to weaning in Iranian Makooei sheep [In this study, estimates of maternal heritability were on average approximately twice as large as those of direct heritability in the presence of maternal genetic effects. The smaller values of the h studies ,29,30,32 studies . Wilson  studies  found loei sheep .2m for litter size traits were found to have much higher values than those reported in the literature [2m estimates of piglet weight traits were consistent with the ranges reported in the literature, which ranged from 0.119 to 0.315 for MW0 and from 0.06 to 0.24 for MW4 [Our estimates of the hterature ,15,16,28terature . However for MW4 ,35.2m estimates from the univariate animal models were similar to the h2 estimates when the maternal genetic effects were ignored, the maternal influence on an individual generally decreases as an animal gets older, and thus the h2m estimates decrease as a piglet ages; the values were 0.237 and 0.222 for LS0 and LS4 and 0.252 and 0.125 for MW0 and MW4, respectively. We should also note that higher estimates of the h2d and h2m were found for litter size traits than piglet weight traits in both the univariate and multivariate analyses. Similar phenomena were also observed in the strength of the direct–maternal genetic correlation (rdm) within and between traits when using the same analysis. The univariate analysis showed that maternal genetic effects (h2m) on litter size and piglet body weight at birth to weaning decreased as the piglets were weaned, but this was not the case for the multivariate analysis, which showed inconsistent results for litter size traits and piglet weight traits.Although the hdm both within and between traits, low h2r and h2T estimates would be expected. One possible explanation is that the traits are influenced by both direct and maternal genetic effects , but these two effects oppose each other, which is consistent with others’ results [2r and h2T values are low or that there may even be no heritable variation.Negative correlation estimates shown in  were obs results ,30,31,352r and h2T, the h2T showed a larger estimate than the h2r due to the fact that the maternal genetic effects were greater than the direct–maternal genetic effects in this study. The h2r and h2T values were estimated using equations proposed by Willham [2r as the fraction of the selection differential realized for phenotypic selection , which is also known as the regression coefficient of the TBV and the phenotype [2T as the fraction of an individual’s total breeding value variance (h2T = σ2TBV/σ2P). As was stated by Eaglen and Bijma [2r is the proportion of phenotypic variance attributable to additive genetic effects and thus refers to the realized heritability of mass selection, whereas h2T is the ratio of the total heritable variance (σ2TBV) available for a selection response to a phenotypic variation. Thus, h2T appears to be more relevant for mammalian farm species because mass selection is rarely performed alone in livestock breeding programs.When a comparison was made between the estimates of the h Willham  and Eagl Willham , respect Willham  referredhenotype . Eaglen henotype  defined nd Bijma , in the d was stronger than the corresponding estimate of the genetic correlation (rg) under the model that ignored the maternal genetic effects. The possible reason for this might be due to σ2m and σdm being separated from the genetic variance with negatively strong direct–maternal genetic covariance in all traits evaluated when maternal genetic effects were considered in the model. In this study, the estimates of rdm between the traits were only available in a three-trait model. Therefore, a three-trait animal model showed more advantages than a single-trait one when early or indirect selection was preferred. It was shown by the rdm that weaker direct–maternal genetic associations were observed in terms of piglet body weight traits when compared with litter size traits, which implies a stronger maternal genetic influence on prolificacy than on pre-weaning growth.In the presence of maternal genetic effects, the estimate of rLitter size  and mean piglet weight  from farrowing to weaning in KHAPS Black Pigs had direct heritability in the range of 0.11–0.18 and 0.08–0.11 and maternal heritability in the range of 0.22–0.24 and 0.10–0.25, respectively. A negative correlation between direct and maternal effects was found in all traits, but significance was found in LS0, LS3, LS4, and MW3 when using a three-trait model and in LS3 and LS4 when using a single-trait model. We conclude, therefore, that direct–maternal covariance is essential in litter size traits and mean piglet weight at birth in KHAPS Black Pigs.


Genomic selection (GS) has great potential to increase genetic gain in aquaculture breeding; however, its implementation is hindered owing to high genotyping cost and the large number of individuals to genotype. This study investigated the efficiency of genomic prediction in four aquaculture species. In total, 749 to 1481 individuals with records for disease resistance and growth traits were genotyped using SNP arrays ranging from 12K to 40K. We compared the prediction accuracies and bias of breeding values obtained from BLUP, genomic BLUP (GBLUP), Bayesian mixture (BayesR), weighted GBLUP (WGBLUP), and genomic feature BLUP (GFBLUP). For GFBLUP, the genomic feature matrix was constructed based on prior information from genome‐wide association studies. Fivefold cross‐validation was performed with 20 replicates. Moreover, to reduce the cost of GS, we reduced the SNP density based on linkage disequilibrium as well as the reference population size. The results showed that the methods with marker information produced more accurate predictions than the pedigree‐based BLUP method. For the genomic model, BayesR performed prediction with a similar or higher accuracy compared to GBLUP. For the four traits, WGBLUP yielded an average of 1.5% higher accuracy than GBLUP. However, the accuracy of genomic prediction decreased by an average of 6.2% for GFBLUP compared to GBLUP. When the density of SNP panels was reduced to 3K, which was sufficient to obtain accuracies similar to those using the whole dataset in the four species, the cost of GS was estimated to be 50% lower than that of genotyping all animals with high‐density panels. In addition, when the reference population size was reduced by 10%, evenly from full‐sib family, the accuracy of genomic prediction was almost unchanged, and the cost reduction was 8% in the four populations. Our results have important implications for translating the benefits of GS to most aquaculture species. The challenged fish belonged to 84 different full‐sib families, with 1–39 fish in each family , from four factorial crosses of five females × ten males , were measured for body weight. These fish belonged to 195 full‐sib families, with 1–21 fish in each family , originating from a factorial cross between 67 broodfish , were challenged by 30‐min immersion with 1 × 105 CFU Photobacterium damselae (causative agent of pasteurellosis) and the number of days to death was recorded. These fish belonged to 73 full‐sib families, with 2–144 fish in each family  belonged to 58 full‐sib families generated from 58 females and 20 males of rainbow trout from the 2014 year class, with 10–18 fish in each family. These fish were challenged with infectious pancreatic necrosis, and the number of days to death was recorded  Atlantic salmon ( Atlantic2.2(1) Atlantic salmon, a total of 1481 Atlantic salmon were genotyped using an Illumina combined species of Atlantic salmon and rainbow trout 17K SNP array (17156 SNPs), designed from a subset of SNPs from a higher density array , where σa2 is the additive genetic variance, and G is the genomic relationship matrix , where pj is the reference allele frequency of A2 for genotypes A1A1, A1A2, and A2A2 at locus j; and Z is a centered genotyped matrix where genotypes are subtracted 2 * reference allele frequency. In this study, the allele frequencies pj were estimated from current marker data. X and L are the incidence matrices that relate b and g to y, respectively; e is the vector of random errors with distribution of N, where σe2 is the residual variance, and I is the identity matrix. The different fixed effects included in the model for different species were overall mean, collection date (three levels), and tank (two levels) in Atlantic salmon; overall mean and factorial‐cross group (4 levels) for common carp; overall mean in sea bream; and overall mean and tagging weight as a covariate in rainbow trout.The GBLUP  design matrix allocating records to the marker effects; and u is an (m × 1) vector of SNP effects assumed to be normally distributed ui∼N. The variance of the ith SNP effect had four possible values: σ12=0, σ22=0.0001σg2, σ32=0.001σg2, σ42=0.01σg2, where σg2 is the total genetic variance; v is a vector of random residual polygenic effects with a normal distribution g ~ N, where σa2 is the polygenic variance, and A is the pedigree relationship matrix. The Markov chain Monte Carlo (MCMC) was run for 50,000, and the first 10,000 cycles were discarded as burn‐in, and every 10th sample of the remaining 40,000 iterations was saved for estimating SNP effects and the variance components.The BayesR model  = I, and G(t)=ZD(t)Z′1∑2pj(1‐pj), where t is the iteration number and Z, I, and pj are the same as those described in the GBLUP method.Set t = 0, G(t)∗=0.95∗G(t)+0.05∗I  using GBLUP;Calculate GEBV (u^=1∑2pj(1‐pj)D(t)Z′(G(t)∗)‐1g^;Calculate SNP effects as D(t+1)∗ as djj(t+1)∗=u^j22pj(1‐pj);Calculate SNP weight matrix D(t+1) as D(t+1)=tr(D(0))tr(D(t+1)∗)D(t+1)∗;Normalize matrix G(t+1)∗=0.95∗ZD(t+1)Z′1∑2pj(1‐pj)+0.05∗I, t = t+1;Construct the matrix Go back to step (3) when t is less than or equal to 4. The result from the third iteration was used for GS analysis, as there was no difference between the results of the third and fourth iterations in this study.The WGBLUP ; r is the vector of genomic effects captured by the remaining set of genetic markers, following a normal distribution N; and L is an incidence matrix that links f and r to y. Matrices fG and rG were constructed in the same way as G, but using only the genetic marker set defined by a genomic feature, as described below, and the remaining markers, respectively.The GFBLUP . The genotyped individuals were randomly split into five folds, phenotypes from onefold  were removed from the dataset, and the remaining folds (reference population) were used to predict the GEBV in the validation population. This fivefold CV was replicated 20 times, resulting in 20 average accuracies of genomic prediction. The validation population was the same in each replicate of fivefold CV for all the four methods, GBLUP, BayesR, WGBLUP, and GFBLUP, and for assessing the impact of reducing SNP density and reference population size. The accuracy of genomic prediction was evaluated as r /2.6We evaluated direct savings when genotyping a proportion of animals using different density SNP panels. Costs were calculated for four different species in this study. The genotyping cost was calculated assuming prices of $60, $50, $30, $25, $20, and $10 per sample for HD (from 12K to 40K for four species), 10K, 5K, 3K, 1K, and 0.5K, respectively. Here, we did not assume a price reduction when more animals were genotyped, as the population was not large in the four species in this study.33.1The descriptive statistics and genetic parameters for the analyzed traits of the four species are presented in Tables 3.2Table 3.3p = 0.05, when using GWAS prior information, did not yield higher prediction accuracies than GBLUP and even produced the lowest prediction accuracies compared with other methods with marker information. The accuracy of genomic prediction decreased by an average of 6.2% for GFBLUP compared to GBLUP. This result indicated that inappropriate prior information was used in the GFBLUP method. One possible explanation is that the reference population size was not large enough to obtain accurate GWAS results.Two methods, WGBLUP and GFBLUP, were performed using 20 replicates of fivefold CV to improve the accuracy of genomic prediction. As shown in Table The bias of genomic prediction of four traits in four populations, assessed with 20 replicates of fivefold CV, is presented in Table 3.4Since genotyping with medium‐ or high‐density SNP arrays is relatively expensive in aquaculture species, we evaluated the impact of low‐density SNP panel reduction based on LD on prediction accuracy for the four populations in this study. For GBLUP, five SNP panels with different densities  were used to assess prediction accuracy. In the four populations, the accuracy of genomic prediction tended to be stable with decreasing density of SNP panels from HD to 3K and then rapidly decreased, except that the prediction accuracy slightly increased when the SNP panel was 1K in the Atlantic salmon population, as shown in Figure 3.5To investigate additional cost‐effective ways of genotyping, reduced reference population sizes were tested by evenly sampling different ratios (10%–50%) of the reference population from each full‐sib family to assess the accuracy of genomic prediction. As shown in Figure 3.6In scenario (1), where all animals were genotyped with an HD panel, the total cost of genotyping was estimated to be $88860, $72840, $46620, and $44940 for Atlantic salmon, common carp, sea bream, and rainbow trout populations, respectively ; for example, Teissier et al.  reportedp = 0.05, when using GWAS prior information, yielded lower prediction accuracies and larger bias than GBLUP in this study. One possible reason is that the reference population size  was not large enough to obtain accurate GWAS results. A similar study was performed by Lu et al.  to construct the genomic feature matrix in GFBLUP, which may be insufficient to construct an accurate genomic relationship matrix. In addition, different p values  were set to select markers in the GWAS. However, similar results showed that GFBLUP had a lower prediction accuracy than GBLUP (results not shown).Moreover, the GFBUP with GWAS prior information was used to improve the accuracy of genomic prediction. Theoretically, GFBLUP has advantages over GBLUP, mainly because it allows the assignment of different weights to the genomic variants in the different genomic relationships based on their estimated genomic parameters, which can better fit the genetic architecture of the trait . However, the efficiency of these strategies in actual genomic prediction requires further investigation.In addition, several other strategies for reducing GS costs could be explored: (1) genotype imputation, imputing from low‐to‐high‐density SNP markers, whole‐genome sequence SNP markers, and HD SNP markers, could increase the accuracy of GS without increasing the cost (Tsairidou et al., 5p = 0.05 when using GWAS prior information yielded lower prediction accuracies and larger bias than GBLUP in this study. In addition, reducing SNP density based on LD pruning of SNP arrays and reducing the size of the full‐sib family are effective strategies to reduce the cost of GS.It is very important to explore how to improve the accuracy of genomic prediction and develop cost‐effective strategies to accelerate the application of GS in aquaculture species. Our results showed that the methods with marker information were more accurate than the method based only on pedigree. The WGBLUP method yielded higher genomic prediction accuracy than GBLUP, while the GFBLUP model with The authors declare that they have no competing interests.Fig S1Click here for additional data file.Fig S2Click here for additional data file.