The ALL data, consisting of the GENO data plus females with phenotypes but not genotyped , were analyzed using pBLUP and ssGBLUP. Animals were genotyped with the Affymetrix 90k buffalo genotyping array. After quality control, 60,827 single-nucleotide polymorphisms were used for downward analysis. A pedigree file containing 2,642 animals was used for pBLUP and ssGBLUP. Accuracy of prediction was calculated as the correlation between the predicted BVs of the test set and adjusted phenotypes, which were corrected for fixed effects, divided by the square root of the heritability of the trait, corrected for the number of lactations used in the test set. To assess the bias of the prediction, the regression coefficient (slope) of the adjusted phenotypes on the predicted BVs was also calculated. Results showed that genomic methods (GBLUP and ssGBLUP) provide more accurate predictions compared to pBLUP. Average GBLUP and ssGBLUP accuracies were 0.24 and 0.29, respectively, whereas average pBLUP accuracies (for GENO and ALL data) were 0.21 and 0.22, respectively. Slopes of the two genomic methods were also closer to one, indicating lesser bias, compared to pBLUP. Average GBLUP and ssGBLUP slopes were 0.89 and 0.84, respectively, whereas the average pBLUP (for GENO and ALL data) slopes were 0.80 and 0.54, respectively.The objective of this study was to compare the accuracies of genomic prediction for milk yield, fat yield, and protein yield from Philippine dairy buffaloes using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with the accuracies based on pedigree BLUP (pBLUP). To also assess the bias of the prediction, the regression coefficient (slope) of the adjusted phenotypes on the predicted breeding values (BVs) was also calculated. Two data sets were analyzed. The GENO data consisting of all female buffaloes that have both phenotypes and genotypes ( The Philippine Carabao Center (PCC) has put in place a genetic improvement program that includes a system of evaluating genetically superior individual animals for milk and milk component traits and maintenance of nucleus herds of dairy buffaloes as source of breeding animals and provision of frozen semen from the best riverine buffalo germplasm (identified through progeny testing) for artificial insemination (AI). PCC maintains 12 institutional herds of dairy buffaloes [mostly Bulgarian Murrahs (BUL)] dispersed throughout the archipelago as source of breeding animals and frozen semen from the best riverine buffalo germplasm for AI to riverine, crossbred, and swamp buffaloes. Recording and evaluation of performance are presently limited to animals in these herds, numbering ∼1,200 females, of which ∼400 can be considered as elite dams (open-nucleus scheme). However, present constraints of the breeding program are as follows: the number of recorded cows is not expected to increase substantially in the immediate future; currently progeny is testing only eight bulls per year; accuracies of progeny test bulls are low due to small number of daughters with lactation records; and generation interval is long for AI sires, ∼8 years .The availability of the Affymetrix 90K Buffalo Genotyping Array  in 2013 made it possible to do genomic studies in the bubaline species . When thGenomic prediction studies in dairy buffaloes are very limited and were based on small data sets. The objective of this study was to determine the accuracy of genomic prediction and bias for milk yield (MY), fat yield (FY), and protein yield (PY) from Philippine dairy buffaloes using GBLUP and ssGBLUP compared to prediction accuracy and bias based on pedigree BLUP (pBLUP).Phenotype data and blood samples used in this study were obtained from the PCC. All animals are housed in institutional farms and cooperatives managed by PCC. Data collection and storage are managed by the center’s Animal Breeding and Genomics Section (ABGS).Traits investigated in this study are 305-days MY, FY, and PY. Descriptive statistics of the phenotypic data are presented in Two data sets were analyzed. One contains only female buffaloes that have both phenotypes and genotypes (hereby referred to as GENO) . Analysep < 1 × 10–15), has no genome location, and is not found in the autosomes. After applying the quality control measures, only 60,827 SNPs in 29 autosomes were used for the determination of accuracy of genomic prediction and bias.Genomic DNA was extracted using the Promega ReliaPrep Blood gDNA Miniprep System according to the manufacturer’s protocol. DNA quantification was done using the Promega Quantus Fluorometer. Samples were first subjected to RNA purification prior to shipment to Affymetrix, Inc. Submitted samples were genotyped using the Axiom 90k Buffalo Genotyping Array. Generated “.CEL” files were analyzed using the Axiom Analysis Suite using default settings, wherein polymorphic markers were identified. Additional quality control measures applied include a single-nucleotide polymorphism (SNP) removed if its minor allele frequency is less than 0.05, is out of Hardy-Weinberg equilibrium ; μ is the general mean; breed is the fixed breed effect; lactation number is the fixed effect for lactation number; HYS is the fixed effect for herd-year-season; and animal and permanent env are the individual effect and permanent environmental effect on animal p; and e is random residual with e ∼ N.BVs were estimated using three methods: pBLUP, GBLUP, and ssBLUP. The three methods used the following model:22 matrix .The difference among the three methods is the type of relationships that was used. pBLUP uses a numerator relationship matrix  based on the pedigree (family relationships). The creation of the genomic relationship matrix (GRM), also known as the G-matrix, was used in GBLUP, and ssGBLUP is based on A threefold cross-validation scheme was used to compare accuracy of prediction and bias using GBLUP and ssGBLUP with those of pBLUP. Animals were assigned to one of three test sets: one lactation record, two lactation records, and three lactation records  and 4. Or is the accuracy of prediction; corr is the correlation; BV is the predicted BV; adj. pheno is the adjusted phenotype corrected for fixed effects; h2 is the heritability of the trait; rep is the repeatability of test set; and n is the number of lactations records used in test set. Note that if n = 1, denominator is equal to h.Accuracy of prediction was calculated as the correlation between the predicted BVs of the test set and its corresponding adjusted phenotypes, which were corrected for fixed effects, divided by the square root of the heritability of the trait, corrected for the number of lactations used in the test set:The average of the accuracies of the three test sets is the accuracy of prediction of a trait.To assess the bias of prediction, the regression coefficient (slope) of the adjusted phenotypes on the predicted BVs was also calculated, with slopes of approximately 1 showing zero bias. Slopes greater than or less than 1 indicate underestimation and overestimation, respectively, of BVs. The average of the slopes of the three test sets is the slope of a trait.Accuracies of genomic prediction of the three traits through cross-validation are shown in In the case of bias of prediction, slopes for all methods were less than 1, indicating overestimation of BVs . HoweverWith a limited number of progeny-tested bulls, a reference population of females with at most three lactations per animal was used in this study to determine the accuracy of genomic prediction and bias for MY, FY, and PY using GBLUP and ssGBLUP and compared to prediction accuracy and bias based on pedigree pBLUP. The accuracy of prediction was based on threefold cross-validation scheme , and bias was calculated as the regression coefficient (slope) of the adjusted phenotypes on the predicted BVs.Several genomic prediction studies in dairy cattle have been done wherein the reference populations are cows. Combining different breeds is another option to increase the reference population . In thisThe increase in accuracy in GBLUP could be due to the realized relationships of animals in GBLUP compared to just expected relationships of animals in pBLUP. For example, full sibs would have an expected relationship of 0.5 in pBLUP, but this could be 0.3 to 0.6 in GBLUP. The increase in accuracy in ssGLUP could also be due to the above plus the linking of unrelated families, which is not possible with pBLUP. As an example, two families in pBLUP are not related because they do not share a common ancestor. In ssGBLUP, genotyping only one animal in each family would serve as a link between these two families; this relationship between these two genotyped animals will now create relationships among all animals in both families.The accuracy of prediction for MY in this study using GBLUP and ssGBLUP was 0.28 and 0.30, respectively. These were lower than reported studies using dairy cows as the reference population. A limitation of this study is the small data set. Female animals with production and genotype data will be added yearly to increase the reference population. Potential semen donor bulls will be genotyped to determine their BVs using the population of cows as the reference population.At present, the generation interval of AI buffalo sires is ∼8 years. With GS, young genotyped candidate bulls can be given BVs using females in the institutional herds as the reference population . ssGBLUPThis study determined the accuracy of genomic prediction and bias for MY, FY, and PY in Philippine dairy buffaloes wherein the reference population is composed solely of cows. GBLUP and ssGBLUP accuracies were, on average, 0.03 and 0.07 higher, respectively, compared to pBLUP accuracies. Moreover, prediction bias of the two genomic methods is lesser (closer to 1) compared to pBLUP. With the higher accuracy of prediction and lesser bias, it is suggested that PCC adopts the genomic method, that is, GLUP or ssGBLUP, in its genetic evaluation.


Observational research suggests that periodontitis affects pulmonary function; however, observational studies are subject to confounding and reverse causation, making causal inference and the direction of these associations difficult. We used Mendelian randomization (MR) to assess the potential causal association between genetic liability to periodontitis and pulmonary function.P < 5 × 10−6) from a genome-wide association study (GWAS) of 17,353 European descent periodontitis cases and 28,210 controls from the GeneLifestyle Interactions in Dental Endpoints consortium and the UK Biobank, and related these to SNPs from a lung function GWAS including 79,055 study participants of the SpiroMeta Consortium.We used six single-nucleotide polymorphisms (SNPs) associated with periodontitis  =  − 0.004 ). Replication analysis using genetic instruments from two different GWAS and sensitivity analyses to address potential pleiotropy led to no substantial changes in estimates.Collectively, these findings do not support a relationship between genetic liability for periodontitis and pulmonary function.Periodontitis does not seem to be a risk factor for worsening of pulmonary function. Periodontitis is a chronic, bacteria-initiated inflammatory disease, affecting ~ 50% of adults, with 11.2% suffering from severe periodontitis . The latYet, the observed association between periodontitis and pulmonary function could also be due to confounding, such as tobacco smoking , which iP < 5 × 10−6), after accounting for linkage disequilibrium , as instruments from GWAS of 17,353 European decent periodontitis cases and 28,210 controls from the GeneLifestyle Interactions in Dental Endpoints consortium and UK Biobank [1), lower forced vital capacity (FVC), and FEV1/FVC ratio by the corresponding log OR of the same SNP in the GWAS for periodontitis; and obtained standard errors by the delta method. Wald ratios were pooled using the primary multiplicative random effects inverse-variance-weighted (IVW) analyses [P < 5 × 10−4, P < 5 × 10−5) to strengthen the instrument and applied weak instrument analyses [www.phenoscanner.medschl.cam.ac.uk/) and the GWAS Catalog (www.ebi.ac.uk/gwas/); investigated the Cochran Q heterogeneity test, the I2 statistic, and the MR-Egger intercept test; performed leave-one-SNP-out analysis; and applied a suite of pleiotropy-robust methods  [We performed two-sample MR analysis using summary statistics from the largest available GWAS on periodontitis and pulmonary function , 8. We uanalyses . We trananalyses , 11. We outlier) . We usedoutlier) , 14 of aoutlier) . Height 1, FVC, and FEV1/FVC  per doubling in the odds of periodontitis. The primary analyses were supported when using more liberal SNP selection thresholds and in weak instrument analyses. We found no previously reported associations of genetic variants with tobacco smoking. The IVW estimates were consistent with estimates from robust methods. There was minimal heterogeneity between Wald ratios, and the MR-Egger intercept analyses did not indicate directional pleiotropy. IVW leave-one-out analysis did not identify any leverage points with high influence. In replication analysis, when summary data from two different GWAS of periodontitis were used, effect sizes were similar to the primary analysis. In negative control outcome analysis, the association  with height at 10 years provided additional reassurance for the robustness of the primary analysis.The IVW analysis did not suggest an effect of genetic liability to periodontitis on FEVThe association between periodontitis and pulmonary function seen in previous observational studies was not replicated when applying MR and could have been due to unobserved environmental confounding or selection bias . The stuCollectively, our data provided preliminary evidence that genetically proxied long-term exposure to periodontitis does not worsen pulmonary function. We emphasize the importance of triangulating multiple lines of experimental and observational research to strengthen causal inference .


Science in which the authors demonstrated that the relationship between stress and depression is moderated by a polymorphism in the promoter region (5-HTTLPR) of the gene SLC6A4. Replication has been weak and led many to challenge the overall significance of GxE research. Here, we utilize data from Add Health, a large, nationally representative, and well-powered longitudinal study to re-examine the genetic determinants of stress sensitivity. We characterize environmental sensitivity using a genome-wide polygenic indicator rather than relying on one polymorphism in a single candidate gene. Our results provide support for the stress-diathesis perspective and validate the scientific contributions of the original paper.We re-evaluate the findings of one of the most cited and disputed papers in gene-environment interaction (GxE) literature. In 2003, a paper was published in  Their work demonstrated that carriers of the “short” allele in the promoter region (5-HTTLPR) of the SLC6A4 gene were more sensitive to the effects of stress on depression compared to those who were homozygous for the longer repeat allele. To date, replication efforts have been inconsistent2 and the authors of a large meta-analysis concluded that there is “no evidence that the serotonin transporter genotype alone or in interaction with stressful life events is associated with an elevated risk of depression in men alone, women alone, or in both sexes combined”3. Similarly, researchers have elsewhere detailed the theoretical and statistical shortcomings of GxE research in when it is limited to the candidate gene-environment interaction (cGxE) work4. Together, these important criticisms of the cGxE work linking 5-HTTLPR genotype, stress exposure, and mental health may have inadvertently challenged the overall significance of the GxE perspective in general and the genetic origins of environmental sensitivity in particular.Nearly twenty years ago, Caspi et al.1 paper but we do not use the cGxE approach. Rather, we use a genome-wide polygenic indicator of overall environmental sensitivity5. We also examine the relevance of three similar but distinct GxE models and discuss the importance of considering the phenotype and environmental moderator when examining different models of genetic sensitivity6. It is our hope that the results of this paper continue to highlight the far-reaching significance of genetic determinants of environmental sensitivity across the medical, biological, and social sciences.In this paper, we use a large, nationally representative, and contemporary sample to demonstrate the significance of considering the genetic origins of environmental sensitivity as a genome-wide characteristic rather than a single polymorphism in one gene. To illustrate the importance of this perspective we examine a comparable model to that presented in the Caspi et al.7. Each of the three models anticipates that the most sensitive individuals will respond more strongly to stress exposure than the least sensitive (LS), but they differ from one another with respect to the intercept, which has important substantive implications. The results presented by Caspi et al. are best characterized by the stress-diathesis (SD) model. Here, environmentally sensitive individuals and their less sensitive counterparts do not differ from one another with respect to depressive symptoms in the least stressful environments. Differences in overall sensitivity, however, lead to a departure such that the environmentally sensitive individuals report significantly higher levels of depressive symptoms in increasingly stressful environments. The emphasis of this model is on the toxicity of stressful environments rather than the benefits of the least stressful environments, per se.Figure 9. As with the SD model, the VS model anticipates that environmentally sensitive individuals may respond more strongly to stress but suggests that these differences will be the most evident in the most positive (rather than low-stress) environments; that is, sensitive individuals derive the greatest psychological benefits in nurturing, supportive, and stress-free environments.This distinction is made clearer when one considers the vantage sensitivity model (VS) shown with the bottom, thick-dashed line of Fig. 110. This relationship is shown by the cross-over line with small dashes in Fig. Finally, the Differential Susceptibility (DS) model combines elements of the SD and VS models and suggests that the most environmentally sensitive individuals will report both higher levels of depressive symptoms in the most stressful environments and lower levels of depressive symptoms in the least stressful environmentsleast sensitive to the environment. Thus, the other lines represent points at which other genotypes are comparatively more sensitive to broad environmental stress than the LS group. The thin, solid line represents the Stress-Diathesis Model (SD), the small-dashed line represents the Differential Susceptibility Model (DS), and the large-dashed line shows the Vantage Sensitivity Model (VS).The solid bold line (LS) represents the comparison group for all three models; that is, the genotype that is 11, a PGS is a value that is assigned to each individual that is simply the product of an individual’s genotype at a single nucleotide polymorphism (SNP) and the value of the effect for that loci identified in an independent and well-powered discovery sample, and then summed across the total number of SNPs for which the individual was genotyped. These scores tend to be normally distributed and are standardized to have an intuitive interpretation. An important contribution to work on PGS construction came from Keers and colleagues5 who used comparable techniques but instead of focusing on the mean level of an outcome to derive the effect size estimates for each SNP, they focused on discordance among twin pairs to identify the phenotype of environmental sensitivity. Genome-wide regression models were then used to retrieve the beta estimates and risk allele for their overall environmental sensitivity PGS. Thus, reassessing the results of the Caspi et al. paper using an indicator of genetically oriented environmental sensitivity beyond the one candidate gene  denotes an important contribution to work in this area. To our knowledge, ours is the second paper to apply this PGS to depression longitudinally, but offers a larger and more diverse sample and focuses more broadly on the stress-diathesis relationship12.The second important contribution of our paper is the application of polygenic score (PGS) techniques to the evaluation of the three models of genetically oriented environmental sensitivity. As described in great detail elsewhere13. Add Health is a nationally-representative, admixed sample of young adults in the U.S., allowing us to expand our analysis beyond individuals of European genetic ancestry, which has unfortunately become the norm14. The original paper by Caspi and colleagues only included “Caucasian non-Maori study members” (n = 387) and research since that time, especially work utilizing PGS estimates, has limited the application of summary statistics to individuals from the same genetic ancestral group of the discovery sample. In our analyses, we analyze all genetic ancestry and racial/ethnic groups together for three reasons: (1) theoretically, we do not agree with the belief that the genetic associations for environmental sensitivity differ as a function of one’s racial identity and experience; (2) substantively, the continued stratification of individuals by ethnic classification when examining genetic associations is a problematic practice foreseen nearly 30 years ago in Troy Duster’s Backdoor to Eugenics (1990) and the scientific community must work diligently to stop such practices15; and (3) methodologically, we are concerned not with a single, causal biological pathway but instead an overall indicator of genetic associations . In ancillary analyses we estimate the same models only with those within the European genetic ancestry group and who self-identify as non-Hispanic White to assuage any further concerns; as expected, the results are virtually identical (available upon request). Another possibility is that the sensitivity genotype is correlated with stress exposure . Those who are more sensitive to stressors may make greater efforts to avoid situations in which they may be exposed to additional sources of stress or strain. As others have pointed out16, this active form of rGE can make it difficult to interpret the meaning of a GxE interaction term. Accordingly, we estimated a weak baseline correlation between stress and our PGS for environmental sensitivity  that loses all significance  once controls for genetic ancestry are added.Finally, we add to the literature by considering all respondents in the Add Health study for whom genotyped data are available In summary, in this paper, we reassess the work of Caspi et al. by (1) examining the utility of a genome-wide approach to understanding environmental sensitivity; (2) evaluating our results in terms of an updated theoretical backdrop; and (3) examining similar associations in a different environmental setting , birth cohort and historical period, among a broader and older age group, and without restrictions to a single race/ethnic group.18. The three rows at the top of this table summarize the primary findings of our paper. We report a main effect of stress exposure  described in the Methods. Given that the environmental sensitivity PGS is standardized, this estimate reflects the effect of stress on depression for those with an average PGS value. The second value presents the beta estimate for the effect of the PGS on depression. As expected by the stress-diathesis (SD) model, the PGS  is not significantly associated with depression among those with 0 stressful life events. The primary estimates are in bold and provide additional support for the SD model. Specifically, the interaction between stress and the PGS is positive and statistically significant . This suggests that the positive association between stress and depressive symptoms is roughly 14.4% stronger among those with a one standard deviation-increase in a genome-wide measure of environmental sensitivity. Figure Tables Estimates are derived from Model 3 of Table 19. Other work has shown that the relationship between school-level norms regarding cigarette and alcohol consumption and individual-level behaviors is stronger among carriers of the S’-allele in the 5-HTT gene21. Such “environmentally susceptible” individuals smoke and drink more than they would in other contexts and do so relative to their peers in schools with a high prevalence of these behaviors. These different examples are precisely what Keers and others were trying to capture with their broad indicator of environmental sensitivity linked to genetic loci across the genome5. To further illustrate this point we estimate comparable models in which the PGS is calculated for depressive symptoms or major depressive disorder . Add Health is a nationally representative cohort drawn from a probability sample of 80 U.S. high schools and 52 U.S. middle schools, representative of U.S. schools in 1994–1995 with respect to region, urban setting, school size, school type, and race or ethnic background . Our analyses use data from Wave V which was conducted during 2016–2018 to collect social, environmental, behavioral, and biological data with which to track the emergence of chronic disease as the cohort advanced through their fourth decade of life. Importantly, the Wave V survey was expanded to obtain retrospective reports of birth and childhood circumstances to supplement existing early life data.Wave V contains a total of 12,300 respondents of which 7033 had genome-wide data. After removing those with missing information on depressive symptom, our final sample contained a total of 6472 respondents. Descriptive statistics for this sample are shown in Table 2. Genotyping was completed over three years funded by R01 HD073342 (PI Harris) and R01 HD060726 . Add Health utilized two Illumina platforms for genotyping: the Illumina Human Omni1-Quad BeadChip for the majority of samples and the Illumina Human Omni-2.5 Quad BeadChip for the remainder. The two platforms utilized tag SNP technology to identify and include over 1.1 million and 2.5 million genetic markers, respectively, from Omni1 and Omni2.5 derived from the International HapMap Project and the most informative markers from the 1000 Genomes Project (1KGP). The genetic markers include known disease-associated SNPs from multiple sources, ancestry-informative markers, sex chromosomes, and ABO blood typing markers. The platforms also included probes for the detection of copy number variation (CNV) covering all common CNV regions and more than 5000 rare CNV regions. After quality control procedures, genotype data were available for 9974 individuals: n = 7917 from the Illumina HumanOmni1-Quad chip and for 2057 individuals from the Illumina HumanOmni2.5-Quad chip. After filtering, the Add Health genotype data contained n = 609,130 single-nucleotide polymorphisms (SNPs) common to both chips.At Wave IV, Add Health collected Oragene saliva samples from consenting participants , and requested a second consent to archive their samples for future genomic studies. Approximately 80% consented to archive and were thus eligible for genome-wide genotyping1. Specifically, we incorporated questions from Wave V concerning employment/job stress, financial stress, housing stress, physical/mental health stress, and relationship stress into an overall, five-point summative measure, with a value of 1 representing generally low stress and 5 representing generally high stress. Our measure of genetic susceptibility to stress is captured by a PGS based on summary statistics from Keers et al.5, who instead of focusing on the mean level of an outcome to derive the effect size estimates for each polymorphism, emphasized discordance among twin pairs to identify the phenotype of environmental sensitivity. Genome-wide regression models were then used to retrieve the beta estimates and risk allele for their overall environmental sensitivity PGS. Our models also control for the first five genetic principal components, as well as age, biological sex, race/ethnicity, and educational attainment.Our primary outcome of interest, depression, is a concatenation of several questions asked in the interview. Specifically, we create a four-point scale measuring how frequently the respondent reported (1) being unhappy, (2) unable to “shake the blues,” (3) felt sad, or (4) felt depressed (self-diagnosed). Our scale is coded such that 1 = Generally Happy/Good Mood, while 4 = Extremely Unhappy across the aforementioned variables. Our measure of environmental stress was designed to capture the components/dimensions of stressed referenced in the original paper by Caspi et al.SNPij is the allele frequency of the jth SNP for the ith individual and βj is the estimated association between SNPj and within-pair variability in emotional problems among monozygotic twins as reported by Keers et al.5. The raw PGSs are then standardized (μ = 0 and σ = 1) within ancestry groups to account for between-group population stratification.PGSs are calculated as a weighted sum, such that the raw PGSs for environmental sensitivity are calculated as:The Add Health genotyped sample is restricted to four genetic ancestry groups: (1) European, (2) African, (3) Latin American, and (4) East Asian. To identify respondents in these four genetic ancestry groups, a principal component analysis is conducted on all unrelated members of the full genotyped sample. Estimates are then projected onto the remaining related individuals. Each genetic ancestry group is defined by distance from the mean of the first two principal components of the genetic data. To be included in the Latin American, East Asian, and European ancestry groups individuals must be within ± 1 standard deviation of the mean of the first two principal components of the genetic data estimated from all individuals in the Add Health genome-wide data who self-identified as Hispanic, Asian, and non-Hispanic White, respectively. To be included in the African ancestry group individuals must be within ± 2 standard deviations of the mean of the first principal component and ± 1 standard deviation of the mean of the second principal component estimated from all individuals in the genome-wide data who self-identified as non-Hispanic Black.22 for more detail). However, genetic ancestry should not be conflated with race or ethnicity. Race and ethnicity are social constructs based on a multitude of factors, of which genetic ancestry may or may not be included depending on historical and societal differences in racialization23. Consequently, not all individuals included in a given genetic ancestry group may self-identify or be classified by others as the same race and/or ethnicity as other members of their genetic ancestry group.While genetic ancestry and race/ethnicity are correlated (r = 0.89), they are distinct constructs and attempts to conflate the two are problematic. More specifically, population stratification refers to differences in genetic variation between geographical ancestry groups. Due primarily to the genetic bottle neck created by the small number of humans (~ 2000) who migrated out of Africa early in human history and the tendency for people to procreate with individuals from the same or nearby geographic regions, genetic variance across the entire genome is highly correlated with geography  with full coding of variables and models is available upon request.


Genetic association data from national biobanks and large-scale association studies have provided new prospects for understanding the genetic evolution of complex traits and diseases in humans. In turn, genomes from ancient human archaeological remains are now easier than ever to obtain, and provide a direct window into changes in frequencies of trait-associated alleles in the past. This has generated a new wave of studies aiming to analyse the genetic component of traits in historic and prehistoric times using ancient DNA, and to determine whether any such traits were subject to natural selection. In humans, however, issues about the portability and robustness of complex trait inference across different populations are particularly concerning when predictions are extended to individuals that died thousands of years ago, and for which little, if any, phenotypic validation is possible. In this review, we discuss the advantages of incorporating ancient genomes into studies of trait-associated variants, the need for models that can better accommodate ancient genomes into quantitative genetic frameworks, and the existing limits to inferences about complex trait evolution, particularly with respect to past populations. The last decade has seen dramatic advances in our understanding of the genetic architecture of polygenic traits . The advSkin, hair and eye pigmentation are among the least polygenic complex traits; though more than a hundred pigmentation-associated loci have been found, their heritability is largely dominated by large-effect common SNPs . AdditioRecently, ancient DNA (aDNA) studies have attempted to reconstruct pigmentation phenotypes in ancient human populations, although the extent to which these predictions are accurate remains uncertain. These reconstructions have been mostly focused on ancient individuals from Western Eurasia, due to the relatively higher abundance of SNP-phenotype associations from European-centric studies, and the poor portability of gene-trait associations to more distantly related populations , 2019. FSome aDNA studies have sought to systematically investigate how pigmentation-associated variants were introduced and evolved in the European continent. The other trait that has shared comparable prominence with pigmentation in the aDNA literature is standing height. In contrast to pigmentation, the genetic architecture of height is highly polygenic . The herHowever, the inference of standing height from skeletal remains is not without its own problems. Both The availability of genome sequences from archaic humans, like Neanderthals and Denisovans, has greatly expanded our understanding of their demographic history and interactions with modern humans , 2017. HKnowledge about admixture between archaic and modern humans has led to a recent flurry of exploratory studies concerning the potential impact of archaic variants on complex traits in present-day populations. Various approaches have been used to identify introgressed archaic DNA putatively under positive selection in modern humans . OverallAdditionally, comparisons between the combined phenotypic effects of Neandertal variants and frequency-matched non-archaic variants have revealed that Neanderthal DNA is over-proportionally associated with neurological and behavioural phenotypes, as well as viral immune responses and type 2 diabetes . These gOne key limitation to these approaches is that only about 40–50% of the Neandertal genome can be recovered in present-day humans, and therefore discoverable in such analyses . FurtherFurthermore, it remains questionable how transferable any phenotypic associations are between modern and archaic humans, given the difficulties of transferring associations between present-day populations . All of Recent studies have also aimed to predict the phenotypic effects of archaic DNA without relying on introgression in present-day populations see . ColbranPerhaps the most fascinating question about the evolution of complex traits in humans is whether they were subject to natural selection. Current methods to detect polygenic adaptation have mainly focused on present-day populations; using either differences between populations, or variation within them, to identify polygenic adaptation. For example, A prominently reported example of polygenic adaptation is that of selection for increasing height across a north-south gradient in Europe . Most stResidual stratification is a major concern for GWAS, even among a relatively homogeneous cohort like the UK Biobank. For large sample sizes, low-frequency variants (MAF ≤ 0.05) make a significant contribution to the heritability of many complex traits , but theA recent analysis indicated that a substantial component of the unidentified heritability for anthropometric traits like height and BMI lies within large effect rare variants, some with MAF as low as 0.01% . HoweverIn addition to these issues, the majority of SNP associations inferred from GWAS are likely not the causal alleles. Instead, GWAS predominantly identifies SNPs which are in high linkage disequilibrium (LD) with causal alleles. Most GWAS also assume a model in which all complex trait heritability is additive and well tagged by SNPs segregating in the cohort; although some GWAS do include non-additive models e.g., . ConsequOver the last decade, paleogenomic studies have repeatedly demonstrated that the evolutionary histories of human populations are characterized by recurrent episodes of divergence, expansion, migration and admixture  via the construction of tree sequences , which hAnother area in which ancient genomes offer unique potential is in detecting polygenic adaptation in response to environmental change. The time-series nature of ancient genomes provides the potential for the incorporation of paleoclimate reconstructions e.g.,  into tesUltimately, the ancient genomics community must come to terms with the limitations of genetic hindcasting. Ancient genomes provide an unprecedented window into our past, but this window is often blurry and distorted. There is still a lot of information waiting to be obtained from ancient DNA, and some of the blurriness might ultimately come into focus as computational methods continue to improve. But we must also accept the fact that many aspects of past human biology—including physical characteristics and disease susceptibility—might be irrevocably lost to the tides of history. Ancient genome sequences are, after all, molecular fossils: imperfect and degraded records of lives that ceased to exist long ago.EI-P and FR reviewed and edited the manuscript. All authors wrote the original draft of the manuscript and approved the submitted version.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.


Estimation of direct and indirect  genetic effects on phenotypes is becoming increasingly important. We compare several multivariate methods that utilize summary results statistics from genome-wide association studies to determine how well they estimate direct and indirect genetic effects. Using data from the UK Biobank, we contrast point estimates and standard errors at individual loci compared to those obtained using individual level data. We show that Genomic structural equation modelling (SEM) outperforms the other methods in accurately estimating conditional genetic effects and their standard errors. We apply Genomic SEM to fertility data in the UK Biobank and partition the genetic effect into female and male fertility and a sibling specific effect. We identify a novel locus for fertility and genetic correlations between fertility and educational attainment, risk taking behaviour, autism and subjective well-being. We recommend Genomic SEM be used to partition genetic effects into direct and indirect components when using summary results from genome-wide association studies. Estimating direct and indirect effects of genotypes on phenotypes is important for genetic analyses such as Mendelian randomization. Here the authors compare five different methods to estimate direct and indirect genetic effects using summary results statistics that account for sample overlap. Likewise, it is also important to estimate the direct effect of an individual’s own genotype on their own phenotype independent of any indirect parental effects. These estimates can subsequently be used in Mendelian randomization studies to make causal inferences, without introducing biases due to dynastic effects and assortative mating8. However, for correct inference to be made regarding maternal genetic effects and an individual’s own genetic effect independent of parental effects, analyses must adjust for the individual’s own genetic effect on their own outcome as well as one or both parents. This adjustment has traditionally been performed using conditional analyses applied to individual level genotypes from mother-offspring pairs or parent-offspring trios; however, there are few cohorts worldwide with large numbers of genotyped mother-offspring pairs or parent-offspring trios with offspring phenotypes, leading to limited statistical power in many studies. We have developed a structural equation model (SEM) that can partition genetic effects into maternal and offspring mediated components9. This model can incorporate data from genotyped mother-offspring pairs with offspring phenotypes, mother-offspring pairs with maternal genotypes and both mother and offspring phenotypes, individuals with their own genotype and phenotype and mothers with their own genotype and their offspring’s phenotype. We have recently described how the maternal and offspring partitioning from this SEM can be used to facilitate large-scale two-sample Mendelian randomization studies investigating whether maternal exposures are causally related to offspring outcomes10.There is growing interest in estimating maternal genetic effects, via the intrauterine environment, on offspring outcomes , we hypothesize that some of these methods could be appropriate. However, because we are interested in using the adjusted maternal and offspring genetic effect in downstream analyses, such as Mendelian randomization, we need methods that would provide unbiased estimates of parental and offspring genetic effects (and standard errors) for each variant. In addition, if we are to use publicly available summary statistics from large GWAS, such a method would need to account for any known or unknown overlap of individuals contributing to maternal  and offspring GWAS.Although our SEM is flexible in terms of incorporating many study designs, it is computationally intensive when using individual level data, prohibiting its use for genome-wide association studies (GWAS). Therefore, we wanted to identify other existing methods that could be used on summary results statistics from GWAS to estimate the conditional maternal  and offspring genetic effects on a trait. There are a number of multivariate methods available that utilize summary statistics from GWAS of multiple traits. For example, metaCCAIn this manuscript, we compare several different multivariate methods to identify the most appropriate method for partitioning the genetic effect of a trait into maternal and offspring components, based on how well the effect estimates compare to those from our SEM using individual level data, the computational time and how well the method accounts for unknown sample overlap. We use birth weight to compare the different methods as we have a substantial number of known associated genetic loci for birth weight, with the genetic effect partitioned into maternal and offspring genetic components. We subsequently use the most appropriate method to conduct conditional GWAS of fertility, partitioning the effects into parental and offspring mediated components providing evidence for how these different loci exert their effect on number of children in a family.9  and linear approximation of the SEM3, we identified three published methods including multi-trait analysis of GWAS (MTAG)13, multi-trait-based conditional and joint analysis using GWAS summary data (mtCOJO)16 and Genomic SEM17. MTAG is a multivariate method, which uses genome-wide GWAS summary results from multiple correlated phenotypes to increase power to detect pleiotropic loci. mtCOJO is another multivariate method, which uses summary results data but is designed to estimate genetic effects on a trait conditional on a correlated phenotype(s). Although MTAG and mtCOJO are not specifically designed to partition genetic effects into maternal and offspring components (i.e. by conditioning on a correlated genotype), they are user friendly and computationally efficient, and given the dearth of existing software packages to generate conditional genetic effect estimates using genome-wide summary results data, we were interested in investigating whether they would approximate the effects of interest accurately. Genomic SEM on the other hand is a highly flexible  method that allows users to specify a wide range of models to fit to the data. A summary of each of the methods and their underlying assumptions is provided in Table We searched the literature for multivariate methods that fit the following four criteria: 1) had published code or software, (2) used summary results statistics and did not require individual level data, (3) accounted for sample overlap and (4) produced an effect size estimate and standard error for each trait. In addition to our published structural equation model (SEM) had publApproximately 19 million genetic variants were included in the GWAS analysis of own and offspring birth weight that passed our filtering criteria . We excluded variants from the SEM using summary statistics if the minor allele frequency in the sample was <0.5%; this led to ~11 million genetic variants with results. MTAG also implements additional filtering criteria; variants with missing values, variants that are not SNPs, variants with duplicated rs numbers and variants that are strand ambiguous are excluded leading to ~14 million genetic variants with results.18 to estimate the sample overlap between the GWAS of own and offspring birth weight. We observed a regression intercept of 0.1287 (0.0078) in the analyses where there were individuals in both the GWAS of own and offspring birth weight, indicating that ~91,790 individuals were in both GWAS . In the analyses where there were unique individuals in the GWAS of own and offspring birth weight, the observed regression intercept from LD score regression was 0.0161 (0.0064), indicating that ~8396 individuals were in both GWAS . These estimates of sample overlap were used in the SEM analysis using covariance matrices derived from the GWAS summary statistics.We used bivariate LD score regression3. Due to the additional exclusions, MTAG had 258 SNPs available for comparison and the SEM using summary statistics had 298 SNPs. We use the estimates from the SEM using individual level data as a baseline comparator as we have previously shown that they are asymptotically unbiased estimates of the maternal and offspring genetic effects9. Figure We compared the effect size and standard errors estimated using the SEM with the individual level data to those estimated using methods based on the summary statistics from the GWAS of own and offspring birth weight for the 300 autosomal genome-wide significant SNPs identified in the latest GWAS of birth weight9 that there is an increase in power, due to a reduction in the standard error, when individuals with both their own and their offspring’s phenotype are included in a model that specifies this relationship. Therefore, the 8396 individuals estimated to overlap between the GWAS will result in a reduction of the standard error in the SEM using summary statistics (where we model this relationship) in comparison to the SEM using individual level data (where no sample overlap is modelled). As expected due to the difference in purpose of the method, the standard errors estimated using MTAG and mtCOJO were smaller than those estimated by the SEM using individual level data for the maternal and offspring genetic effect, with and without sample overlap, with the largest difference for MTAG.The comparison of the standard errors for the genetic effects for each of the 300 genetic variants are displayed in Fig. min = 0.039, maternal effect Pmin = 0.083), the SEM based on covariance matrices derived from the summary statistics , the linear approximation of the SEM  or Genomic SEM . Similar results were seen when there was no sample overlap between the GWAS of own and offspring birth weight  SNPs are presented in Table Manhattan plots and Q–Q plots for each of the conditional GWAS are presented in Supplementary Figs. 3. Although a large number of birth weight associated loci were detected using some of the methods , particularly when there is no sample overlap, which is in line with the inflated LD score intercepts. MTAG also detected a substantial number of false positives, whereas the three other methods only detected up to three false positives.We defined a birth weight associated locus as being 500 kb from a previously identified birth weight associated sentinel SNPThrough the methods comparison, we have shown that Genomic SEM outperforms the other methods in terms of its ability to accurately estimate conditional effect sizes and standard errors at individual genetic variants and its ability to account for sample overlap appropriately. It is also a highly flexible method, allowing the estimation of other genetic effects, such as paternal-specific effects. To illustrate application of this method and how it can be extended to simultaneously estimate conditional maternal, paternal and offspring genetic effects, we applied it to fertility data from the UK Biobank. Given an offspring’s genotype is correlated ~0.5 with both parental genotypes, and the offspring could influence parental decision to have additional children , it is important to adjust for offspring specific genetic effects when investigating the genetic determinants of fertility. We will refer to this offspring specific genetic effect as a sibling-specific effect as we are estimating it using the number of siblings an individual has. Additionally, we will estimate the female-specific genetic effect on fertility using the number of children mothered and the male-specific genetic effect on fertility using the number of children fathered.18, we estimated the genetic correlations between the unconditional and conditional GWAS. There was a strong genetic correlation between the number of children mothered  and the female-specific effect on fertility  and similarly between the number of children fathered and the male-specific effect on fertility . In contrast, the genetic correlation was weaker between the number of siblings and the sibling-specific effect, particularly once male-specific effects were incorporated . We also used LD score regression to estimate the genetic correlation between the conditional GWAS for male and female fertility , male fertility and sibling effects  and female fertility and sibling effects .Results from the unconditional GWAS analysis conducted in BOLT-LMM of the number of children fathered, the number of children mothered and the number of siblings can be visualized in Supplementary Fig. g = −0.17, SE = 0.03, P = 7 × 10−8; female rg = −0.20, SE = 0.03, P = 3 × 10−13) and positively genetically correlated with risk-taking behaviours . Subjective well-being was also genetically correlated with fertility; positively correlated with male fertility  and negatively correlated with sibling effects .Both male and female fertility from the conditional analysis were negatively genetically correlated with years of education  associated with the number of children mothered, after conditioning on the number of siblings, and one locus associated with the number of siblings, conditional on the number of children mothered. When we extended the Genomic SEM model to estimate female, male and sibling-specific genetic effects in analysis two, we identified six loci associated with maternal-specific effects, one locus associated with paternal-specific effects  and one locus associated with sibling-specific effects , even though it is a different locus to the one identified on chromosome 3 for the male and female-specific effects. The full results for each of these genome-wide significant loci are presented in Supplementary Data ESR1, CADM2), number of sexual partners (CADM2), educational attainment  and risk-taking behaviour (MDFIC).The results from analysis one estimating the female and sibling-specific effects only on fertility, where we identified four loci . In particular, MTAG and mtCOJO are multivariate methods that were specifically developed for other purposes . Previous work has shown that both methods perform excellently when applied to the situations for which they were originally developedOf the comparisons that we made across all the different methods , Genomic SEM performed best on all comparisons except computational time. In contrast, mtCOJO and MTAG did not yield accurate estimates or SEs of conditional maternal and/or offspring genetic effects. Although we based our conclusions on findings from a single large dataset, we believe that our results are likely to hold more generally and are a reflection of Genomic SEM’s flexibility in being able to accurately model the relationship between parental and offspring genotypes (i.e. neither MTAG nor mtCOJO specifies this relationship accurately—see below for further discussion of this point) and Genomic SEM’s ability to take into account sample overlap and cryptic relatedness across the different GWAS .18, which was overestimated in both of our analyses with and without sample overlap. This overestimation has been described previously when there is population stratification19, and the authors suggest a modified formula to calculate the overlap. We did not use this modified formula in the current analysis as we and others have previously used LD score regression to estimate sample overlap and we wanted to get an idea of how this would perform20. It could also be due to cryptic relatedness between the GWAS; for example, there might be close relatives in both GWAS of own and offspring birth weight that are adding to this overestimation. Second, estimates of the maternal and offspring specific genetic effects can vary dramatically if the phenotypic correlation between the maternal and offspring phenotype is misspecified (results not shown). Given we had access to the phenotypic data that was used for the GWAS analysis, we were able to obtain a good estimate of the phenotypic correlation; however, this might be more difficult to estimate accurately if using publically available GWAS results. Third, this method assumes that the effect size estimates from the unadjusted GWAS are estimated accurately and does not account for the standard errors. Therefore, for low frequency genetic variants that have large standard errors, we saw the method performed poorly. We also found the method performed poorly for a subset of genetic variants where the maternal-specific genetic effect on the offspring outcome went in the opposite direction to the offspring specific genetic effect, particularly when there was no sample overlap between the unadjusted GWAS. However, as we showed in the initial paper describing the SEM9, including some raw data in addition to the covariance matrices estimated from the summary statistics improves estimation of the maternal and offspring specific genetic effects. It is likely that the SNPs that reached genome-wide significance using this method, but are unknown birth weight associated loci, are false positives for three main reasons; (1) as seen in the Manhattan plots presented in the Supplementary Material, the majority of these SNPs are singletons and not part of LD blocks, (2) none of the other methods included in the comparison identified these loci and (3) the most recent GWAS of birth weight3, which included the data in this study in addition to data from many birth cohorts and also partitioned the genetic effect into maternal and offspring components, did not identify these loci.There were several limitations with using summary statistics in the SEM, which resulted in inflation in the test statistics. First, we estimated the sample overlap using LD score regressionThe linear approximation of the SEM assumes no sample overlap, so performed well when there was no overlap; however, the standard errors were overestimated when there was sample overlap, deflating the test statistics. The formula for estimating the standard error of the maternal and offspring specific effects could be extended to account for any sample overlap, but it would need to rely on LD score regression to estimate the sample overlap which has issues as described previously. Otherwise, this method performed well in terms of accurately estimating the maternal and offspring specific effects and was one of the fastest methods to perform the conditional analysis.13. In contrast, our results suggest that if the focus is on locus characterization/accurately partitioning effects into maternal and offspring components  then one of the SEM based procedures discussed in this manuscript will be more appropriate.Given that MTAG is not designed to estimate maternal and offspring specific effects at individual loci, it was not surprising that it performed poorly in terms of accurately partitioning the genetic effect into maternal and offspring components. This is because the MTAG model estimates combined pleiotropic genetic effects on both phenotypes  that other methods be used.Finally, mtCOJO was originally developed to condition the outcome on one or more covariate phenotypes21, and the variance explained by common genetic variants (SNP based heritability) has been estimated to be ~10%22. Although there have been two GWAS previously conducted on number of children born24, no study to date has estimated the conditional male, female and sibling genetic effects at individual genetic loci. Both previous studies observed significant genetic correlations for the number of children between men and women , which our findings from the conditional analysis are consistent with . We also identified a strong negative genetic correlation between both male/female fertility and sibling effects; this is likely to be due to a technical artefact of the analyses as described by Wu and colleagues25. We replicate the negative genetic correlation between years of education and fertility described in Barban et al.24. Furthermore, we found a positive genetic correlation between risk-taking behaviour and both male and female fertility, showing that having more increasing alleles for the number of children is associated with a higher genetic risk for partaking in risk-taking behaviours. Due to our ability to partition the genetic effect, we were also able to identify a negative genetic correlation between male fertility and autism, indicating that fathers at genetically increased risk of autism are more likely to have fewer children. This relationship between fertility and autism, particularly in males, has previously been shown in a large population based study in Sweden using patients with various psychiatric disorders and their unaffected siblings26. Interestingly, we identified a positive genetic correlation between male fertility and subjective well-being and a negative genetic correlation with the sibling effect. We identified six of the previous 28 statistically independent loci for fertility, and one of the 16 loci for childlessness23. In addition, we identified a novel locus on chromosome 9 that is associated with female fertility. This locus is near RFX3, which harbours genetic variants that have previously been associated with smoking initiation. Our genetic correlation analysis shows a positive genetic correlation between both male and female fertility and smoking initiation; however, it does not meet our multiple testing threshold.We performed the first GWAS partitioning the genetic effect into male and female fertility specific genetic effects and a sibling-specific effect. The heritability of number of children ever born has been estimated to be between 0.24 and 0.43In conclusion, when estimating maternal and offspring  specific genetic effects on an offspring outcome using GWAS summary statistics, we recommend using Genomic SEM.The UK Biobank has ethical approval from the North West Multi-Centre Research Ethics Committee (MREC), which covers the UK, and all participants provided written informed consent. UK Biobank phenotype data was available on 502,543 individuals, of which 280,142 reported their own birth weight at either the baseline or first two follow-up visits. There were 7701 individuals who were part of a multiple birth and were excluded from the analyses. There were 10,670 individuals who reported their birth weight at more than one visit, with 83 individuals reporting the two values to be different by more than 1 kg; these individuals were excluded from the analyses. For those individuals who reported different values between baseline and follow-up (<1 kg) we took the measure from the first reported visit for the analyses. Finally, we excluded individuals who reported their birth weight to be <2.5 kg or >4.5 kg, as these are implausible for live term births before 1970. In total, 234,154 individuals had data on their birth weight matching our inclusion criteria.N = 216,782). We excluded individuals with multiple measures that differed by >1 kg (N = 29) or if their birth weight was <2.2 kg (5 pounds) or >4.6 kg (10 pounds), leaving 210,423 individuals with birth weight of their first child matching our inclusion criteria.Women in the UK Biobank were also asked to report the birth weight of their first child to the nearest pound and were converted to kilograms for analyses  in the 1000 genomes samples. The UK Biobank samples were then projected into this PC space using the single nucleotide polymorphism (SNP) loadings obtained from the PC analysis using the 1000 genomes samples. The UK Biobank participants’ ancestry was classified using K-means clustering centered on the three main 1000 genomes populations . Those clustering with the European cluster were classified as having European ancestry. The UK Biobank participants were asked to report their ethnic background. Only those reporting as either “British”, “Irish”, “White” or “Any other white background” were included in the clustering analysis. Second, to identify a subset of unrelated individuals in the UK Biobank, we generated a genetic relationship matrix in the GCTA software package28 (version 1.90.2) and excluded one of every pair of related individuals with a genetic relationship greater than 9.375%. A subset of 257,696 individuals with genotype data, a valid birth weight for themselves or their first child, were unrelated and were genetically of white European ancestry remained for analysis. Of these, 72,274 were men so only reported their own birth weight, 25,951 women reported only their own birth weight, 73,968 reported only the birth weight of their first child and 85,503 reported both. We adjusted both the individuals’ own birth weight and the birth weight of their first child for the principal components provided by the UK Biobank, assessment center and genotyping array, and sex for own birth weight, and then created z-scores.We analysed genetic data from the April 2018 release of imputed data from the UK Biobank, a resource that is described extensively elsewhereN = 183,728) and the GWAS of offspring birth weight . The second contained all of the data for the GWAS of offspring birth weight  but excluded those individuals from the GWAS of own birth weight that were included in the GWAS of offspring birth weight .Because we were interested in how each of the statistical methods handled sample overlap, we created two sets of data Fig. . The fir29 to account for population structure and subtle relatedness. Only autosomal genetic variants which were common , had Hardy-Weinberg equilibrium P-value > 1 × 10−6 and missingness <0.1 were included in the genetic relationship matrix (GRM). We excluded genetic variants with an INFO score < 0.4 and minor allele frequency <0.1% from the analysis in BOLT-LMM . We then used the summary statistics from the GWAS of own and offspring birth weight in the following analyses to estimate the conditional maternal and offspring genetic effects at each genetic variant.GWAS of own and offspring birth weight was conducted using a linear mixed model implemented in BOLT-LMM v2.3.29  and the sample size. We assume that the allele frequency, beta coefficient and variance is consistent across the following three groups, but the sample size will differ: individuals with their own phenotype only, individuals with their offspring’s phenotype only and individuals with both. We therefore need to estimate the sample overlap from the summary statistics in order to include the sample size for each of the covariance matrices. To do this, we performed bivariate linkage disequilibrium (LD) score regression (version 1.0.0) analysis using the summary statistics from the GWAS of own birth weight and the GWAS of offspring birth weight and used the regression intercept to estimate the number of individuals in both analyses. We used a phenotypic correlation between own and offspring birth weight of 0.24 in the calculation, which was estimated using the cleaned phenotype data that was included in the GWAS analysis. We have previously shown that the SEM has difficulty optimizing with low frequency variants, so we excluded SNPs with a minor allele frequency less than 0.5%. For each genetic variant we then calculated the observed covariance matrices from the summary statistics and fit the SEM with the relevant estimated sample sizes. We calculated a Wald P-value for the maternal and offspring genetic effects using the effect size estimates and their standard errors. We conducted the analysis of each chromosome in parallel to reduce the computational time. Analyses were conducted in R (version 3.4.3) using the OpenMx package (version 2.6.9).The SEM to estimate the adjusted maternal and offspring genetic effects has been described in detail previously3. This model uses a linear transformation of the effect sizes from the GWAS of own birth weight and the GWAS of offspring birth weight based on the principles of ordinary least squares linear regression. The offspring effect at each genetic variant is estimated as:We have previously derived a weighted linear model that is a good approximation of the SEM but substantially less computationally intensiveβ̂_(o_adj) is the offspring effect adjusted for the effect of maternal genotype, β̂_(m_unadj) is the unadjusted maternal effect from the GWAS of offspring birth weight and β̂_(o_unadj) is the unadjusted offspring effect from the GWAS of own birth weight. Likewise, the maternal effect is estimated as:And the corresponding standard error is:β̂_(m_adj) is the maternal effect adjusted for the effect of offspring genotype. The full derivation can be found in Warrington et al. (2019)3. Similar to the SEM using summary statistics, we calculated a Wald P-value for the maternal and offspring genetic effects using the effect size estimates and their standard errors. This method assumes that the two unadjusted GWAS are independent, and do not contain any sample overlap. We performed the linear transformation in R (version 3.4.3) and each of the chromosomes were run in parallel to reduce the computational time.And standard error is:13. It is based on the idea that when GWAS estimates from different traits are correlated, the SNP effect estimates can be improved by incorporating information from the other correlated traits. This method was not developed to estimate SNP effects conditional on parental genotypes; however, we were interested in investigating how well it approximated the maternal and offspring genetic effects on birth weight , we wanted to investigate how well this approach would approximate conditioning on the genotype. In other words, we were interested in investigating how well β̂_ approximates β̂_o from the SEM for SNP i, and β̂_ approximates β̂_m.mtCOJOTo conduct analysis in mtCOJO, we needed a reference sample with individual level genotypes for LD estimation. Therefore, we randomly sampled 50,000 individuals from the UK Biobank and extracted their imputed genetic data, using Plink2 (released 18 March 2019), for each of the unique genetic variants that were included in the cleaned GWAS summary statistics. mtCOJO could not handle genetic variants that had the same rs number but different alleles , so we removed all duplicate rs numbers from the reference dataset. Using this reference dataset, we conducted the mtCOJO analysis with the default parameters in GCTA (version 1.92.0beta3), using the summary statistics from the GWAS of own birth weight as the outcome and conditioning on offspring birth weight and then using the summary statistics from the GWAS of offspring birth weight as the outcome and conditioning on own birth weight.17 is a highly flexible, two stage multivariate statistical method for analysing the joint genetic architecture of traits using GWAS summary results statistics. In stage one, a K order genetic covariance matrix is estimated from the genome-wide summary results data of K GWAS using LD score regression17. Estimates of the standard errors for each of the variance-covariance terms, which account for sample overlap between the GWAS are also obtained. This stage contrasts with ordinary structural equation modelling, which uses a covariance matrix obtained from individual level data . In stage two, a user specified model is then fit to the genetic covariance matrix in an attempt to explain the underlying pattern of genetic correlations across the traits in terms of a series of latent genetic variables. The model can be augmented through the addition of observed SNP variables, providing the opportunity to perform multivariate tests of association between individual SNPs and phenotypes, estimate the conditional effect of SNPs, and in some cases increase statistical power to detect association. In this manuscript, we create a path model based on standard biometrical genetics theory to model the genetic relationship between own and offspring birth weight, and use Genomic SEM to estimate conditional maternal  and offspring specific genetic effects. The specific model that we fit to the birth weight data is depicted in Supplementary Fig. Genomic SEMWe conducted GWAS analysis using the userGWAS function in Genomic SEM v0.0.2 , which creates genetic covariance matrices for individual SNPs and estimates SNP effects for a user specified multivariate GWAS. Following the workflow described on the github Wiki, we ran multivariate LD score regression to estimate the genetic covariance matrix and corresponding sampling covariance matrix, which accounts for any potential sample overlap between the GWAS summary statistics. After preparing the summary statistics for analysis, we used the estimated matrices to run the GWAS using 50 cores on a computing cluster.P < 5 × 10−8 in the latest GWAS of birth weight3; we excluded rs2428362 from the comparison as it is tri-allelic. For the SEM using the data with no sample overlap , we did not estimate the correlation between the birth weight measures as we had no data to estimate the parameter. We visualized the difference between the effect size estimates and standard errors from this SEM and those estimated using the GWAS summary statistics and methods described above. We conducted a heterogeneity test to assess the difference in the beta coefficients using the rmeta package (version 3.0) in R (version 3.5.2).We fit the same SEM as described above, but using the individual level data rather than observed covariance matrices, for the 300 autosomal genetic variants that reached We were also interested in how the methods compared in terms of computational time, inflation of the test statistics and number of genome-wide significant SNPs identified. We saved the computational time for each of the methods  for comparison purposes. We note that the computational time will differ between computing resources and we present them here to compare the methods relative to each other. We conducted an LD score regression (version 1.0.0) analysis to estimate the inflation in test statistics for each method.N = 493,181) and sisters  they had at each follow-up. There were 28,609 women who reported how many children they mothered at more than one visit, 263 (0.9%) of whom changed their response over time so were excluded. Similarly, there were 26,171 men who reported how many children they had fathered at more than one visit, 1103 (4%) of whom changed their response over time so were excluded. In terms of siblings, 54,480 participants reported how many full brothers they had and 54,489 how many full sisters they had at more than one visit, with 2430 (4%) and 1802 (3%) excluded, respectively, because their response changed over time. We added the number of brothers and sisters to get the total number of siblings, with 489,701 participants reporting how many siblings they had available for analysis. Participants reporting greater than 10 siblings  or children  were recoded to have 10 in case these were data errors and to avoid a distribution with a large tail. We excluded individuals who were not part of our white European ancestry cluster, leaving 237,768 women reporting how many children they mothered, 199,570 men reporting how many children they had fathered and 430,466 individuals reporting how many siblings they have available for GWAS analysis.In the UK Biobank, 272,579 women and 225,349 men reported how many children they had given birth to (live births only) or fathered, respectively. Additionally, each of the participants reported how many full brothers  to estimate genetic correlations between the conditional estimates from Genomic SEM and a range of developmental, reproductive, behavioural, neuropsychiatric and anthropometric phenotypes that were investigated in Barban et al.24. We also investigated the genetic correlation between the conditional estimates and risk-taking behaviour as one of the genome-wide significant loci was previously associated with this trait. Due to the different linkage disequilibrium structure across ancestry groups, we only used summary statistics from LD Hub that were of European origin. There were several traits that had summary statistics from multiple GWAS available in LD Hub, so we used the latest GWAS to estimate the genetic correlations with fertility.To investigate how Genomic SEM performed on a genome-wide scale, we estimated genetic correlations between the unconditional GWAS conducted in BOLT-LMM and the conditional GWAS conducted in Genomic SEM using LD score regressionFurther information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Peer Review FileReporting Summary


Autochthonous taurine and later introduced zebu cattle from Cameroon differ considerably in their resistance to endemic pathogens with little to no reports of the underlying genetic make-up. Breed history and habitat variations are reported to contribute significantly to this diversity worldwide, presumably in Cameroon as well, where locations diverge in climate, pasture, and prevalence of infectious agents. In order to investigate the genetic background, the genotypes of 685 individuals of different Cameroonian breeds were analysed by using the BovineSNP50v3 BeadChip. The variance components including heritability were estimated and genome-wide association studies (GWAS) were performed. Phenotypes were obtained by parasitological screening and categorised in Tick-borne pathogens (TBP), gastrointestinal nematodes (GIN), and onchocercosis (ONC). Estimated heritabilities were low for GIN and TBP (0.079 (se = 0.084) and 0.109 (se = 0.103) respectively) and moderate for ONC (0.216 (se = 0.094)). Further than revealing the quantitative nature of the traits, GWAS identified putative trait-associated genomic regions on five chromosomes, including the chromosomes 11 and 18 for GIN, 20 and 24 for TBP, and 12 for ONC. The results imply that breeding for resistant animals in the cattle population from Northern Cameroon might be possible for the studied pathogens; however, further research in this field using larger datasets will be required to improve the resistance towards pathogen infections, propose candidate genes or to infer biological pathways, as well as the genetic structures of African multi-breed populations. Toxocara, Strongyles, Strongyloides, and Trichuris [Rhipicephalus (Boophilus) microplus is progressively invading new areas with published reports of displacing established populations of closely-related species [In cattle breeds, the genetic makeup has been shown to create different phenotypes related to their ability to sustain environmental pressure, including pathogens. Particularly in developing countries, many of the endemic pathogens considerably affect the cattle livestock industry , for insrichuris ,3. Theserichuris . The resrichuris ,6. This  species .Bos taurus) and zebu (Bos indicus) cattle breeds, as well as to all agents of the above-mentioned infectious diseases. The respective distribution of those pathogens is thereby dependent on the climatic zone, habitat preference and vector abundance. The whole genomes of individuals of the investigated breeds have been recently published [Onchocerca volvulus were not explainable by exposure to parasite transmission, suggesting a substantial genetic predisposition [Onchocercaochengi infected individuals have been described as putative immune [Onchocerca (ONC) parasites have been reported non- or low-pathogenic [Cameroon is a country situated in Central Africa and home to both autochthonous taurine  and oral-faecal-transmitted (GIN) pathogens in cattle breeds from Cameroon.Bos indicus breeds Fulani (n = 100) and Gudali (n = 372), as well as the autochthonous Bos taurus breeds Kapsiki (n = 137) and Namchi (n = 110). Additional information is available from [n = 123/125) and Faro et Deo (n = 105/128) in the Adamawa region; Faro (n = 106/110) and Mayo-Rey (n = 215/219) in the North region, and Mayo Tsanaga (n = 136/137) in the Far North region. The quality control ensured that individuals without reliable phenotypic records or more than 10% missing genotypes were discarded. The sample collections have been carried out with the consent of the regional state representatives and traditional authorities from each of the sampling areas. Furthermore, oral consent was given by the cattle owners, herdsmen , and with the participation and approval of the National Institute of Agricultural Research for Development (IRAD) in Cameroon, which is the country’s government institution for animal health and livestock husbandry improvement.As large datasets are indispensable for reliable results in genomic studies , a pooleble from ,21. The Onchocerca spp. were investigated by intradermal nodule palpation [O. ochengi, O. gutturosa and O. armillata according to Wahl et al. [DNA was extracted from blood, as previously reported . Phenotyalpation  and isoll et al.  for ONC.p < 0.001), and segregating in all of the breeds were favoured for downstream analyses. The final dataset contained the phenotypes of 608 to 685 animals depending on the trait and their genotype status of 35,195 SNPs . After quality control, all annotated autosomal single nucleotide polymorphisms (SNPs) with a minor allele frequency (MAF) ≥ 0.05, no significant deviation from Hardy-Weinberg-equilibrium (195 SNPs .p < 0.05) to determine the effects to be included in the evaluation model. Since not all breeds were present at all sites, and samples were not taken in all seasons in each year, combined effects (breed_site and season_year) were considered. The heritabilities were calculated based on the estimated variance components using standard notations. For the variance component analyses, the following model was applied:I is the identity matrix. As the traits were binary coded, heritabilities were calculated on the observed, as well as on the unobserved liability scale  . Initialp-values smaller than the threshold of n denotes the number of SNPs.Thereby, Parasitological examinations representing phenotypic data are reported in The estimated variance components, as well as heritabilities based on the observed and the liability scale are given in Bos taurus and Bos indicus breeds and have shown that genetic variation plays a significant role in terms of an animal’s resistance towards parasites as reviewed by Mapholi et al. [As in many parts of the world, the distribution and phenotypic adaptation of cattle in Cameroon has been strongly influenced by herd migration history, climate, wildlife abundance, vector and pathogen occurrence, and food and water accessibility ,27,28,29i et al. ,32. Givei et al. . The obsi et al. . Indeed,i et al. ,33 variaThe variance component estimation uncovered the susceptibility of individuals towards infections with the studied pathogens to be to some extent controlled by genetics. As expected for binary coded traits, a high or low observed pathogen prevalence in the population came along with considerably smaller variance component estimates . CompareA plethora of constantly updated reports ,40,41 ofThe present study is the first in-depth investigation of the genetic makeup towards pathogen resistance of the cattle population in Cameroon. For all the studied groups of endemic pathogens genetic determinants were found, but due to the combination of high population variability and the limited sample size, a clear association with the involved genes was not possible. Future genetic association studies should consider these circumstances already in their planning phase.