The body mass-neutral component predisposing to abdominal fat deposition, likely reflecting a shift from subcutaneous to visceral fat, exhibited health effects that were weaker but specifically linked to lipotoxicity, such as ischemic heart disease  and diabetes . Combining their independent predicted effects significantly improved the prediction of obesity-related diseases (p < 10−10). The presented decomposition approach sheds light on the biological mechanisms underlying the heterogeneity of body morphology and its consequences on health and lifestyle.Obesity is a major risk factor for a wide range of cardiometabolic diseases, however the impact of specific aspects of body morphology remains poorly understood. We combined the GWAS summary statistics of fourteen anthropometric traits from UK Biobank through principal component analysis to reveal four major independent axes: body size, adiposity, predisposition to abdominal fat deposition, and lean mass. Mendelian randomization analysis showed that although body size and adiposity both contribute to the consequences of BMI, many of their effects are distinct, such as body size increasing the risk of cardiac arrhythmia ( Jonathan Sulc et al. use principal component analysis and Mendelian randomization to conduct a comprehensive analysis of 14 body morphology metrics using data from the UK Biobank. Their results suggest that body size and adiposity have distinct impacts on obesity-related outcomes and highlight a BMI-neutral component affecting body fat distribution, providing further insight into the genetic basis of body shape and its consequences on human health and lifestyle. The associated disease risk and underlying biology of obesity are generally studied through the lens of body mass index , which uses excess body mass as a surrogate for adiposity. This approximation provides a reasonably accurate predictor at the population level4 but is blind to many aspects of body shape and composition that may be critical to disease etiology. Indeed, disease risk and progression have been shown to be affected by the location and type of the adipose tissue in which excess calories are stored6. Abdominal obesity in particular, usually assessed using waist circumference or waist-to-hip ratio (WHR), is associated with increased disease risk and mortality independent of BMI3.Obesity is one of the main risk factors for many non-communicable diseases, such as type 2 diabetes  around organs in a process similar to a mild form of lipodystrophy. Ectopic and visceral fat are thought to play a central role in many of the direct consequences of obesity.Although the exact mechanisms underlying the consequences of adiposity on health have not been fully elucidated, the predominant hypothesis is that of adipose tissue expandability: that excess calories are preferentially stored in subcutaneous adipose tissue (SAT), which can expand with little to no deleterious impact on health10. However, such techniques remain costly and are therefore generally restricted to smaller sample sizes.Much remains unknown about the genetic and environmental factors affecting body fat distribution and its contribution to health outcomes. Currently available non-invasive imaging techniques such as magnetic resonance imaging (MRI) and dual-energy X-ray absorptiometry (DXA) allow the accurate measurement of adipose mass in different parts of the body and have considerably improved our understanding of the impact of different subtypes of adiposity on health11. While this has improved our understanding of the genetic basis underlying common and distinct components of anthropometric traits, their impact on health and quality of life remains unknown. Other approaches such as clustering and canonical correlation analysis have identified single nucleotide polymorphisms (SNPs) associated with healthier metabolic profiles, despite higher BMI and/or body fat percentage15. However, these hypothesis-driven approaches  are not suited to determine the causality of these correlated differences or the directionality of potential causal effects because the SNP groups have different health consequences by construction.An alternative approach is the concurrent analysis of multiple traits, leveraging the co-occurring changes in multiple phenotypes to understand the underlying causes and mechanisms. Analysis of variance methods, such as principal component analysis (PCA), have been used to investigate the complex architecture underlying body morphology, revealing the main axes of phenotypic variation and increasing the statistical power to detect novel loci affecting body morphology16 to extract orthogonal components, each representing different features of body shape . Summary statistics for WHR were not available in the UK Biobank, therefore we performed a GWAS in the UK Biobank following the same procedure as that used by the Neale lab. SNPs which were then genome-wide significant  for any trait were pruned to be independent. The resulting SNP x traits matrix of effect estimates was subjected to PCA. The loadings of each PC were then rescaled according to the phenotypic correlation between these traits in the UK Biobank so as to obtain standardized effect sizes, i.e. the resulting PC phenotypes would have a variance of 1. SNP-PC associations were then calculated genome-wide and SNPs for each PC were re-pruned individually.A schematic representation of our composite MR analysis framework is shown in Fig. The top four PCs explained more than 99% of the total variance Fig. . PC1  or were less relevant to obesity-related health outcomes .For brevity and clarity, we mainly describe the PCs in comparison with BMI and occasionally weight or WHR. Other traits had causal effects similar to or weaker than these traits , 641 with PC2 (adiposity), 354 with PC3 , and 610 with PC4 (lean mass). Among these, respectively 3, 83, 137, and 330 SNPs did not reach genome-wide significance for any individual trait. For comparison, BMI had 532 independent GWS SNPs.17 through the FUMA interface18 in both GTEx v819 and brain development and aging data from BrainSpan20. We also tested for their enrichment of molecular pathway terms from the DEPICT dataset21 using PASCAL22. The tissue-enrichment results of the four PCs as well as weight, BMI, and WHR are shown in Fig. We tested the PCs for the enrichment of genes expressed in specific tissues using the MAGMA methodp ≤ 2.2 × 10−5) and the pituitary gland (p ≤ 5.7 × 10−4). Using data from BrainSpan20, we found PC2 to be further enriched for genes expressed in the brain specifically during the early to late mid-prenatal phases of development (p ≤ 6.1 × 10−4).Loci associated with both PC1 (body size) and PC2 (adiposity) were mainly enriched for genes expressed in the cerebellum (p = 3.0 × 10−14), followed by VAT (p = 4.2 × 10−10), female reproductive tissues , nerves , arteries (p ≤ 2.2 × 10−9), and digestive system (p ≤ 8.8 × 10−5). Note that using sex-specific  summary statistics and PC loadings with the same gene expression datasets produced a similar enrichment for female-specific tissues. Data from BrainSpan showed PC3 to be enriched for genes expressed in the late prenatal brain (p = 9.3 × 10−8).PC3-  associated SNPs were harbored by genes most enriched for expression in SAT (p ≤ 1.6 × 10−10) and some female reproductive tissues , and weaker for adipose tissue and tibial nerve. PC4 was also enriched for genes expressed in the prostate (p = 2.9 × 10−4). In BrainSpan, PC4 also showed enrichment for genes expressed prenatally in the brain.PC4 (lean mass) showed similar enrichment to that of PC3, though stronger for the digestive system  MR to test for causal effects of the 14 anthropometric traits and PCs on disease outcomes, continuous measures of health, lifestyle factors, and diet, as well as the reverse. To avoid bias in the MR estimates due to sample overlap, we used sex-specific effects from opposite sexes for exposures and outcomes . This produces unbiased estimates of the sex-specific causal effects in the sex used in the outcome, provided the strength of the instruments’ association with the exposure was not different between sexes. The two cross-sex causal effect estimates  were then meta-analyzed using inverse-variance weighting. Where the strength of the instruments’ association with the exposure differed between sexes, namely for WHR and PC3 , the meta-analyzed causal effect estimates may be biased . Although the effect size estimates may be slightly over- or underestimated, this should not affect the type I error rate (see “Methods”).PC1 (body size) increased the risk of many diseases Fig. . An incrPC2 (adiposity) had much stronger effects on many obesity-related diseases Fig. , where aPC3 , despite being weight- and BMI-neutral, was a risk factor for many of the same obesity-related diseases as PC2 Fig. . A 1 SD PC4 (lean mass) had few consequences on health, only significantly increasing the risk of nerve disorders and diseases, carpal tunnel syndrome, and joint disorders Fig. .2 in women, 4.2 kg/m2 in men) increased the risk of diabetes by 3.8% (95% CI: 3.4–4.2), hypertension by 9.9% (95% CI: 8.8–11.0), hypercholesterolemia by 3.0% (95% CI: 2.3–3.7), and IHD by 1.8% (95% CI: 1.4–2.2). Weight and WHR had similar, if somewhat weaker, effects . Glucose levels were similarly affected, though the effects were weaker. All three PCs also increased triglyceride levels (b between 0.095 and 0.24) and decreased HDL cholesterol (b between −0.22 and −0.16), but only PC1 decreased LDL cholesterol  and total cholesterol. They all increased blood pressure (systolic and/or diastolic), with the strongest effects from PC2 (b > 0.15). Plasma concentrations of several liver enzymes, such as γ-glutamyltransferase (GGT) and alanine aminotransferase (ALT), were found to be increased as well, possibly indicating liver damage.Consistent with the increased risk of diabetes, PCs 1–3 all increased the levels of glycated hemoglobin in blood, though the effect was strongest for PC2 . C-reactive protein (CRP) was also increased by PCs 1 and 2  but unaffected by PC3. PCs 1–3 all strongly decreased levels of sex hormone-binding globulin (SHBG) with the strongest effects from PC2 , as well as testosterone where the effects of PCs 1 and 3 were stronger .PCs 1 and 2 also increased levels of cystatin C and decreased albumin, possibly indicative of impaired kidney functionb = −0.090, 95% CI: −0.11 to −0.067) and the maximum heart rate during fitness test. The levels of triglycerides and the liver function markers GGT and ALT were slightly decreased but only alkaline phosphatase reached Bonferroni-corrected significance .PC4 mainly increased creatinine levels while decreasing CRP  and glucose , as well as increasing the levels of triglycerides while decreasing both HDL  and LDL  cholesterol. Systolic and diastolic blood pressure was also increased, as were levels of CRP  and liver function markers such as ALT and GGT. SHBG  and testosterone  were also decreased.The effects of BMI were comparable to a combination of PCs 1 and 2, increasing levels of glycated hemoglobin . This was accompanied by a longer working week, as well as an increase in smoking and alcohol consumption, particularly spirits , as shown by increased Townsend deprivation index  and the likelihood of having a job involving heavy physical work , but strongly linked to decreased income, fluid intelligence score, and education . Accompanying these were lifestyle changes similar to those of PC1, increasing smoking and the frequency of alcohol consumption, with a decrease in wine in favor of spirits and alcohol being taken more often outside of meals. Although PC2 increased the duration of walks  and vigorous activity, the duration of walking for pleasure was decreased , as were several other measures of physical activity, namely the frequency of stair climbing and the usual walking pace. Unlike PC1, PC2 decreased the time spent using the computer in favor of time spent watching TV.The effects of PC2 on SES were similar but much more pronounced Fig. , not onlb = 0.046, 95% CI: 0.028– 0.065) and napping during the day   Fig. .b = 0.043, 95% CI: 0.024–0.062)  and physical activity while increasing smoking and alcohol consumption. Additional comparisons of overall effects on lifestyle can be seen in Supplementary Figs. The effects of BMI on lifestyle were most similar to those of PC2, linked to decreased SES  and increased consumption of healthy foods such as fresh fruit, vegetables, and water .PC1 was associated with greater reported variation in diet , as well as salt added to food . Other changes in diet reflect increased consumption of cheaper meats, namely pork and poultry, while decreasing intake of grain products , cheese, and dried fruits .PC3 was associated with increased bread consumption (b > 0.041), while decreasing processed meat intake  and decreased intake of grain products and cheese .Effects of BMI on diet were similar to those of PC2, such as increased variation in diet  and were unsuitable for this purpose. Full results are available in Supplementary Data The genetic effects of the selected IVs on PCs 1, 2, and 4, as well as BMI and weight, were not significantly different between men and women, which made it possible to obtain unbiased sex-specific causal effects (see “Methods”). Briefly, if the IV effects on the exposure do not differ between sexes, summary statistics for the opposite sex can be used for the exposure while using those of the sex of interest as outcome. Those of PC3 (and WHR) were stronger in women  and heart diseases such as IHD . However, many blood molecular traits and other continuous health outcomes were differently affected by PCs in men and women.None of the PCs had significantly different effects on diet and lifestyle in men and women, though PC2 did have a tendency for stronger effects on disease risk in men (TLS slope = 1.27), mainly driven by diabetes  and PC2 , whereas in women it was unaffected by PC1 and increased by PC2 . SHBG was similarly affected by PC2 in both sexes, but the decrease caused by PC1 was twice as strong in women .Testosterone in particular was differently affected between sexes, strongly decreased in men by both PC1  and increasing glycated hemoglobin only in men . The effects of PC2 in men were stronger for ALT, GGT, and pulse rate, and an increase in total protein was only seen in men.Other differences include PC1 causing a stronger decrease in albumin in women . LDL was decreased only in men . The opposite effects on testosterone levels were also observed for BMI .The effects of BMI were again similar to those of PCs 1 and 2. The increase in glycated hemoglobin was also stronger in men and was accompanied a larger increase in glucose as well , likely due to confounding, and are not considered here. For example, forced vital capacity (FVC), which is dependent on lung—and therefore body—size, showed strong bi-directional associations with most body traits. The main results are summarized here, full results are listed in Supplementary Data b ≤ −0.068), as well as apolipoprotein A and diagnosis of hypercholesterolemia. PC1 also showed a positive feedback loop with cystatin C but a negative one with IGF-1, reducing IGF-1 levels in the blood  but being increased by it .While PC1 reduced cholesterol, it was in turn reduced by LDL and total cholesterol .PC2 showed a positive feedback loop with CRP and mutual negative loop with creatinine. PC2 was also reduced by IGF-1 , forming a negative feedback loop. It also formed a positive feedback loop with diabetes (as did WHR), although this seems to be due to a negative effect of diabetes on hip circumference  rather than an increase in waist circumference (WC). PC3 also had a positive feedback loop with triglycerides (from decreased HC), and mutual negative effects on apolipoprotein A and HDL cholesterol (from decreased WC). We also found the negative effects of PC3 on testosterone and SHBG to be reciprocal (b ≤ −0.058).PC3 was decreased by hypertension . SHBG and testosterone both decreased PC4, though for testosterone this appears to have been driven by an effect in women only .PC4 had a positive feedback loop with creatinine and was also increased by IGF-1 (b ≤ −0.035), similar to PC1.BMI increased CRP in a positive feedback loop, similar to that of PC2, and reciprocal negative effects with LDL and total cholesterol (24. We were able to impute many such traits based on the subset of ~5000 UK Biobank participants with these phenotypes (Supplementary Data r2 above 0.75), had effects on disease comparable to BMI and PC2 (correlation r ≥ 0.97) but with a tendency for slightly larger effects . However, with fewer GWS SNPs (~28% fewer than BMI), these phenotypes revealed no additional diseases affected by obesity  curves, in a subsample of the UK Biobank similar to that used by the Neale lab.p = 1.3 × 10−72), while WHR was more accurate for high cholesterol  and slightly more for diabetes . In all cases, the PC-based predictors significantly outperformed both BMI and WHR with AUCs of 0.68 for hypertension, 0.67 for high cholesterol, and 0.78 for diabetes  ranging from 0.62 to 0.74 “within-sample.” BMI was more accurate in predicting hypertension . The accuracy of PCs and BMI in predicting diabetes out-of-sample decreased to 0.75 and 0.68, respectively, and although WHR was no less accurate out-of-sample (AUC = 0.74) it remained less accurate than PCs (p = 5.1 × 10−11).To explore the transferability of PCs, we replicated these results out-of-sample in 76,756 non-”white British” UK Biobank participants, who had therefore been excluded from the original summary statistics. Despite the considerable potential for disparities between these samples, the AUCs for high cholesterol and hypertension were almost exactly identical to those obtained above Fig. , with PCp < 3.1 × 10−12). This is likely due to the fact that these risk factors are not independent and hence their conferred risk is not simply additive.We also tested whether a weighted linear combination of BMI and WHR  would improve the prediction, and found that in most (5 out of 6) cases the AUC of the combined predictor performed worse than the better of the two traits alone. In all cases, this predictor did not exceed the accuracy of the PCs ; avPC2 showed opposing directions for height and BMI/WHR (though WHR had much more importance than BMI); avPC3 was dominated by WHR (however both height and BMI contributed as well); and loadings for avPC4 showed an increase in weight and BMI despite decreased waist and hip circumference, suggesting increased density of body mass and consistent with decreased body fat percentage. The robustness of the obtained PCs to changes in the trait selection or the use of male- or female-specific data supports the hypothesis that these represent true biological mechanisms underlying the shared variance across these anthropometric traits. Furthermore, the molecular basis of PCs 3 and 4 in particular appear to be more homogenous, as shown by enrichment for genes expressed in many tissues, such as SAT and VAT but also in the digestive, reproductive, and vascular systems, which were not picked up by individual traits. This is also reflected in the much broader spectrum of pathways enriched only for these PCs.By combining multiple traits through PCA, we found that more than 99% of genetically determined variation in body shape and size (as defined by the 14 selected traits) can be summarized using four PCs affecting (1) body size, (2) adiposity, (3) predisposition to abdominal fat deposition, and (4) lean mass. PCs 1–3 showed some similarity with weight, BMI, and WHR, respectively, and the latter two especially have been widely studied in the context of obesity. However, these traits are highly correlated and partially redundant, making it difficult to understand their individual contributions to health outcomes, whereas PCs are orthogonal by design and their effects additive. The PCs obtained here also share some similarities with the average PCs (avPCs) obtained by Ried et al.25 suggesting that behavioral changes are likely one of the major factors underpinning heritable susceptibility to obesity (reviewed in ref. 26). Shared effects between PC1 and PC2 highlight diseases whose etiology involves elements common to both, such as metabolic overexertion in the case of diabetes27 or the physical burden of a larger body in the case of arthrosis28. Differences between their effects can provide insight into disease etiology which single traits cannot. For example, Hyppönen et al.29 performed a phenome-wide MR analysis to assess the effects of BMI on a number of diseases and found that BMI increased the risk of IHD, cardiac ar-/dysrhythmia, and diseases of the veins (phlebitis and thrombophlebitis), among others. However, our results show that these do not all arise through the same mechanisms, IHD being increased by PCs 2 and 3 but not PC1, consistent with the predominant role of adipose tissue-related dyslipidemia in this disease8, whereas cardiac arrhythmia and diseases of the veins were only increased by PC1. BMI is also a known risk factor for prolapsed disc30, though it is unknown whether this occurs due to mechanical overstraining, dysregulation of the metabolic and immune systems, or a combination of these31. That PC1 alone increased the risk of prolapsed disc strongly supports the role of mechanical stress over any form of lipotoxicity.Leveraging the orthogonality of PCs, we can dissect the etiology of obesity-related health and lifestyle consequences by comparing the effects of different PCs. Both PCs 1 and 2 embody the excessive accumulation of body mass and their enrichment for genes expressed in the brain and involved in neuronal signaling is consistent with findings from BMI33, although recent results suggest that this is due to residual population stratification and non-genetic familial effects35. Many other differences in lifestyle can be considered as concurrent with this change in SES  whereas others are likely secondary to these . Despite a moderate association with lower SES, PC1 was not associated with any of these secondary/concurrent changes, though the increased time spent using a computer may be indicative of white-collar occupations rather than the physical jobs associated with PC2. The similarity of the effects of both PCs on smoking and alcohol consumption suggests mechanisms independent of SES. For example, increased smoking may reflect the use of smoking as a strategy for weight loss, leveraging the appetite suppressant effects of nicotine37, and is consistent with the observational correlation found between the number of cigarettes smoked and the risk of obesity38. Although we cannot exclude residual population stratification, the increased alcohol consumption found for both PC1 and PC2 suggests a directionality of causal effects which is rarely considered, as obesity is typically viewed as a consequence of alcohol consumption rather than the cause39. The lack of robust genetic instruments to explore the distinct effect of different alcoholic beverages render MR analysis suboptimal to resolve such reverse causations. The increased variation in diet and salt added to food may reflect a greater desire for palatable foods, possibly due to altered or reduced activation of the reward system which has been shown to occur in obese individuals (reviewed in ref. 40). The healthy aspects of the dietary shift from PC1, e.g., greater consumption of fruits and vegetables, could possibly represent attempts to eat healthier, however, this is likely to be highly confounded by biased reporting41. The weak to non-existent effects of cholesterol and other health risks or diseases on these measures of body size suggests that any lifestyle changes upon disease diagnosis are generally minimal and likely insufficient to have a real impact.The decreased SES associated with PC2 is consistent with what has been reported for BMI42. The increased waist circumference at the expense of that of the hips suggests a change in body fat distribution, with a shift from subcutaneous (contributing to both hips and waist) to VAT . This is supported by the enrichment for terms related to energy homeostasis and adipose tissue function, similar to those previously reported for WHR adjusted for BMI43, and the impacts on health which appear specific to lipotoxicity and are similar to what has been found for genetic variants affecting subcutaneous-to-visceral adipose tissue ratio13. The implication of genes expressed in adipose tissues, particularly subcutaneous, is in line with the hypothesis that adipose tissue function, in particular its expandability, may be a critical factor in determining the distribution and impact of excess adiposity7 and its study may provide pharmaceutical avenues which may reduce health risks associated with obesity. The enrichment of both PCs 3 and 4 (as well as WHR) for genes expressed in several tissues of the digestive tract is not entirely surprising44, however, their strong enrichment for all female-specific tissues tested was unexpected. It is possible that these tissues, and the genes expressed therein or associated sexual hormones, contribute to this phenotype, which could explain the increased variance of PC3  observed in women compared to men. Additional studies will be required to determine how these genes and tissues are involved in the regulation of body fat distribution.PC3, i.e., predisposition to abdominal fat deposition, was of particular interest as a hypothesis-free emergent component affecting body shape while remaining independent of its size or composition. This is in contrast to WHR which is highly correlated with BMI and body fat percentage, particularly in men45, which exceed the scope of this study.The additivity implied by the independence of the PCs’ causal effects provides a straightforward estimation of disease risk through linear combination, which proved more accurate than single trait-based prediction. Although causal effects are not ideally suited for within-sample outcome prediction, since they do not take advantage of non-causal correlates, their basis in causality proved reliable even out-of-population. The reason for the drop in accuracy seen for out-of-population PC- and BMI-based prediction of diabetes is unclear, though a number of factors may contribute, such as ethnicity-dependent differences in susceptibility, lifestyle, or ascertainment bias46. Although such biases may be present in the exposure, they would only bias MR estimates if they affect the outcome in a similar fashion35, which is difficult to test in a real setting. This study is also subject to other limitations of the MR approach, such as the estimated univariable causal effects summarizing the consequences of a lifetime exposure of a difference in phenotype at the population level. For example, the effect of 1 SD difference in weight (~14 kg) in the population is not equivalent to a sole gain/loss of 14 kg, since this difference in the population distribution will be accompanied by a difference in average height as well (as illustrated by PC1 loadings). The causal effect being modeled linearly, it also averages what may be different effects in different strata of the population, i.e. if the causal effect is non-linear. For example, the effects of BMI on all cause mortality have been shown to be non-linear47, although the linearity of effects on specific diseases such as diabetes remains unclear48. In the case of non-linear effects, the MR causal effect estimates can be interpreted as an average effect at the population mean.The systematic application of MR over a broad range of phenotypes carries the risk of violating its assumptions. Despite the steps taken to avoid any such violations or mitigate their impact, some sources of bias may remain inherent to the methods employed. GWAS effect estimates themselves have been shown to be biased due to population effects, such as parental effects, population stratification, and assortative mating49. We also showed that cross-sex MR can reduce the bias by avoiding sample overlap, while preserving much of the statistical power from a potential one-sample MR using the full sample, still relying only on publicly available summary statistics. Applying these to the PCs of anthropometric traits, body size, adiposity, abdominal fat distribution, and lean mass, we showed that their distinct effects on health- and lifestyle-related outcomes can aid in understanding the etiology of the consequences of obesity. These components can be visualized and their effects on hundreds of outcomes compared through the shiny app which can be downloaded following the instructions at http://wp.unil.ch/sgg/pca-mr/. Finally, we showed their effectiveness in predicting obesity-related diseases, confirming the additivity of the conferred risks and emphasizing their relevance to disease etiology. Although we identified four PCs here, larger sample sizes or more detailed anthropometric traits may achieve a finer scale identification of obesity subtypes.In summary, we established a scale-preserving method for the linear combination of traits at the summary-statistics level, mimicking a GWAS performed on a standardized composite trait but circumventing the need for individual-level data. Furthermore, this allows the combination of traits across different cohorts, since the covariance of effect size estimates between different traits can be estimated from summary statistics via cross-trait LD score regression intercept16, a cohort of ~500,000 participants aged 37–73 (median 58), recruited between 2006 and 2010. UK Biobank obtained ethics approval from the North West Multi-centre Research Ethics Committee (MREC) and obtained informed consent from all participants. Phenome-wide GWAS in UK Biobank data were performed and the summary statistics made available by the Neale Lab  for three different sex groups: men, women, and both sexes combined. This includes effect estimates for 13.7 million SNPs which were tested for association with 4203 unique phenotypes across 361,194 unrelated, white British individuals . All were adjusted for age, age2, sex, age ∗ sex, age2 ∗ sex, and the top 20 genetic PCs to correct for population stratification. We excluded SNPs from the HLA region . For continuous phenotypes, we used effect estimates for the inverse rank-normalized trait. Summary statistics for WHR were calculated in the UK Biobank across a similar sample of 378,139 unrelated, white British individuals  and correcting for the same covariates after inverse rank-normalization. For binary traits, we divided the effect estimates and standard errors by the square root of the variance of the trait . As such, all effects are expressed on the SD scale and comparable with continuous traits. The linear models used to estimate the SNP-binary trait association provide well-calibrated p-values, as long as rare SNPs (MAF < 0.1%) are not evaluated for a trait with a highly imbalanced case fraction (<10%) or as long as the product of MAF and disease prevalence exceeds 0.000150. Since we analyzed diseases only with >1% prevalence, any SNPs with MAF > 1% are safe to use as instruments. The largest fraction of low-frequency (MAF < 1%) IVs were observed for height  and the lowest disease prevalence were between 1 and 2%. Still the resulting causal effects from MR analysis between height and these diseases did not change appreciably upon the exclusion of IVs below 1% MAF (Supplementary Note We used GWAS summary statistics data derived from the UK Biobankp < 5 × 10−8) were divided into five mutually exclusive categories: body measures (14 phenotypes), continuous measures of health (37 phenotypes), dietary habits (19 phenotypes), diseases (108 phenotypes), and lifestyle factors (54 phenotypes), which are briefly described below. The full list of selected phenotypes can be found in Supplementary Data In total, 232 phenotypes that had at least one GWS SNP p < 5 × 0−8 were Body measures included BMI, height, weight, hip and waist circumference, and WHR, as well as bioimpedance-derived fat and lean mass estimates in arms, legs, trunk, and overall body fat percentage. For arms and legs, summary statistics were available for left and right sides. As these were almost identical, the statistics for the left side alone were used. Basal metabolic rate was also included in body measurements as it is derived from bioimpedance measures.Continuous health outcomes included the biomarker panel of the UK Biobank, including 34 biomarkers measured in either blood or urine, as well as systolic and diastolic blood pressure (BP), heart rate at rest and during effort, and FVC.Dietary habits were obtained from a food frequency questionnaire (FFQ). UK Biobank also includes more specific questions such as 24-h recall (food consumed on the last day) which is more reliable but less representative, as well as specific questions such as the type of bread or milk typically consumed, however, these were not included in the present analysis.www.finngen.fi). We included data from both self-reported answers and the FinnGen curated diseases, excluding the raw ICD10 diagnoses which were considered less informative. We included any diseases which had a prevalence of at least 1% in the analyzed sample.Disease summary statistics in the UK Biobank were available for self-reported disease status, ICD-10-classified hospital diagnoses, and diseases curated by the Neale Lab in collaboration with the FinnGen consortium  for their inclusion as body phenotypes in the PCA. We did, however, investigate whether approximating these traits through linear combination of other available traits  could provide a hypothesis-driven alternative to the hypothesis-free PCA approach. We were able to estimate 57 body composition measurements using the same 14 anthropometric and bioimpedance-based traits with varying accuracy (see Supplementary Data p < 5 × 10−8) on at least one of the body traits and pruned them using the same procedure as for single traits (distance > 10 Mb or r2 < 0.01). SNPs were prioritized according to the highest rank within any significantly associated trait, i.e., for each trait, all SNPs significantly associated with it were ranked by p-value; then each SNP has attributed a priority based on the highest rank obtained with any significantly associated trait. This was done to avoid the well-powered traits  overshadowing other traits and driving the SNP selection. Any missing effect estimates in the resulting matrix were set to 0. Data were neither centered nor scaled prior to the PCA, as the effect estimates were standardized and should therefore have zero mean and their variance for a given trait is informative (distantly related to trait heritability).PCA was performed on a matrix of effect estimates of independent SNPs on the 14 body traits described above. We selected all SNPs with a GWS effect  loading vectors for PC i, respectively, and K is the pairwise phenotypic correlation matrix of body trait phenotypes, which was calculated on a subset of the UK Biobank similar to that used for the summary statistics  and the phenotypes were corrected for the same covariates as used by the Neale lab prior to calculating the correlation. The effect estimates were then calculated using these adjusted loadings, i.e.51:j is a diagonal matrix with the standard error of the association of SNP j with each trait. Since we use only UK Biobank summary statistics, the correlation between effect estimates simplifies to the phenotypic correlation between the traits. The advantage of this approach is that we do not need to calculate the composite trait and run a GWAS but can directly compute the association summary statistics.Although the SNP-phenotype associations are standardized , a linear combination of effects would yield non-standard effects since the resulting traits do not have a variance of 1. To maintain comparable effects between PCs and traits, the PC loadings should be scaled such that the resulting βs would represent SNP effects on composite traits with unit variance, which would typically involve performing the rotation on the phenotypes, standardizing, and rerunning a GWAS on the resulting composite trait with variance 1. Instead, we can consider the linear regression coefficients for trait 17 through the FUMA v 1.3.5e interface18 using the default parameters, with a Bonferroni-adjusted p-value threshold. We used gene expression data in 54 tissues from GTEx v819 as well as brain development and age data from BrainSpan20.We tested PC- and trait-associated SNPs for the enrichment of tissue-specific genes using the MAGMA tool for gene set analysis21 and tested for enrichment using PASCAL22. As with most gene-based pathway enrichment methods, we assume that most regulatory variants for a given gene are in close (50 kb) physical proximity to the gene body52, hence we may ignore more distant regulatory variants.We used the molecular pathway gene sets defined by DEPICT53. MR relies on three key assumptions to infer causality: (1) the IV is associated with a change in the exposure; (2) the IV is independent of the outcome, except through its association with the exposure; (3) the IV is independent of any confounders of the exposure-outcome association. If these assumptions are verified, MR provides an unbiased estimate of the causal effect of the exposure on the outcome and can be done using summary statistics data alone. We used IVW MR as the default method for all our analyses, but we compared IVW causal effect estimates to those obtained from weighted median-based MR to ensure robustness.We used IVW MR to estimate causal effects of body traits and PCs on all non-body traits, as well as the reverse. MR mimics a randomized controlled trial (RCT) where the treatment corresponds to the random allocation of an exposure-associated allele, called an instrumental variable (IV)54. To circumvent this, we used the existing summary statistics for two non-overlapping samples from the Neale Lab, those for men and women separately. Each sex was used as exposure on the other as outcome and then both causal effect estimates were meta-analyzed (using inverse variance weighting). This removed the correlation between the error terms of the effect estimates of IVs on exposure and outcome, considerably reducing the bias from sample overlap, while minimizing loss of power. This would lead to a slight bias away from zero even when there is no true difference in the genetic effects on the exposure in men and women, given that:βm and βf are the coefficients of association for a given IV with the exposure for men and women, respectively. This bias increases if there is a difference between sexes in the strength of association with the exposure, but its magnitude remains small. For example, if the SNP effects on the exposure were consistently 1.37 times stronger in one sex, the relative theoretical bias would only be 5%. In practice, however, the IVs were filtered for genome-wide significance in each sex prior to being used as exposure, resulting in fewer IVs being selected in the sex with weaker effects (on the exposure). This reduced the power and increased the variance in the overestimated causal effect, which was correspondingly down-weighted by the IVW meta-analysis. For example, we found PC3 to have a strong sex-specificity, with effects on exposure tending to be ~2.2 times stronger in women, which would theoretically lead to a 34% bias away from the null. However, this yielded 242 GWS IVs in women but only 33 in men. This increased the variance in the male exposure–female outcome causal effect estimate by a median of 6.8-fold, resulting in a corresponding down-weighting of the overestimated causal effect. In this example, we expect a meta-analyzed estimate which is biased towards the null by ~32%  ∗ (1/1.7) + (1/7.1) ∗ 1.7) ∗ In our case, the use of summary data from the UK Biobank for both the exposure and outcome effect sizes  would lead to a bias in MR causal effect estimate in the direction of the observed correlation of the phenotypesated see  8–9.Although the PCs were constructed using combined-sex summary statistics, the selected IVs were filtered for genome-wide significance in the sex-specific summary statistics prior to MR analysis to avoid weak instrument bias inflating causal effect estimates. This may slightly exacerbate Winner’s curse, inflating the SNP-exposure association, which would result in a small bias towards the null. In the presence of a true causal effect, however, the SNP-outcome association (being assessed in the same sample) may be proportionally increased, which would mitigate this bias. Such biases are difficult to correct for without using three independent samples.t-test and removed if the magnitude of the effects in the outcome were significantly greater (p < 0.05). To avoid unreliable causal effect estimates, MR was only performed if at least 10 IVs remained. This was done using the TwoSampleMR R package v0.5.455.For each sex, the GWS IVs associated with the exposure formed the initial set of IVs. Those with significantly larger effects on the outcome than on the exposure were removed, as these would indicate a violation of MR assumptions . The effect sizes being on a standardized scale, they were compared directly using a one-sided Q test:βiexp and βiout are the coefficients of association of SNP i with the exposure and outcome, respectively, with σiexp and σiout the corresponding standard errors, and βMR and σMR are the MR estimate and standard error of the causal effect of the exposure on the outcome. The test statistic Qi follows a χ2 distribution with 1 degree of freedom. If any of the IVs used had an associated p < 10−3, the most heterogeneous one  was removed. If at least 5 IVs remained, the MR was then repeated with the remaining IVs. In practice, few SNPs were filtered as outliers (on average <1%), though we also explored alternative methods for dealing with heterogeneity, namely performing no filtering, using weighted median MR with or without filtering, or using exact Q statistics56 instead of Cochran’s Q  on type 2 diabetes (disease) is 0.05/(number of body phenotypes ∗ number of diseases).To account for multiple testing, we adjusted the Although the forward MR analyses using anthropometric traits  as exposures were well-powered to find many associations, the reverse/bi-directional MR analyses often lacked sufficient GWS IVs. This can be seen in Supplementary Data The simplest method to test for sex-specific effects would be to use sex-specific summary statistics for the sex of interest for both exposure and outcome, however, this would bias results away from the null due to correlated errors in the exposure and outcome. In most cases, we were instead able to obtain unbiased causal effect estimates using the exposure summary statistics from the opposite sex on those of the sex of interest in the outcome. This relies on the strength of the association between the IVs and the exposure to be identical  between sexes. We tested this using both paired Wilcoxon signed-rank test on the absolute genetic effects of the IVs and total least squares (TLS) regression. Exposures with non-significant results for both tests were considered identical in both sexes for this purpose and the summary statistics for the opposite sex were used for the exposure.25, we were able to confirm that the use of opposite-sex summary statistics for the exposure produced effect estimates which were highly consistent with those using two-sample same-sex MR  was computed using a jackknife procedure, which was then used to compare the obtained estimate with the null hypothesis that the true causal effects were identical . The TLS procedure was performed using the deming R package v1.458.In some cases, it was useful to compare the causal effects of two exposures on all outcomes of a category  or the same exposure in different experimental settings . In these cases, we obtained the slope estimate using TLS regression with no intercept, which considers error in both axes rather than ordinary least squares (OLS) which minimizes only the vertical offset. The standard error on the angle of the regression line  for diabetes, high cholesterol, and hypertension. PCs were calculated based on the 14 scaled and centered anthropometric traits for each individual. These were then combined in a disease-specific linear combination based on the estimated causal effect on the disease. Rather than ignoring PCs with non-significant effects, the effect estimates were IVW to account for the uncertainty of the effect:pROC R package (version 1.16.2)59.The accuracy of this predictor was assessed using ROC curves by comparing the AUC with those of BMI and WHR. The significance of the difference between the AUCs for each predictor was determined using DeLong’s test in the −4).Out-of-sample prediction accuracy was assessed in a sample of 76,756 UK Biobank participants  who were not flagged as “in.white.British.ancestry.subset” in the sample QC file. This includes individuals who either did not self-report ‘white British’ or whose genetic ancestry (as determined by the genomic PCs) was dissimilar from other white British individuals. This sample thereby excluded all the individuals included in the original analysis and any relatives in the UK Biobank. The phenotypes were scaled according to the original distribution, i.e. the mean and SD of the original sample were used to scale individuals without relying on the distribution in the second sample. Using the values of the second distribution for scaling changed little (the AUC increased by at most 4.4 × 10Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Supplementary FilesSupplementary DataReporting Summary


N2–3 computational effort dependency on sample size of classical likelihood approaches. We calculated for 60 regional and 1.3 × 105 voxel-wise traits in N = 1,206 twin and sibling participants from the Human Connectome Project (HCP)  and N = 37,432  participants from the UK Biobank (UKBB). The FPHI estimates were in excellent agreement with heritability values calculated using Genome-wide Complex Trait Analysis software (r = 0.96 and 0.98 in HCP and UKBB sample) while significantly reducing computational (102–4 times). The regional and voxel-wise traits heritability estimates for the HCP and UKBB were likewise in excellent agreement . In summary, the hardware-accelerated FPHI made it practical to calculate heritability values for voxel-wise neuroimaging traits, even in very large samples such as the UKBB. The patterns of additive genetic variance in neuroimaging traits measured in a large sample of related and unrelated individuals showed excellent agreement regardless of the estimation method. The code and instruction to execute these analyses are available at www.solar-eclipse-genetics.org.Imaging genetics analyses use neuroimaging traits as intermediate phenotypes to infer the degree of genetic contribution to brain structure and function in health and/or illness. Coefficients of relatedness (CR) summarize the degree of genetic similarity among subjects and are used to estimate the heritability – the proportion of phenotypic variance explained by genetic factors. The CR can be inferred directly from genome-wide genotype data to explain the degree of shared variation in common genetic polymorphisms (SNP-heritability) among related or unrelated subjects. We developed a central processing and graphics processing unit (CPU and GPU) accelerated Fast and Powerful Heritability Inference (FPHI) approach that linearizes likelihood calculations to overcome the ~ Classically, heritability is calculated using variance component models that use coefficients of relatedness (CR) to represent the shortest self-reported ancestral path for a pair of individuals as the degree of genetic variance shared among individuals. CR can also be calculated empirically from high-throughput genome-wide single nucleotide polymorphism (SNP) data, in which case the heritability measures the proportion of the observed variation explained by common SNPs (SNP-h2)  software (https://cnsgenomics.com/software/gcta/). We compared heritability estimates for complex polygenic neuroimaging traits in a twin-and-siblings sample collected by HCP and mainly unrelated sample provided by UKBB. We finally showed a good agreement in heritability estimates measured in UKBB and these reported by large meta-and-mega analyses performed by Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) studies (Big data research initiatives - including the Human Connectome Project (HCP) and UK Biobank (UKBB) - collect comprehensive multimodal neuroimaging datasets and allow researchers to quantify genetic and environmental risk and protective factors that affect human brain in health and illness . Genetic(SNP-h2) . In fami(SNP-h2) . The SNPicipants . Here, w studies .www.solar-eclipse-genetics.org) and SNP-heritability measured using the GCTA software which pioneered the SNP-h2 measurements. SNP-h2 refers to the proportion of phenotypic variance explained by the individual variances in the SNP data collected from genotyping arrays. SNP-h2 values can be calculated using classical variance component such as these implemented in FPHI or fitting the linear model across all SNP as implemented in GCTA. It can also be calculated using linkage disequilibrium (LD) score regression (LDSR) approaches that use summary statistics for trait from a genome-wide association study analysis (GWAS)  , 2017. AN × N matrix of CR values (where N is the sample size), known as the pedigree or kinship matrix to map the sharing of genetic variance among subjects. Traditionally, CR values were fixed to the theoretical values of the expected degree of autosomal genomic sharing for a given kinship type: 1 for the similarity with oneself, or with a monozygotic twin; Classical heritability analyses use variance models that partition the phenotypic variance into the additive genetic and environmental components See . These mvariance , 2007. Nvariance .h2 values calculated by FPHI and by the GCTA software that was specifically developed for SNP-based heritability  pedigree matrix  approach to calculate the empirical CR. The WAC–CR values provided more stable empirical heritability measures than those from other methods, including self-reported CR, although the differences were minor . The WAC−1, or 0 , 2017. A studies , 2017. Hanalyses . The WACe matrix . This ishallenge .N > 35,000 based on our previous work on linearizing likelihood calculation  healthy individuals in the Human Connectome Project (HCP) (humanconnectome.org) for whom imaging and genetic data were released after passing the HCP quality control and assurance standards (https://www.humanconnectome.org/study/hcp-young-adult/document/1200-subjects-data-release). Participants in the HCP study were recruited from the Missouri Family and Twin Registry of individuals born in Missouri  whose imaging and genetic data were released from 2015 to 2021. The full set of inclusion and exclusion criteria are detailed elsewhere (The UK BioBank (UKBB) dataset included lsewhere . All par2.2.https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001364.v1.p1). Briefly, all participants were genotyped using the Illumina Multi-Ethnic Global Array (MEGA) SNP-array. This array provides extended coverage for European, East Asian, and South Asian populations. Overall, 1,580,642 SNPs satisfied the following quality control exclusion criteria: MAF < 1%, genotype call rate < 95%, and Hardy–Weinberg equilibrium < 1 × 10−6.We used genotyping data provided by HCP and UKBB projects with minimal post-processing as recommended by GCTA software manual. The genotyping data for the HCP is available through the dbGAP database (https://biobank.ndph.ox.ac.uk/showcase/showcase/docs/genotyping_qc.pdf) and (https://biobank.ndph.ox.ac.uk/showcase/showcase/docs/impute_ukb_v1.pdf). In summary, all participants were genotyped using the UKBB Axiom array from Affymetrix and imputed using Haplotype Reference Consortium (HRC) and UK10K haplotype resource. Overall, there were 8,521,984 SNPs remaining after the same exclusion criteria as used for HCP data.Genotyping data for the UKBB was downloaded as version 3 imputed data from the UKBB showcase website. The protocol for genotyping, imputation and quality control is described in sections of the 2.3.We selected traits from four neuroimaging domains: cortical gray matter thickness, subcortical gray matter volumes, fractional anisotropy FA values of water diffusion measured for regions of interest , and voxhttps://www.humanconnectome.org/study/hcp-young-adult/document/1200-subjects-data-release). Diffusion data was collected using a single-shot, single refocusing spin-echo, echo-planar imaging sequence with 1.25 mm isotropic spatial resolution . Three gradient tables of 90 diffusion-weighted directions and 6 b = 0 images each, were collected with right-to-left and left-to-right phase encoding polarities for each of the three diffusion weightings . The diffusion data were then processed using the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) pipeline for structural and diffusion tensor imaging, including skeletonized voxel-wise FA values .The UKBB imaging data were collected using three sites each equipped with a Siemens Skyra 3T scanner and 32-channel RF head coil with high resolution T1-weighted . Diffusion data was acquired with the following parameters: a resolution = 2 × 2 × 2 mm and two diffusion-weighted shells with all 100 distinct diffusion-encoding directions, 5 https://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/brain_mri.pdf)  . All datmri.pdf) . This wamri.pdf) , 2019a.2.4.i,j) (twice the coefficients of kinship) to represent the probability that two alleles from individuals i and j are identical by descent. The coefficient of relationship is a function of identity by descent sharing statistics, ri,j = π1i,j/2 + π2i,j, where π1i,j and π2i,j are the probabilities that two individuals share one and two alleles through a common ancestry. Empirical ri,j were calculated using methods implemented in the SOLAR-Eclipse software (www.solar-eclipse-genetics.org). The pedifromsnps function uses the allelic data stored in a PLINK file as the input and produces a pedigree file. We calculated empirical ri,j using weighted allelic correlation (WAC) /empirical kinship matrix value between individual i and individual j. m is the total number of SNP loci that are not missing values for both individual i and individual j. SNPik and SNPjk are allelic scores  for the k-th SNP in individuals i and j. μk is the frequency of the k-th major allele.SOLAR-Eclipse uses CR (ron (WAC) . This fu2.5.The ELRT method is used by SOLAR-Eclipse software to evaluate the statistical power of a pedigree for heritability analysis and to compare power between two pedigrees. This function is based on the functionality proposed by  and furt2.6.fphi function. The multivariate normal covariance matrix Ω for a pedigree of individuals is given by σe2 is the variance caused by environmental effects and measurement errors, and I is an identity matrix under the assumption that all environmental effects are uncorrelated among family members.The algorithms used to estimate variance components employ a variance decomposition approach based on an extension of the strategy developed by  and optih2) is the proportion of the total phenotypic variance (σp2) that can be explained by the additive effects of genes (σg2):Heritability .GCTA also estimates the GRM using the WAC approach . The var2.8.Large-scale imaging genetic analyses such as voxel-wise heritability calculations in large datasets, such as the UKBB, may benefit from modern computational hardware. The highly parallel and non-iterative nature of the SOLAR-Eclipse FPHI algorithms calls for efficient implementation using modern hardware optimized for massively parallel computations see . Here, w3.3.1.N and therefore the large UKBB sample provided more power than the HCP sample  between the h2 values estimated from FPHI and GCTA in both the HCP and UKBB samples . However, the average FPHI h2 estimates were higher than GCTA-derived h2 values in the UKBB .The scatter plots of the heritability estimates showed an excellent agreement  and GCTA . However, the heritability estimates in UKBB were approximately 50% lower than those for HCP .The regional heritability analyses showed good agreement between HCP and UKBB  and D whr = 0.76, p < 10−10). However, the voxel-wise heritability estimates in the UKBB sample were lower than those for HCP .The plot of voxel-wise heritability values of skeletonized FA values for 32,215 voxels that overlapped between UKBB and HCP skeletons is shown in 3.4.r = 0.76–0.82, p < 0.01) .ENIGMA has published regional white matter heritability meta- and mega- analytical multi-site estimates from a multi-site heritability analysis. The FPHI and GCTA heritability estimates for white matter tracts in UKBB showed good agreement with the published values (linear regression  < 0.01)  and B. T3.5.FPHI-CPU analyses in the HCP required ~0.02 ± 0.01 s per trait versus 3.0 ± 0.10 s per trait for GCTA. The heritability analyses of regional phenotypes in the UKBB took about 1.1 ± 0.10 s per trait using FPHI-CPU and 2046 ± 470 s for GCTA.N) was approximately linear for both CPU and GPU versions of FPHI versus ~N2–3 for GCTA.The timing of voxel-wise analyses was limited to FPHI due to the long execution time of GCTA . We performed a timing analysis for the CPU and GPU versions of FPHI in SOLAR-Eclipse. The FPHI-CPU voxel-wise heritability analyses took approximately 2 min for HCP and 22 h for UKBB. The FPHI-GPU version took approximately 36 s for HCP and 58.33 min for UKBB. The scaling of computational burden with respect to the number of participants (4.2) derived using a classical variance component model and empirical coefficients of relatedness (CR) with the SNP-h2 estimated from an independent analytic approach using samples of related   consortium. Overall, our findings demonstrated good agreement among genetic contribution to neuroimaging traits regardless of the study/sample design. The small degree of shared genotypic variance in sufficiently large samples such as UKBB can enable standard heritability analyses. We discussed the finding of lower heritability estimates in UKBB versus HCP and attributed it to several well-known factors. Nonetheless, the patterns of additive genetic contribution across the brain were consistent and readily replicable across diverse samples and study designs.We compared the estimates of SNP-heritability (SNP-h related  and unre related  genetic s (GCTA) , 2007. Wp-values as compared to those from analyses using self-reported CR and recommend this approach for genetic analyses in related samples . Prior work demonstrated that heritability values derived using the empirical CR had better confidence intervals and lower  samples . GCTA apividuals , 2007. H samples . Here, wr = 0.7–0.8) agreement in the regional genetic variance across the brain between the HCP and UKBB datasets despite the differences in the study design (twin-siblings versus unrelated), sample size  and sample characteristics such as differences in average age (28.8 ± 3.7 versus 63.7 ± 7.5 years for HCP and UKBB respectively) and imaging protocols. The HCP imaging protocol was focused on collecting data at twice  to four (diffusion) times higher spatial resolution than the UKBB images. Despite the differences in protocols, we observed good agreement in the patterns of heritability values among the HCP, UKBB, as well as data published by ENIGMA. This demonstrates that the substantial genetic variance influencing individual differences in brain structure can be readily and consistently measured across diverse samples, study designs, imaging protocols, and software approaches. Importantly, the agreement in the patterns of heritability between UKBB and HCP data provides an opportunity to exploit the greater statistical power of large and inclusive samples such as UKBB for the classical genetic analyses that were previously limited to twins, siblings, and extended pedigree samples.We demonstrated significant heritability for a series of neuroanatomical phenotypes that cover structural and diffusion properties of the human brain. We observed an excellent (h2 = 0.72 versus 0.36) observed in the UKBB sample, and for white matter approximately 40% smaller than ENIGMA (average h2 = 0.42 versus 0.67). Likewise, the voxel-wise heritability estimates for the HCP cohort were ~60% higher than those calculated in the UKBB. These absolute differences were independent of the software used to estimate heritability. The SNP-h2 values depend on study design, sample characteristics, and the fidelity and ‘closeness’ of the trait to underlying genetic processes. The higher heritability of the neuroimaging traits in the HCP cohort is likely to be the product of three factors: study design, sample differences, and quality of the imaging data. Heritability is the proportion of the variance attributed to the additive genetic variance after correction for covariates. In the HCP sample, we found that sex was the only significant covariate. The HCP sample was designed to reduce the effects of age on the brain measurements by limiting recruitment to an age range that corresponds to a plateau in the brain-aging-versus-development trend (22–35 years)  . The foc5 years) .The HCP study used a twin-sibling recruitment design. Heritability estimates obtained using this study design are typically higher than heritability estimates obtained other study designs such as extended-family-based pedigrees or unrelated samples . For ins2–4 -fold acceleration in heritability analyses versus GCTA, which makes the approach especially valuable for studies using data from the UKBB . The progress of methodological developments in imaging genetics enables the transition from an interrogation of only a few traits to massive voxel-wise analyses in order to study regional variations in genetic influences across the brain.The SOLAR-Eclipse FPHI is an extension of the standard variance component model that has served the biomedical genetics community for over seven decades. Empirical relatedness is a logical extension of this method, allowing the estimation of additive genetic variation captured by SNP arrays and informative of the genetic architecture of complex traits . The hig5.Empirical CR methods also have a few limitations. The threshold for empirical CR was set at 0 because WAC can produce negative CR values for some unrelated individuals. The negative CR reflect violations of Hardy-Weinberg equilibrium, i.e. ancestral differences in linkage disequilibrium structures, overlapping generations, and deviations from the assumption that genotype frequencies in a population will remain constant from generation to generation . GCTA, c6.h2 values calculated by the GCTA software, suggesting stability of these estimates independent of the analytic methods. Overall, this suggests that large and inclusive samples of unrelated individuals such as data collected by the UKBB can be used to estimate the proportion of phenotypic variance explained by additive genetic factors.We show that heritability measurements for complex neuroimaging traits based on empirically measured genetic variance among the largely unrelated participants in the UKBB sample were in agreement with those measured in the twin- and family-based HCP sample. This agreement was observed for both region-based and voxel-wise traits. We likewise observed an excellent agreement between empirical heritability values derived by SOLAR-Eclipse and SNP-1


Humans have a great diversity in phenotypes, influenced by genetic, environmental, nutritional, cultural, and social factors. Understanding the historical trends of physiological traits can shed light on human physiology, as well as elucidate the factors that influence human diseases. Here we built genome-wide polygenic scores for heritable traits, including height, body mass index, lipoprotein concentrations, cardiovascular disease, and intelligence, using summary statistics of genome-wide association studies in Europeans. Subsequently, we applied these scores to the genomes of ancient European populations. Our results revealed that after the Neolithic, European populations experienced an increase in height and intelligence scores, decreased their skin pigmentation, while the risk for coronary artery disease increased through a genetic trajectory favoring low HDL concentrations. These results are a reflection of the continuous evolutionary processes in humans and highlight the impact that the Neolithic revolution had on our lifestyle and health. Important human traits such as height, body mass index (BMI), or diseases susceptibility vary greatly between populations, both geographically and temporally. Local conditions on different continents and various climates influence human phenotypes through a combination of environmental and genetic factors . UnderstA very limited number of complex traits, most notably height, can be relatively easily assessed on historical samples based on physical measurements of skeletal remains in different populations during history. Such studies have shown that the height of modern humans decreased from the Early Upper Paleolithic to Mesolithic period and thereafter slightly increased towards the Bronze Age . InteresHomo sapiens since the Early Upper Paleolithic, through Mesolithic, Neolithic, Bronze Age, medieval, and modern time periods.GWAS has also identified thousands of genomic variants that are significantly associated with diverse phenotypes . AlthougAncient DNA genotype data was downloaded from version 37.2 of the published aDNA genotype database, compiled by and available on the David Reich Lab website. The ancient DNA samples consisted of pseudo-haploid genotype data. This was due to the low genotyping coverage. Samples with variant missingness above 96 percent were filtered out using Plink version 1.9 . This waBoth carbon-dated age of origin, as well as latitudinal and longitudinal data, was available for these 827 ancient European samples. Broad time periods were assigned to these samples with the Early Upper Paleolithic era for all samples originating from before 25,000 years before the common era (BCE) standardized to 1950. The Late Upper Paleolithic era follows until 11,000 BCE. The Mesolithic era ranges from 11,000 to 5500 BCE. The Neolithic era ranges from 8,500 to 3900 BCE, and the Post-Neolithic era ranges from 5000 BCE and more recent ages. Using the geographical data in combination with archeological clues and the genetic data, the broad time period of origin was also available for samples that were dated to a point in time with overlapping broad time periods. This allowed the samples to be classified as either Early Upper Paleolithic, Late Upper Paleolithic, Mesolithic, Neolithic, or Post-Neolithic. The sample age of the 250 modern European samples was set to 0.Summary statistics for complex traits, like, standing height, skin tanning ease, skin color, BMI, and intelligence, were obtained from the United Kingdom Biobank and the GWAS catalog. The intelligence score of each participant was assessed employing different neurocognitive tests, SAT test scores, and WAIS IQ score . Some coS being the weighted sum of a variant’s dosage Xn multiplied by its associated weight or beta βn calculated using m variants.Polygenic risk scores were then calculated by first intersecting the filtered variants from the summary statistics with the variants present in the DNA samples. Starting at the most significant variant, all variants within 250 kb of that variant that was less significant were excluded. We then multiplied the dosage of the remaining variants with the effect size and these values were summed. If a variant is missing in a sample, the dosage is substituted with the average genotyped dosage for that variant within the entire dataset. This way, the PRS is not skewed in any specific direction. The formula for this is described below with the score p values with the sign of the correlation coefficients.We constructed piecewise linear models for each trait by separating the samples into two groups. These two groups consisted of all samples preceding the Neolithic era and those of the Neolithic era and later, respectively. We correlated PRS with the carbon-dated age of our samples. We then multiplied the -log10 of the Pearson correlation t-test. We compared pre-Neolithic, Neolithic, post-Neolithic, and Mesolithic samples with their respective adjacent historical periods to show the difference between other historical transitions besides the pre- and post-Neolithic.In addition, we plotted LOESS regression models to highlight the change in PRS at each point in time independent of any predefined breakpoint between historical periods. We also performed a group-based comparison using a student st calculated with trait-specific SNP’s between two adjacent periods and a reference distribution of 10,000 random LD and MAF matched mean Fst scores calculated using an equal amount of SNP’s . The aDNA data was used in conjunction with DNA data of 250 randomly selected modern individuals from Western Europe, which were downloaded from 1000G database , which can be seen in p-value threshold of 10–6, in 250 kb windows. Multiple thresholds were further tested for assuring the robustness of the findings. We supplemented the effect of missing variants in a sample with the average of the entire population. We then scaled the PRS distribution to have a range of 1 to −1 before correlating them with the carbon-dated sample age. The general overview of the analysis can be seen in We used ancient DNA (aDNA) data of 827 ancient individuals from Western Eurasia, which were publicly available and aggregated into a database released 22 February 2019, by the David Reich lab (version 37.2) . A down-sampling analysis shows that despite the reduction in power, due to the lower sample size in linear regression models, the correlation coefficients remain significantly similar between the complete dataset and the down-sampled dataset . Lastly,resholds .The first set of traits in which this pattern is apparent are height and skin color. The genetic modeling shows that standing height remained relatively constant between the Paleolithic and Neolithic periods . The secSince, some genetic polymorphisms in cholesterol-related pathways are connected to cognitive functions, while variations in the levels of HDL and LDL have been linked with alterations in intelligence, learning, and memory , in the st) to calculate mean Fst scores per trait to analyze if they differed significantly from the distribution of 10,000 LD and MAF matched mean Fst scores  could be a confounding factor which needs caveats in interpreting results. From the technical point, data imputation was applied for handling missing data, which could limit the power of the study. Also, although there are temporal changes in PRS for various traits coinciding with large changes in lifestyle such as the Neolithic revolution, these results should only be taken as evidence for changes in risk allele frequencies and their effect on the genetic susceptibility for their respective phenotypes. The changes in lifestyle and diet seen in European populations in the same era could be an additional possible explanation, but further research would be required to ascertain this.


Inclusion of other secondary CKD measurements with these three kidney function traits identified the KCNIP4 locus with GWAS significance (pmin = 1.59 × 10–9). Finally, we identified a group of two SNPs with increased minor allele frequencies as potential functional variants. With the use of genetic isolate and the PCA-based multi-phenotype GWAS approach, we have revealed a potential pleotropic effect locus for CKD. Further studies are required to assess functional relevance of this locus.Chronic kidney disease (CKD) is a persistent impairment of kidney function. Genome-wide association studies (GWAS) have revealed multiple genetic loci associated with CKD susceptibility but the complete genetic basis is not yet clear. Since CKD shares risk factors with cardiovascular diseases and diabetes, there may be pleiotropic loci at play but may go undetected when using single phenotype GWAS. Here, we used multi-phenotype GWAS in the Norfolk Island isolate (n = 380) to identify new loci associated with CKD. We performed a principal components analysis on different combinations of 29 quantitative traits to extract principal components (PCs) representative of multiple correlated phenotypes. GWAS of a PC derived from glomerular filtration rate, serum creatinine, and serum urea identified a suggestive peak (p CKD can result in end-stage kidney disease (ESKD) whereby kidney replacement therapy is required. CKD prevalence and burden is steadily rising, with an estimated 10–15% of the world’s population affected2. Increased serum levels of creatinine, cystatin C or urea are often used to indicate kidney dysfunction3. The best current marker of CKD is glomerular filtration rate (GFR), which can be directly measured using exogeneous markers or estimated (eGFR) based on concentrations of endogenous filtration markers such as serum creatinine or cystatin C4.Chronic kidney disease (CKD) is the gradual deterioration of kidney function or structure over at least 3 months8. A recent GWAS meta-analysis of eGFR  identified 308 associated loci, which together explained 19.6% of eGFR heritability5. In addition, the GWASs to date have mostly focused on populations with European ancestry, hence the complete genetic architecture underlying CKD has not yet been established2.The pathophysiology underlying CKD is not yet fully understood, which has hindered the early detection and prevention of CKD as well as development of effective therapeutic treatments. Genome-wide association studies (GWAS) have identified a number of loci in relation to CKD, eGFR or complementary biomarkers, e.g. serum creatinine and blood urea10. Furthermore, many endophenotypes for CKD risk exhibit substantial intercorrelation. This suggests that a genetic commonality, perhaps acting via pleiotropic mechanisms, may play a role in CKD and related disorders. This is supported by a phenome-wide association study (PheWAS) that revealed association of eGFR index SNPs with 7 phenotypes out of 23 cardiovascular and diabetes-related traits11. Principal component analysis (PCA) of multiple correlated quantitative endophenotypes can capture important underlying structure in the phenotypic data, which when analysed as outcomes in GWASs may reveal loci that would remain undetected when traits are analysed individually. Avery et al.12 sucessfully identified three new loci associated with multiple-phenotype domains of metabolic syndrome by using the PCA-based GWAS approach. Applying the same method, Fatumo et al.13 identified new susceptibility genes for blood cell traits that were not identified in the standard univariate GWAS. Thus, investigating multiple CKD-related phenotypes via PCA might reveal new insights into the genetic basis of CKD.Other chronic disorders such as diabetes, high blood pressure, and obesity exhibit co-morbidity with CKD14. Norfolk Island (NI) is a small and remote island located in the Pacific Ocean and is about 1400 km off the east coast of mainland Australia. Most of the modern-day Norfolk Island population are direct descendants of 11 European Mutineers of the HMS Bounty and 6 Polynesian women from the late eighteenth century. Thus, the NI population now exhibits founder effects, admixture and increased homozygosity. For almost 20 years, the NI isolate has been a valuable resource for genetic research19. Notably, a study of multiple cardiovascular disease (CVD) risk traits in the NI cohort group identified a potential pleiotropic effect locus on chromosome 1p22.2. This locus was only revealed from a GWAS of a multiple quantitative endophenotypes for CVD19.The use of genetically isolated populations can empower genetic mapping studies of complex traits. Genetic isolates are often defined by founder effects resulting in reduced genetic diversity and increased frequency of variants that are rare in other populationsKCNIP4 gene, which encodes for the Potassium Voltage-Gated (Kv) channel-interacting protein 4, as a potential pleiotropic locus of CKD. In addition, we also identified two SNPs with MAFs substantially enriched in NI isolate to be the potential functional variants.In this study, we performed a GWAS of multiple phenotypes in 380 individuals of the NI isolate with the aim of identifying pleiotropic loci associated with CKD. The phenotypes included for CKD were primary traits, i.e. creatinine-based eGFR, serum creatinine level, and serum urea level, as well as 26 secondary phenotypes including anthropometric and biochemical measurements. We applied PCA on the 3 CKD-primary traits to identify components representing covariance among them and then performed GWAS on the principal components yielding statistically significant heritability. We also included 26 secondary CKD traits into the analysis based on correlation clusters and combined these with the primary traits to perform the same PCA and GWAS workflow. As a result, we were able to identify variants in the 20 (UKBB)  Fig. . The numBB) Fig. a. Furtheory Fig. b. Collec21.The high rate of reduced kidney function observed in the NI isolate may also reflect the influence of Polynesian ancestry in the gene pool. We note that there is not comparable eGFR data available for Polynesian populations, however, many studies have shown that the prevalence of ESKD is much higher in Polynesian populations in comparison to Europeans22, heritability estimates of eGFR, serum urea, and serum creatinine were 44%, 31%, and 37% respectively. In our study, these traits showed lower heritability estimates, ranging from 0.27 to 0.3 (P < 0.05)  generated 3 principal components (PC): CGU-PC1, CGU-PC2, and CGU-PC3 .KCNIP4 gene on chromosome 4 forming a clear GWAS peak for CGU-PC1 . Given the high capacity of CGU-PC1 in explaining kidney dysfunction and the negative effect size of the lead SNP, the identified KCNIP4 locus could be relevant in decreased susceptibility of CKD.GWASs of CGU-PC1 and CGU-PC2 did not identify any genome-wide significant loci. However, there was a cluster of SNPs mapped to the Following analysis of the three primary CKD traits, we expanded the analysis to include other CKD-secondary phenotypes. Along with eGFR, serum creatinine, and serum urea, there were 26 other continuous measurements that were assessed /HDL-C ratio, HDL, triglyceride, and waist hip ratio (i.e. CGU-CHTW-PC2); body fat and height (i.e. CGU-BH-PC1). Of note, the association of KCNIP4 in the CGU-CHTW-PC2 combination was identified with the the lowest P-value of pmin = 1.59 × 10–9  with all the 29 phenotypic traits showed this locus to be highly associated with eGFR, creatinine, and urea . UGT1A8 is an established bilirubin-associated gene.Along with KCNIP4 gene (rs12640604) and CGU-PC1 extracted from the three CKD-primary traits. The UKBB is comprised of multiple different ethnic  subgroups and these were analyzed separately (Supplementary Table –6) was several times higher than those in the Caribbean and Indian. Finally, only the Indian subgroup effect size was in the same direction as that in the NI isolate. Of note, the index SNP showed some evidence for association to the individual CKD traits in several subgroups (P < 0.05) although the modest level of statistical significance would not implicate this SNP in large GWASs.We utilized the UKBB data to test for replication of the association between the index SNP for KCNIP4 of which, there were 47 SNPs that exhibited some LD (R2 > 0.2) with the index SNP (rs12640604). These SNPs showed MAFs ranging from 0.146 to 0.491. Comparison with the MAFs taken from the UKBB, in which 46 SNPs were available, showed that most of the SNPs had comparable MAFs to NI cohort (the ratios of NI MAFs to the UKBB MAFs ranged from 0.6 to 2.3). In contrast, two SNPs (rs148583816 and rs143182955), which are a few base pairs apart from one another, have MAFs approximately tenfold higher than their MAFs in NI isolate , we localized the expression of KCNIP4 locus was identified in principal component traits derived from three CKD-primary traits as well as with various secondary trait combinations. The three derived composite phenotypes, i.e. CGU-PC1, CGU-BH-PC1, and CGH-CHTW-PC2, have encapsulated information that were not entirely expressed in the individual traits, hence, we were able to identify the KCNIP4 locus in these components but not in any single-phenotype.For the past decade, multiple genetic loci associated with kidney function have been identified via GWAS. However, the genetic basis of CKD is still not completely understood and requires a range of approaches to tackle the problem. In this study, we aimed to identify novel susceptible loci underlying kidney function using a multi-phenotype approach in combination with the use of the genetically isolated cohort of Norfolk Island (NI). As a result, the KCNIP4 which encodes for the Potassium Voltage-Gated (Kv) channel-interacting protein 4 has several transcripts with varied cellular expressions, and potentially different protein functions. In kidney, the three active transcripts are KCNIP4-IbΔII, KCNIP4-IcΔII, and KCNIP4-IeΔII25. Previously, the disruption of KCNIP4 has been observed in patients with renal-cell carcinoma. This is possibly due to alteration of the normal transcriptional regulation since the breakpoint interval coincides with the promoter of transcript KCNIP4-IcΔII26. In our study, the associated SNPs were located in the region corresponding to the promoter of the transcript KCNIP4-IeΔII, hence the associated variants may have a regulatory role in gene expression. In a recent publication, Gerhardt et al.27 found that KCNIP4 was markedly up-regulated at the late injury stage in proximal tubule cells in an ischemia–reperfusion injury model for studying acute kidney injury (AKI). Kidney proximal tubule is particularly vulnerable to injury and maladaptive repair of injured tubules after AKI can lead to CKD29. Therefore, the up-regulation of KCNIP4 can be one of the factors involved in the consistent impairment of the proximal tubule in kidney injury. In support of this we were also able to identify spatial gene expression of KCNIP4 in two human cortical kidney tissue sections. In these samples, KCNIP4 expression was localized to the tubules, with no expression in the glomeruli or vasculature of the cortical kidney tissue.The gene To pinpoint the potential regulatory SNPs, we analyzed all the SNPs in LD with the index SNP and found two nearby SNPs with MAFs much greater in NI isolate compared to other world populations. Consistent with the hypothesis that the associated SNPs involved in transcriptional regulation, this group was in a region where the histone modification H3K4me3 occurred. The founder effect of NI isolate, along with the high inbreeding rate, has mostly likely increased the frequencies of these alleles, which in turn has increased the power to detect the association relative to other major ancestral populations.3. However, because the initial aim of the Norfolk Island Health Study (NIHS) was to study cardiovascular diseases, only serum biomarkers were collected. Also, the inclusion of both eGFR and serum creatinine in PCA was not entirely justified because these were highly correlated variables i.e. eGFR was calculated based on serum creatinine, age, and sex. However, the exclusion of serum creatinine did not alter the current findings.One of the limitations of the study was the lack of urine biomarkers for measuring albuminuria, which are especially important to detect CKD when eGFR is in normal rangeKCNIP4 locus to be associated with CKD. Two associated variants which minor allele frequencies that were about tenfold higher in the NI isolate than in other world populations potentially play a regulatory role in the KCNIP4 gene expression. Further studies are needed to assess the biological functions of the identified variants in relation to CKD.In conclusion, the use of the NI genetic isolate in combination with the PCA-based multi-phenotype approach revealed the 30.The Norfolk Island Health Study (NIHS) is a well-established study aimed at identifying genetic and environmental risk factors for CVD and related diseasesIn this study, we included 380 individuals with available genomic and phenotypic data. These individuals consisted of 196 females (mean age: 49.8 ± 16.5) and 184 males (mean age: 47.1 ± 15.3) all of whom were members of the core-pedigree and at least 18 years of age at the time of collection. Serum samples were drawn from each individual to measure serum biomarkers as well as to collect blood-based DNA.Ethical approval was granted prior to the commencement of the study by the Griffith University Human Research Ethics Committee . Ethics approval and management of the NIHS has since been transferred to Queensland University of Technology . All individuals gave written informed consent prior to the study and all methods were carried out in accordance with relevant guidelines and regulations.31. Missing data were imputed using the MissMDA 1.3 package32. The package can perform multiple imputation using principal component analysis (PCA), meaning the imputed values will not affect PCA results.In total there were 29 quantitative phenotypic traits  analyses were performed with the package FactoMineR 1.4234, which integrated multiple PCA exploratory methods and illustrations.After the imputation of missing data, the PCA method was applied to transform multi-dimensional data into fewer components. PCA is one of the optimal approaches in multiphenotype analysis, especially in the case of association studies35 as a reference, identified over 26 million SNPs. In total, genomic data was available for 520 individuals.NI genomic data were generated from two platforms: Illumina HiSeq X10 sequencing (n = 108) and Illumina 610-Quad array (n = 506). Whole genome sequencing (WGS) directly revealed nearly 20 million SNPs, while the SNP-array, which was imputed using the 1000 genomes project36. For overlapping individuals, WGS data was given preference such that SNP-array data was removed.To merge the SNP-array and WGS data, the common SNPs across both data sets were identified, and data associated with the common SNPs extracted. The merge procedure was performed using the default parameters of the ‘bmerge’ function in PLINK 1.9–7 was applied as the HWE threshold—a specific value calculated to account for widespread linkage disequilibrium in the NI cohort. A 5% missing genotyping filter was also applied on individuals and variants to ensure high quality data for subsequent analysis. Finally, variants with a minor allele frequency less than 0.05 and samples without phenotype data were removed. A total of 380 individuals and 4,753,086 SNPs remained after filtering.Quality control filters were applied to the unified genomic data set to ensure high quality SNP data for subsequent analysis. A P-value of 1.84 × 1037 to estimate heritability for each individual trait and phenotypic PCs. The extended pedigree information of the NI isolate38 was integrated to account for the high degree of relatedness among the samples. All traits and PCs with a high excess kurtosis were inverse-normal transformed prior to heritability estimation.We utilized the SOLAR 8.5.1 program39 to avoid loss of power when double-fitting of the candidate variants in the model.Genome-wide association analysis (GWAS) was only performed for traits that yielded heritability estimates that were statistically significant at a nominal level (P < 0.05). To account for the family relatedness in association testing, we applied the mixed linear model, where a genetic relationship matrix (GRM) containing all the genotype correlations between all pairs of individuals was fitted as a random effect with age and sex fitted as additional fixed effects when computing the associations between phenotype and genetic markers. However, tested SNPs were excluded from calculating the GRM as implemented in the GCTA-LOCO approachWe examined the inclusion of principal components of all genotyped SNPs’ GRM as a common method to address confounding due to population structure. PCA was performed with PLINK 1.9 and the first numbers of PCs were included as covariates along with age and sex in the GWAS. We found that as more PCs were included, the deflation in the statistics tests increased  of these SNPs were then compared to those in the gnomAD database (v3.1.1)40 and the UKBB data. Genes and SNPs were visualized on the UCSC Genome Browser.We used the WGS data of 108 NI samples to explore allele frequencies of variants that are in LD with the GWAS most associated SNP rs12640604 at the 41 were used to identify the spatial gene expression of KCNIP4. These data were visualized using the gene_plot function of stLearn42 and compared to existing data from the Human Protein Atlas.Existing Visium spatial gene expression data of two human cortical kidney tissue sectionKCNIP4 gene and CGU-PC1 extracted from the 3 CKD-primary traits in populations included in the UKBB20 data (Supplementary Table 31. CGU-PC1 were newly constructed for each sub-group. Genotypic association analysis for each population was performed with simple linear models using the allele counts of rs12640604-A as independent variable and CGU-PC1 as the outcomes.We performed association analysis between the index SNP mapped to the Supplementary Information.