Microarray technology has been used to measure genome-wide DNA methylation in thousands of individuals. These studies typically test the associations between individual DNA methylation sites (“probes”) and complex traits or diseases. The results can be used to generate methylation profile scores (MPS) to predict outcomes in independent data sets. Although there are many parallels between MPS and polygenic (risk) scores (PGS), there are key differences. Here, we review motivations, methods, and applications of DNA methylation-based trait prediction, with a focus on common diseases. We contrast MPS with PGS, highlighting where assumptions made in genetic modeling may not hold in epigenetic data.The online version contains supplementary material available at 10.1186/s13059-023-02855-7. In mammals, DNAm predominantly occurs at cytosine-guanine dinucleotides (CpGs). In some instances, DNAm can block the binding of transcription factors to DNA and is therefore associated with reduced gene expression [The most characterized epigenetic marker is DNA methylation (DNAm). DNAm is a reversible modification to DNA involving the covalent addition of methyl groups .cis (where cis implies a close proximity on the chromosome between the DNA polymorphism and the site of methylation). Measures of DNAm at mQTLs are correlated between relatives, i.e., they are heritable quantitative traits [While the DNA sequence is stable across cell types throughout lifetime (other than somatic mutations), DNAm varies between cell types  and between people (within a cell type) for several reasons Fig. . First, e traits . Third, e traits , wherease traits –18. While traits . If DNAmIt is notable that the use of DNAm technology in the context of a cancer diagnosis from blood samples is already well-advanced . The celIn this review, we focus on DNAm measured by array technology. We consider how MWAS data sets can be used to calculate trait-associated methylation profiles scores (MPS) in independent data sets that have DNAm measured. MPS are also referred to as episcores  or simplEvaluation of trait prediction using DNAm data requires at least two independent data sets with measures of both genome-wide DNAm and the trait of interest Fig. . The MWAWe use the word trait “prediction” for MPS with hesitation since it is important to emphasize a key difference between MPS and PGS. In principle, PGS can be calculated at birth and will not change over lifetime (unless the SNPs and their weights used to construct the PGS are updated). PGS can therefore be considered as predictors of future, not-yet observed events. Since measured DNAm levels can fluctuate, any trait-specific MPS for an individual can change throughout an individual’s lifetime. Hence, much more thought is needed, compared to PGS applications, about the time point at which biological samples  are taken in the discovery, target, and final application samples as to whether the MPS developed can be considered as a predictor of a future event. While some MPS scenarios will fit the criteria of prediction, we emphasize that this is not always the case. Consider the goal of using DNAm as a blood biomarker to aid in early risk stratification of subsequent (incident) disease onset. To have clinical utility as a diagnostic biomarker, the MPS needs to be valid in blood samples taken at or before the time diagnosis is achieved under current practice  Fig. , whereasFor GWAS and PGS, the tissue (or cell type) from which DNA is derived is rarely considered since germline-inherited DNA polymorphisms are the same in all tissues . While tissue source  can be identified in the principal component analysis of SNP array data pre-quality control (QC), differences are handled through routine QC steps and do not impact upon genotype calls. In contrast, cell type-specific DNAm is a major contributor to DNAm variation, and tissues from which DNA is usually isolated for genomic analysis comprise a heterogeneous mix of different cell types. In a later section, we consider issues associated with cell-type proportions from the whole blood in the context of MPS.The majority of large MWAS studies, to date, have quantified DNAm in the whole blood, which is an easily accessible tissue relevant to biomarker development. Notably, DNAm can be measured accurately from dried blood spots with good concordance with measures from matched blood samples . To achiM-values and beta values. M-values are the log2 ratio of the intensities of the methylated versus unmethylated probes at a given CpG site, across all cells sampled, with positive values indicating a site is more methylated than unmethylated [To date, Illumina is the only commercial provider of DNAm arrays, and their most recent “EPIC” array is designed to measure DNAm at over 850K genomic locations , 36. Arrthylated . Beta vathylated . Beta vathylated .The Illumina iScan System is designed to read both SNP and DNAm arrays, although the probe design is more complex for DNAm analysis . While bIt is notable that DNAm arrays have traditionally been priced several-fold higher than SNP arrays, which has been a contributing factor to the smaller sample sizes for MWAS compared to GWAS. As a likely consequence of the smaller market, there has been less activity in the development of DNAm array content compared to SNP arrays. We understand that Thermo Fisher has a product in development which may provide competition in array content and price, which may drive DNAm measurement in larger cohorts. Although the EPIC array includes 850K probes, 120K are reported as non-variable between individuals in the blood , and furThe probes and their weights used in MPS are generated from the discovery MWAS data; hence, guidelines for the optimal design of MWAS  are critIn GWAS, the phenotype is always the dependent variable (y~SNP), but in MWAS, sometimes DNAm is analyzed as the dependent variable (DNAm~y), reflecting the two possible directions of dependency. For the purposes of trait prediction, we assume the y~DNAm model for analysis. For example, while DNAm changes associated with smoking most likely reflect smoking being causal for the changes, supporting the logic of the DNAm~y model, when the goal is to develop an MPS in independent data for those with unknown smoking status the analysis model must be y~DNAm. While DNAm measures are continuous they may not be normally distributed .λ, ratio of the observed median test statistic to the expected median test) is used to demonstrate that the quality control pipeline has retained no residual population stratification associated with the trait that could generate the identification of false positives [λ is expected to be 1, since it is reasonable to assume that fewer than half of the sites are truly associated. MWAS data must be afforded more consideration in this regard. The simulations conducted by Zhang et al. [λ = 7.67). This observation reflects, in part, cell type proportion differences between individuals with cell type-specific methylation patterns correlated across chromosomes. However, including cell-type proportion values  as covariates only reduced the λ to 4.95, implying other factors  generate a correlation of DNAm across chromosomes. Many MWAS methods have been introduced with the goal to control for unmeasured cell-type proportions and other unmeasured potential confounders, e.g., [λ only reduced to 1.67. They introduced linear mixed model methods to estimate the effect of each probe while controlling for the background genome-wide DNAm, which reduced λ to the expected value of 1. These simulations were conducted for a quantitative trait. In real data, while technical confounding with a quantitative trait is not expected, for binary disease traits, this can be problematic. For example, in case-control cohorts, the DNA collection and processing of cases and controls are frequently achieved by different protocols which can lead to technical confounding in DNAm levels . Statischnology .ith individual, the PGS is j which has values of i. Similarly, MPS are calculated as j and j in the ith individual. PGS are calculated as a weighted sum of trait-associated alleles , so for .g., see  for a comentclass2pt{minimmentclass2pt{minimp-value of association). The correlation structure between SNPs is integrated through knowledge of linkage disequilibrium (LD) among genetic variants, derived from a reference panel with individual-level genotypic data. The simplest PGS method selects independent SNPs associated with a p-value less than a specified threshold to select the p-value thresholding method, denoted here C+PT). Other PGS methods, in essence, use the genome-wide set of GWAS summary statistics to learn the trait-specific genetic architecture that can lead to choices of For PGS, individual-level GWAS data are often not available to researchers due to logistical and privacy concerns. Thus, the choice of SNPs and their weights are usually derived from meta-analyses of GWAS summary statistics . So. Sop-valT 2) and daWe identify four key applications for use of MPS for the prediction of an unmeasured/unknown phenotype Fig. . First, A second application of MPS is the potential use of DNAm as a direct outcome measure in the clinic , for exaA third application for phenotype prediction could be in forensics: when a biological sample is available, but the person associated with the sample is unidentified. In contrast to DNA profiling, which is used as evidence, MPS can be used in the investigative process adding to suspect profiling . In criminal investigations, demographic traits can be crucial to help identify offenders. Whereas highly heritable traits such as height can be predicted from genetic data, less heritable traits such as weight, or body mass index (BMI) could be better predicted by MPS or a combination of MPS and PGS , 93. A pFinally, DNAm data could prove useful in clinical settings as biomarkers of disease risk Fig. . DNAm diWhen DNAm is measured in bulk tissue such as whole blood, trait associations could reflect a mixture of intrinsic and extrinsic signals Fig. . The “inN case/control of 612/782 and 1159/637 in discovery and target cohorts, respectively). Moreover, we found that MPS based on weights applied to DNAm-derived cell-type proportions generated an AUC of 0.67, higher than our MPS approach and close to the maximum AUC achieved from combining the predicted cell-type proportions with the MPS (AUC = 0.69). However, a higher proportion of neutrophil granulocytes estimated from DNAm in cases compared to controls was found for many diseases , demonstrating non-specificity [Careful consideration of study aims is needed when deciding the relevance of trait-associated cell-type proportions. If the goal is a biological interpretation of DNAm differences, then correction for cell-type proportions is appropriate. However, when DNAm in the blood is simply considered as a biomarker, and when the goal is trait prediction, any signal that can be derived from the DNAm that maximizes out-of-sample prediction accuracy should be included. For example, in our MWAS of amyotrophic lateral sclerosis , we founcificity . We testcificity  on thesecificity . Althougcificity  and Cellcificity ) have beR2—the proportion of phenotypic variance (σ2𝑃) in the trait explained by the PGS. The R2 is on the same scale as and can be contrasted to heritability (h2) which is the proportion of y after removing variation attributed to fixed effects, such as sex and age). Whereas h2 reflects all factors contributing to genetic variation, the PGS can only capture the genetic variation associated with the SNPs measured in the GWAS, so the upper bound on the R2 from PGS (under assumptions of the same trait drawn from the same population) is the SNP-based heritability (M refers to the number of independent SNPs represented by the genome-wide SNP array). Given that effect sizes of individual SNPs are estimated with error the R2 that is expected from a PGS can be approximated as N is the sample size (of the discovery sample). In theory, as sample sizes increase, the variance explained by PGS will also increase and tend to the SNP-based heritability [First, we consider the evaluation metrics for PGS when calculated in target samples (those where the MPS trait has been measured) and then draw parallels for MPS. For quantitative traits , the accuracy of prediction of a PGS is evaluated as M/(NhM2) , where Ntability , 106.R2 statistic, Nagelkerke’s R2 (P) and the lifetime risk of disease (K), z is the height of the normal curve when thresholded by proportion K [R2 [R2 statistics [For binary traits, a logistic regression pseudo-ortion K . Anotheron K [R2  so increatistics ).R2 for quantitative traits, and In the context of MPS, ediction , 69, 71.reported , 52, 69 c theory , 109 than = 1,366). The PGS and MPS explained 8% and 7%, respectively, of the variance in BMI and 14% when fitted jointly demonstrating that PGS and MPS contributions were mostly independent and additive. Most notably, the discovery sample was ~ 350K for the PGS but only 750 for MPS. The same study reported that height MPS explained no variation in height. The difference in the success of MPS for BMI and height likely reflects that the current diet impacts blood DNAm which is associated with concurrent BMI. In contrast, variation in height between older people is likely not captured in their blood . It is hildhood , 73, likR2). For smoking behavior defined as the number of pack-years, they reported that 46% of the phenotypic variance was captured by methylation probes and 6% by genome-wide SNPs  for GWAS compared to MWAS and so PGS and MPS will be generated independently. Tuning samples will be needed to determine how to weigh PGS and MPS when combined into a single predictor.The BayesRR  method umentclass2pt{minimIt is well-recognized that most GWAS discovery samples are from participants of European ancestry . The cosThere are few data sets that compare DNAm across ancestries. DNAm age predictors were originally derived from multi-ancestry samples , 114, anN ≥ 725) and projected into the Generation Scotland cohort  were associated with the incidence of 11 major age-related morbidities, with 137 MPS disease associations reported for 11 common diseases over 14 years of electronic health linkage [MPS are increasingly being developed to stratify risk associated with diseases and traits associated with diseases. The methodology used to calculate these scores parallels that used to define PGS for common complex diseases, which are derived from common single nucleotide variants. PGS can be considered trait or disease predictors, since in principle they can be calculated at any time over the lifespan. In contrast, DNAm can be dynamic across the lifespan and therefore must be developed using data relevant to a specific application context, or at least evaluated in the application context before the real-life utility can be confirmed. MPS likely capture signatures that are a consequence of the trait, in addition to potential trait-associated causal pathways and MPS development must recognize this nuance. For MPS where DNAm is largely a direct outcome of the trait, e.g., smoking and BMI, MPS capture a large proportion of variation in out-of-sample evaluation, much more than PGS, despite much smaller sample sizes. In the context of disease prediction, less variance is often explained by MPS, but there is sufficient evidence to date to support further evaluation of DNAm as a biomarker of disease onset or disease progression. This requires careful ascertainment of cases and controls and efforts in all stages of experimental design to minimize batch effects and to understand contributions arising from cell-type effects. MPS can also be trained to predict disease-associated traits, which  could be more cost-effective or simply more feasible than direct measurement of the trait. For example, MPS for 109 plasma proteins trained in independent cohorts ( linkage . Investm linkage ) would aAdditional file 1.Additional file 2. Review history.


Feed efficiency (FE) related traits play a key role in the economy and sustainability of beef cattle production systems. The accurate knowledge of the physiologic background for FE-related traits can help the development of more efficient selection strategies for them. Hence, multi-trait weighted GWAS (MTwGWAS) and meta-analyze were used to find genomic regions associated with average daily gain (ADG), dry matter intake (DMI), feed conversion ratio (FCR), feed efficiency (FE), and residual feed intake (RFI). The FE-related traits and genomic information belong to two breeding programs that perform the FE test at different ages: post-weaning  and post-yearling .p -value) > 5) regions mapped on BTA 1, 2, 3, 4, 7, 8, 11, 14, 15, 18, 21, and 29. These regions explained a large proportion of the total genetic variance for FE-related traits across-population ranging from 20% (FCR) to 36% (DMI) in the IZ population and from 22% (RFI) to 28% (ADG) in the QLT population. Relevant candidate genes within these regions  highlighted that the physiological mechanisms related to neuropeptides and the metabolic signals controlling the body's energy balance are responsible for leading to greater feed efficiency. Integrated meta-analysis results and functional pathway enrichment analysis highlighted the major effect of biological functions linked to energy, lipid metabolism, and hormone signaling that mediates the effects of peptide signals in the hypothalamus and whole-body energy homeostasis affecting the genetic control of FE-related traits in Nellore cattle.The meta-analyze MTwGWAS identified 14 genomic regions (-log10(Genes and pathways associated with common signals for feed efficiency-related traits provide better knowledge about regions with biological relevance in physiological mechanisms associated with differences in energy metabolism and hypothalamus signaling. These pleiotropic regions would support the selection for feed efficiency-related traits, incorporating and pondering causal variations assigning prior weights in genomic selection approaches.The online version contains supplementary material available at 10.1186/s12864-022-08671-w. The genetic improvement of animal feed efficiency (FE) has been considered a relevant aspect in breeding programs to achieve reductions in feed costs and environmental impacts . Indeed,Feed efficiency-related traits are controlled by a complex interaction of different physiological mechanisms and biological processes regulating feed intake and energy expenditure –6. GenomAssembling a large enough population for accurate detection of QTLs is the major challenge for GWAS, especially for FE-related traits which are commonly evaluated in small populations. A limited number of animals with genotype and phenotypic information lead to lower accuracy of SNP markers effect, making it difficult to identify causative mutations directly associated with the target trait . In thisThe FE-related traits  and genomic information were obtained for 1,024 animals belonging to an experimental breeding program at Beef Cattle Research Center , and 918 animals from a commercial breeding program Nellore Qualitas (QLT). The animal procedures realized in this research agreed with Animal Care of the São Paulo State University (UNESP), School of Agricultural and Veterinary Science Ethical Committee (protocol number 18.340/16).IZ population is divided into three selection herds: Nellore control (NeC), Nellore Selection (NeS), and Nellore Traditional (NeT). In NeC, animals are selected based on differential selection for YBW close to zero. On the other hand, animals are selected for the maximum differential selection for YBW in NeS and NeT [The experimental breeding program was established in 1980, selecting animals based on yearling body weight (YBW) measured at 378 days of age in young bulls and 550 days of age in heifers. The  and NeT . In the The FE-related traits were measured on 1,156 animals , born from 2004 to 2015, being 146 of NeC , 300 of NeS , and 710 of NeT  herd. The animals were evaluated in a feeding trial, in which they were either housed in individual pens  or group pens equipped with the GrowSafe feeding system . In both situations, the feeding trial comprised at least 21 days for adaptation to the feedlot diet and management and at least 56 days for the data collection period. During the feeding trial period, the animals were grouped according to sex, males exhibited an average age at the beginning of the trial of 275 and 366 ± 27.5 days in the end, while females showed an average age at the beginning of the trial of 302 and 384 ± 45.4 days at the end of the feeding trial.ad libitum and formulated with 67% of total digestible nutrients (TDN) and 13% of crude protein (CP), allowing an average daily gain (ADG) of 1.1 kg/day.Animals were weighed without fasting at the beginning and the end of the feeding trial, as well as every 14 days during the experimental period. The mixed diet  was offered QLT) breeding program comprises 34 farms distributed in two regions of Brazil (Midwest and Southeast) and Bolivia. The animals are selected based on a selection index that includes body weight at weaning (24%), weight gain from weaning to yearling (38%), scrotal circumference (19%), and muscling visual score (19%), both measured around 15 months of age. In the QLT population, FE-related traits from 947 young bulls, born from 2008 to 2015, were evaluated in feeding trials in individual pens  or group pens equipped with the Intergado feeding system . The Intergado System operates like the GrowSafe System, measuring the animals' feed intake and feed frequency. Each feeding trial comprised 28 days for adaptation to the feedlot diet and 56 days for data recording. Animals were weighed without fasting, at the beginning and ending, as well as every 21 days during the experimental period. The diet  was offered ad libitum and formulated with 82% of total digestible nutrients (TDN) and 12,5% of CP, allowing an ADG of 1.5 kg/day. Animals were, on average, 652 ± 38.43 days old at the beginning and 712.02 ± 38.43 days old at the end of the feeding trial.Nelore Qualitas  was defined as the slope from the linear regression of body weight (BW) on feeding trial days. The ratio between ADG and DMI was used to calculate the feed efficiency (FE), and the ratio between DMI and ADG was used to estimate the feed conversion rate (FCR). Residual feed intake (RFI) was calculated in each test year as the difference between the observed and expected feed intake considering the each animal's average metabolic body weight (MBW) and ADG, using the gold-standard equation proposed by Koch et al. [During the feeding trials, in both where IZ and Qualitas), excluding records outside the interval of ± 3.5 SD below or above the mean of each contemporary group (CG). After phenotypic quality control, the number of animals per CG ranged from 10 to 70 for the IZ population and from 27 to 119 for the QLT population. The CG in the IZ population was defined by sex and month and year of birth and in the QLT population by month and year of birth and year of feed trial. In both cases, for animals in the group pens, the feed trial pen was added. Descriptive statistics and genetic parameter estimate for FE-related traits are reported in Table Phenotypic data quality control was performed for each breeding program (IZ) and 582 (QLT) animals were genotyped using the Illumina BovineHD BeadChip assay  and 310 (IZ) and 372 (QLT) animals using GeneSeek® HDi 75 K . For each population, animals genotyped with the lower density panel were imputed to the HD panel using FImpute v2.2 [–5), and with call rate less than 0.90. In addition, samples with a call rate lower than 0.90 were also removed from the analyses. After quality control, 1,024 animals from IZ and 918 animals from QLT, and 387,035 SNP markers in common between the two populations remained in the dataset.A total of 846  evaluated the population substructure based on the SNP markers using the ade4 R package . Four grMulti-trait weighted genome-wide association analysis (MTwGWAS) was performed for each population, separately, considering the following general model:IZ population, the three first principal components that explain 7.55% of the genotypic variability and the linear and quadratic effect of cow age were also considered.where G matrix used in the MTwGWAS method was constructed as follows: jth SNP marker.The random effects of animal and residual were assumed to be normally distributed: VanRaden , ⊗ is thThe SNP marker effect and weights were calculated based on genomic breeding values (GEBV) of genotyped animals obtained from a multi-trait model through the algorithm proposed by Wang et al. . The posD = I and second step D = w (w were the weight estimates obtained in step 6); 2) Calculate the G matrix  In tjth SNP marker and jth SNP within the window with 100 markers.Results from MTwGWAS was used to estimate the proportion of genetic variance explained by SNPs markers , a multi-trait meta-analysis statistical method, described by Bolormaa et al. , was perk degrees of freedom, where k is the number of traits included in the multi-trait statistical test. For each SNP marker  the statistic was: t -value of SNPk for the 5 FE-related traits in the 2 Nellore cattle population, t -values correlation matrix between the t -values (10 × 10). The p -value adjustment for multiple tests was performed using the false discovery rate (FDR) test [p -value < 0.05) and p -value < A multi-trait meta-analysis statistic test was used to evaluate the association and influence of SNP effects for FE-related traits across two Nellore cattle populations . This st element . This co element . The p -DR) test : \documep -value) > 5.0 (5% FDR) and grouped in SNP-window regions within each BTA when the markers did not show a gap greater than 0.2 Mb among them. Genes in significant SNP-window regions were identified using the NCBI BioSystems database for cattle using the map Bos taurus ARS-UCD1.2 assembly as reference. The candidate gene list from meta-analysis GWAS was used as the target gene list for functional classification for biological process  and KEGG pathways using the string R Package [p -value < 0.05 and an FDR of 5% for multiple test correction as described by Boyle et al. [The SNP markers from statistical combination were deemed significant when –log10 explained more than 2% of the genetic variance for FE-related traits  explained more than 2.80% of additive genetic variance  associated with feeding control [P-window regions on BTA 3, 4, and 11 might directly affect FE-related traits due to the potential regulation of energy metabolism and neuroendocrine pathways, with major effects on catabolic and anabolic pathways involved in feed intake control and energy homeostasis.The genes identified on BTA3 (78.99 – 80.84 Mb), BTA4 70.88 – 71.85 Mb), and BTA11 , lipid regulation (ANGPTL8), and energy metabolism  and affecting the oxidative metabolism in muscle fibers (CNN1) [ANGPTL8 plays functions in physiological adaptation through lipid and glucose homeostasis, affecting bovine's adipogenesis [EPOR), energy balance by specific anabolic and catabolic pathways (INSR), as well as in glucose and lipid metabolism by the action of the resistin gene (RETN). The genes EPOR, INSR, and RETN could be involved as peripheral signals of energy homeostasis controlled by glucose and insulin homeostasis leading to important feedback with NPY regulating the feed intake [The major candidate genes identified on BTA5 (65.95—67.03 Mb) were: BP Table . The IGFng score . The PARsistance . The gens (CNN1) . The genogenesis . These fogenesis . Reyer eogenesis  observedd intake .LPL gene mapped on BTA8 (65.95 – 67.82 Mb) is directly involved in the metabolism and transport of lipids. Montanholi et al. [CAPN5 and MYO7A identified on BTA15 (56.22 – 56.63 Mb) are associated with muscle metabolism  formation leading to a reduction in the capacity to transport cholesterol from adipose tissue to liver and muscle. The PLA2G15 gene regulates the hydrolysis of phospholipids into free fatty acids. There is evidence that high feed efficient animals exhibit reduced hepatic usage of fatty acids [AGRP is associated with hypothalamic integration of energy balance, nutrient partition control, and feed intake increase by antagonizing the effects of the orexigenic peptides [The genomic region mapped on BTA18 (35.00 – 35.79 Mb) accounted from 3.30% to 4.95% of genetic variance for FE-related traits  and TGFB1. It is associated with skeletal muscle development and growth, a main biological factor affecting feed-related traits. Jing et al. [TGFB1 signaling pathway plays a key effect in feed efficiency by skeletal muscle growth stimulation and metabolism. The gene set  is a member of cytochrome P450 proteins involved in synthesizing steroids and lipids. Tizioto et al. [LIPE gene codifies an enzyme with function in lipid hydrolysis, mainly hormone-sensitive lipase (HSL). The LIPE gene increases lipolysis during the negative energy balance in dairy cattle to attend to energy homeostasis [The genomic region on BTA18 49.66 – 50.93 Mb) explained a significant amount of genetic variance from 2.05% to 4.07% , AMPK (bta04152), and insulin signaling (bta04910) pathways which play a key role in molecular signals that act on energy homeostasis leading to the greatest feed efficiency in cattle Table . The MAPficiency .NPY, POMC, PENK, GHSR, LEPR, and LEPROT) [The AMPK pathway (bta04152) is associated with metabolic energy balance 67 and increases protein metabolism, fatty acid oxidation, glucose uptake, and glycolysis Table . Thus, t LEPROT) . This reGHSR, INS, INSR, LIPE, JAK1, and LEPR) are directly associated with regulating these pathways. These metabolic responses act to keep metabolic homeostasis, leading to different feed efficiency by catabolic process, mainly energy mobilization and protein degradation [GHSR, LIPE, ADCY3, PDE4B, PLD1, INSR, INS, and NPY) related to cAMP and regulation of lipolysis in adipocytes lead to higher feed efficiency in animals by differences in lipid synthesis and degradation of the lipid content of adipocytes.The significant metabolic pathways JAK-STAT signaling bta04630), insulin signaling (bta04910), and growth hormone  across the Nellore cattle population. Thus, the pleiotropic effects of the key-regulatory genomic regions are directly implicated in the regulation of energy metabolism, and hypothalamus signaling may have an essential effect on FE-related traits in the two Nellore cattle populations. Overall, the major genomic regions uncovered across the Nellore cattle population are related to major modulators linking the metabolic homeostasis and genes that regulate the feeding intake allowing greater feed efficiency.Additional file 1: Supplementary Table S1. Functional enriched Biological Process (BP) related to metabolism among the candidate genes identified using the multi-trait meta-analysis statistical test for feed efficiency-related traits. Supplementary Table S2. Functional enriched Biological Process (BP) related to metabolism among the candidate genes identified using the multi-trait meta-analysis statistical test for feed efficiency-related traits. Supplementary Figure S1. Principal component analysis of animals based on the first two principal components based on SNP information to evaluate the extent of the population structure in the IZ population  and Qualitas population (QLT). Supplementary Figure S2. Manhattan plots of the percentage of the additive genetic variance explained by SNP-windows of 100 adjacent SNPs for average daily gain (ADG) and feed conversion rate (FCR) in IZ and Qualitas (QLT) population. Supplementary Figure S3. Manhattan plots of the percentage of the additive genetic variance explained by chromosome regions of 100 adjacent SNP windows for residual feed intake (RFI) and dry matter intake (DMI) in IZ and Qualitas (QLT) population.Supplementary Figure S4. Manhattan plots of the percentage of the additive genetic variance explained by chromosome regions of 100 adjacent SNP windows for feed efficiency (FE) in the IZ and Qualitas (QLT) population.


Expanding a statistical approach called Mendelian randomization to include multiple variables may help researchers to identify new molecular causes of specific traits. Related research article Auwerx C, Sadler MC, Woh T, Reymond A, Kutalik Z, Porcu E. 2023. Exploiting the mediating role of the metabolome to unravel transcript-to-phenotype associations. eLife12:e81097. doi: 10.7554/eLife.81097.Understanding how variations in our genome influence our susceptibility to diseases is one of the most compelling research topics in the life sciences. Researchers have used genome-wide association studies – experiments that analyze the DNA sequences of multiple individuals – to identify statistical relationships between genetic variants and specific human traits, such as susceptibility to a disease or various body parameters.Despite the success of this approach, major challenges persist. First, associations between variants that are located close to each other within the genome can make it difficult to determine which of these genetic changes are responsible for the phenotype of interest . Second, even if specific variants can be identified, it is often not straightforward to determine the molecular mechanism by which they impact the trait .To overcome these difficulties, studies often include information about other modalities such as transcriptomes, proteins and metabolites . Some ‘mMendelian randomization is considered to be an ‘experiment of nature’, as it uses variations already present in the genetic code to determine if exposure to certain conditions  affects a specific trait . The genetic variants act as a proxy, or ‘instrument’, for exposures that are difficult or impossible to manipulate in the population being studied. Mediation analysis can then be applied to ask if the exposure is responsible for the effects of the instrumental variable on the trait of interest. However, it is necessary to proceed carefully : for exaThe computational framework presented by Auwerx et al. integrates results from genome-wide association studies with data on genetic variants that affect the level of transcripts or the composition of metabolites. These variants are typically referred to as eQTL (short for expression quantitative trait loci) and mQTL (metabolite QTL), and can be derived from separate population cohorts, allowing researchers to tap into the vast resources of information that are already available.First, causal links between transcripts and metabolites are established using overlapping mQTL and eQTL as instrumental variables. Causal effects of metabolites on traits of interest are then determined in the same manner using mQTL and genetic variants identified in genome-wide association studies. The next step in the framework is purely based on this established causality: transcripts that causally affect trait-modifying metabolites have to be causally linked to the same trait, resulting in transcript-metabolite-trait triplets . A statiANKH that has been implicated in mineralization disorders. The resulting change to the export of citrate seems to affect the level of calcium present in the serum of individuals – a connection that was not detected when only transcript levels were correlated with the calcium trait.Auwerx et al. highlight an intriguing example of genetic variants affecting the transcription of a citrate-exporting protein encoded by a gene called By extending the Mendelian randomization approach to include two modalities (transcripts and metabolites), this new framework can detect causal relationships that could not be identified by comparing the genome wide association data to a single modality only. It also provides new insights into how the transcript impacts the phenotype through metabolic changes. With multi-omics studies increasing further in size, it is highly probable that even more advanced statistical approaches may become feasible in the future.


PDE3B as an important modulator of fat distribution. Local adiposity traits (1) highlight depot-specific genetic architecture and (2) enable construction of depot-specific polygenic scores that have divergent associations with type 2 diabetes and coronary artery disease. These results – using MRI-derived, BMI-independent measures of local adiposity – confirm fat distribution as a highly heritable trait with important implications for cardiometabolic health outcomes.For any given level of overall adiposity, individuals vary considerably in fat distribution. The inherited basis of fat distribution in the general population is not fully understood. Here, we study up to 38,965 UK Biobank participants with MRI-derived visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes. Because these fat depot volumes are highly correlated with BMI, we additionally study six local adiposity traits: VAT adjusted for BMI and height (VATadj), ASATadj, GFATadj, VAT/ASAT, VAT/GFAT, and ASAT/GFAT. We identify 250 independent common variants (39 newly-identified) associated with at least one trait, with many associations more pronounced in female participants. Rare variant association studies extend prior evidence for  The inherited basis of body fat distribution is not fully understood. Here, the authors use genetic data and MRI-derived measures of local adiposity to highlight fat depot-specific genetic architecture with implications for cardiometabolic health. An extreme example of this paradigm occurs in Mendelian lipodystrophies, such as those caused by missense mutations in the LMNA and PPARG genes10. By contrast, the genetic architecture of more subtle variation in fat distribution across the general population warrants further attention.Overall fat mass and fat distribution represent two correlated but distinct axes of variation that determine the health impacts of adipose tissue. Individuals with high body mass index (BMI)—defining obesity—are at elevated risk of type 2 diabetes and cardiovascular events, but increased cardiometabolic risk has also been noted in individuals with the same BMI when fat is disproportionally depleted in more favorable gluteofemoral fat depots and deposited instead in visceral and ectopic fat depots12. Another recent GWAS aimed to examine fat distribution using estimates of body composition based on stepping on a scale equipped with impedance technology, known to be reasonably accurate for total fat volume but less so for fat distribution15. Despite the considerable value of these studies, a central limitation is an unclear relationship between each anthropometric trait and each fat depot of biological interest—for example, an increase in WHRadjBMI could be capturing increased visceral adipose tissue , increased abdominal subcutaneous adipose tissue , decreased gluteofemoral adipose tissue , or some combination of these perturbations17. Variation in WHRadjBMI could also reflect variation in muscle and bone mass, rather than adipose tissue burden.In general, prior studies aiming to elucidate common genetic variation contributing to fat distribution can be categorized into three study types: (1) genome-wide association studies (GWAS) on anthropometric proxies of fat distribution, (2) studies combining GWAS summary statistics of metabolic and anthropometric traits, and (3) GWASs on imaging-based measures of fat distribution. The first type has been spearheaded by the Genetic Investigation of ANthropometric Traits (GIANT) consortium and others, leading to the discovery of over 300 loci associated with waist-to-hip ratio adjusted for BMI (WHRadjBMI) in an analysis of nearly 700,000 individuals23. These studies have succeeded in establishing a common variant basis for metabolically distinct fat depots, with seminal work demonstrating that an insulin resistance polygenic score is associated with lower hip circumference in the general population, and that individuals with familial partial lipodystrophy type 1 (FPLD1) have a higher burden of this polygenic score19. Along with their reliance on anthropometric proxies of fat distribution, these studies are limited by their inclusion requirement of nominal significance across multiple metabolic traits which is likely leading to only a fraction of the genetic architecture of fat distribution being described.A second category of studies has aimed to gain further resolution into anthropometric loci by combining summary statistics of metabolic and anthropometric traits, generating clusters of metabolically favorable and unfavorable loci29. These include GWASs of CT-quantified VAT and ASAT in nearly 20,000 individuals, GWASs on MRI-quantified VAT and ASAT, and a GWAS of a predicted VAT trait using several anthropometric traits trained on over 4000 DEXA-measured VAT values29. These studies have been important for translating insights from anthropometric and metabolic trait GWASs to image-derived measurements of the fat depots of interest, but have been limited by (1) the absence of GFAT, which appears to have a metabolically protective role in contrast to VAT and ASAT, and frequently (2) a reliance on raw, unadjusted fat depot metrics which are highly correlated with both each other and BMI.Finally, the third category of studies performed GWASs on measurements derived from body imaging30.In this study, we investigate the common and rare variant genetic architecture of three fat depots as quantified by MRI in up to 38,965 UK Biobank participants. Beyond study of raw VAT, ASAT, and GFAT volumes, we analyze six measures that better reflect local adiposity and fat distribution: VAT adjusted for BMI and height (VATadj), ASATadj, GFATadj, VAT/ASAT, VAT/GFAT, and ASAT/GFAT. We show that these local adiposity traits (1) highlight depot-specific genetic architecture, (2) reflect sex-dimorphism previously appreciated with anthropometric traits, and (3) can be used to construct depot-specific polygenic scores that have divergent associations with type 2 diabetes and coronary artery disease. This study is to our knowledge the largest imaging-based study to date to disentangle the genetic architecture of different fat depots—including GFAT, a fat depot that appears to confer protection from adverse cardiometabolic health5. Among those with MRI-quantified fat depot volumes, 39,076 had genotyping array data available, enabling common variant association studies in up to 38,965 participants after quality control (“Methods”). Mean age in the genotyped cohort was 64.5 years, 51% were female, and 87% were of white British ancestry as previously defined in this study , VATadj, ASATadj, GFATadj, and VAT/ASAT were nearly independent of BMI (Pearson r ranging from 0–0.18), while VAT/GFAT (Pearson r = 0.42) and ASAT/GFAT (Pearson r = 0.56) displayed attenuated correlations with BMI ) . In contrast, WHRadjBMI, an anthropometric proxy for local adiposity, was less heritable than these traits . In sex-stratified analyses, most adiposity traits were more heritable in females as compared to males, with the greatest heritability across all analyses for GFATadj in females .To quantify the inherited component to each of these nine adiposity traits, we used the BOLT-REML algorithm to estimate SNP-heritability. Heritability estimates for VAT, ASAT, and GFAT ranged from 0.31–0.36 (standard error (SE) = 0.01), comparable to that observed for BMI in the same individuals (rg) between the adiposity and related anthropometric traits, we used LD-score regression34. Results were generally consistent with observational correlations—raw VAT, ASAT, and GFAT volumes were highly genetically correlated with BMI (rg ranging from 0.66–0.82), while the three adjusted fat depots, VAT/ASAT, and VAT/GFAT exhibited low genetic correlation with BMI (rg ranging from −0.16–0.28) , but nearly independent of the other two fat depots (rg ranging from –0.24–0.15), suggesting that adjusted-for-BMI traits can enable fat depot-specific genetic analyses. Finally, WHRadjBMI exhibited positive genetic correlations with VATadj (rg: 0.65) and ASATadj (rg: 0.25), and a negative genetic correlation with GFATadj (rg: −0.29), consistent with the perturbations needed in each fat depot to drive a change in WHRadjBMI.To study the genetic correlations (28) Fig. . In sex-p value threshold of 5 × 10−8   (Supplementary Table λGC) was observed with GFATadj (λGC: 1.14)—the LD-score regression intercept was 1.05, consistent with polygenicity rather than significant population structure  > 0.005. Across all 27 association studies, 250 loci were associated with at least one adiposity trait at a g) Table 35. Of thFTO variant (rs56094641) previously associated with childhood and adult obesity38. ASAT harbored the most significant association with this locus (p = 1.3 × 10−22), followed by GFAT (p = 1.2 × 10−12), and finally VAT (p = 3.3 × 10−10), reflecting the strength of observational and genetic correlation of each fat depot with BMI. Given observational and genetic evidence that a large component of each fat depot volume trait is accounted for by BMI—or “overall adiposity”—we focused further common variant analyses to the three adjusted-for-BMI-and-height measures and three fat depot ratios, aiming to study the genetic architecture of “local adiposity.”We began by investigating the genetic architecture of VAT, ASAT, and GFAT volumes   previously associated with WHRadjBMI and serum triglycerides, and an intronic PEPD variant  previously associated with waist circumference adjusted for BMI (WCadjBMI) and type 2 diabetes41. Newly-identified loci in association with VATadj included an intronic GPR158 variant , and an intronic ARHGEF3 variant exclusively in females . Prior work has similarly noted female-specific effects of variation in this gene including an association with postmenopausal osteoporosis in humans and Arhgef3-KO mice being found to have improved muscle regeneration following injury, with an enhanced rate in females, although the role of this gene on fat distribution is uncertain43.For VATadj, 30 genome-wide significant associations were identified p < 5 × 0−8  Fig. . The twoADAMTSL3 variant , which was in near-perfect linkage disequilibrium (R2 = 0.97) with another intronic ADAMTSL3 variant (rs11856122) previously associated with bioelectrical impedance-derived arm fat ratio, leg fat ratio, and trunk fat ratio . Rare variants in PPARG have previously been associated with familial partial lipodystrophy7. The minor alleles at this locus (MAF = 0.12), which additionally consisted of rs17036328 and rs71304101 (R2 > 0.90), were associated with increased ASATadj , increased GFATadj , decreased VAT/ASAT ratio , and decreased VAT/GFAT ratio . These three SNPs are also in high LD (R2 ≥ 0.94) with rs1801282, a missense variant in PPARG previously associated with reduced risk of type 2 diabetes46. These data suggest that common variation at PPARG can lead to adiposity variation along the lipodystrophy axis—for this locus, the minor alleles associated with a pattern of favorable adiposity. FST is another gene that promotes adipogenesis and may have a causal role in insulin resistance—an intronic variant in FST (rs55744247) associated with ASATadj (p = 5.1 × 10−10), but not VATadj (p = 0.80) or GFATadj (p = 0.25)47. Finally, a newly-identified intronic DMRT2 variant  associated with ASATadj. In a study investigating fat depot-specific transcriptome signatures before and after exercise, DMRT2 was one of three genes with higher expression in ASAT vs. GFAT both before and after exercise48.The most statistically significant association with ASATadj was an intronic RSPO3 variant  that has previously been shown to be a top signal for WHRadjBMI  associated with marked reduction of GFATadj  and increased of VATadj , but a nonsignificant effect on ASATadj . Three independent intronic COBLL1 variants (R2 < 0.1) were associated with GFATadj , but not VATadj (pmin = 0.009) or ASATadj (pmin = 2.7 × 10−3). One of these variants (rs13389219) is in LD with another intronic COBLL1 variant (rs6738627) which has previously been implicated in a metabolically healthy obesity phenotype characterized by increased HDL cholesterol and reduced triglycerides despite increased body fat percentage50. In this study, aligning rs13389219 to the BMI-increasing direction  revealed a concurrent increase in GFATadj (beta = 0.073), consistent with a metabolically healthy fat depot shift. Finally, a GFATadj association was observed at an intronic PDGFC variant —PDGFC was recently prioritized as a candidate causal gene for insulin resistance in human preadipocytes and adipocytes47.The top GFATadj signal was an intronic BMI Fig. 12. RecenACVR1C significantly reduced VAT/GFAT ratio . Prior work demonstrated that sequence variation in ACVR1C—including this variant—reduces WHRadjBMI and risk of type 2 diabetes51. Another missense variant in ACVR1C was nominally associated with reduced VAT/GFAT ratio, strengthening the importance of this gene (rs56188432 (p.Ile195Thr); beta = −0.21, p = 0.006) . Homozygous carriers of this variant are known to harbor alpha-1-antitrypsin deficiency, and heterozygous carriers have higher serum ALT and increased risk of cirrhosis52. Interestingly, this missense variant has also been associated with reduced risk of type 2 diabetes  and coronary artery disease 53. The present association with reduced VAT/GFAT ratio suggests that a shift toward a metabolically healthy fat distribution could partially explain a reduced risk of cardiometabolic disease. In a large meta-analysis, this SERPINA1 variant had only a nominally significant association with waist-to-hip ratio —the closest anthropometric correlate of VAT/GFAT ratio—highlighting the utility of image-derived phenotypes for this discovery12.Several associations were exclusive to GWASs of fat depot ratios  with any of the nine adiposity traits in this study, we selected 101 LD-pruned (r2 = 0.1) SNPs that were nominally associated (p < 0.05) with GFATadj. Each SNP was aligned to the GFATadj increasing direction. We used Bayesian non-negative matrix factorization (bNMF)—a soft clustering approach—with 32 cardiometabolic traits including anthropometric traits , lipid traits , and diabetes-related traits  to identify clusters  with unadjusted GFAT. The three aforementioned clusters were reproduced along with a fourth cluster representing overall adiposity—the top locus in this cluster was FTO and the most strongly weighted trait was increased BMI  as compared to ASATadj or GFATadj (rg = 0.80 and 0.79 respectively) (Supplementary Table pdiff <0.05/220 independent loci-trait pairs tested = 2.3 × 10−4). The majority of these signals were driven by a greater magnitude of effect in female participants, which is consistent with prior investigations of WHRadjBMI55. Across all six local adiposity traits, 26 trait-loci associations were only genome-wide significant in females, while 9 loci were only genome-wide significant in males.Given prior work has noted significant sex heterogeneity in the genetic basis of anthropometric traits, we next tested for such heterogeneity for each of the six local adiposity traits 12. Of the 345 loci, 10 (3%) achieved genome-wide significance in association with VATadj (p < 5 × 10−8), 2 with ASATadj (0.6%), and 14 (4%) with GFATadj. A unit increase in WHRadjBMI might be expected to be reflecting a unit increase in VATadj or ASATadj, or a unit decrease in GFATadj. We quantified how often a locus was discordant from this pattern , excluding loci where the fat depot effect size was smaller in magnitude than the SE. Fifteen of 242 loci (6%) were VATadj-discordant, 71 of 166 loci (43%) were ASATadj-discordant, and 22 of 231 loci (10%) were GFATadj-discordant —our work further clarifies that this signal is driven by an effect on VATadj  and GFATadj , but not ASATadj . In contrast, a WHRadjBMI signal near LINC02029  is driven by ASATadj  and GFATadj , but has a VATadj-discordant signal .Two illustrative examples indicate how follow-up of WHRadjBMI associations from a very large study in a smaller study with specific fat depots quantified may prove useful. The top WHRadjBMI signal is located at an intronic 27. Of the 76 SNP-trait associations across the traits of VAT, ASAT, VATadj, and VAT/ASAT ratio in this study, association results for 17 were available for comparison in published summary statistics27. Of these, 16 (94%) had directionally consistent effects , and VAT/ASAT ratio in up to 18,332 individuals60. Across all traits, the most significant association was observed between GFATadj and CCDC92  in subcutaneous adipose tissue  and DNAH10 . Prior work demonstrated that knockdown of CCDC92 or DNAH10 led to significant reduction of lipid accumulation in an adipocyte model19. Of note, predicted VATadj associations with CCDC92 and DNAH10 in visceral adipose tissue samples demonstrated the opposite direction of effect , suggesting fat depot discordant effects.To prioritize genes, we conducted a transcriptome-wide association study (TWAS) using gene expression data from visceral and subcutaneous adipose tissue from GTEx v7IRS1  with the corresponding association with ASATadj having the same direction of effect . Prior work has demonstrated that decreased IRS1 expression, the gene encoding the insulin receptor substrate, causes insulin resistance—our work further suggests that impaired expansion of the gluteofemoral and abdominal subcutaneous fat depots may be involved in this physiological insult61. Finally, a significant association was observed between VEGFB and GFATadj , but not ASATadj . Endothelial VEGFB is known to facilitate endothelial targeting of fatty acids to peripheral tissues and induce adipocyte thermogenesis, and transduction of VEGFB into mice improved metabolic health without changes in body weight63. These results suggest that maintenance of the gluteofemoral fat depot may partially explain the metabolic effects of VEGFB.Another top TWAS signal was observed with GFATadj and 64. A marked dichotomy was observed between the three raw fat depot volumes —each highly genetically correlated with BMI—and the six derived local adiposity traits . While VAT, ASAT, and GFAT showed a pattern of central nervous system (CNS) tissue enrichment—consistent with the enrichment pattern for BMI—local adiposity traits were characterized by adipose tissue signals with reduced CNS signals  —consistent with a metabolically favorable fat distribution—and that these effects are attenuated in males   and a second combining pLoF with missense variants predicted to be deleterious by 5 out of 5 in silico prediction algorithms (pLoF + missense). We tested the association between the aggregated rare variant score with each mask and each inverse normal transformed phenotype using multivariable regression. Analyses were restricted to genes with at least ten variant carriers in the analyzed cohort, yielding up to 12,020 tested genes. Exome-wide significance was considered to be 27) Fig. .Fig. 5RaPCSK1 associated with GFAT in sex-combined analysis  and pLoF + missense variants in ACAT1 associated with VAT in females . Both of these genes have previously been implicated in altering adiposity. Rare mutations in PCSK1 are known to cause monogenic obesity—here, a relatively symmetric pattern of increased GFAT, VAT , and ASAT  were observed in sex-combined analyses , consistent with a lipodystrophic-pattern of reduced peripheral adipose tissue deposition. We were unable to detect a significant association among the 51 carriers of rare LMNA variants, potentially related to inadequate statistical power or variant annotation.Finally, we investigated if rare variants in known familial partial lipodystrophy genes 70. In the context of the traits defined in this study, a lipodystrophy-like phenotype might be characterized by increased VATadj, decreased ASATadj, and/or decreased GFATadj. We set out to quantify the potential for genetic prediction of these traits by generating polygenic scores consisting of up to 1,125,301 variants for VATadj, ASATadj, and GFATadj traits using the LDpred2 algorithm71. To ensure no overlap between summary statistics and tested individuals, GWAS was conducted using a randomly selected 70% of participants. An additional 10% of participants was used as training data to select optimal LDpred2 hyperparameters and the remaining 20% of participants were held out for testing. In the test set, VATadj, ASATadj, and GFATadj polygenic scores explained 5.8%, 3.6%, and 7.0% of the corresponding trait variance, respectively ) and disease outcomes  Fig. .Fig. 7Efp = 8.2 × 10−107), lower serum triglycerides , lower serum ALT , lower risk of type 2 diabetes , and lower risk of coronary artery disease . By contrast, those in the top 5% of the VATadj polygenic score tended to have increased risk of these disease outcomes with odds ratios for type 2 diabetes, coronary artery disease, and hypertension of 1.18, 1.12, and 1.09, respectively.Within an independent dataset of 447,486 individuals of the UK Biobank who were genotyped, but not imaged, individuals in the top 5% of the GFATadj polygenic score had higher HDL-cholesterol , lower serum triglycerides , and lower risk of prevalent type 2 diabetes   studySERPINA1 that predisposes to a metabolically healthier fat distribution. Polygenic scores for local adiposity traits were highly enriched among those with “lipodystrophy-like” fat distributions and were associated with cardiometabolic traits in a depot-specific fashion. These results have at least four implications.In this study, we investigated the inherited basis of body fat distribution using VAT, ASAT, and GFAT volumes quantified from body MRI in up to 38,965 individuals. Local adiposity traits derived from these fat depots had a significant inherited component, enabling identification of 250 unique loci across all traits. The increased precision afforded by image-derived quantification confirmed and extended prior work indicating significant sex-dimorphism, refined depot-specific associations for loci previously identified for WHRadjBMI, and led to the discovery of newly-associated loci, including a missense variant in FTO variant—characteristic of BMI—as a top signal, and cell-enrichment analyses of each unadjusted fat depot displayed a pattern of CNS cell-enrichment, consistent with the signal for BMI64. By contrast, fat depot volumes adjusted-for-BMI-and-height and fat depot ratios—traits that capture local adiposity—were more heritable than measures of overall adiposity, revealed depot-specific genetic architecture, and displayed a pattern of adipose tissue cell-enrichment. As large cohorts with body imaging become more prominent, careful consideration of this correlation structure is warranted to enable interpretation of genetic association results. For example, a measurement of VAT predicted from a model using primarily anthropometric traits was very highly genetically correlated with BMI (rg = 0.93), suggesting that the resultant genetic associations may predominantly reflect a component of VAT that is complementary to VATadj (rg with BMI = −0.16) in this study29. Additional investigation of how best to utilize composite phenotypes that jointly represent several correlated adiposity traits may prove useful74.First, traits aiming to quantify variation in body habitus—even when they are image-derived measurements of specific fat depot volumes as in this study—tend to be highly observationally and genetically correlated with one another and with BMI. GWAS of raw VAT, ASAT, and GFAT volumes each identified a well-known intronic h2 = 0.41)—particularly in females (GFATadj h2 = 0.52)—with a genetic architecture that is distinct from VAT and ASAT when adjusted for overall adiposity. Most prior genetic studies of imaging-derived adiposity traits to date have been limited to VAT and ASAT—in this study, only 13 of 54 genome-wide significant loci for GFATadj overlapped with either VATadj or ASATadj28. Individuals with a GFATadj polygenic score in the bottom 5% were enriched for adverse cardiometabolic biomarker profiles and increased risk of type 2 diabetes and coronary artery disease. These observations lend further support to the hypothesis that a primary insult in a metabolically unhealthy fat distribution is the inability of the gluteofemoral fat depot to adequately expand75. Additional study of GFAT depots—or related measures such as gynoid fat from DEXA scans—in future biobank-scale studies is warranted to determine the consistency of these genetic associations across diverse age and ancestry groups.Second, GFAT is highly heritable  are known to be caused by monogenic variation in genes like LMNA and PPARG, FPLD type 1 has not been linked to a single mutation, leading some to suggest that this disease may be polygenic in nature10. Lotta et al. provided evidence for this by demonstrating that individuals with FPLD1 had a higher burden of a 53-SNP insulin resistance polygenic score compared to the general population19. In this study, individuals who harbor lower than average GFATadj or ASATadj and/or higher than average VATadj tended to manifest a mild lipodystrophy-like phenotype. We demonstrate that individuals at the extremes of these local adiposity traits are enriched in extreme polygenic scores suggesting that polygenic scores may be helpful in identifying this subgroup of individuals for future focused investigations. For example, growth hormone releasing hormone analogs—such as tesamorelin—have previously been shown to lead to a selective reduction of VAT in patients with obesity or HIV-associated lipodystrophy77. Whether a local adiposity polygenic score—perhaps in combination with emerging imaging tools for identifying lipodystrophies—could identify a subset of individuals with obesity and polygenic lipodystrophy who may benefit from these fat redistribution agents in addition to traditional obesity therapy is an area for future investigation78.Third, this study extends prior work suggesting that common genetic variation—as captured by a polygenic score—contributes to extreme fat distribution phenotypes79. Such targeted perturbation studies in subcutaneous and visceral adipocyte cell lines may reveal key biological pathways driving fat distribution, and may generate therapeutic hypotheses for adverse fat distribution-related traits80.Fourth, these results lay the scientific foundation for variant-to-function studies to link fat distribution-associated genetic risk loci to effector genes and mechanisms of action in depot-specific adipocyte model systemsIn conclusion, we carried out genetic association studies of local adiposity traits in a large cohort of individuals with MRI imaging. Our work characterizes the depot-specific genetic architecture of visceral, abdominal subcutaneous, and gluteofemoral adipose tissue, and extends efforts to define and identify individuals with polygenic lipodystrophy.82. Our group previously estimated VAT, ASAT, and GFAT volumes in 40,032 individuals of the imaged cohort after excluding 3489 (8.0%) scans based on technical problems or artifacts5. A subset of 39,076 individuals with genotype array data available was studied here. Compared to non-imaged individuals of the UK Biobank at enrollment, imaged individuals were younger (mean age 56 years vs. 57 years), less likely to be female (51% vs. 55%), and more likely to be of white British ancestry (87% vs. 84%) . Several prior studies have suggested that adjusting for heritable covariates can lead to spurious genetic associations due to collider bias85. Imputation was done using the UK10K and 1000 Genomes Phase 3 reference panels87. Prior to analysis, genotyped SNPs were filtered based on the following criteria, only including variants if: (1) MAF ≥ 1%, (2) Hardy-Weinberg equilibrium (HWE) p > 1 × 10−15, (3) genotyping rate ≥ 99%, and (4) LD pruning using R2 threshold of 0.9 with window size of 1000 markers and step size of 100 marker89. This process resulted in 433,616 SNPs available for genetic relationship matrix (GRM) construction. Imputed SNPs with MAF < 0.005 or imputation quality (INFO) score <0.3 were excluded. Note that the MAF filter was applied to the UK Biobank imputed file prior to subsetting to the imaged substudy. These criteria resulted in a total of 11,485,690 imputed variants available for analysis.Genotyping in the UK Biobank was done with two custom genotyping arrays: UK BiLEVE and AxiomParticipant were excluded from analysis if they met any of the following criteria: (1) mismatch between self-reported sex and sex chromosome count, (2) sex chromosome aneuploidy, (3) genotyping call rate <0.95, or (4) were outliers for heterozygosity. Up to 38,965 participants were available for analysis .91. Genetic correlations between traits were estimated using cross-trait LD-score regression (ldsc v1.0.1) using default settings34.Nine traits were analyzed  in three contexts , leading to 27 analyses in total. SNP-heritability was estimated using BOLT-REML v2.3.491. After the QC protocol detailed above, 433,616 SNPs were available for GRM construction. A threshold of p < 5 × 10−8 was used to denote genome-wide significance, while a threshold of p < 5 × 10−8/27 = 1.9 × 10−9 was used to denote study-wide significance.Prior to conducting GWAS, each trait was inverse-normal transformed. Each analysis was adjusted for age at the time of MRI, age squared, sex , the first ten principal components of genetic ancestry, genotyping array, and MRI imaging center. BOLT-LMM v2.3.4 was used to carry out GWAS accounting for cryptic population structure and sample relatednessp < 5E−08 are chosen starting with the lowest p value, and for each variant chosen, all other variants with p < 5E−06 within a 1000 kb region and r2 > 0.1 with the index variant are assigned to that index variant. This process is repeated until all variants with p < 5E−08 are assigned an LD clump. An LD reference panel for this task was constructed using a random sample of 3000 individuals from the studied.Lead SNPs were prioritized with LD clumping. LD clumping was done with the -clump function in PLINK to isolate independent signals for each GWAS. The parameters were as follows: -clump-p1 5E−08, -clump-p2 5E−06, -clump-r2 0.1, -clump-kb 1000, which can be interpreted as follows: variants with ldsc v1.0.1) using default settings33.The extent of genomic inflation vs. polygenicity was assessed by computing the LD-score regression intercept (R2 < 0.1) with any SNP in the GWAS catalog  with genome-wide significant association (p < 5 × 10−8) with any “DISEASE/TRAIT” containing the following characters: (1) “body mass”, (2) “BMI”, (3) “adipos”, (4) “fat”, (5) “waist”, (6) “hip circ”, or (7) “whr”. These characters captured key anthropometric traits of interest  as well as other related traits of interest .A lead SNP was defined as newly-identified if it was not in LD (p < 0.05) for each analysis. To ensure that only independent signals were used for the clustering, variants were LD-pruned using a LD threshold of r2 = 0.1. When two SNPs were found to be in LD above this threshold, the variant with the lower p value was retained.Clustering analysis was performed for GFATadj and GFAT association signals. We started with all 250 lead SNPs significantly associated with any of the nine adiposity traits and extracted those associated with the primary trait  with nominal significance . When two traits had highly correlated Z-scores (|r| > 0.85), the trait with the lower minimum p value was kept and the other removed. The remaining standardized effect sizes made up the variant-trait association matrix, Z (N variants by M traits).The clustering traits were then filtered to retain those relevant to the analysis by removing any that were not associated with at least one variant at a Bonferroni Z-scores and the other with the absolute value of the negative Z-scores. This means that the final association matrix, X, contained N variants by 2M traits.In order to satisfy the non-negative requirement of Bayesian non-negative matrix factorization (bNMF), each column was split into two arrays: one with the positive 20. The procedure attempts to approximate the association matrix by factorizing X into two matrices, W (2M by K) and HT (N by K), with an optimal rank K. bNMF is designed to suggest an optimal K best explaining X at the balance between an error measure, ||X − WH|2, and a penalty for model complexity derived from a non-negative half-normal prior for W and H. In addition, bNMF exploits an automatic relevance determination technique to iteratively regress out irrelevant components in explaining the observed data X. The exact objective function optimized by bNMF is a posterior, which has two opposing contributions from the likelihood (Frobenius norm) and the regularization penalty (L2-norm of W and H coupled by the relevance weights). For all analyses, bNMF was run with 100 iterations for each. All analyses converged in ≥92% of iterations to their given K solution. Code used in the bNMF clustering is available on GitHub: https://github.com/kwesterman/bnmf-clustering.The bNMF clustering was performed as previously describedGenetic correlations between sexes for each of the adiposity traits were computed using cross-trait LD-score regression as described above.t-statistic:r is the genome-wide Spearman rank correlation coefficient between males and females. The t-statistic and associated p value (pdiff) were computed using the EasyStrata software92. Given that 220 independent loci were tested, a significance threshold of pdiff < 0.05/220 = 2.3 × 10−4 was used.Using sex-specific GWAS summary statistics for each of the six local adiposity traits , we tested each of the 220 genetic loci that were genome-wide significant for any of the six local adiposity traits in either sex-combined or sex-stratified analyses for sex dimorphism by computing the 12. Of these 346 loci, the primary signals for 345 loci were among the imputed variants available for analysis in this study. We plotted the effect sizes for VATadj, ASATadj, and GFATadj for each of these 345 loci and further quantified the frequency of “WHRadjBMI-discordance” defined as either (1) WHRadjBMI and VATadj effects going in opposite directions, (2) WHRadjBMI and ASATadj effects going in opposite directions, or (3) WHRadjBMI and GFATadj effects going in the same direction. For each adiposity trait in the “WHRadjBMI-discordance” analysis, we excluded loci for which the effect size beta was smaller than the SE to avoid inflating the fraction of “WHRadjBMI-discordant” loci.A recent meta-analysis for the WHRadjBMI trait across 694,649 individuals revealed 346 unique associated loci35. Alleles were aligned and the z-score for each SNP from the previous study were compared with the effect sizes in the current study to determine concordance.External validation for 76 genome-wide significant SNP-trait associations with VAT, ASAT, VATadj, and VAT/ASAT ratio was pursued using summary statistics downloaded from the GWAS catalog of a multiethnic genome-wide meta-analysis of ectopic fat depots in up to 2.6 million SNPs in up to 18,332 individuals94. Pre-computed gene expression weights from GTEx v7 were used as downloaded from the FUSION website (http://gusevlab.org/projects/fusion/)60. Reference weights for visceral adipose tissue were used for VATadj, while those for subcutaneous adipose tissue were used for ASATadj, GFATadj, and ASAT/GFAT ratio. Weights from both visceral and subcutaneous adipose tissue were used for VAT/ASAT and VAT/GFAT ratios.For each of the six local adiposity traits , we performed a TWAS to prioritize genes on the basis of imputed cis-regulated gene expression using FUSION with default settings64. We carried out this analysis using ldsc v1.0.1 with default settings and using two gene expression datasets that are described in the manuscript outlining stratified LD-score regression64: GTEx95 and the “Franke lab”97 dataset.We used stratified LD-score regression to identify cell types that are most relevant for each of the nine adiposity traits  and BMI98. Whole-exome sequencing was performed by the Regeneron Genetics Center using an updated Functional Equivalence protocol that retains original quality scores in the CRAM files (referred to as the OQFE protocol) as previously described98. The DTxGen Exome Research Panel v1.0 including supplemental probes was used for exome capture for this dataset (https://biobank.ctsu.ox.ac.uk/showcase/label.cgi?id=170). In total, 19,396 genes in the targets of 38Mbp were covered. In total, 75 × 75 bp paired-end reads were sequenced on the Illumina NovaSeq 6000 platform. For each sample in the targeted region, more than 95.2% of sites were covered by more than 20 reads. We downloaded the pVCF file provided by the UK Biobank, and then applied additional genotype call, variant, and sample quality control99.We conducted rare-variant association studies using data from the 200,643 exomes released by the UK Biobank100.Variants in low-complexity regions of the genome that preclude accurate read alignment as previously defined101.Variants in segmental duplication region of the genomep value <1 × 10−15.Hardy-Weinberg disequilibrium (HWE) Variant call rate <90%.Monomorphic sites after the above genotype call quality control.The individual genotype call was set as missing if reads depth (DP) ≤ 10 or DP ≥ 200, if homozygous reference allele with genotype quality (GQ) ≤ 20 or the ratio of alt allele reads over all of the covered reads >0.1, if heterozygous with the ratio of alt allele reads over all of the covered reads <0.2 or Phred-scaled likelihood (PL) of the reference allele <20, or if homozygous alternate with the ratio of alt allele reads over all of the covered reads <0.9 or PL of reference allele <20. The variant quality control was performed using the following exclusion criteria:p > 10−6. We further pruned the variants using PLINK2 software102 with a window size of 200, step size 100, and R2 = 0.1 and removed indels and strand ambiguous SNPs. Based on these variants, we used KING (version 2.2.5)103 to infer the genetic kinship matrix. We further selected X-chromosomal variants, not within the pseudo-autosomal regions, based on the sample variant QC criteria as for the autosome variants and did the same variant pruning procedure. We then inferred the genetic sex based on the F statistics by PLINK2 software, F > 0.8 was set to male, while samples with F < 0.5 were set to female. Eighty samples were removed because of the discordance of genetic sex with self-reported sex. We further removed samples if:N = 100 samples removed).The ratio of heterozygote/homozygote beyond 8 standard deviations (N = 1 samples removed).The ratio of the number of SNVs/indels beyond 8 standard deviations (N = 111 samples removed).The number of singletons was beyond 8 standard deviations (N = 1 sample removed).Genotype call rate <90% (N = 13 samples removed).Withdrawal of informed consent . Of all the above QC passed samples, 19,255 samples out of the 40,032 having image-derived traits were used in the downstream rare variant burden test. We converted the genetic coordinates from GRCh38 to GRCh37 using CrossMap software (version: v0.3.3)104.We further randomly removed one sample if a pair of samples had second-degree relative or closer kinship, defined as kinship coefficient >0.088474 .To identify rare (MAF <0.1%) high-confidence predicted inactivating variants, we applied the previously validated Loss-Of-Function Transcript Effect Estimator (LOFTEE) algorithm implemented within the Ensembl Variant Effect Predictor (VEP) software program as a plugin, VEP version 96.0109. In brief, predictions were retrieved from the dbNSFP database110, version 2.9.3, with the most severe prediction across multiple transcripts used. We focused on five prediction algorithms: SIFT111 (including variants annotated as damaging), PolyPhen2-HDIV and PolyPhen2-HVAR112 (including variants annotated as possibly or probably damaging), LRT113 (including variants annotated as deleterious), and MutationTaster114 (including variants annotated as disease-causing-automatic or disease-causing). Within the association testing framework, this class of variants was given a gene-specific weight based on the relative cumulative frequency of these predicted damaging missense variants as compared to the cumulative frequency of high-confidence predicted inactivating variants identified by LOFTEE algorithm using a previously recommended approach:116 given the cumulative allele frequency of all of the LOFTEE high-confidence rare variants of a gene (G) as fL, the cumulative allele frequency of all of the predicted damaging missense variants as fM, the weight for the missense variants was estimated as the quantity in Eq. (To identify rare (MAF <0.1%) predicted damaging missense variants, we included variants predicted to be damaging by all of five computational prediction algorithmsFor genes without LOFTEE high-confidence rare variants, the weight for missense variants is 1.0. This aggregation strategy will be referred to hereafter as putative loss-of-function plus missense (“pLoF + missense”).p value threshold of 0.05/12,020 = 4.2 × 10−6.We tested the association between the aggregated rare variant score  and each inverse normal transformed phenotype using a multivariable regression model in sex-combined and sex-stratified models. Analyses were restricted to genes that had at least ten variant carriers in the analyzed cohort. An individual’s gene-specific score was computed according to the weighting strategy described above and capped at one. The covariates were the same as the common variant association test. Given the filter of ten variant carriers, sex-combined analyses tested 12,020 genes and so a gene was recognized as exome-wide significant if the gene’s p value was smaller than the Bonferroni-corrected 71 to derive genome-wide polygenic scores for each trait. We randomly selected 350,000 White British ancestry individuals from the UK Biobank to use as the LD reference panel85, and used HapMap3 variants with MAF > 0.5% in the LD reference panel to compute the LD correlation matrix. For each trait, we partitioned the samples into three independent portions: 70% to run the GWAS for making the summary statistics, 10% to select the optimal hyperparameters, and 20% to test performance. We randomly removed one sample in a pair if the pair had a genetic relationship closer than a second-degree genetic relationship in the last two partitions of samples, and checked the pairwise relationship across the whole dataset. For the hyperparameters of the LDpred2 algorithm, we grid searched three parameters: (1) 0.7, 1, and 1.4 times of genome-wide heritability estimation, (2) whether or not to use a sparse LD correlation matrix, and (3) 17 different estimates of the proportion of causal variants selecting from  × 10 and 0.0001. In total, we tested 3 × 2 × 17 = 102 grid points.We used the LDpred2 algorithmFor all downstream analyses, each polygenic score was residualized against the first ten principal components of genetic ancestry prior to regression with the dependent variable of interest, and each regression was adjusted for age at the time of imaging, sex, and the first ten principal components of genetic ancestry.72. Genotype and clinical data were retrieved from the National Center for Biotechnology Information dbGAP server (accession number phg000035.v1). VATadj, ASATadj, and GFATadj polygenic scores were computed using identical LDpred2 weights and the optimal hyperparameter set for UK Biobank analyses. Circulating biomarkers and clinical risk factor ascertainment was performed at time of enrollment as previously described72.The ARIC study is a prospective cohort study that—beginning in 1987—enrolled white and black participants between the ages of 45 and 64 yearsFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-22Reporting Summary


Genetic influences are ubiquitous as virtually all phenotypes and most exposures typically classified as environmental have been found to be heritable. A polygenic score summarises the associations between millions of genetic variants and an outcome in a single value for each individual. Ever lowering costs have enabled the genotyping of many samples relevant to child psychology and psychiatry research, including cohort studies, leading to the proliferation of polygenic score studies. It is tempting to assume that associations detected between polygenic scores and phenotypes in those studies only reflect genetic effects. However, such associations can reflect many pathways  and biases.Here, we provide a comprehensive overview of the many reasons why associations between polygenic scores, environmental exposures, and phenotypes exist. We include formal representations of common analyses in polygenic score studies using structural equation modelling. We derive biases, provide illustrative empirical examples and, when possible, mention steps that can be taken to alleviate those biases.Structural equation models and derivations show the many complexities arising from jointly modelling polygenic scores with environmental exposures and phenotypes. Counter‐intuitive examples include that: (a) associations between polygenic scores and phenotypes may exist even in the absence of direct genetic effects; (b) associations between child polygenic scores and environmental exposures can exist in the absence of evocative/active gene–environment correlations; and (c) adjusting an exposure‐outcome association for a polygenic score can increase rather than decrease bias.Strikingly, using polygenic scores may, in some cases, lead to more bias than not using them. Appropriately conducting and interpreting polygenic score studies thus requires researchers in child psychology and psychiatry and beyond to be versed in both epidemiological and genetic methods or build on interdisciplinary collaborations. A polygenic score aims to capture individuals' genetic predispositions for a phenotype. Most often, a weighted sum is computed, with weights obtained from a Genome‐Wide Association Study (GWAS), resulting in a single value per individual and a single variable per sample (e.g. the polygenic score for height). The first (complementary) article of this issue, Allegrini, Baldwin, Barkhuizen, and Pingault , provideBecause a polygenic score is derived from a linear combination of effect alleles for a given phenotype, it is tempting to assume it captures the genetic liability for this phenotype. Although polygenic scores aim to and can capture genetic effects, several limitations must be acknowledged. First, polygenic scores currently capture only a fraction of SNP‐heritability, which is a measure of heritability due to the effects of all measured common Single Nucleotide Polymorphisms (SNPs)  to a perfectly measured phenotype (latent variable Y*). In a standardised model, the square of Gβ*Y* equals additive heritability. However, a simple regression of a measured phenotype Y on a polygenic score G : Y* is imperfectly associated with Y . The polygenic score G can also be conceived as a measure of the true additive genetic factor with substantial measurement error . The fact that polygenic scores are an imperfect measure of the true genetic values has considerable implications for polygenic score studies. As depicted in Figure Y on G (GYb) is a biased approximation of Gβ*Y*. As shown in the Appendix GYb is attenuated as a function of the imperfect measurement of both Y * and G *. The bias, that is, the fitted parameter minus the true effect is thus:Yl = Gl = 1, there is no measurement error, and the bias is null. Conversely, if the polygenic score or the outcome is pure noise (Yl or Gl = 0), then the bias is −Gβ*Y* and the observed association will be null. As a numerical example, consider a highly heritable trait as the outcome, that is,Y of 20%, that is, 1 − l2Y = 0.20, so thatG* explains 10% of the variance in G, soGYb = GYr. The variance explained by the polygenic score in the observed phenotype (2GYr) is therefore close to 6% (0.252), much lower than the underlying heritability. Note that removing measurement error in Y increases GYb but not massively,h2SNP) can be approximated based on the variance explained by the polygenic score and SNP‐heritability  confounds the true association between the exposure (X  *) and the outcome (Y *)   is low (corresponding to high measurement error), which is often the case. Let’s take the example of (i) an exposure X* with no effect on the outcome Y*, that is,The fact that polygenic scores currently explain a small percentage of (SNP) heritability has wider consequences on models commonly implemented in child and adolescent psychiatry and beyond. Let us extend the previous model by adding a risk factor X Figure . Figure Y* with a SNP‐heritabilityY* mediated by the exposure. We also have substantial genetic effects on the exposure, that is,Y*. We thus have:(ii) a heritable outcome G*→X*→Y*. Several studies have aimed to examine to what extent the effect of a polygenic score on an outcome is mediated by important risk factors throughout development  via X. In a sense, all genetic effects are indirect, whether mediated by physiological pathways or environmental risk factors. To add to the confusion, in mediation analyses, the term direct effect has a standard meaning as the effect that is not mediated by the mediator(s) being modelled. The term ‘direct genetic effect’ defined above thus only refers to the fact that the individual phenotype Y* is ultimately explained by the individual genetic factor G* without the additional biases discussed below, and does not preclude mediation. We come back to this issue and propose a new terminology below.A related question of interest, also represented in Figure G* to Y*, that is,Gl (i.e. GβMl) but there is also an additional term corresponding to an additional ‘mediation’ path via the bias in X*Y*. This is because, as we have shown above, the fitted Xb*Y* is under‐corrected for genetic confounding. This undercorrection leads to an overestimation of Xb*Y* which, in turn, leads to an overestimation of the fitted mediation effect. The counter‐intuitive consequence is that, when using a polygenic score, the proportion of genetic effects mediated by a given risk factor X is over – rather than underestimated. The estimated proportion mediated is:Mπ is the true proportion mediated, to which an additional proportion is added that includes the additional ‘mediation’ path via the bias in X*Y* in the numerator and the true total association between the genetic factor and the outcomes in the denominator (Gr*Y*). It is therefore possible to have a nonnull mediated effect when using the polygenic score even if the true percentage is null . In the example above, because the study used a polygenic score for educational attainment, which was not entirely reliable, the path from child self‐control to later socioeconomic outcomes is likely under‐corrected for genetic confounding and thus exaggerated. In turn, the fitted mediated effect from the polygenic score for education to adult socioeconomic outcomes via child self‐control is likely exaggerated.The true mediated effect is obtained by multiplying betas along the path from h2SNP or even additive heritability measured from twin studies.Recently, we have proposed a method to account to some extent for the low reliability of polygenic scores based on external estimates of heritability  and the second is a dichotomised environmental risk (high vs. low risk). Let us assume for simplicity that the two are independent and that being a high risk on either factor necessarily leads to referral. Because the two are independent in the population, knowing that one individual is at high environmental risk gives no information on risk status for the polygenic score (i.e. the two predictors are not associated). However, within the referred adolescents only (i.e. one stratum of the outcome), the two become strongly negatively associated. This is because we know that for an adolescent to be referred they have to be high risk on either or both variables. Therefore, if we know that a referred adolescent has a low‐risk polygenic score, we also know with certainty that they must be at high environmental risk. Polygenic and environmental risks become associated by adjusting for the collider (the adjustment is represented here by stratifying for the collider when testing for the association in referred adolescents only). Similarly, collider bias can generate spurious associations between genetic variables (variants or polygenic scores) and environmental risk factors if analyses are restricted to cases of a disease.Collider bias, that is, the bias incurred from adjusting for a collider should also be considered when adjusting for polygenic scores. In Figure U* stands for unknown (or unmeasured) nongenetic confounders of the association between X* and Y *. The fitted model, Figure U* . Importantly, G* is not a confounder here as it causes X* but does not cause Y* directly. In theory, adjusting for G* should not modify the association between X* and Y *. In practice, in a model regressing Y * on both X* (the collider) and G*, the association between X* and Y * is biased. This specific case has been termed bias amplification as the bias can be so severe that the adjusted estimate of X*→Y * is further from the causal effect than the unadjusted correlation between X* and Y * . The bias thus comes from the denominator of the second term, which is equal to the standardised variance of X* (1) that is not explained by the effect of G* on the exposure X* (β2G*X*). As such, the more variance G* explains in X* the smaller the denominator will be and the larger the bias. In other words, once the effect of G* is removed, U * explains a larger proportion of the variance in X *, which magnifies the bias via U*  refers to the collider bias and is similar to what we obtained in the simpler expression of Xb*Yb* above. The third term includes Gβ*X*Gβ*Y* and refers to the genetic confounding effect that remains unadjusted because of the measurement error in G (1 − l2G). Taken together, these different components mean that, as polygenic scores become more accurate (increasing Gl), genetic confounding should be better adjusted for while the collider bias will worsen. The complexity of this expression also demonstrates why simply adjusting for a polygenic score does not adjust for genetic confounding.When allowing for measurement error in GEr), which can be active/evocative, discussed in this section or passive, discussed later.As noted in the introduction, polygenic scores have been associated with many variables that are typically considered environmental, such as maltreatment, parenting, and bullying. This is typically referred to as gene–environment correlation   for Y * need not be associated with X* in case of reverse causation   informs the perception of the risk . Risk perception is also influenced by a phenotype Y* (e.g. schizophrenia symptoms) itself influenced by genetic factors (G*). This will lead to observed correlations between G and XP  but not between G and XO. The objective risk factor XO is also unrelated to the phenotype Y. The observed correlations between G, XP, and Y do not imply that the polygenic score has an environmentally mediated effect; rather, the polygenic score influences the outcome (e.g. schizophrenia symptoms) which leads to perceived environmental risk. Even if measures of perceived and objective environmental exposures are associated . For example, we could observe an association between a polygenic score for schizophrenia and self‐reported neighbourhood violence: (a) without any path from the polygenic score to objective levels of neighbourhood violence ; and (b) without any role of neighbourhood violence in the aetiology of schizophrenia. This is illustrated in Figure d Figure , the potY2* in Figure XO* to Y 1* indicating an absence of direct effect of XO* after accounting for XP*). This mediation scenario might explain evidence showing that objective measures of adverse childhood experiences  are not associated with psychopathology after accounting for perceptions of these experiences . But interventions targeting the perception itself could be beneficial when intervening on the objective exposure is complex or impossible (e.g. when an adult has been exposed to early adversity). Note that genetic effects can also tag perception processes (i.e. a polygenic score for Y2* would reflect the path G*→Y1*→XP*→Y2*).However, the perceived environmental risk might also feed into later psychiatric symptoms, as illustrated in Figure Selection bias and attrition – that is, the biases resulting from a nonrandom (self‐) selection or retention into the sample – can be considered as special cases of collider bias  or other indirect mechanisms (e.g. the school that the child is sent to). However, as highlighted above, the terms direct and indirect genetic effects are also unsatisfactory as (i) all genetic effects are indirect via physiological and environmental pathways and (ii) confusion arises with standard terminology used in mediation analyses when assessing whether either direct or indirect genetic effects are mediated by intermediate variables. We therefore propose to replace direct genetic effects with individual genetic effects defined as effects that originate in the individual genome. And to replace indirect genetic effects with familial genetic effects defined as effects that originate in the genome of family members, such as parents or siblings, independent of genetic transmission  but because hyperactivity–impulsivity in the parents impacts their parenting (leading to passive GEr); and (c) environmental exposures and child outcomes can be associated in the absence of a causal effect of the environment .Figure Note that family‐based studies are starting to address these challenges. As mentioned above, sibship GWAS can better estimate individual genetic effects, from which polygenic scores can be derived. Designs capitalising on trio genomic data  can be implemented to estimate familial genetic effects as well as gauge the presence of passive and/or active/evocative gene–environment correlations . Empirical findings show that adjusting for maternal polygenic scores does not eliminate the relationship between the child's polygenic score and household chaos, thereby suggesting evocative GEr . After careful examination, Agnew‐Blais et al. concluded that household chaos is unlikely to directly influence the development of child ADHD symptoms.Most polygenic score studies will be susceptible to multiple sources of bias, which, when combined, can make the interpretation of results challenging. Let’s take the example examined in Agnew‐Blais et al.  of a plaImportantly, the consequences of the biases discussed in this review are not necessarily negative, depending on the intended purpose of the analysis. For example, the fact that child polygenic scores potentially capture familial genetic effects and population stratification can be useful. Adjusting for such polygenic scores removes confounding beyond (strictly defined) genetic confounding. Such a polygenic score will also be useful when building a prediction model, where what counts is the strength of the association rather than understanding the underlying mechanisms.When conducting polygenic score studies, key sources of biases should be either addressed through analyses or discussed as limitations. A nonexhaustive list is presented in Table As in other fields, triangulation of evidence is required, both within polygenic score studies, for example, comparing population and within‐family analyses .Appendix S2. Exposure model .Appendix S3. Collider bias .Click here for additional data file.