eXclusionarY: 10 years later, where are the sex chromosomes in GWASs?

10 years ago, a detailed analysis showed that only 33% of genome-wide association study (GWAS) results included the X chromosome. Multiple recommendations were made to combat such exclusion. Here, we re-surveyed the research landscape to determine whether these earlier recommendations had been translated. Unfortunately, among the genome-wide summary statistics reported in 2021 in the NHGRI-EBI GWAS Catalog, only 25% provided results for the X chromosome and 3% for the Y chromosome, suggesting that the exclusion phenomenon not only persists but has also expanded into an exclusionary problem. Normalizing by physical length of the chromosome, the average number of studies published through November 2022 with genome-wide-significant findings on the X chromosome is ∼1 study/Mb. By contrast, it ranges from ∼6 to ∼16 studies/Mb for chromosomes 4 and 19, respectively. Compared with the autosomal growth rate of ∼0.086 studies/Mb/year over the last decade, studies of the X chromosome grew at less than one-seventh that rate, only ∼0.012 studies/Mb/year. Among the studies that reported significant associations on the X chromosome, we noted extreme heterogeneities in data analysis and reporting of results, suggesting the need for clear guidelines. Unsurprisingly, among the 430 scores sampled from the PolyGenic Score Catalog, 0% contained weights for sex chromosomal SNPs. To overcome the dearth of sex chromosome analyses, we provide five sets of recommendations and future directions. Finally, until the sex chromosomes are included in a whole-genome study, instead of GWASs, we propose such studies would more properly be referred to as "AWASs," meaning "autosome-wide scans."

SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals.

MOTIVATION: Colocalization analysis is commonly used to assess whether two or more traits share the same genetic signals identified in genome-wide association studies (GWAS), and is important for prioritizing targets for functional follow-up of GWAS results. Existing colocalization methods can have suboptimal performance when there are multiple causal variants in one genomic locus. RESULTS: We propose SharePro to extend the COLOC framework for colocalization analysis. SharePro integrates linkage disequilibrium (LD) modeling and colocalization assessment by grouping correlated variants into effect groups. With an efficient variational inference algorithm, posterior colocalization probabilities can be accurately estimated. In simulation studies, SharePro demonstrated increased power with a well-controlled false positive rate at a low computational cost. Compared to existing methods, SharePro provided stronger and more consistent colocalization evidence for known lipid-lowering drug target proteins and their corresponding lipid traits. Through an additional challenging case of the colocalization analysis of the circulating abundance of R-spondin 3 GWAS and estimated bone mineral density GWAS, we demonstrated the utility of SharePro in identifying biologically plausible colocalized signals. AVAILABILITY AND IMPLEMENTATION: SharePro for colocalization analysis is written in Python and openly available at https://github.com/zhwm/SharePro_coloc.

Predicting ExWAS findings from GWAS data: a shorter path to causal genes.

GWAS has identified thousands of loci associated with disease, yet the causal genes within these loci remain largely unknown. Identifying these causal genes would enable deeper understanding of the disease and assist in genetics-based drug development. Exome-wide association studies (ExWAS) are more expensive but can pinpoint causal genes offering high-yield drug targets, yet suffer from a high false-negative rate. Several algorithms have been developed to prioritize genes at GWAS loci, such as the Effector Index (Ei), Locus-2-Gene (L2G), Polygenic Prioritization score (PoPs), and Activity-by-Contact score (ABC) and it is not known if these algorithms can predict ExWAS findings from GWAS data. However, if this were the case, thousands of associated GWAS loci could potentially be resolved to causal genes. Here, we quantified the performance of these algorithms by evaluating their ability to identify ExWAS significant genes for nine traits. We found that Ei, L2G, and PoPs can identify ExWAS significant genes with high areas under the precision recall curve (Ei: 0.52, L2G: 0.37, PoPs: 0.18, ABC: 0.14). Furthermore, we found that for every unit increase in the normalized scores, there was an associated 1.3-4.6-fold increase in the odds of a gene reaching exome-wide significance (Ei: 4.6, L2G: 2.5, PoPs: 2.1, ABC: 1.3). Overall, we found that Ei, L2G, and PoPs can anticipate ExWAS findings from widely available GWAS results. These techniques are therefore promising when well-powered ExWAS data are not readily available and can be used to anticipate ExWAS findings, allowing for prioritization of genes at GWAS loci.

Colocalization of expression transcripts with COVID-19 outcomes is rare across cell states, cell types and organs.

Identifying causal genes at GWAS loci can help pinpoint targets for therapeutic interventions. Expression studies can disentangle such loci but signals from expression quantitative trait loci (eQTLs) often fail to colocalize-which means that the genetic control of measured expression is not shared with the genetic control of disease risk. This may be because gene expression is measured in the wrong cell type, physiological state, or organ. We tested whether Mendelian randomization (MR) could identify genes at loci influencing COVID-19 outcomes and whether the colocalization of genetic control of expression and COVID-19 outcomes was influenced by cell type, cell stimulation, and organ. We conducted MR of cis-eQTLs from single cell (scRNA-seq) and bulk RNA sequencing. We then tested variables that could influence colocalization, including cell type, cell stimulation, RNA sequencing modality, organ, symptoms of COVID-19, and SARS-CoV-2 status among individuals with symptoms of COVID-19. The outcomes used to test colocalization were COVID-19 severity and susceptibility as assessed in the Host Genetics Initiative release 7. Most transcripts identified using MR did not colocalize when tested across cell types, cell state and in different organs. Most that did colocalize likely represented false positives due to linkage disequilibrium. In general, colocalization was highly variable and at times inconsistent for the same transcript across cell type, cell stimulation and organ. While we identified factors that influenced colocalization for select transcripts, identifying 33 that mediate COVID-19 outcomes, our study suggests that colocalization of expression with COVID-19 outcomes is partially due to noisy signals even after following quality control and sensitivity testing. These findings illustrate the present difficulty of linking expression transcripts to disease outcomes and the need for skepticism when observing eQTL MR results, even accounting for cell types, stimulation state and different organs.

The roles of 6K protein on Getah virus replication and pathogenicity.

Alphavirus is a type of arbovirus that can infect both humans and animals. The amino acid sequence of the 6K protein, being one of the structural proteins of the alphavirus, is not conserved. Deletion of this protein will result in varying effects on different alphaviruses. Our study focuses on the function of the Getah virus (GETV) 6K protein in infected cells and mice. We successfully constructed infectious clone plasmids and created resulting viruses (rGETV and rGETV-Δ6K). Our comprehensive microscopic analysis revealed that the 6 K protein mainly stays in the endoplasmic reticulum. In addition, rGETV-Δ6K has lower thermal stability and sensitivity to temperature than GETV. Although the deletion of the 6K protein does not reduce virion production in ST cells, it affects the release of virions from host cells by inhibiting the process of E2 protein transportation to the plasma membrane. Subsequent in vivo testing demonstrated that neonatal mice infected with rGETV-Δ6K had a lower virus content, less significant pathological changes in tissue slices, and milder disease than those infected with the wild-type virus. Our results indicate that the 6K protein effectively reduces the viral titer by influencing the release of viral particles. Furthermore, the 6K protein play a role in the clinical manifestation of GETV disease.

Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models.

Complex traits are known to be influenced by a combination of environmental factors and rare and common genetic variants. However, detection of such multivariate associations can be compromised by low statistical power and confounding by population structure. Linear mixed effects models (LMM) can account for correlations due to relatedness but have not been applicable in high-dimensional (HD) settings where the number of fixed effect predictors greatly exceeds the number of samples. False positives or false negatives can result from two-stage approaches, where the residuals estimated from a null model adjusted for the subjects' relationship structure are subsequently used as the response in a standard penalized regression model. To overcome these challenges, we develop a general penalized LMM with a single random effect called ggmix for simultaneous SNP selection and adjustment for population structure in high dimensional prediction models. We develop a blockwise coordinate descent algorithm with automatic tuning parameter selection which is highly scalable, computationally efficient and has theoretical guarantees of convergence. Through simulations and three real data examples, we show that ggmix leads to more parsimonious models compared to the two-stage approach or principal component adjustment with better prediction accuracy. Our method performs well even in the presence of highly correlated markers, and when the causal SNPs are included in the kinship matrix. ggmix can be used to construct polygenic risk scores and select instrumental variables in Mendelian randomization studies. Our algorithms are available in an R package available on CRAN (https://cran.r-project.org/package=ggmix).

Block coordinate descent algorithm improves variable selection and estimation in error-in-variables regression.

Medical research increasingly includes high-dimensional regression modeling with a need for error-in-variables methods. The Convex Conditioned Lasso (CoCoLasso) utilizes a reformulated Lasso objective function and an error-corrected cross-validation to enable error-in-variables regression, but requires heavy computations. Here, we develop a Block coordinate Descent Convex Conditioned Lasso (BDCoCoLasso) algorithm for modeling high-dimensional data that are only partially corrupted by measurement error. This algorithm separately optimizes the estimation of the uncorrupted and corrupted features in an iterative manner to reduce computational cost, with a specially calibrated formulation of cross-validation error. Through simulations, we show that the BDCoCoLasso algorithm successfully copes with much larger feature sets than CoCoLasso, and as expected, outperforms the naïve Lasso with enhanced estimation accuracy and consistency, as the intensity and complexity of measurement errors increase. Also, a new smoothly clipped absolute deviation penalization option is added that may be appropriate for some data sets. We apply the BDCoCoLasso algorithm to data selected from the UK Biobank. We develop and showcase the utility of covariate-adjusted genetic risk scores for body mass index, bone mineral density, and lifespan. We demonstrate that by leveraging more information than the naïve Lasso in partially corrupted data, the BDCoCoLasso may achieve higher prediction accuracy. These innovations, together with an R package, BDCoCoLasso, make error-in-variables adjustments more accessible for high-dimensional data sets. We posit the BDCoCoLasso algorithm has the potential to be widely applied in various fields, including genomics-facilitated personalized medicine research.

Polygenic risk score as a possible tool for identifying familial monogenic causes of complex diseases.

PURPOSE: The study aimed to evaluate whether polygenic risk scores could be helpful in addition to family history for triaging individuals to undergo deep-depth diagnostic sequencing for identifying monogenic causes of complex diseases. METHODS: Among 44,550 exome-sequenced European ancestry UK Biobank participants, we identified individuals with a clinically reported or computationally predicted monogenic pathogenic variant for breast cancer, bowel cancer, heart disease, diabetes, or Alzheimer disease. We derived polygenic risk scores for these diseases. We tested whether a polygenic risk score could identify rare pathogenic variant heterozygotes among individuals with a parental disease history. RESULTS: Monogenic causes of complex diseases were more prevalent among individuals with a parental disease history than in the rest of the population. Polygenic risk scores showed moderate discriminative power to identify familial monogenic causes. For instance, we showed that prescreening the patients with a polygenic risk score for type 2 diabetes can prioritize individuals to undergo diagnostic sequencing for monogenic diabetes variants and reduce needs for such sequencing by up to 37%. CONCLUSION: Among individuals with a family history of complex diseases, those with a low polygenic risk score are more likely to have monogenic causes of the disease and could be prioritized to undergo genetic testing.

Individuals with common diseases but with a low polygenic risk score could be prioritized for rare variant screening.

PURPOSE: Identifying rare genetic causes of common diseases can improve diagnostic and treatment strategies, but incurs high costs. We tested whether individuals with common disease and low polygenic risk score (PRS) for that disease generated from less expensive genome-wide genotyping data are more likely to carry rare pathogenic variants. METHODS: We identified patients with one of five common complex diseases among 44,550 individuals who underwent exome sequencing in the UK Biobank. We derived PRS for these five diseases, and identified pathogenic rare variant heterozygotes. We tested whether individuals with disease and low PRS were more likely to carry rare pathogenic variants. RESULTS: While rare pathogenic variants conferred, at most, 5.18-fold (95% confidence interval [CI]: 2.32-10.13) increased odds of disease, a standard deviation increase in PRS, at most, increased the odds of disease by 5.25-fold (95% CI: 5.06-5.45). Among diseased patients, a standard deviation decrease in the PRS was associated with, at most, 2.82-fold (95% CI: 1.14-7.46) increased odds of identifying rare variant heterozygotes. CONCLUSION: Rare pathogenic variants were more prevalent among affected patients with a low PRS. Therefore, prioritizing individuals for sequencing who have disease but low PRS may increase the yield of sequencing studies to identify rare variant heterozygotes.

Polygenic risk for coronary heart disease acts through atherosclerosis in type 2 diabetes.

BACKGROUND: Type 2 diabetes increases the risk of coronary heart disease (CHD), yet the mechanisms involved remain poorly described. Polygenic risk scores (PRS) provide an opportunity to understand risk factors since they reflect etiologic pathways from the entire genome. We therefore tested whether a PRS for CHD influenced risk of CHD in individuals with type 2 diabetes and which risk factors were associated with this PRS. METHODS: We tested the association of a CHD PRS with CHD and its traditional clinical risk factors amongst individuals with type 2 diabetes in UK Biobank (N = 21,102). We next tested the association of the CHD PRS with atherosclerotic burden in a cohort of 352 genome-wide genotyped participants with type 2 diabetes who had undergone coronary angiograms. RESULTS: In the UK Biobank we found that the CHD PRS was strongly associated with CHD amongst individuals with type 2 diabetes (OR per standard deviation increase = 1.50; p = 1.5 × 10- 59). But this CHD PRS was, at best, only weakly associated with traditional clinical risk factors, such as hypertension, hyperlipidemia, glycemic control, obesity and smoking. Conversely, in the angiographic cohort, the CHD PRS was strongly associated with multivessel stenosis (OR = 1.65; p = 4.9 × 10- 4) and increased number of major stenotic lesions (OR = 1.35; p = 9.4 × 10- 3). CONCLUSIONS: Polygenic predisposition to CHD is strongly associated with atherosclerotic burden in individuals with type 2 diabetes and this effect is largely independent of traditional clinical risk factors. This suggests that genetic risk for CHD acts through atherosclerosis with little effect on most traditional risk factors, providing the opportunity to explore new biological pathways.

Indirect effect inference and application to GAW20 data.

BACKGROUND: Association studies using a single type of omics data have been successful in identifying disease-associated genetic markers, but the underlying mechanisms are unaddressed. To provide a possible explanation of how these genetic factors affect the disease phenotype, integration of multiple omics data is needed. RESULTS: We propose a novel method, LIPID (likelihood inference proposal for indirect estimation), that uses both single nucleotide polymorphism (SNP) and DNA methylation data jointly to analyze the association between a trait and SNPs. The total effect of SNPs is decomposed into direct and indirect effects, where the indirect effects are the focus of our investigation. Simulation studies show that LIPID performs better in various scenarios than existing methods. Application to the GAW20 data also leads to encouraging results, as the genes identified appear to be biologically relevant to the phenotype studied. CONCLUSIONS: The proposed LIPID method is shown to be meritorious in extensive simulations and in real-data analyses.

A fast workflow for identification and quantification of proteomes.

The current in-depth proteomics makes use of long chromatography gradient to get access to more peptides for protein identification, resulting in covering of as many as 8000 mammalian gene products in 3 days of mass spectrometer running time. Here we report a fast sequencing (Fast-seq) workflow of the use of dual reverse phase high performance liquid chromatography - mass spectrometry (HPLC-MS) with a short gradient to achieve the same proteome coverage in 0.5 day. We adapted this workflow to a quantitative version (Fast quantification, Fast-quan) that was compatible to large-scale protein quantification. We subjected two identical samples to the Fast-quan workflow, which allowed us to systematically evaluate different parameters that impact the sensitivity and accuracy of the workflow. Using the statistics of significant test, we unraveled the existence of substantial falsely quantified differential proteins and estimated correlation of false quantification rate and parameters that are applied in label-free quantification. We optimized the setting of parameters that may substantially minimize the rate of falsely quantified differential proteins, and further applied them on a real biological process. With improved efficiency and throughput, we expect that the Fast-seq/Fast-quan workflow, allowing pair wise comparison of two proteomes in 1 day may make MS available to the masses and impact biomedical research in a positive way.

A unified model for interpretable latent embedding of multi-sample, multi-condition single-cell data.

Single-cell analysis across multiple samples and conditions requires quantitative modeling of the interplay between the continuum of cell states and the technical and biological sources of sample-to-sample variability. We introduce GEDI, a generative model that identifies latent space variations in multi-sample, multi-condition single-cell datasets and attributes them to sample-level covariates. GEDI enables cross-sample cell state mapping on par with state-of-the-art integration methods, cluster-free differential gene expression analysis along the continuum of cell states, and machine learning-based prediction of sample characteristics from single-cell data. GEDI can also incorporate gene-level prior knowledge to infer pathway and regulatory network activities in single cells. Finally, GEDI extends all these concepts to previously unexplored modalities that require joint consideration of dual measurements, such as the joint analysis of exon inclusion/exclusion reads to model alternative cassette exon splicing, or spliced/unspliced reads to model the mRNA stability landscapes of single cells.

Vitamin D, Cognition, and Alzheimer's Disease: Observational and Two-Sample Mendelian Randomization Studies.

Background: Observational studies have found that vitamin D supplementation is associated with improved cognition. Further, recent Mendelian randomization (MR) studies have shown that higher vitamin D levels, 25(OH)D, may protect against Alzheimer's disease. Thus, it is possible that 25(OH)D may protect against Alzheimer's disease by improving cognition. Objective: We assessed this hypothesis, by examining the relationship between 25(OH)D levels and seven cognitive measurements. Methods: To mitigate bias from confounding, we performed two-sample MR analyses. We used instruments from three publications: Manousaki et al. (2020), Sutherland et al. (2022), and the Emerging Risk Factors Collaboration/EPIC-CVD/Vitamin D Studies Collaboration (2021). Results: Our observational studies suggested a protective association between 25(OH)D levels and cognitive measures. An increase in the natural log of 25(OH)D by 1 SD was associated with a higher PACC score (BetaPACC score = 0.06, 95% CI = (0.04-0.08); p = 1.8×10-10). However, in the MR analyses, the estimated effect of 25(OH)D on cognitive measures was null. Specifically, per 1 SD increase in genetically estimated natural log of 25(OH)D, the PACC scores remained unchanged in the overall population, (BetaPACC score = -0.01, 95% CI (-0.06 to 0.03); p = 0.53), and amongst individuals aged over 60 (BetaPACC score = 0.03, 95% CI (-0.028 to 0.08); p = 0.35). Conclusions: In conclusion, our MR study found no clear evidence to support a protective role of increased 25(OH)D concentrations on cognitive performance in European ancestry individuals. However, our study cannot entirely dismiss the potential beneficial effect on PACC for individuals over the age of 60.

Circulating Metabolite Abundances Associated With Risks of Bipolar Disorder, Schizophrenia, and Depression: A Mendelian Randomization Study.

BACKGROUND: Preventive measures and treatments for psychiatric disorders are limited. Circulating metabolites are potential candidates for biomarker and therapeutic target identification, given their measurability and essential roles in biological processes. METHODS: Leveraging large-scale genome-wide association studies, we conducted Mendelian randomization analyses to assess the associations between circulating metabolite abundances and the risks of bipolar disorder, schizophrenia, and depression. Genetic instruments were selected for 94 metabolites measured in the Canadian Longitudinal Study on Aging cohort (N = 8299). We repeated Mendelian randomization analyses based on the UK Biobank, INTERVAL, and EPIC (European Prospective Investigation into Cancer)-Norfolk studies. RESULTS: After validating Mendelian randomization assumptions and colocalization evidence, we found that a 1 SD increase in genetically predicted circulating abundances of eicosapentaenoate and docosapentaenoate was associated with odds ratios of 0.72 (95% CI, 0.65-0.79) and 0.63 (95% CI, 0.55-0.72), respectively, for bipolar disorder. Genetically increased Ω-3 unsaturated fatty acids abundance and Ω-3-to-total fatty acids ratio, as well as genetically decreased Ω-6-to-Ω-3 ratio, were negatively associated with the risk of bipolar disorder in the UK Biobank. Genetically increased circulating abundances of 3 N-acetyl-amino acids were associated with an increased risk of schizophrenia with a maximum odds ratio of 1.31 (95% CI, 1.18-1.44) per 1 SD increase. Furthermore, a 1 SD increase in genetically predicted circulating abundance of hypotaurine was associated with an odds ratio of 0.85 (95% CI, 0.78-0.93) for depression. CONCLUSIONS: The biological mechanisms that underlie Ω-3 unsaturated fatty acids, NAT8-catalyzed N-acetyl-amino acids, and hypotaurine warrant exploration to identify new biomarkers and potential therapeutic targets.

Large-scale circulating proteome association study (CPAS) meta-analysis identifies circulating proteins and pathways predicting incident hip fractures.

Hip fractures are associated with significant disability, high cost, and mortality. However, the exact biological mechanisms underlying susceptibility to hip fractures remain incompletely understood. In an exploratory search of the underlying biology as reflected through the circulating proteome, we performed a comprehensive Circulating Proteome Association Study (CPAS) meta-analysis for incident hip fractures. Analyses included 6430 subjects from two prospective cohort studies (Cardiovascular Health Study and Trøndelag Health Study) with circulating proteomics data (aptamer-based 5 K SomaScan version 4.0 assay; 4979 aptamers). Associations between circulating protein levels and incident hip fractures were estimated for each cohort using age and sex-adjusted Cox regression models. Participants experienced 643 incident hip fractures. Compared with the individual studies, inverse-variance weighted meta-analyses yielded more statistically significant associations, identifying 23 aptamers associated with incident hip fractures (conservative Bonferroni correction 0.05/4979, P < 1.0 × 10-5). The aptamers most strongly associated with hip fracture risk corresponded to two proteins of the growth hormone/insulin growth factor system (GHR and IGFBP2), as well as GDF15 and EGFR. High levels of several inflammation-related proteins (CD14, CXCL12, MMP12, ITIH3) were also associated with increased hip fracture risk. Ingenuity pathway analysis identified reduced LXR/RXR activation and increased acute phase response signaling to be overrepresented among those proteins associated with increased hip fracture risk. These analyses identified several circulating proteins and pathways consistently associated with incident hip fractures. These findings underscore the usefulness of the meta-analytic approach for comprehensive CPAS in a similar manner as has previously been observed for large-scale human genetic studies. Future studies should investigate the underlying biology of these potential novel drug targets.

Hip fractures are associated with significant disability, high cost, and mortality. However, the exact biological mechanisms underlying susceptibility to hip fractures remain incompletely understood. To increase the understanding of the underlying mechanisms, we performed a meta-analysis of the associations between 4860 circulating proteins and risk of fractures using two large cohorts, including 6430 participants with 643 incident hip fractures. We identified 23 proteins/aptamers associated with incident hip fractures. Two proteins of the growth hormone/insulin growth factor system (GHR and IGFBP2), as well as GDF15 and EGFR were most strongly associated with hip fracture risk. High levels of several inflammation-related proteins were also associated with increased hip fracture risk. Pathway analysis identified reduced LXR/RXR activation and increased acute phase response signaling to be overrepresented among those proteins associated with increased hip fracture risk. Future mechanistic studies should investigate the underlying biology of these novel protein biomarkers which may be potential drug targets.

A plasma protein-based risk score to predict hip fractures.

As there are effective treatments to reduce hip fractures, identification of patients at high risk of hip fracture is important to inform efficient intervention strategies. To obtain a new tool for hip fracture prediction, we developed a protein-based risk score in the Cardiovascular Health Study using an aptamer-based proteomic platform. The proteomic risk score predicted incident hip fractures and improved hip fracture discrimination in two Trøndelag Health Study validation cohorts using the same aptamer-based platform. When transferred to an antibody-based proteomic platform in a UK Biobank validation cohort, the proteomic risk score was strongly associated with hip fractures (hazard ratio per s.d. increase, 1.64; 95% confidence interval 1.53-1.77). The proteomic risk score, but not available polygenic risk scores for fractures or bone mineral density, improved the C-index beyond the fracture risk assessment tool (FRAX), which integrates information from clinical risk factors (C-index, FRAX 0.735 versus FRAX + proteomic risk score 0.776). The developed proteomic risk score constitutes a new tool for stratifying patients according to hip fracture risk; however, its improvement in hip fracture discrimination is modest and its clinical utility beyond FRAX with information on femoral neck bone mineral density remains to be determined.

Beyond apples and pears: sex-specific genetics of body fat percentage.

Introduction: Biological sex influences both overall adiposity and fat distribution. Further, testosterone and sex hormone binding globulin (SHBG) influence adiposity and metabolic function, with differential effects of testosterone in men and women. Here, we aimed to perform sex-stratified genome-wide association studies (GWAS) of body fat percentage (BFPAdj) (adjusting for testosterone and sex hormone binding globulin (SHBG)) to increase statistical power. Methods: GWAS were performed in white British individuals from the UK Biobank (157,937 males and 154,337 females). To avoid collider bias, loci associated with SHBG or testosterone were excluded. We investigated association of BFPAdj loci with high density cholesterol (HDL), triglyceride (TG), type 2 diabetes (T2D), coronary artery disease (CAD), and MRI-derived abdominal subcutaneous adipose tissue (ASAT), visceral adipose tissue (VAT) and gluteofemoral adipose tissue (GFAT) using publicly available data from large GWAS. We also performed 2-sample Mendelian Randomization (MR) using identified BFPAdj variants as instruments to investigate causal effect of BFPAdj on HDL, TG, T2D and CAD in males and females separately. Results: We identified 195 and 174 loci explaining 3.35% and 2.60% of the variation in BFPAdj in males and females, respectively at genome-wide significance (GWS, p<5x10-8). Although the direction of effect at these loci was generally concordant in males and females, only 38 loci were common to both sexes at GWS. Seven loci in males and ten loci in females have not been associated with any adiposity/cardiometabolic traits previously. BFPAdj loci generally did not associate with cardiometabolic traits; several had paradoxically beneficial cardiometabolic effects with favourable fat distribution. MR analyses did not find convincing supportive evidence that increased BFPAdj has deleterious cardiometabolic effects in either sex with highly significant heterogeneity. Conclusions: There was limited genetic overlap between BFPAdj in males and females at GWS. BFPAdj loci generally did not have adverse cardiometabolic effects which may reflect the effects of favourable fat distribution and cardiometabolic risk modulation by testosterone and SHBG.

Development of risk prediction models for depression combining genetic and early life risk factors.

Background: Both genetic and early life risk factors play important roles in the pathogenesis and progression of adult depression. However, the interplay between these risk factors and their added value to risk prediction models have not been fully elucidated. Methods: Leveraging a meta-analysis of major depressive disorder genome-wide association studies (N = 45,591 cases and 97,674 controls), we developed and optimized a polygenic risk score for depression using LDpred in a model selection dataset from the UK Biobank (N = 130,092 European ancestry individuals). In a UK Biobank test dataset (N = 278,730 European ancestry individuals), we tested whether the polygenic risk score and early life risk factors were associated with each other and compared their associations with depression phenotypes. Finally, we conducted joint predictive modeling to combine this polygenic risk score with early life risk factors by stepwise regression, and assessed the model performance in identifying individuals at high risk of depression. Results: In the UK Biobank test dataset, the polygenic risk score for depression was moderately associated with multiple early life risk factors. For instance, a one standard deviation increase in the polygenic risk score was associated with 1.16-fold increased odds of frequent domestic violence (95% CI: 1.14-1.19) and 1.09-fold increased odds of not having access to medical care as a child (95% CI: 1.05-1.14). However, the polygenic risk score was more strongly associated with depression phenotypes than most early life risk factors. A joint predictive model integrating the polygenic risk score, early life risk factors, age and sex achieved an AUROC of 0.6766 for predicting strictly defined major depressive disorder, while a model without the polygenic risk score and a model without any early life risk factors had an AUROC of 0.6593 and 0.6318, respectively. Conclusion: We have developed a polygenic risk score to partly capture the genetic liability to depression. Although genetic and early life risk factors can be correlated, joint predictive models improved risk stratification despite limited improvement in magnitude, and may be explored as tools to better identify individuals at high risk of depression.

Identifying Rare Genetic Determinants for Improved Polygenic Risk Prediction of Bone Mineral Density and Fracture Risk.

Osteoporosis and fractures severely impact the elderly population. Polygenic risk scores for bone mineral density have demonstrated potential clinical utility. However, the value of rare genetic determinants in risk prediction has not been assessed. With whole-exome sequencing data from 436,824 UK Biobank participants, we assigned White British ancestry individuals into a training data set (n = 317,434) and a test data set (n = 74,825). In the training data set, we developed a common variant-based polygenic risk score for heel ultrasound speed of sound (SOS). Next, we performed burden testing to identify genes harboring rare determinants of bone mineral density, targeting influential rare variants with predicted high deleteriousness. We constructed a genetic risk score, called ggSOS, to incorporate influential rare variants in significant gene burden masks into the common variant-based polygenic risk score. We assessed the predictive performance of ggSOS in the White British test data set, as well as in populations of non-White British European (n = 18,885), African (n = 7165), East Asian (n = 2236), South Asian (n = 9829), and other admixed (n = 1481) ancestries. Twelve genes in pivotal regulatory pathways of bone homeostasis harbored influential rare variants associated with SOS (p < 5.5 × 10-7 ), including AHNAK, BMP5, CYP19A1, FAM20A, FBXW5, KDM5B, KREMEN1, LGR4, LRP5, SMAD6, SOST, and WNT1. Among 4013 (5.4%) individuals in the test data set carrying these variants, a one standard deviation decrease in ggSOS was associated with 1.35-fold (95% confidence interval [CI] 1.16-1.57) increased hazard of major osteoporotic fracture. However, compared with a common variant-based polygenic risk score (C-index = 0.641), ggSOS had only marginally improved prediction accuracy in identifying at-risk individuals (C-index = 0.644), with overlapping confidence intervals. Similarly, ggSOS did not demonstrate substantially improved predictive performance in non-European ancestry populations. In summary, modeling the effects of rare genetic determinants may assist polygenic prediction of fracture risk among carriers of influential rare variants. Nonetheless, improved clinical utility is not guaranteed for population-level risk screening. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).