A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

Genetic predisposition to mosaic Y chromosome loss in blood.

Mosaic loss of chromosome Y (LOY) in circulating white blood cells is the most common form of clonal mosaicism1-5, yet our knowledge of the causes and consequences of this is limited. Here, using a computational approach, we estimate that 20% of the male population represented in the UK Biobank study (n = 205,011) has detectable LOY. We identify 156 autosomal genetic determinants of LOY, which we replicate in 757,114 men of European and Japanese ancestry. These loci highlight genes that are involved in cell-cycle regulation and cancer susceptibility, as well as somatic drivers of tumour growth and targets of cancer therapy. We demonstrate that genetic susceptibility to LOY is associated with non-haematological effects on health in both men and women, which supports the hypothesis that clonal haematopoiesis is a biomarker of genomic instability in other tissues. Single-cell RNA sequencing identifies dysregulated expression of autosomal genes in leukocytes with LOY and provides insights into why clonal expansion of these cells may occur. Collectively, these data highlight the value of studying clonal mosaicism to uncover fundamental mechanisms that underlie cancer and other ageing-related diseases.

Effect of duck interferon-α and an anti-cap protein polyclonal antibody against duck circovirus.

Duck circovirus (DuCV) is one of the most prevalent viruses in the duck breeding industry, and causes persistent infection and severe immunosuppression. Currently, there is a serious lack of prevention and control measures and no commercial vaccine against DuCV. Therefore, effective antiviral drugs are important for treating DuCV infection. Interferon (IFN) is an important component of antiviral innate immunity, but it remains unclear whether duck IFN-α has a clinical effect against DuCV. Antibody therapy is an important way to treat viral infections. The DuCV structural protein (cap) is immunogenic, and it remains to be determined whether an anti-cap protein antibody can effectively block DuCV infection. In this study, the duck IFN-α gene and the DuCV structural protein cap gene were cloned, expressed and purified in Escherichia coli to prepare duck recombinant IFN-α and the cap protein. Then, rabbits were immunized with the recombinant cap protein to prepare a rabbit polyclonal antibody. This study investigated the antiviral effect of duck recombinant IFN-α and the anti-cap protein antibody and their combined effect on Cherry Valley ducks infected with DuCV. The results showed that the treatment significantly alleviated the clinical symptoms of immune organ atrophy and immunosuppression compared with the control. The histopathological damage of the target organs was alleviated, and replication of DuCV in the immune organs was significantly inhibited. The treatment also reduced the damage caused by DuCV to the liver and immune function, and increased the level of the DuCV antibody in the blood, thereby improving antiviral activity. Notably, the combination of duck IFN-α and the polyclonal antibody completely blocked DuCV infection after 13 days under the experimental conditions, showing a better inhibitory effect on DuCV infection than single treatments. These results showed that duck recombinant IFN-α and the anti-cap protein antibody can be used as antiviral drugs to clinically treat and control DuCV infection, particularly the vertical transmission of the virus in breeding ducks.

Comparison study of protective effects of porcine bile acids and sheep bile acids against heat stress in chickens.

BACKGROUND: Heat stress (HS) is known to exert negative effects on the poultry and breeding industry, resulting in severe economic losses. Bile acids (BAs), an important component of bile, play a crucial role in improving the production performance of livestock and poultry, alleviating stress injury, and ensuring the health of livestock and poultry. At present, porcine BAs are widely used because of their therapeutic effects on HS; however, it remains unclear whether the same effects are exerted by sheep BAs, which are different from porcine BAs and have different compositions. In this study, we compared the anti-HS effects of porcine BAs and sheep BAs in the diet by establishing an HS model of chicks and investigating the chicken performance, HS-related genes' expression, oxidative stress markers, jejunal histoarchitecture, inflammatory cytokines' expression, jejunal secreted immunoglobulin A concentration, and cecal bacterial flora. RESULTS: The results showed that the addition of sheep BAs to the diet increased the average daily weight gain and the feed conversion ratio of chicks. Under HS, sheep BAs were more effective than porcine BAs in improving the activities of lactate dehydrogenase and glutamic pyruvic transaminase in serum and the content/activity of malondialdehyde, superoxide dismutase, and reduced glutathione in serum and tissue, in reducing the messenger RNA (mRNA) expression of heat shock proteins (HSP60, HSP70, and HSP90) in the liver and jejunum, and in improving the histological structure and the expression of tight junction proteins (occludin and zonula occludens-1) and enriching intestinal bacterial flora. However, porcine BAs were significantly inferior to sheep BAs in reducing the mRNA expression of inflammatory factors (interleukin-6, interleukin-1ß, and tumor necrosis factor-α). CONCLUSION: The effect of sheep BAs was more significant than porcine BAs was in alleviating HS injury in chicks, suggesting that sheep BAs have great potential as new feed nutrition and health additive to improve poultry production performance and prevent HS. © 2023 Society of Chemical Industry.

Genetic Architectures of Childhood- and Adult-Onset Asthma Are Partly Distinct.

The extent to which genetic risk factors are shared between childhood-onset (COA) and adult-onset (AOA) asthma has not been estimated. On the basis of data from the UK Biobank study (n = 447,628), we found that the variance in disease liability explained by common variants is higher for COA (onset at ages between 0 and 19 years; h2g = 25.6%) than for AOA (onset at ages between 20 and 60 years; h2g = 10.6%). The genetic correlation (rg) between COA and AOA was 0.67. Variation in age of onset among COA-affected individuals had a low heritability (h2g = 5%), which we confirmed in independent studies and also among AOA-affected individuals. To identify subtype-specific genetic associations, we performed a genome-wide association study (GWAS) in the UK Biobank for COA (13,962 affected individuals) and a separate GWAS for AOA (26,582 affected individuals) by using a common set of 300,671 controls for both studies. We identified 123 independent associations for COA and 56 for AOA (37 overlapped); of these, 98 and 34, respectively, were reproducible in an independent study (n = 262,767). Collectively, 28 associations were not previously reported. For 96 COA-associated variants, including five variants that represent COA-specific risk factors, the risk allele was more common in COA- than in AOA-affected individuals. Conversely, we identified three variants that are stronger risk factors for AOA. Variants associated with obesity and smoking had a stronger contribution to the risk of AOA than to the risk of COA. Lastly, we identified 109 likely target genes of the associated variants, primarily on the basis of correlated expression quantitative trait loci (up to n = 31,684). GWAS informed by age of onset can identify subtype-specific risk variants, which can help us understand differences in pathophysiology between COA and AOA and so can be informative for drug development.

Genome-wide association study identifies nine novel loci for 2D:4D finger ratio, a putative retrospective biomarker of testosterone exposure in utero.

The ratio of the length of the index finger to that of the ring finger (2D:4D) is sexually dimorphic and is commonly used as a non-invasive biomarker of prenatal androgen exposure. Most association studies of 2D:4D ratio with a diverse range of sex-specific traits have typically involved small sample sizes and have been difficult to replicate, raising questions around the utility and precise meaning of the measure. In the largest genome-wide association meta-analysis of 2D:4D ratio to date (N = 15 661, with replication N = 75 821), we identified 11 loci (9 novel) explaining 3.8% of the variance in mean 2D:4D ratio. We also found weak evidence for association (ß = 0.06; P = 0.02) between 2D:4D ratio and sensitivity to testosterone [length of the CAG microsatellite repeat in the androgen receptor (AR) gene] in females only. Furthermore, genetic variants associated with (adult) testosterone levels and/or sex hormone-binding globulin were not associated with 2D:4D ratio in our sample. Although we were unable to find strong evidence from our genetic study to support the hypothesis that 2D:4D ratio is a direct biomarker of prenatal exposure to androgens in healthy individuals, our findings do not explicitly exclude this possibility, and pathways involving testosterone may become apparent as the size of the discovery sample increases further. Our findings provide new insight into the underlying biology shaping 2D:4D variation in the general population.

Powerful and robust cross-phenotype association test for case-parent trios.

There has been increasing interest in identifying genes within the human genome that influence multiple diverse phenotypes. In the presence of pleiotropy, joint testing of these phenotypes is not only biologically meaningful but also statistically more powerful than univariate analysis of each separate phenotype accounting for multiple testing. Although many cross-phenotype association tests exist, the majority of such methods assume samples composed of unrelated subjects and therefore are not applicable to family-based designs, including the valuable case-parent trio design. In this paper, we describe a robust gene-based association test of multiple phenotypes collected in a case-parent trio study. Our method is based on the kernel distance covariance (KDC) method, where we first construct a similarity matrix for multiple phenotypes and a similarity matrix for genetic variants in a gene; we then test the dependency between the two similarity matrices. The method is applicable to either common variants or rare variants in a gene, and resulting tests from the method are by design robust to confounding due to population stratification. We evaluated our method through simulation studies and observed that the method is substantially more powerful than standard univariate testing of each separate phenotype. We also applied our method to phenotypic and genotypic data collected in case-parent trios as part of the Genetics of Kidneys in Diabetes (GoKinD) study and identified a genome-wide significant gene demonstrating cross-phenotype effects that was not identified using standard univariate approaches.

Flexible and robust methods for rare-variant testing of quantitative traits in trios and nuclear families.

Most rare-variant association tests for complex traits are applicable only to population-based or case-control resequencing studies. There are fewer rare-variant association tests for family-based resequencing studies, which is unfortunate because pedigrees possess many attractive characteristics for such analyses. Family-based studies can be more powerful than their population-based counterparts due to increased genetic load and further enable the implementation of rare-variant association tests that, by design, are robust to confounding due to population stratification. With this in mind, we propose a rare-variant association test for quantitative traits in families; this test integrates the QTDT approach of Abecasis et al. [Abecasis et al., ] into the kernel-based SNP association test KMFAM of Schifano et al. [Schifano et al., ]. The resulting within-family test enjoys the many benefits of the kernel framework for rare-variant association testing, including rapid evaluation of P-values and preservation of power when a region harbors rare causal variation that acts in different directions on phenotype. Additionally, by design, this within-family test is robust to confounding due to population stratification. Although within-family association tests are generally less powerful than their counterparts that use all genetic information, we show that we can recover much of this power (although still ensuring robustness to population stratification) using a straightforward screening procedure. Our method accommodates covariates and allows for missing parental genotype data, and we have written software implementing the approach in R for public use.

A permutation procedure to correct for confounders in case-control studies, including tests of rare variation.

Many case-control tests of rare variation are implemented in statistical frameworks that make correction for confounders like population stratification difficult. Simple permutation of disease status is unacceptable for resolving this issue because the replicate data sets do not have the same confounding as the original data set. These limitations make it difficult to apply rare-variant tests to samples in which confounding most likely exists, e.g., samples collected from admixed populations. To enable the use of such rare-variant methods in structured samples, as well as to facilitate permutation tests for any situation in which case-control tests require adjustment for confounding covariates, we propose to establish the significance of a rare-variant test via a modified permutation procedure. Our procedure uses Fisher's noncentral hypergeometric distribution to generate permuted data sets with the same structure present in the actual data set such that inference is valid in the presence of confounding factors. We use simulated sequence data based on coalescent models to show that our permutation strategy corrects for confounding due to population stratification that, if ignored, would otherwise inflate the size of a rare-variant test. We further illustrate the approach by using sequence data from the Dallas Heart Study of energy metabolism traits. Researchers can implement our permutation approach by using the R package BiasedUrn.

Susceptibility to childhood-onset rheumatoid arthritis: investigation of a weighted genetic risk score that integrates cumulative effects of variants at five genetic loci.

OBJECTIVE: Children with childhood-onset rheumatoid arthritis (RA) include those with rheumatoid factor or anti-citrullinated protein antibody-positive juvenile idiopathic arthritis. To test the hypothesis that adult-onset RA-associated variants are also associated with childhood-onset RA, we investigated RA-associated variants at 5 loci in a cohort of patients with childhood-onset RA. We also assessed the cumulative association of these variants in susceptibility to childhood-onset RA using a weighted genetic risk score (wGRS). METHODS: A total of 155 children with childhood-onset RA and 684 healthy controls were genotyped for 5 variants in the PTPN22, TRAF1/C5, STAT4, and TNFAIP3 loci. High-resolution HLA-DRB1 genotypes were available for 149 cases and 373 controls. We tested each locus for association with childhood-onset RA via logistic regression. We also computed a wGRS for each subject, with weights based on the natural log of the published odds ratios (ORs) for the alleles investigated, and used logistic regression to test the wGRS for association with childhood-onset RA. RESULTS: Childhood-onset RA was associated with TNFAIP3 rs10499194 (OR 0.60 [95% confidence interval 0.44-0.83]), PTPN22 rs2476601 (OR 1.61 [95% confidence interval 1.11-2.31]), and STAT4 rs7574865 (OR 1.41 [95% confidence interval 1.06-1.87]) variants. The wGRS was significantly different between cases and controls (P < 2 × 10(-16) ). Individuals in the third to fifth quintiles of wGRS had a significantly increased disease risk compared to baseline (individuals in the first quintile). Higher wGRS was associated with increased risk of childhood-onset RA, especially among males. CONCLUSION: The magnitude and direction of the association between TNFAIP3, STAT4, and PTPN22 variants and childhood-onset RA are similar to those observed in RA, suggesting that adult-onset RA and childhood-onset RA share common genetic risk factors. Using a wGRS, we have demonstrated the cumulative association of RA-associated variants with susceptibility to childhood-onset RA.

Assessing the impact of population stratification on association studies of rare variation.

AIMS: The study of rare variants, which can potentially explain a great proportion of heritability, has emerged as an important topic in human gene mapping of complex diseases. Although several statistical methods have been developed to increase the power to detect disease-related rare variants, none of these methods address an important issue that often arises in genetic studies: false positives due to population stratification. Using simulations, we investigated the impact of population stratification on false-positive rates of rare-variant association tests. METHODS: We simulated a series of case-control studies assuming various sample sizes and levels of population structure. Using such data, we examined the impact of population stratification on rare-variant collapsing and burden tests of rare variation. We further evaluated the ability of 2 existing methods (principal component analysis and genomic control) to correct for stratification in such rare-variant studies. RESULTS: We found that population stratification can have a significant influence on studies of rare variants especially when the sample size is large and the population is severely stratified. Our results showed that principal component analysis performed quite well in most situations, while genomic control often yielded conservative results. CONCLUSIONS: Our results imply that researchers need to carefully match cases and controls on ancestry in order to avoid false positives caused by population structure in studies of rare variants, particularly if genome-wide data are not available.

An Ensemble Penalized Regression Method for Multi-ancestry Polygenic Risk Prediction.

Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination of ℒ1 (lasso) and ℒ2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.

An ensemble penalized regression method for multi-ancestry polygenic risk prediction.

Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination of L 1 (lasso) and L 2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.

MUSSEL: Enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups.

Polygenic risk scores (PRSs) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in summary statistics from genome-wide association studies (GWASs) across multiple ancestry groups via Bayesian hierarchical modeling and ensemble learning. In our simulation studies and data analyses across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. For example, MUSSEL has an average gain in prediction R2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, trait architecture, and linkage disequilibrium reference samples; thus, ultimately a combination of methods may be needed to generate the most robust PRSs across diverse populations.

Hierarchy of risk of childhood-onset rheumatoid arthritis conferred by HLA-DRB1 alleles encoding the shared epitope.

OBJECTIVE: Associations between shared epitope (SE)-encoding HLA-DRB1 alleles and rheumatoid arthritis (RA) are well established. However, only a limited number of studies have investigated these alleles in patients with childhood-onset RA, which is defined as rheumatoid factor- and/or anti-citrullinated protein antibody-positive juvenile idiopathic arthritis. The aims of this study were to investigate the largest cohort of patients with childhood-onset RA for association with SE alleles and to determine whether there is a hierarchy of risk based on the amino acid sequence of the SE. METHODS: High-resolution HLA-DRB1 genotypes were obtained for 204 patients with childhood-onset RA and 373 healthy control subjects. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated for different SE-encoding HLA-DRB1 alleles. In addition, genotype ORs were calculated for combinations of SE alleles classified into S(2) , S(3P) , or L alleles, based on amino acid sequences in position 70-74 of the DRß1 chain, as proposed by Tezenas du Montcel et al. RESULTS: We confirmed associations between HLA-DRB1 SE alleles and childhood-onset RA (76% of patients carried 1 or 2 SE alleles compared with 46% of control subjects; OR 3.81, 95% CI 2.4-6.0, P < 1 × 10(-7) ). We also observed associations between individual SE alleles (HLA-DRB1*0101, *0401, *0404, *0405, *0408, and *1001) and childhood-onset RA. Genotype-specific risk estimates suggested a hierarchy of risk, with the highest risk among individuals heterozygous for S(2) /S(3P) (OR 22.3, 95% CI 9.9-50.5, P < 0.0001). CONCLUSION: We confirm the association between SE-encoding HLA-DRB1 alleles and susceptibility to childhood-onset RA. The excess risk conferred by carriage of the combination of S(2) and S(3P) risk alleles suggests that children with DRß1 chains containing the KRAA and QRRAA or RRRAA sequences are especially susceptible to RA.

Pathogenicity of duck circovirus and fowl adenovirus serotype 4 co-infection in Cherry Valley ducks.

Duck circovirus (DuCV) is one of the most prevalent infectious viruses in the duck industry in China. Although the clinical symptoms vary, it often causes immunosuppression in the host and leads to secondary infection with other pathogens. Fowl adenovirus serotype 4 (FAdV-4) mainly infects chickens and causes hydropericardium hepatitis syndrome. However, the incidence of infection in ducks has increased in recent years, and the phenomenon of mixed infection with DuCV is very common, resulting in more severe clinical morbidity. However, there is no systematic study evaluating the presence of mixed infection. To explore the synergistic pathogenicity of DuCV and FAdV-4 co-infection in Cherry Valley ducks, a comparative experiment was established between DuCV and FAdV-4 co-infection and single infection animal models. It was found that DuCV and FAdV-4 co-infected ducks showed more pronounced clinical signs of pericardial effusion, hepatitis and immunosuppression; more severe tissue damage in target organs; and more significant levels of viral load, biochemical indicators and immune indicators in various organs compared with Cherry Valley ducks infected with just one virus. The results showed that co-infection with DuCV and FAdV-4 may promote greater viral replication, causing more severe tissue damage and immunosuppression than infection with just one virus. Therefore, the monitoring and prevention of the two viruses should be strengthened clinically, with a particular focus on the potential harm of DuCV as it carries the highest infection rate.

Hexavalent chromium disrupts the skin barrier by targeting ROS-mediated mitochondrial pathway apoptosis in keratinocytes.

Hexavalent chromium (Cr(VI)), a toxic heavy metal, is ubiquitous in daily life. Exposure to this toxic substance in occupational settings can cause dermatitis and cancer. As the body's largest organ, the skin plays a crucial role in protecting the organism against external aggressions. While previous studies have focused on the effects of Cr(VI) on skin inflammation, this study investigates the potential toxicity of Cr(VI) from the skin barrier and integrity perspective. The in vivo results of this study showed that mice exposed to Cr(VI) experienced skin deterioration and hemorrhaging, as well as a reduction in the thickness of the collagen fiber layer. TUNEL and Occludin staining results revealed that Cr(VI)'s toxicity primarily targeted keratinocytes. Experiments in vitro demonstrated that Cr(VI) treatment decreased the activity of HaCaT cells, altered cell morphology, and increased LDH secretion. Further research revealed that Cr(VI) could modify membrane permeability, impair membrane integrity, and reduce the protein expression of ZO-1 and Occludin. In addition, it was discovered that Cr(VI) promoted cell apoptosis and inhibited AKT activation. However, the addition of a caspase inhibitor and an AKT activator prevented Cr(VI)-induced injury to the cell membrane barrier, indicating that apoptosis plays a crucial role in this process. The addition of three apoptotic pathway inhibitors, confirmed that Cr(VI) damaged the cell barrier through ROS-mediated mitochondrial pathway apoptosis. Moreover, the use of a ROS inhibitor significantly reduced Cr(VI)-induced apoptosis and cell barrier injury. In conclusion, this study provides an experimental foundation for the treatment of skin injury caused by Cr(VI).

MUSSEL: Enhanced Bayesian Polygenic Risk Prediction Leveraging Information across Multiple Ancestry Groups.

Polygenic risk scores (PRS) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across different populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in the summary statistics from genome-wide association studies (GWAS) across multiple ancestry groups. MUSSEL conducts Bayesian hierarchical modeling under a MUltivariate Spike-and-Slab model for effect-size distribution and incorporates an Ensemble Learning step using super learner to combine information across different tuning parameter settings and ancestry groups. In our simulation studies and data analyses of 16 traits across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. The method, for example, has an average gain in prediction R2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African Ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, underlying trait architecture, and the choice of reference samples for LD estimation, and thus ultimately, a combination of methods may be needed to generate the most robust PRS across diverse populations.

A new method for multiancestry polygenic prediction improves performance across diverse populations.

Polygenic risk scores (PRSs) increasingly predict complex traits; however, suboptimal performance in non-European populations raise concerns about clinical applications and health inequities. We developed CT-SLEB, a powerful and scalable method to calculate PRSs, using ancestry-specific genome-wide association study summary statistics from multiancestry training samples, integrating clumping and thresholding, empirical Bayes and superlearning. We evaluated CT-SLEB and nine alternative methods with large-scale simulated genome-wide association studies (~19 million common variants) and datasets from 23andMe, Inc., the Global Lipids Genetics Consortium, All of Us and UK Biobank, involving 5.1 million individuals of diverse ancestry, with 1.18 million individuals from four non-European populations across 13 complex traits. Results demonstrated that CT-SLEB significantly improves PRS performance in non-European populations compared with simple alternatives, with comparable or superior performance to a recent, computationally intensive method. Moreover, our simulation studies offered insights into sample size requirements and SNP density effects on multiancestry risk prediction.

Characterizing mood disorders in the AFFECT study: a large, longitudinal, and phenotypically rich genetic cohort in the US.

There has recently been marked progress in identifying genetic risk factors for major depression (MD) and bipolar disorder (BD); however, few systematic efforts have been made to elucidate heterogeneity that exists within and across these diagnostic taxa. The Affective disorders, Environment, and Cognitive Trait (AFFECT) study presents an opportunity to identify and associate the structure of cognition and symptom-level domains across the mood disorder spectrum in a prospective study from a diverse US population.Participants were recruited from the 23andMe, Inc research participant database and through social media; self-reported diagnosis of MD or BD by a medical professional and medication status data were used to enrich for mood-disorder cases. Remote assessments were used to acquire an extensive range of phenotypes, including mood state, transdiagnostic symptom severity, task-based measures of cognition, environmental exposures, personality traits. In this paper we describe the study design, and the demographic and clinical characteristics of the cohort. In addition we report genetic ancestry, SNP heritability, and genetic correlations with other large cohorts of mood disorders.A total of 48,467 participants were enrolled: 14,768 with MD, 9864 with BD, and 23,835 controls. Upon enrollment, 47% of participants with MD and 27% with BD indicated being in an active mood episode. Cases reported early ages of onset (mean = 13.2 and 14.3 years for MD and BD, respectively), and high levels of recurrence (78.6% and 84.9% with >5 episodes), psychotherapy, and psychotropic medication use. SNP heritability on the liability scale for the ascertained MD participants (0.19-0.21) was consistent with the high level of disease severity in this cohort, while BD heritability estimates (0.16-0.22) were comparable to reports in other large scale genomic studies of mood disorders. Genetic correlations between the AFFECT cohort and other large-scale cohorts were high for MD but not for BD. By incorporating transdiagnostic symptom assessments, repeated measures, and genomic data, the AFFECT study represents a unique resource for dissecting the structure of mood disorders across multiple levels of analysis. In addition, the fully remote nature of the study provides valuable insights for future virtual and decentralized clinical trials within mood disorders.