Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr

Fast and scalable ensemble learning method for versatile polygenic risk prediction

Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, but existing methods face several limitations, encompassing issues related to computational burden, predictive accuracy, and adaptability to a wide range of genetic architectures. To address these issues, we propose Aggregated L0Learn using Summary-level data (ALL-Sum), a fast and scalable ensemble learning method for computing PRS using summary statistics from genome-wide association studies (GWAS). ALL-Sum leverages a L0L2 penalized regression and ensemble learning across tuning parameters to flexibly model traits with diverse genetic architectures. In extensive large-scale simulations across a wide range of polygenicity and GWAS sample sizes, ALL-Sum consistently outperformed popular alternative methods in terms of prediction accuracy, runtime, and memory usage by 10%, 20-fold, and threefold, respectively, and demonstrated robustness to diverse genetic architectures. We validated the performance of ALL-Sum in real data analysis of 11 complex traits using GWAS summary statistics from nine data sources, including the Global Lipids Genetics Consortium, Breast Cancer Association Consortium, and FinnGen Biobank, with validation in the UK Biobank. Our results show that on average, ALL-Sum obtained PRS with 25% higher accuracy on average, with 15 times faster computation and half the memory than the current state-of-the-art methods, and had robust performance across a wide range of traits and diseases. Furthermore, our method demonstrates stable prediction when using linkage disequilibrium computed from different data sources. ALL-Sum is available as a user-friendly R software package with publicly available reference data for streamlined analysis.

A maize semi-dwarf mutant reveals a GRAS transcription factor involved in brassinosteroid signaling

Brassinosteroids (BR) and gibberellins (GA) regulate plant height and leaf angle in maize (Zea mays). Mutants with defects in BR or GA biosynthesis or signaling identify components of these pathways and enhance our knowledge about plant growth and development. In this study, we characterized three recessive mutant alleles of GRAS transcription factor 42 (gras42) in maize, a GRAS transcription factor gene orthologous to the DWARF AND LOW TILLERING (DLT) gene of rice (Oryza sativa). These maize mutants exhibited semi-dwarf stature, shorter and wider leaves, and more upright leaf angle. Transcriptome analysis revealed a role for GRAS42 as a determinant of BR signaling. Analysis of the expression consequences from loss of GRAS42 in the gras42-mu1021149 mutant indicated a weak loss of BR signaling in the mutant, consistent with its previously demonstrated role in BR signaling in rice. Loss of BR signaling was also evident by the enhancement of weak BR biosynthetic mutant alleles in double mutants of nana plant1-1 and gras42-mu1021149. The gras42-mu1021149 mutant had little effect on GA-regulated gene expression, suggesting that GRAS42 is not a regulator of core GA signaling genes in maize. Single-cell expression data identified gras42 expressed among cells in the G2/M phase of the cell cycle consistent with its previously demonstrated role in cell cycle gene expression in Arabidopsis (Arabidopsis thaliana). Cis-acting natural variation controlling GRAS42 transcript accumulation was identified by expression genome-wide association study (eGWAS) in maize. Our results demonstrate a conserved role for GRAS42/SCARECROW-LIKE 28 (SCL28)/DLT in BR signaling, clarify the role of this gene in GA signaling, and suggest mechanisms of tillering and leaf angle control by BR.

The role of co-occurring conditions and genetics in the associations of eating disorders with attention-deficit/hyperactivity disorder and autism spectrum disorder

Eating disorders (EDs) commonly co-occur with other psychiatric and neurodevelopmental disorders including attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD); however, the pattern of family history and genetic overlap among them requires clarification. This study investigated the diagnostic, familial, and genetic associations of EDs with ADHD and ASD. The nationwide population-based cohort study included all individuals born in Denmark, 1981-2008, linked to their siblings and cousins. Cox regression was used to estimate associations between EDs and ADHD or ASD, and mediation analysis was used to assess the effects of intermediate mood or anxiety disorders. Polygenic scores (PGSs) were used to investigate the genetic association between anorexia nervosa (AN) and ADHD or ASD. Significantly increased risk for any ED was observed following an ADHD [hazard ratio = 1.97, 95% confidence interval = 1.75-2.22] or ASD diagnosis [2.82, 2.48-3.19]. Mediation analysis suggested that intermediate mood or anxiety disorders could account for 44-100% of the association between ADHD or ASD and ED. Individuals with a full sibling or maternal halfsibling with ASD had increased risk of AN [1.54, 1.33-1.78; 1.45, 1.08-1.94] compared to those with siblings without ASD. A positive association was found between ASD-PGS and AN risk [1.06, 1.02-1.09]. In this study, positive phenotypic associations between EDs and ADHD or ASD, mediation by mood or anxiety disorder, and a genetic association between ASD-PGS and AN were observed. These findings could guide future research in the development of new treatments that can mitigate the development of EDs among individuals with ADHD or ASD.

Exome-wide evidence of compound heterozygous effects across common phenotypes in the UK Biobank

The phenotypic impact of compound heterozygous (CH) variation has not been investigated at the population scale. We phased rare variants (MAF ∼0.001%) in the UK Biobank (UKBB) exome-sequencing data to characterize recessive effects in 175,587 individuals across 311 common diseases. A total of 6.5% of individuals carry putatively damaging CH variants, 90% of which are only identifiable upon phasing rare variants (MAF < 0.38%). We identify six recessive gene-trait associations (p < 1.68 × 10-7) after accounting for relatedness, polygenicity, nearby common variants, and rare variant burden. Of these, just one is discovered when considering homozygosity alone. Using longitudinal health records, we additionally identify and replicate a novel association between bi-allelic variation in ATP2C2 and an earlier age at onset of chronic obstructive pulmonary disease (COPD) (p < 3.58 × 10-8). Genetic phase contributes to disease risk for gene-trait pairs: ATP2C2-COPD (p = 0.000238), FLG-asthma (p = 0.00205), and USH2A-visual impairment (p = 0.0084). We demonstrate the power of phasing large-scale genetic cohorts to discover phenome-wide consequences of compound heterozygosity.

GRPa-PRS: A risk stratification method to identify genetically-regulated pathways in polygenic diseases

Background

Polygenic risk scores (PRS) are tools used to evaluate an individual's susceptibility to polygenic diseases based on their genetic profile. A considerable proportion of people carry a high genetic risk but evade the disease. On the other hand, some individuals with a low risk of eventually developing the disease. We hypothesized that unknown counterfactors might be involved in reversing the PRS prediction, which might provide new insights into the pathogenesis, prevention, and early intervention of diseases.

Methods

We built a novel computational framework to identify genetically-regulated pathways (GRPas) using PRS-based stratification for each cohort. We curated two AD cohorts with genotyping data; the discovery (disc) and the replication (rep) datasets include 2722 and 2854 individuals, respectively. First, we calculated the optimized PRS model based on the three recent AD GWAS summary statistics for each cohort. Then, we stratified the individuals by their PRS and clinical diagnosis into six biologically meaningful PRS strata, such as AD cases with low/high risk and cognitively normal (CN) with low/high risk. Lastly, we imputed individual genetically-regulated expression (GReX) and identified differential GReX and GRPas between risk strata using gene-set enrichment and variational analyses in two models, with and without APOE effects. An orthogonality test was further conducted to verify those GRPas are independent of PRS risk. To verify the generalizability of other polygenic diseases, we further applied a default model of GRPa-PRS for schizophrenia (SCZ).

Results

For each stratum, we conducted the same procedures in both the disc and rep datasets for comparison. In AD, we identified several well-known AD-related pathways, including amyloid-beta clearance, tau protein binding, and astrocyte response to oxidative stress. Additionally, we discovered resilience-related GRPs that are orthogonal to AD PRS, such as the calcium signaling pathway and divalent inorganic cation homeostasis. In SCZ, pathways related to mitochondrial function and muscle development were highlighted. Finally, our GRPa-PRS method identified more consistent differential pathways compared to another variant-based pathway PRS method.

Conclusions

We developed a framework, GRPa-PRS, to systematically explore the differential GReX and GRPas among individuals stratified by their estimated PRS. The GReX-level comparison among those strata unveiled new insights into the pathways associated with disease risk and resilience. Our framework is extendable to other polygenic complex diseases.

Popularity at first sight: Dominant behaviours mediate the link between extraversion and popularity in face-to-face and virtual group interactions

Although there is robust evidence that being more extraverted is related to higher popularity, only few studies have examined which actual behaviours (e.g., verbal content, body language) might explain this association. The current study examined whether observer-rated dominant behaviours (nonverbal, paraverbal, verbal, and general cues) mediate the relationship between self-rated extraversion and its facets (assertiveness, sociability, and activity) and other-rated popularity in zero-acquaintance settings. In two studies, we analysed data from face-to-face (Study 1, N = 124) and virtual (Study 2, N = 291) group interactions where participants were videotaped while performing a task and subsequently rated each other on popularity. Across studies, extraversion and the facets assertiveness and sociability were consistently associated with higher popularity, while the role of dominant behaviours differed. In Study 1, only two nonverbal behaviours, dominant gestures and upright posture, mediated the association between extraversion and popularity. In Study 2, all four types of behavioural cues mediated the association between extraversion (facets) and popularity. We discuss how these findings provide insights into the mechanisms of attaining popularity at zero acquaintance in diverse social settings.

Archetypal clustering reveals physiological mechanisms linking milk yield and fertility in dairy cattle

Fertility in dairy cattle has declined as an unintended consequence of single-trait selection for high milk yield. The unfavorable genetic correlation between milk yield and fertility is now well documented; however, the underlying physiological mechanisms are still uncertain. To understand the relationship between these traits, we developed a method that clusters variants with similar patterns of effects and, after the integration of gene expression data, identifies the genes through which they are likely to act. Biological processes that are enriched in the genes of each cluster were then identified. We identified several clusters with unique patterns of effects. One of the clusters included variants associated with increased milk yield and decreased fertility, where the "archetypal" variant (i.e., the one with the largest effect) was associated with the GC gene, whereas others were associated with TRIM32, LRRK2, and U6-associated snRNA. These genes have been linked to transcription and alternative splicing, suggesting that these processes are likely contributors to the unfavorable relationship between the 2 traits. Another cluster, with archetypal variant near DGAT1 and including variants associated with CDH2, BTRC, SFRP2, ZFHX3, and SLITRK5, appeared to affect milk yield but have little effect on fertility. These genes have been linked to insulin, adipose tissue, and energy metabolism. A third cluster with archetypal variant near ZNF613 and including variants associated with ROBO1, EFNA5, PALLD, GPC6, and PTPRT were associated with fertility but not milk yield. These genes have been linked to GnRH neuronal migration, embryonic development, or ovarian function. The use of archetypal clustering to group variants with similar patterns of effects may assist in identifying the biological processes underlying correlated traits. The method is hypothesis generating and requires experimental confirmation. However, we have uncovered several novel mechanisms potentially affecting milk production and fertility such as GnRH neuronal migration. We anticipate our method to be a starting point for experimental research into novel pathways, which have been previously unexplored within the context of dairy production.

Multi-tissue transcriptome-wide association studies identified 235 genes for intrinsic subtypes of breast cancer

BACKGROUND

Although genome-wide association studies (GWAS) of breast cancer (BC) identified common variants which differ between intrinsic subtypes, genes through which these variants act to impact BC risk have not been fully established. Transcriptome-wide association studies (TWAS) have identified genes associated with overall BC risk, but subtype-specific differences are largely unknown.

METHODS

We performed two multi-tissue TWAS for each BC intrinsic subtype, including an expression-based approach that collated TWAS signals from expression quantitative trait loci (eQTLs) across multiple tissues and a novel splicing-based approach that collated signals from splicing QTLs (sQTLs) across intron clusters and subsequently across tissues. We used summary statistics for five intrinsic subtypes including Luminal A-like, Luminal B-like, Luminal B/HER2-negative-like, HER2-enriched-like, and triple-negative BC, generated from 106 278 BC cases and 91 477 controls in the Breast Cancer Association Consortium.

RESULTS

Overall, we identified 235 genes in 88 loci that were associated with at least one of the five intrinsic subtypes. Most genes were subtype-specific, and many have not been reported in previous TWAS. We discovered common variants that modulate expression of CHEK2 confer increased risk to Luminal A-like BC, in contrast to the viewpoint that CHEK2 primarily harbors rare, penetrant mutations. Additionally, our splicing-based TWAS provided population-level support for MDM4 splice variants that increased the risk of triple-negative BC.

CONCLUSION

Our comprehensive, multi-tissue TWAS corroborated previous GWAS loci for overall BC risk and intrinsic subtypes, while underscoring how common variation that impacts expression and splicing of genes in multiple tissue types can be used to further elucidate the etiology of BC.

Temporal genomics help in deciphering neutral and adaptive patterns in the contemporary evolution of kelp populations

The impact of climate change on populations will be contingent upon their contemporary adaptive evolution. In this study, we investigated the contemporary evolution of 4 populations of the cold-water kelp Laminaria digitata by analyzing their spatial and temporal genomic variations using ddRAD-sequencing. These populations were sampled from the center to the southern margin of its north-eastern Atlantic distribution at 2 time points, spanning at least 2 generations. Through genome scans for local adaptation at a single time point, we identified candidate loci that showed clinal variation correlated with changes in sea surface temperature (SST) along latitudinal gradients. This finding suggests that SST may drive the adaptive response of these kelp populations, although factors such as species' demographic history should also be considered. Additionally, we performed a simulation approach to distinguish the effect of selection from genetic drift in allele frequency changes over time. This enabled the detection of loci in the southernmost population that exhibited temporal differentiation beyond what would be expected from genetic drift alone: these are candidate loci which could have evolved under selection over time. In contrast, we did not detect any outlier locus based on temporal differentiation in the population from the North Sea, which also displayed low and decreasing levels of genetic diversity. The diverse evolutionary scenarios observed among populations can be attributed to variations in the prevalence of selection relative to genetic drift across different environments. Therefore, our study highlights the potential of temporal genomics to offer valuable insights into the contemporary evolution of marine foundation species facing climate change.

Inflammatory markers in pregnancy - surprisingly stable. Mapping trajectories and drivers in four large cohorts

Adaptations of the immune system throughout gestation have been proposed as important mechanisms regulating successful pregnancy. Dysregulation of the maternal immune system has been associated with adverse maternal and fetal outcomes. To translate findings from mechanistic preclinical studies to human pregnancies, studies of serum immune markers are the mainstay. The design and interpretation of human biomarker studies require additional insights in the trajectories and drivers of peripheral immune markers. The current study mapped maternal inflammatory markers (C-reactive protein (CRP), interleukin (IL)-1β, IL-6, IL-17A, IL-23, interferon- γ ) during pregnancy and investigated the impact of demographic, environmental and genetic drivers on maternal inflammatory marker levels in four multi-ethnic and socio-economically diverse population-based cohorts with more than 12,000 pregnant participants. Additionally, pregnancy inflammatory markers were compared to pre-pregnancy levels. Cytokines showed a high correlation with each other, but not with CRP. Inflammatory marker levels showed high variability between individuals, yet high concordance within an individual over time during and pre-pregnancy. Pre-pregnancy body mass index (BMI) explained more than 9.6% of the variance in CRP, but less than 1% of the variance in cytokines. The polygenic score of CRP was the best predictor of variance in CRP (>14.1%). Gestational age and previously identified inflammation drivers, including tobacco use and parity, explained less than 1% of variance in both cytokines and CRP. Our findings corroborate differential underlying regulatory mechanisms of CRP and cytokines and are suggestive of an individual inflammatory marker baseline which is, in part, genetically driven. While prior research has mainly focused on immune marker changes throughout pregnancy, our study suggests that this field could benefit from a focus on intra-individual factors, including metabolic and genetic components.

Phenotypic and ancestry-related assortative mating in autism

BACKGROUND

Positive assortative mating (AM) in several neuropsychiatric traits, including autism, has been noted. However, it is unknown whether the pattern of AM is different in phenotypically defined autism subgroups [e.g., autism with and without intellectually disability (ID)]. It is also unclear what proportion of the phenotypic AM can be explained by the genetic similarity between parents of children with an autism diagnosis, and the consequences of AM on the genetic structure of the population.

METHODS

To address these questions, we analyzed two family-based autism collections: the Simons Foundation Powering Autism Research for Knowledge (SPARK) (1575 families) and the Simons Simplex Collection (SSC) (2283 families).

RESULTS

We found a similar degree of phenotypic and ancestry-related AM in parents of children with an autism diagnosis regardless of the presence of ID. We did not find evidence of AM for autism based on autism polygenic scores (PGS) (at a threshold of |r|> 0.1). The adjustment of ancestry-related AM or autism PGS accounted for only 0.3-4% of the fractional change in the estimate of the phenotypic AM. The ancestry-related AM introduced higher long-range linkage disequilibrium (LD) between single nucleotide polymorphisms (SNPs) on different chromosomes that are highly ancestry-informative compared to SNPs that are less ancestry-informative (D2 on the order of 1 × 10-5).

LIMITATIONS

We only analyzed participants of European ancestry, limiting the generalizability of our results to individuals of non-European ancestry. SPARK and SSC were both multicenter studies. Therefore, there could be ancestry-related AM in SPARK and SSC due to geographic stratification. The study participants from each site were unknown, so we were unable to evaluate for geographic stratification.

CONCLUSIONS

This study showed similar patterns of AM in autism with and without ID, and demonstrated that the common genetic influences of autism are likely relevant to both autism groups. The adjustment of ancestry-related AM and autism PGS accounted for < 5% of the fractional change in the estimate of the phenotypic AM. Future studies are needed to evaluate if the small increase of long-range LD induced by ancestry-related AM has impact on the downstream analysis.

Polygenic Scores and Networks of Psychopathology Symptoms

Importance

Studies on polygenic risk for psychiatric traits commonly use a disorder-level approach to phenotyping, implicitly considering disorders as homogeneous constructs; however, symptom heterogeneity is ubiquitous, with many possible combinations of symptoms falling under the same disorder umbrella. Focusing on individual symptoms may shed light on the role of polygenic risk in psychopathology.

Objective

To determine whether polygenic scores are associated with all symptoms of psychiatric disorders or with a subset of indicators and whether polygenic scores are associated with comorbid phenotypes via specific sets of relevant symptoms.

Design, Setting, and Participants

Data from 2 population-based cohort studies were used in this cross-sectional study. Data from children in the Avon Longitudinal Study of Parents and Children (ALSPAC) were included in the primary analysis, and data from children in the Twins Early Development Study (TEDS) were included in confirmatory analyses. Data analysis was conducted from October 2021 to January 2024. Pregnant women based in the Southwest of England due to deliver in 1991 to 1992 were recruited in ALSPAC. Twins born in 1994 to 1996 were recruited in TEDS from population-based records. Participants with available genetic data and whose mothers completed the Short Mood and Feelings Questionnaire and the Strength and Difficulties Questionnaire when children were 11 years of age were included.

Main Outcomes and Measures

Psychopathology relevant symptoms, such as hyperactivity, prosociality, depression, anxiety, and peer and conduct problems at age 11 years. Psychological networks were constructed including individual symptoms and polygenic scores for depression, anxiety, attention-deficit/hyperactivity disorder (ADHD), body mass index (BMI), and educational attainment in ALSPAC. Following a preregistered confirmatory analysis, network models were cross-validated in TEDS.

Results

Included were 5521 participants from ALSPAC (mean [SD] age, 11.8 [0.14] years; 2777 [50.3%] female) and 4625 participants from TEDS (mean [SD] age, 11.27 [0.69] years; 2460 [53.2%] female). Polygenic scores were preferentially associated with restricted subsets of core symptoms and indirectly associated with other, more distal symptoms of psychopathology (network edges ranged between r = -0.074 and r = 0.073). Psychiatric polygenic scores were associated with specific cross-disorder symptoms, and nonpsychiatric polygenic scores were associated with a variety of indicators across disorders, suggesting a potential contribution of nonpsychiatric traits to comorbidity. For example, the polygenic score for ADHD was associated with a core ADHD symptom, being easily distracted (r = 0.07), and the polygenic score for BMI was associated with symptoms across disorders, including being bullied (r = 0.053) and not thinking things out (r = 0.041).

Conclusions and Relevance

Genetic associations observed at the disorder level may hide symptom-level heterogeneity. A symptom-level approach may enable a better understanding of the role of polygenic risk in shaping psychopathology and comorbidity.

Consequences of domestication in eastern oyster: Insights from whole genomic analyses

Selective breeding for production traits has yielded relatively rapid successes with high-fecundity aquaculture species. Discovering the genetic changes associated with selection is an important goal for understanding adaptation and can also facilitate better predictions about the likely fitness of selected strains if they escape aquaculture farms. Here, we hypothesize domestication as a genetic change induced by inadvertent selection in culture. Our premise is that standardized culture protocols generate parallel domestication effects across independent strains. Using eastern oyster as a model and a newly developed 600K SNP array, this study tested for parallel domestication effects in multiple independent selection lines compared with their progenitor wild populations. A single contrast was made between pooled selected strains (1-17 generations in culture) and all wild progenitor samples combined. Population structure analysis indicated rank order levels of differentiation as [wild - wild] < [wild - cultured] < [cultured - cultured]. A genome scan for parallel adaptation to the captive environment applied two methodologically distinct outlier tests to the wild versus selected strain contrast and identified a total of 1174 candidate SNPs. Contrasting wild versus selected strains revealed the early evolutionary consequences of domestication in terms of genomic differentiation, standing genetic diversity, effective population size, relatedness, runs of homozygosity profiles, and genome-wide linkage disequilibrium patterns. Random Forest was used to identify 37 outlier SNPs that had the greatest discriminatory power between bulked wild and selected oysters. The outlier SNPs were in genes enriched for cytoskeletal functions, hinting at possible traits under inadvertent selection during larval culture or pediveliger setting at high density. This study documents rapid genomic changes stemming from hatchery-based cultivation of eastern oysters, identifies candidate loci responding to domestication in parallel among independent aquaculture strains, and provides potentially useful genomic resources for monitoring interbreeding between farm and wild oysters.

Analysis of gene expression in the postmortem brain of neurotypical Black Americans reveals contributions of genetic ancestry

Ancestral differences in genomic variation affect the regulation of gene expression; however, most gene expression studies have been limited to European ancestry samples or adjusted to identify ancestry-independent associations. Here, we instead examined the impact of genetic ancestry on gene expression and DNA methylation in the postmortem brain tissue of admixed Black American neurotypical individuals to identify ancestry-dependent and ancestry-independent contributions. Ancestry-associated differentially expressed genes (DEGs), transcripts and gene networks, while notably not implicating neurons, are enriched for genes related to the immune response and vascular tissue and explain up to 26% of heritability for ischemic stroke, 27% of heritability for Parkinson disease and 30% of heritability for Alzheimer's disease. Ancestry-associated DEGs also show general enrichment for the heritability of diverse immune-related traits but depletion for psychiatric-related traits. We also compared Black and non-Hispanic white Americans, confirming most ancestry-associated DEGs. Our results delineate the extent to which genetic ancestry affects differences in gene expression in the human brain and the implications for brain illness risk.

A polygenic score method boosted by non-additive models

Dominance heritability in complex traits has received increasing recognition. However, most polygenic score (PGS) approaches do not incorporate non-additive effects. Here, we present GenoBoost, a flexible PGS modeling framework capable of considering both additive and non-additive effects, specifically focusing on genetic dominance. Building on statistical boosting theory, we derive provably optimal GenoBoost scores and provide its efficient implementation for analyzing large-scale cohorts. We benchmark it against seven commonly used PGS methods and demonstrate its competitive predictive performance. GenoBoost is ranked the best for four traits and second-best for three traits among twelve tested disease outcomes in UK Biobank. We reveal that GenoBoost improves prediction for autoimmune diseases by incorporating non-additive effects localized in the MHC locus and, more broadly, works best in less polygenic traits. We further demonstrate that GenoBoost can infer the mode of genetic inheritance without requiring prior knowledge. For example, GenoBoost finds non-zero genetic dominance effects for 602 of 900 selected genetic variants, resulting in 2.5% improvements in predicting psoriasis cases. Lastly, we show that GenoBoost can prioritize genetic loci with genetic dominance not previously reported in the GWAS catalog. Our results highlight the increased accuracy and biological insights from incorporating non-additive effects in PGS models.

Cross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain

Neuropsychiatric genome-wide association studies (GWASs), including those for autism spectrum disorder and schizophrenia, show strong enrichment for regulatory elements in the developing brain. However, prioritizing risk genes and mechanisms is challenging without a unified regulatory atlas. Across 672 diverse developing human brains, we identified 15,752 genes harboring gene, isoform, and/or splicing quantitative trait loci, mapping 3739 to cellular contexts. Gene expression heritability drops during development, likely reflecting both increasing cellular heterogeneity and the intrinsic properties of neuronal maturation. Isoform-level regulation, particularly in the second trimester, mediated the largest proportion of GWAS heritability. Through colocalization, we prioritized mechanisms for about 60% of GWAS loci across five disorders, exceeding adult brain findings. Finally, we contextualized results within gene and isoform coexpression networks, revealing the comprehensive landscape of transcriptome regulation in development and disease.

Genetically correlated leaf tensile and morphological traits are driven by growing season length in a widespread perennial grass

PREMISE

Leaf tensile resistance, a leaf's ability to withstand pulling forces, is an important determinant of plant ecological strategies. One potential driver of leaf tensile resistance is growing season length. When growing seasons are long, strong leaves, which often require more time and resources to construct than weak leaves, may be more advantageous than when growing seasons are short. Growing season length and other ecological conditions may also impact the morphological traits that underlie leaf tensile resistance.

METHODS

To understand variation in leaf tensile resistance, we measured size-dependent leaf strength and size-independent leaf toughness in diverse genotypes of the widespread perennial grass Panicum virgatum (switchgrass) in a common garden. We then used quantitative genetic approaches to estimate the heritability of leaf tensile resistance and whether there were genetic correlations between leaf tensile resistance and other morphological traits.

RESULTS

Leaf tensile resistance was positively associated with aboveground biomass (a proxy for fitness). Moreover, both measures of leaf tensile resistance exhibited high heritability and were positively genetically correlated with leaf lamina thickness and leaf mass per area (LMA). Leaf tensile resistance also increased with the growing season length in the habitat of origin, and this effect was mediated by both LMA and leaf thickness.

CONCLUSIONS

Differences in growing season length may promote selection for different leaf lifespans and may explain existing variation in leaf tensile resistance in P. virgatum. In addition, the high heritability of leaf tensile resistance suggests that P. virgatum will be able to respond to climate change as growing seasons lengthen.

An approach to identify gene-environment interactions and reveal new biological insight in complex traits

There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the Gene-Lifestyle Interactions Working Group within the Cohorts for Heart and Aging Research in Genetic Epidemiology Consortium has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identify and confirm 5 loci (6 independent signals) interacted with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrate that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated heritability is significant (P < 0.02) for low-density lipoprotein cholesterol and triglycerides in Cross-Population data. Our study improves the understanding of the genetic architecture and environmental contributions to complex traits.

Multi-trait GWAS for diverse ancestries: mapping the knowledge gap

BACKGROUND

Approximately 95% of samples analyzed in univariate genome-wide association studies (GWAS) are of European ancestry. This bias toward European ancestry populations in association screening also exists for other analyses and methods that are often developed and tested on European ancestry only. However, existing data in non-European populations, which are often of modest sample size, could benefit from innovative approaches as recently illustrated in the context of polygenic risk scores.

METHODS

Here, we extend and assess the potential limitations and gains of our multi-trait GWAS pipeline, JASS (Joint Analysis of Summary Statistics), for the analysis of non-European ancestries. To this end, we conducted the joint GWAS of 19 hematological traits and glycemic traits across five ancestries (European (EUR), admixed American (AMR), African (AFR), East Asian (EAS), and South-East Asian (SAS)).

RESULTS

We detected 367 new genome-wide significant associations in non-European populations (15 in Admixed American (AMR), 72 in African (AFR) and 280 in East Asian (EAS)). New associations detected represent 5%, 17% and 13% of associations in the AFR, AMR and EAS populations, respectively. Overall, multi-trait testing increases the replication of European associated loci in non-European ancestry by 15%. Pleiotropic effects were highly similar at significant loci across ancestries (e.g. the mean correlation between multi-trait genetic effects of EUR and EAS ancestries was 0.88). For hematological traits, strong discrepancies in multi-trait genetic effects are tied to known evolutionary divergences: the ARKC1 loci, which is adaptive to overcome p.vivax induced malaria.

CONCLUSIONS

Multi-trait GWAS can be a valuable tool to narrow the genetic knowledge gap between European and non-European populations.

Tissue informative cell-free DNA methylation sites in amyotrophic lateral sclerosis

Cell-free DNA (cfDNA) is increasingly recognized as a promising biomarker candidate for disease monitoring. However, its utility in neurodegenerative diseases, like amyotrophic lateral sclerosis (ALS), remains underexplored. Existing biomarker discovery approaches are tailored to a specific disease context or are too expensive to be clinically practical. Here, we address these challenges through a new approach combining advances in molecular and computational technologies. First, we develop statistical tools to select tissue-informative DNA methylation sites relevant to a disease process of interest. We then employ a capture protocol to select these sites and perform targeted methylation sequencing. Multi-modal information about the DNA methylation patterns are then utilized in machine learning algorithms trained to predict disease status and disease progression. We applied our method to two independent cohorts of ALS patients and controls (n=192). Overall, we found that the targeted sites accurately predicted ALS status and replicated between cohorts. Additionally, we identified epigenetic features associated with ALS phenotypes, including disease severity. These findings highlight the potential of cfDNA as a non-invasive biomarker for ALS.

Adjusting for principal components can induce spurious associations in genome-wide association studies in admixed populations

Principal component analysis (PCA) is widely used to control for population structure in genome-wide association studies (GWAS). Top principal components (PCs) typically reflect population structure, but challenges arise in deciding how many PCs are needed and ensuring that PCs do not capture other artifacts such as regions with atypical linkage disequilibrium (LD). In response to the latter, many groups suggest performing LD pruning or excluding known high LD regions prior to PCA. However, these suggestions are not universally implemented and the implications for GWAS are not fully understood, especially in the context of admixed populations. In this paper, we investigate the impact of pre-processing and the number of PCs included in GWAS models in African American samples from the Women's Women's Health Initiative SNP Health Association Resource and two Trans-Omics for Precision Medicine Whole Genome Sequencing Project contributing studies (Jackson Heart Study and Genetic Epidemiology of Chronic Obstructive Pulmonary Disease Study). In all three samples, we find the first PC is highly correlated with genome-wide ancestry whereas later PCs often capture local genomic features. The pattern of which, and how many, genetic variants are highly correlated with individual PCs differs from what has been observed in prior studies focused on European populations and leads to distinct downstream consequences: adjusting for such PCs yields biased effect size estimates and elevated rates of spurious associations due to the phenomenon of collider bias. Excluding high LD regions identified in previous studies does not resolve these issues. LD pruning proves more effective, but the optimal choice of thresholds varies across datasets. Altogether, our work highlights unique issues that arise when using PCA to control for ancestral heterogeneity in admixed populations and demonstrates the importance of careful pre-processing and diagnostics to ensure that PCs capturing multiple local genomic features are not included in GWAS models.

Investigating the relationship between DNA methylation, genetic variation, and suicide attempt in bipolar disorder

Individuals with bipolar disorder are at increased risk for suicide, and this can be influenced by a range of biological, clinical, and environmental risk factors. Biological components associated with suicide include DNA modifications that lead to changes in gene expression. Common genetic variation and DNA methylation changes are some of the most frequent types of DNA findings associated with an increased risk for suicidal behavior. Importantly, the interplay between genetic predisposition and DNA methylation patterns is becoming more prevalent in genetic studies. We hypothesized that DNA methylation patterns in specific loci already genetically associated with suicide would be altered in individuals with bipolar disorder and a history of suicide attempt. To test this hypothesis, we searched the literature to identify common genetic variants (N=34) previously associated with suicidal thoughts and behaviors in individuals with bipolar disorder. We then created a customized sequencing panel that covered our chosen genomic loci. We profiled DNA methylation patterns from blood samples collected from bipolar disorder participants with suicidal behavior (N=55) and without suicidal behavior (N=51). We identified seven differentially methylated CpG sites and five differentially methylated regions between the two groups. Additionally, we found that DNA methylation changes in MIF and CACNA1C were associated with lethality or number of suicide attempts. Finally, we identified three meQTLs in SIRT1 , IMPA2 , and INPP1 . This study illustrates that DNA methylation is altered in individuals with bipolar disorder and a history of suicide attempts in regions known to harbor suicide-related variants.

Genome-wide association and functional genomic analyses for various hoof health traits in North American Holstein cattle

Hoof diseases are a major welfare and economic issue in the global dairy cattle production industry, which can be minimized through improved management and breeding practices. Optimal genetic improvement of hoof health could benefit from a deep understanding of the genetic background and biological underpinning of indicators of hoof health. Therefore, the primary objectives of this study were to perform genome-wide association studies, using imputed high-density genetic markers data from North American Holstein cattle, for 8 hoof-related traits: digital dermatitis, sole ulcer, sole hemorrhage, white line lesion, heel horn erosion, interdigital dermatitis, interdigital hyperplasia, and toe ulcer, and a hoof health index. De-regressed estimated breeding values from 25,580 Holstein animals were used as pseudo-phenotypes for the association analyses. The genomic quality control, genotype phasing, and genotype imputation were performed using the PLINK (version 1.9), Eagle (version 2.4.1), and Minimac4 software, respectively. The functional genomic analyses were performed using the GALLO R package and the DAVID platform. We identified 22, 34, 14, 22, 28, 33, 24, 43, and 15 significant markers for digital dermatitis, heel horn erosion, interdigital dermatitis, interdigital hyperplasia, sole hemorrhage, sole ulcer, toe ulcer, white line lesion disease, and the hoof health index, respectively. The significant markers were located across all autosomes, except BTA10, BTA12, BTA20, BTA26, BTA27, and BTA28. Moreover, the genomic regions identified overlap with various previously reported quantitative trait loci for exterior, health, meat and carcass, milk, production, and reproduction traits. The enrichment analyses identified 44 significant gene ontology terms. These enriched genomic regions harbor various candidate genes previously associated with bone development, metabolism, and infectious and immunological diseases. These findings indicate that hoof health traits are highly polygenic and influenced by a wide range of biological processes.

Bayesian approach to assessing population differences in genetic risk of disease with application to prostate cancer

Population differences in risk of disease are common, but the potential genetic basis for these differences is not well understood. A standard approach is to compare genetic risk across populations by testing for mean differences in polygenic scores, but existing studies that use this approach do not account for statistical noise in effect estimates (i.e., the GWAS betas) that arise due to the finite sample size of GWAS training data. Here, we show using Bayesian polygenic score methods that the level of uncertainty in estimates of genetic risk differences across populations is highly dependent on the GWAS training sample size, the polygenicity (number of causal variants), and genetic distance (FST) between the populations considered. We derive a Wald test for formally assessing the difference in genetic risk across populations, which we show to have calibrated type 1 error rates under a simplified assumption that all SNPs are independent, which we achieve in practise using linkage disequilibrium (LD) pruning. We further provide closed-form expressions for assessing the uncertainty in estimates of relative genetic risk across populations under the special case of an infinitesimal genetic architecture. We suggest that for many complex traits and diseases, particularly those with more polygenic architectures, current GWAS sample sizes are insufficient to detect moderate differences in genetic risk across populations, though more substantial differences in relative genetic risk (relative risk > 1.5) can be detected. We show that conventional approaches that do not account for sampling error from the training sample, such as using a simple t-test, have very high type 1 error rates. When applying our approach to prostate cancer, we demonstrate a higher genetic risk in African Ancestry men, with lower risk in men of European followed by East Asian ancestry.

Lingo: an automated, web-based deep phenotyping platform for language ability

Background

Language and the ability to communicate effectively are key factors in mental health and well-being. Despite this critical importance, research on language is limited by the lack of a scalable phenotyping toolkit.

Methods

Here, we describe and showcase Lingo - a flexible online battery of language and nonverbal reasoning skills based on seven widely used tasks (COWAT, picture narration, vocal rhythm entrainment, rapid automatized naming, following directions, sentence repetition, and nonverbal reasoning). The current version of Lingo takes approximately 30 minutes to complete, is entirely open source, and allows for a wide variety of performance metrics to be extracted. We asked > 1,300 individuals from multiple samples to complete Lingo, then investigated the validity and utility of the resulting data.

Results

We conducted an exploratory factor analysis across 14 features derived from the seven assessments, identifying five factors. Four of the five factors showed acceptable test-retest reliability (Pearson's R > 0.7). Factor 2 showed the highest reliability (Pearson's R = 0.95) and loaded primarily on sentence repetition task performance. We validated Lingo with objective measures of language ability by comparing performance to gold-standard assessments: CELF-5 and the VABS-3. Factor 2 was significantly associated with the CELF-5 "core language ability" scale (Pearson's R = 0.77, p-value < 0.05) and the VABS-3 "communication" scale (Pearson's R = 0.74, p-value < 0.05). Factor 2 was positively associated with phenotypic and genetic measures of socieconomic status. Interestingly, we found the parents of children with language impairments had lower Factor 2 scores (p-value < 0.01). Finally, we found Lingo factor scores were significantly predictive of numerous psychiatric and neurodevelopmental conditions.

Conclusions

Together, these analyses support Lingo as a powerful platform for scalable deep phenotyping of language and other cognitive abilities. Additionally, exploratory analyses provide supporting evidence for the heritability of language ability and the complex relationship between mental health and language.

Polygenic Scores Clarify the Relationship Between Mental Health and Gender Diversity

Background

Gender-diverse individuals are at increased risk for mental health problems, but it is unclear whether this is due to shared environmental or genetic factors.

Methods

In two SPARK samples, we tested for associations of 16 polygenic scores (PGSs) with quantitative measures of gender diversity and mental health. In study 1, 639 independent adults (59% autistic) reported their mental health with the Adult Self-Report and their gender diversity with the Gender Self-Report (GSR). The GSR has 2 dimensions: binary (degree of identification with the gender opposite that implied by sex designated at birth) and nonbinary (degree of identification with a gender that is neither male nor female). In study 2 (N = 5165), we used a categorical measure of gender identity.

Results

In study 1, neuropsychiatric PGSs were positively associated with Adult Self-Report scores: externalizing was positively associated with the attention-deficit/hyperactivity disorder PGS (β = 0.10 [0.03-0.17]), and internalizing was positively associated with the PGSs for depression (β = 0.07 [0-0.14]) and neuroticism (β = 0.10 [0.03-0.17]). Interestingly, GSR scores were not significantly associated with any neuropsychiatric PGS. However, GSR nonbinary was positively associated with the cognitive performance PGS (β = 0.11 [0.05-0.18]), with the effect size comparable in magnitude to the associations of the neuropsychiatric PGSs with the Adult Self-Report. Additionally, GSR binary was positively associated with the nonheterosexual sexual behavior PGS (β = 0.07 [0-0.14]). In study 2, the cognitive performance PGS effect replicated; transgender and nonbinary individuals had higher PGSs (t316 = 4.16).

Conclusions

We showed that while gender diversity is phenotypically positively associated with mental health problems, the strongest PGS associations with gender diversity were with the cognitive performance PGS, not the neuropsychiatric PGSs.

Efficacy of federated learning on genomic data: a study on the UK Biobank and the 1000 Genomes Project

Combining training data from multiple sources increases sample size and reduces confounding, leading to more accurate and less biased machine learning models. In healthcare, however, direct pooling of data is often not allowed by data custodians who are accountable for minimizing the exposure of sensitive information. Federated learning offers a promising solution to this problem by training a model in a decentralized manner thus reducing the risks of data leakage. Although there is increasing utilization of federated learning on clinical data, its efficacy on individual-level genomic data has not been studied. This study lays the groundwork for the adoption of federated learning for genomic data by investigating its applicability in two scenarios: phenotype prediction on the UK Biobank data and ancestry prediction on the 1000 Genomes Project data. We show that federated models trained on data split into independent nodes achieve performance close to centralized models, even in the presence of significant inter-node heterogeneity. Additionally, we investigate how federated model accuracy is affected by communication frequency and suggest approaches to reduce computational complexity or communication costs.

Whole-exome sequencing in UK Biobank reveals rare genetic architecture for depression

Nearly two hundred common-variant depression risk loci have been identified by genome-wide association studies (GWAS). However, the impact of rare coding variants on depression remains poorly understood. Here, we present whole-exome sequencing analyses of depression with seven different definitions based on survey, questionnaire, and electronic health records in 320,356 UK Biobank participants. We showed that the burden of rare damaging coding variants in loss-of-function intolerant genes is significantly associated with risk of depression with various definitions. We compared the rare and common genetic architecture across depression definitions by genetic correlation and showed different genetic relationships between definitions across common and rare variants. In addition, we demonstrated that the effects of rare damaging coding variant burden and polygenic risk score on depression risk are additive. The gene set burden analyses revealed overlapping rare genetic variant components with developmental disorder, autism, and schizophrenia. Our study provides insights into the contribution of rare coding variants, separately and in conjunction with common variants, on depression with various definitions and their genetic relationships with neurodevelopmental disorders.

Matrisome and Immune Pathways Contribute to Extreme Vascular Outcomes in Williams-Beuren Syndrome

BACKGROUND

Supravalvar aortic stenosis (SVAS) is a characteristic feature of Williams-Beuren syndrome (WBS). Its severity varies: ~20% of people with Williams-Beuren syndrome have SVAS requiring surgical intervention, whereas ~35% have no appreciable SVAS. The remaining individuals have SVAS of intermediate severity. Little is known about genetic modifiers that contribute to this variability.

METHODS AND RESULTS

We performed genome sequencing on 473 individuals with Williams-Beuren syndrome and developed strategies for modifier discovery in this rare disease population. Approaches include extreme phenotyping and nonsynonymous variant prioritization, followed by gene set enrichment and pathway-level association tests. We next used GTEx v8 and proteomic data sets to verify expression of candidate modifiers in relevant tissues. Finally, we evaluated overlap between the genes/pathways identified here and those ascertained through larger aortic disease/trait genome-wide association studies. We show that SVAS severity in Williams-Beuren syndrome is associated with increased frequency of common and rarer variants in matrisome and immune pathways. Two implicated matrisome genes (ACAN and LTBP4) were uniquely expressed in the aorta. Many genes in the identified pathways were previously reported in genome-wide association studies for aneurysm, bicuspid aortic valve, or aortic size.

CONCLUSIONS

Smaller sample sizes in rare disease studies necessitate new approaches to detect modifiers. Our strategies identified variation in matrisome and immune pathways that are associated with SVAS severity. These findings suggest that, like other aortopathies, SVAS may be influenced by the balance of synthesis and degradation of matrisome proteins. Leveraging multiomic data and results from larger aorta-focused genome-wide association studies may accelerate modifier discovery for rare aortopathies like SVAS.

A Bayesian fine-mapping model using a continuous global-local shrinkage prior with applications in prostate cancer analysis

The aim of fine mapping is to identify genetic variants causally contributing to complex traits or diseases. Existing fine-mapping methods employ Bayesian discrete mixture priors and depend on a pre-specified maximum number of causal variants, which may lead to sub-optimal solutions. In this work, we propose a Bayesian fine-mapping method called h2-D2, utilizing a continuous global-local shrinkage prior. We also present an approach to define credible sets of causal variants in continuous prior settings. Simulation studies demonstrate that h2-D2 outperforms current state-of-the-art fine-mapping methods such as SuSiE and FINEMAP in accurately identifying causal variants and estimating their effect sizes. We further applied h2-D2 to prostate cancer analysis and discovered some previously unknown causal variants. In addition, we inferred 369 target genes associated with the detected causal variants and several pathways that were significantly over-represented by these genes, shedding light on their potential roles in prostate cancer development and progression.

Highly parameterized polygenic scores tend to overfit to population stratification via random effects

Polygenic scores (PGSs), increasingly used in clinical settings, frequently include many genetic variants, with performance typically peaking at thousands of variants. Such highly parameterized PGSs often include variants that do not pass a genome-wide significance threshold. We propose a mathematical perspective that renders the effects of many of these non-significant variants random rather than causal, with the randomness capturing population structure. We devise methods to assess variant effect randomness and population stratification bias. Applying these methods to 141 traits from the UK Biobank, we find that, for many PGSs, the effects of non-significant variants are considerably random, with the extent of randomness associated with the degree of overfitting to population structure of the discovery cohort. Our findings explain why highly parameterized PGSs simultaneously have superior cohort-specific performance and limited generalizability, suggesting the critical need for variant randomness tests in PGS evaluation. Supporting code and a dashboard are available at https://github.com/songlab-cal/StratPGS.

A second unveiling: Haplotig masking of the eastern oyster genome improves population-level inference

Genome assembly can be challenging for species that are characterized by high amounts of polymorphism, heterozygosity, and large effective population sizes. High levels of heterozygosity can result in genome mis-assemblies and a larger than expected genome size due to the haplotig versions of a single locus being assembled as separate loci. Here, we describe the first chromosome-level genome for the eastern oyster, Crassostrea virginica. Publicly released and annotated in 2017, the assembly has a scaffold N50 of 54 mb and is over 97.3% complete based on BUSCO analysis. The genome assembly for the eastern oyster is a critical resource for foundational research into molluscan adaptation to a changing environment and for selective breeding for the aquaculture industry. Subsequent resequencing data suggested the presence of haplotigs in the original assembly, and we developed a post hoc method to break up chimeric contigs and mask haplotigs in published heterozygous genomes and evaluated improvements to the accuracy of downstream analysis. Masking haplotigs had a large impact on SNP discovery and estimates of nucleotide diversity and had more subtle and nuanced effects on estimates of heterozygosity, population structure analysis, and outlier detection. We show that haplotig masking can be a powerful tool for improving genomic inference, and we present an open, reproducible resource for the masking of haplotigs in any published genome.

Longitudinal machine learning uncouples healthy aging factors from chronic disease risks

To understand human longevity, inherent aging processes must be distinguished from known etiologies leading to age-related chronic diseases. Such deconvolution is difficult to achieve because it requires tracking patients throughout their entire lives. Here, we used machine learning to infer health trajectories over the entire adulthood age range using extrapolation from electronic medical records with partial longitudinal coverage. Using this approach, our model tracked the state of patients who were healthy and free from known chronic disease risk and distinguished individuals with higher or lower longevity potential using a multivariate score. We showed that the model and the markers it uses performed consistently on data from Israeli, British and US populations. For example, mildly low neutrophil counts and alkaline phosphatase levels serve as early indicators of healthy aging that are independent of risk for major chronic diseases. We characterize the heritability and genetic associations of our longevity score and demonstrate at least 1 year of extended lifespan for parents of high-scoring patients compared to matched controls. Longitudinal modeling of healthy individuals is thereby established as a tool for understanding healthy aging and longevity.

Multi-omic prediction of incident type 2 diabetes

AIMS/HYPOTHESIS

The identification of people who are at high risk of developing type 2 diabetes is a key part of population-level prevention strategies. Previous studies have evaluated the predictive utility of omics measurements, such as metabolites, proteins or polygenic scores, but have considered these separately. The improvement that combined omics biomarkers can provide over and above current clinical standard models is unclear. The aim of this study was to test the predictive performance of genome, proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes.

METHODS

We developed sparse interpretable prediction models in a prospective, nested type 2 diabetes case-cohort study (N=1105, incident type 2 diabetes cases=375) with 10,792 person-years of follow-up, selecting from 5759 features across the genome, proteome, metabolome and clinical biomarkers using least absolute shrinkage and selection operator (LASSO) regression. We compared the predictive performance of omics-derived predictors with a clinical model including the variables from the Cambridge Diabetes Risk Score and HbA1c.

RESULTS

Among single omics prediction models that did not include clinical risk factors, the top ten proteins alone achieved the highest performance (concordance index [C index]=0.82 [95% CI 0.75, 0.88]), suggesting the proteome as the most informative single omic layer in the absence of clinical information. However, the largest improvement in prediction of type 2 diabetes incidence over and above the clinical model was achieved by the top ten features across several omic layers (C index=0.87 [95% CI 0.82, 0.92], Δ C index=0.05, p=0.045). This improvement by the top ten omic features was also evident in individuals with HbA1c <42 mmol/mol (6.0%), the threshold for prediabetes (C index=0.84 [95% CI 0.77, 0.90], Δ C index=0.07, p=0.03), the group in whom prediction would be most useful since they are not targeted for preventative interventions by current clinical guidelines. In this subgroup, the type 2 diabetes polygenic risk score was the major contributor to the improvement in prediction, and achieved a comparable improvement in performance when added onto the clinical model alone (C index=0.83 [95% CI 0.75, 0.90], Δ C index=0.06, p=0.002). However, compared with those with prediabetes, individuals at high polygenic risk in this group had only around half the absolute risk for type 2 diabetes over a 20 year period.

CONCLUSIONS/INTERPRETATION

Omic approaches provided marginal improvements in prediction of incident type 2 diabetes. However, while a polygenic risk score does improve prediction in people with an HbA1c in the normoglycaemic range, the group in whom prediction would be most useful, even individuals with a high polygenic burden in that subgroup had a low absolute type 2 diabetes risk. This suggests a limited feasibility of implementing targeted population-based genetic screening for preventative interventions.

Interpretable trajectory inference with single-cell Linear Adaptive Negative-binomial Expression (scLANE) testing

The rapid proliferation of trajectory inference methods for single-cell RNA-seq data has allowed researchers to investigate complex biological processes by examining underlying gene expression dynamics. After estimating a latent cell ordering, statistical models are used to determine which genes exhibit changes in expression that are significantly associated with progression through the biological trajectory. While a few techniques for performing trajectory differential expression exist, most rely on the flexibility of generalized additive models in order to account for the inherent nonlinearity of changes in gene expression. As such, the results can be difficult to interpret, and biological conclusions often rest on subjective visual inspections of the most dynamic genes. To address this challenge, we propose scLANE testing, which is built around an interpretable generalized linear model and handles nonlinearity with basis splines chosen empirically for each gene. In addition, extensions to estimating equations and mixed models allow for reliable trajectory testing under complex experimental designs. After validating the accuracy of scLANE under several different simulation scenarios, we apply it to a set of diverse biological datasets and display its ability to provide novel biological information when used downstream of both pseudotime and RNA velocity estimation methods.

Inferring disease architecture and predictive ability with LDpred2-auto

LDpred2 is a widely used Bayesian method for building polygenic scores (PGSs). LDpred2-auto can infer the two parameters from the LDpred model, the SNP heritability h2 and polygenicity p, so that it does not require an additional validation dataset to choose best-performing parameters. The main aim of this paper is to properly validate the use of LDpred2-auto for inferring multiple genetic parameters. Here, we present a new version of LDpred2-auto that adds an optional third parameter α to its model, for modeling negative selection. We then validate the inference of these three parameters (or two, when using the previous model). We also show that LDpred2-auto provides per-variant probabilities of being causal that are well calibrated and can therefore be used for fine-mapping purposes. We also introduce a formula to infer the out-of-sample predictive performance r2 of the resulting PGS directly from the Gibbs sampler of LDpred2-auto. Finally, we extend the set of HapMap3 variants recommended to use with LDpred2 with 37% more variants to improve the coverage of this set, and we show that this new set of variants captures 12% more heritability and provides 6% more predictive performance, on average, in UK Biobank analyses.

Isoform-level transcriptome-wide association uncovers genetic risk mechanisms for neuropsychiatric disorders in the human brain

Methods integrating genetics with transcriptomic reference panels prioritize risk genes and mechanisms at only a fraction of trait-associated genetic loci, due in part to an overreliance on total gene expression as a molecular outcome measure. This challenge is particularly relevant for the brain, in which extensive splicing generates multiple distinct transcript-isoforms per gene. Due to complex correlation structures, isoform-level modeling from cis-window variants requires methodological innovation. Here we introduce isoTWAS, a multivariate, stepwise framework integrating genetics, isoform-level expression and phenotypic associations. Compared to gene-level methods, isoTWAS improves both isoform and gene expression prediction, yielding more testable genes, and increased power for discovery of trait associations within genome-wide association study loci across 15 neuropsychiatric traits. We illustrate multiple isoTWAS associations undetectable at the gene-level, prioritizing isoforms of AKT3, CUL3 and HSPD1 in schizophrenia and PCLO with multiple disorders. Results highlight the importance of incorporating isoform-level resolution within integrative approaches to increase discovery of trait associations, especially for brain-relevant traits.

Cerebellar morphological differences and associations with extrinsic factors in bipolar disorder type I

BACKGROUND

The neural underpinnings of bipolar disorder (BD) remain poorly understood. The cerebellum is ideally positioned to modulate emotional regulation circuitry yet has been understudied in BD. Literature suggests differences in cerebellar activity and metabolism in BD, however findings on structural differences remain contradictory. Potential reasons include combining BD subtypes, small sample sizes, and potential moderators such as genetics, adverse childhood experiences (ACEs), and pharmacotherapy.

METHODS

We collected 3 T MRI scans from participants with (N = 131) and without (N = 81) BD type I, as well as blood and questionnaires. We assessed differences in cerebellar volumes and explored potentially influential factors.

RESULTS

The cerebellar cortex was smaller bilaterally in participants with BD. Polygenic propensity score did not predict any cerebellar volumes, suggesting that non-genetic factors may have greater influence on the cerebellar volume difference we observed in BD. Proportionate cerebellar white matter volumes appeared larger with more ACEs, but this may result from reduced ICV. Time from onset and symptom burden were not associated with cerebellar volumes. Finally, taking sedatives was associated with larger cerebellar white matter and non-significantly larger cortical volume.

LIMITATIONS

This study was cross-sectional, limiting interpretation of possible mechanisms. Most of our participants were White, which could limit the generalizability. Additionally, we did not account for potential polypharmacy interactions.

CONCLUSIONS

These findings suggest that external factors, such as sedatives and childhood experiences, may influence cerebellum structure in BD and may mask underlying differences. Accounting for such variables may be critical for consistent findings in future studies.

Rare variants with large effects provide functional insights into the pathology of migraine subtypes, with and without aura

Migraine is a complex neurovascular disease with a range of severity and symptoms, yet mostly studied as one phenotype in genome-wide association studies (GWAS). Here we combine large GWAS datasets from six European populations to study the main migraine subtypes, migraine with aura (MA) and migraine without aura (MO). We identified four new MA-associated variants (in PRRT2, PALMD, ABO and LRRK2) and classified 13 MO-associated variants. Rare variants with large effects highlight three genes. A rare frameshift variant in brain-expressed PRRT2 confers large risk of MA and epilepsy, but not MO. A burden test of rare loss-of-function variants in SCN11A, encoding a neuron-expressed sodium channel with a key role in pain sensation, shows strong protection against migraine. Finally, a rare variant with cis-regulatory effects on KCNK5 confers large protection against migraine and brain aneurysms. Our findings offer new insights with therapeutic potential into the complex biology of migraine and its subtypes.

Transfer learning with false negative control improves polygenic risk prediction

Polygenic risk score (PRS) is a quantity that aggregates the effects of variants across the genome and estimates an individual's genetic predisposition for a given trait. PRS analysis typically contains two input data sets: base data for effect size estimation and target data for individual-level prediction. Given the availability of large-scale base data, it becomes more common that the ancestral background of base and target data do not perfectly match. In this paper, we treat the GWAS summary information obtained in the base data as knowledge learned from a pre-trained model, and adopt a transfer learning framework to effectively leverage the knowledge learned from the base data that may or may not have similar ancestral background as the target samples to build prediction models for target individuals. Our proposed transfer learning framework consists of two main steps: (1) conducting false negative control (FNC) marginal screening to extract useful knowledge from the base data; and (2) performing joint model training to integrate the knowledge extracted from base data with the target training data for accurate trans-data prediction. This new approach can significantly enhance the computational and statistical efficiency of joint-model training, alleviate over-fitting, and facilitate more accurate trans-data prediction when heterogeneity level between target and base data sets is small or high.

A new Approach to Identify Gene-Environment Interactions and Reveal New Biological Insight in Complex traits

There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the CHARGE Gene-Lifestyle Interactions Working Group has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identified and confirmed 5 loci (6 independent signals) interacting with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrated that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated contribution ranges from 1.76% to 14.05% of SNP heritability of serum lipids in Cross-Population data. Our study improves the understanding of the genetic architecture and environmental contributions to complex traits.

Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations

Polygenic risk scores (PRS) have great potential to guide precision colorectal cancer (CRC) prevention by identifying those at higher risk to undertake targeted screening. However, current PRS using European ancestry data have sub-optimal performance in non-European ancestry populations, limiting their utility among these populations. Towards addressing this deficiency, we expand PRS development for CRC by incorporating Asian ancestry data (21,731 cases; 47,444 controls) into European ancestry training datasets (78,473 cases; 107,143 controls). The AUC estimates (95% CI) of PRS are 0.63(0.62-0.64), 0.59(0.57-0.61), 0.62(0.60-0.63), and 0.65(0.63-0.66) in independent datasets including 1681-3651 cases and 8696-115,105 controls of Asian, Black/African American, Latinx/Hispanic, and non-Hispanic White, respectively. They are significantly better than the European-centric PRS in all four major US racial and ethnic groups (p-values < 0.05). Further inclusion of non-European ancestry populations, especially Black/African American and Latinx/Hispanic, is needed to improve the risk prediction and enhance equity in applying PRS in clinical practice.

Common- and rare-variant genetic architecture of heart failure across the allele frequency spectrum

Heart failure (HF) is a complex trait, influenced by environmental and genetic factors, that affects over 30 million individuals worldwide. Historically, the genetics of HF have been studied in Mendelian forms of disease, where rare genetic variants have been linked to familial cardiomyopathies. More recently, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with risk of HF. However, the relative importance of genetic variants across the allele-frequency spectrum remains incompletely characterized. Here, we report the results of common- and rare-variant association studies of all-cause heart failure, applying recently developed methods to quantify the heritability of HF attributable to different classes of genetic variation. We combine GWAS data across multiple populations including 207,346 individuals with HF and 2,151,210 without, identifying 176 risk loci at genome-wide significance (p < 5×10-8). Signals at newly identified common-variant loci include coding variants in Mendelian cardiomyopathy genes (MYBPC3, BAG3), as well as regulators of lipoprotein (LPL) and glucose metabolism (GIPR, GLP1R), and are enriched in cardiac, muscle, nerve, and vascular tissues, as well as myocyte and adipocyte cell types. Gene burden studies across three biobanks (PMBB, UKB, AOU) including 27,208 individuals with HF and 349,126 without uncover exome-wide significant (p < 3.15×10-6) associations for HF and rare predicted loss-of-function (pLoF) variants in TTN, MYBPC3, FLNC, and BAG3. Total burden heritability of rare coding variants (2.2%, 95% CI 0.99-3.5%) is highly concentrated in a small set of Mendelian cardiomyopathy genes, and is lower than heritability attributable to common variants (4.3%, 95% CI 3.9-4.7%) which is more diffusely spread throughout the genome. Finally, we demonstrate that common-variant background, in the form of a polygenic risk score (PRS), significantly modifies the risk of HF among carriers of pathogenic truncating variants in the Mendelian cardiomyopathy gene TTN. These findings suggest a significant polygenic component to HF exists that is not captured by current clinical genetic testing.

Ensembled best subset selection using summary statistics for polygenic risk prediction

Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, yet existing methods face a tradeoff between predictive power and computational efficiency. We introduce ALL-Sum, a fast and scalable PRS method that combines an efficient summary statistic-based L 0 L 2 penalized regression algorithm with an ensembling step that aggregates estimates from different tuning parameters for improved prediction performance. In extensive large-scale simulations across a wide range of polygenicity and genome-wide association studies (GWAS) sample sizes, ALL-Sum consistently outperforms popular alternative methods in terms of prediction accuracy, runtime, and memory usage. We analyze 27 published GWAS summary statistics for 11 complex traits from 9 reputable data sources, including the Global Lipids Genetics Consortium, Breast Cancer Association Consortium, and FinnGen, evaluated using individual-level UKBB data. ALL-Sum achieves the highest accuracy for most traits, particularly for GWAS with large sample sizes. We provide ALL-Sum as a user-friendly command-line software with pre-computed reference data for streamlined user-end analysis.

ADuLT: An efficient and robust time-to-event GWAS

Proportional hazards models have been proposed to analyse time-to-event phenotypes in genome-wide association studies (GWAS). However, little is known about the ability of proportional hazards models to identify genetic associations under different generative models and when ascertainment is present. Here we propose the age-dependent liability threshold (ADuLT) model as an alternative to a Cox regression based GWAS, here represented by SPACox. We compare ADuLT, SPACox, and standard case-control GWAS in simulations under two generative models and with varying degrees of ascertainment as well as in the iPSYCH cohort. We find Cox regression GWAS to be underpowered when cases are strongly ascertained (cases are oversampled by a factor 5), regardless of the generative model used. ADuLT is robust to ascertainment in all simulated scenarios. Then, we analyse four psychiatric disorders in iPSYCH, ADHD, Autism, Depression, and Schizophrenia, with a strong case-ascertainment. Across these psychiatric disorders, ADuLT identifies 20 independent genome-wide significant associations, case-control GWAS finds 17, and SPACox finds 8, which is consistent with simulation results. As more genetic data are being linked to electronic health records, robust GWAS methods that can make use of age-of-onset information will help increase power in analyses for common health outcomes.

Fast and accurate out-of-core PCA framework for large scale biobank data

Principal component analysis (PCA) is widely used in statistics, machine learning, and genomics for dimensionality reduction and uncovering low-dimensional latent structure. To address the challenges posed by ever-growing data size, fast and memory-efficient PCA methods have gained prominence. In this paper, we propose a novel randomized singular value decomposition (RSVD) algorithm implemented in PCAone, featuring a window-based optimization scheme that enables accelerated convergence while improving the accuracy. Additionally, PCAone incorporates out-of-core and multithreaded implementations for the existing Implicitly Restarted Arnoldi Method (IRAM) and RSVD. Through comprehensive evaluations using multiple large-scale real-world data sets in different fields, we show the advantage of PCAone over existing methods. The new algorithm achieves significantly faster computation time while maintaining accuracy comparable to the slower IRAM method. Notably, our analyses of UK Biobank, comprising around 0.5 million individuals and 6.1 million common single nucleotide polymorphisms, show that PCAone accurately computes the top 40 principal components within 9 h. This analysis effectively captures population structure, signals of selection, structural variants, and low recombination regions, utilizing <20 GB of memory and 20 CPU threads. Furthermore, when applied to single-cell RNA sequencing data featuring 1.3 million cells, PCAone, accurately capturing the top 40 principal components in 49 min. This performance represents a 10-fold improvement over state-of-the-art tools.

Neural network on interval-censored data with application to the prediction of Alzheimer's disease

Alzheimer's disease (AD) is a progressive and polygenic disorder that affects millions of individuals each year. Given that there have been few effective treatments yet for AD, it is highly desirable to develop an accurate model to predict the full disease progression profile based on an individual's genetic characteristics for early prevention and clinical management. This work uses data composed of all four phases of the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, including 1740 individuals with 8 million genetic variants. We tackle several challenges in this data, characterized by large-scale genetic data, interval-censored outcome due to intermittent assessments, and left truncation in one study phase (ADNIGO). Specifically, we first develop a semiparametric transformation model on interval-censored and left-truncated data and estimate parameters through a sieve approach. Then we propose a computationally efficient generalized score test to identify variants associated with AD progression. Next, we implement a novel neural network on interval-censored data (NN-IC) to construct a prediction model using top variants identified from the genome-wide test. Comprehensive simulation studies show that the NN-IC outperforms several existing methods in terms of prediction accuracy. Finally, we apply the NN-IC to the full ADNI data and successfully identify subgroups with differential progression risk profiles. Data used in the preparation of this article were obtained from the ADNI database.

A polygenic risk score for Alzheimer's disease constructed using APOE-region variants has stronger association than APOE alleles with mild cognitive impairment in Hispanic/Latino adults in the U.S

INTRODUCTION

Polygenic Risk Scores (PRSs) are summaries of genetic risk alleles for an outcome.

METHODS

We used summary statistics from five GWASs of AD to construct PRSs in 4,189 diverse Hispanics/Latinos (mean age 63 years) from the Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA). We assessed the PRS associations with MCI in the combined set of people and in diverse subgroups, and when including and excluding the APOE gene region. We also assessed PRS associations with MCI in an independent dataset from the Mass General Brigham Biobank.

RESULTS

A simple sum of 5 PRSs ("PRSsum"), each constructed based on a different AD GWAS, was associated with MCI (OR = 1.28, 95% CI [1.14, 1.41]) in a model adjusted for counts of the APOE-[Formula: see text] and APOE-[Formula: see text] alleles. Associations of single-GWAS PRSs were weaker. When removing SNPs from the APOE region from the PRSs, the association of PRSsum with MCI was weaker (OR = 1.17, 95% CI [1.04,1.31] with adjustment for APOE alleles). In all association analyses, APOE-[Formula: see text] and APOE-[Formula: see text] alleles were not associated with MCI.

DISCUSSION

A sum of AD PRSs is associated with MCI in Hispanic/Latino older adults. Despite no association of APOE-[Formula: see text] and APOE-[Formula: see text] alleles with MCI, the association of the AD PRS with MCI is stronger when including the APOE region. Thus, APOE variants different than the classic APOE alleles may be important predictors of MCI in Hispanic/Latino adults.

Multi-PGS enhances polygenic prediction by combining 937 polygenic scores

The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks.

Melanoma risk prediction based on a polygenic risk score and clinical risk factors

Melanoma is one of the most commonly diagnosed cancers in the Western world: third in Australia, fifth in the USA and sixth in the European Union. Predicting an individual's personal risk of developing melanoma may aid them in undertaking effective risk reduction measures. The objective of this study was to use the UK Biobank to predict the 10-year risk of melanoma using a newly developed polygenic risk score (PRS) and an existing clinical risk model. We developed the PRS using a matched case-control training dataset ( N  = 16 434) in which age and sex were controlled by design. The combined risk score was developed using a cohort development dataset ( N  = 54 799) and its performance was tested using a cohort testing dataset ( N  = 54 798). Our PRS comprises 68 single-nucleotide polymorphisms and had an area under the receiver operating characteristic curve of 0.639 [95% confidence interval (CI) = 0.618-0.661]. In the cohort testing data, the hazard ratio per SD of the combined risk score was 1.332 (95% CI = 1.263-1.406). Harrell's C-index was 0.685 (95% CI = 0.654-0.715). Overall, the standardized incidence ratio was 1.193 (95% CI = 1.067-1.335). By combining a PRS and a clinical risk score, we have developed a risk prediction model that performs well in terms of discrimination and calibration. At an individual level, information on the 10-year risk of melanoma can motivate people to take risk-reduction action. At the population level, risk stratification can allow more effective population-level screening strategies to be implemented.