Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood

Genomic insights: Disentangling milk yield and reproduction performance in first-lactation German Holsteins

This study offers insight into the genetic complexity of the interaction between production and reproduction in German dairy cows. The phenotypes and genotypes of 32,352 primiparous German Holstein cows were available for investigation, and datasets (DS) of similar size were generated according to their milk yield. Five distinct DS, each including more than 6,400 animals, from lowest to highest milk yield (DSLowest, DSLow, DSMean, DSHigh, DSHighest) were included for subsequent analysis. Heritabilities and genetic correlations (rG) between traits were estimated, followed by GWAS. The overall heritability estimates were relatively low, with a maximum of h2 = 0.127 for ovary cycle disturbances in DSHighest and a minimum of h2 = 0.026 for retained placenta (NGV) in the complete DS. The rG between milk yield and reproduction traits exhibited a range from rG = -0.436 between milk yield and metritis (MET) in DSHigh to rG = 0.435 between milk yield and NGV in DSHigh. Genetic correlations between the various reproduction traits were moderate, as evidenced by the correlation between calving ease maternal (CEm) and MET in DSHigh (rG = 0.329). In contrast, a striking divergence within the specific traits was evident contingent on the performance subset. The range of rG between CEm and NGV was covered from rG = -0.146 in DSLowest up to rG = 0.318 in DSHighest. The heritabilities and rG estimates did not demonstrate a straightforward linear relationship between milk yield and the analyzed reproduction parameters. Moreover, GWAS identified several significant SNPs across the various DS, with a total of 86 genome-wide and chromosome-wide signals. These findings led to the identification of previously described genes in the context of reproduction, as well as the postulation of additional potential candidate genes, including a high number of zinc finger proteins. Overall, this study provides important insights into the genetic background and interrelations of reproduction traits with special regards to the nonlinear relationship between milk yield and reproduction, and the findings may help to further improve selection decisions.

Unraveling the associations between voice pitch and major depressive disorder: a multisite genetic study

Major depressive disorder (MDD) often goes undiagnosed due to the absence of clear biomarkers. We sought to identify voice biomarkers for MDD and separate biomarkers indicative of MDD predisposition from biomarkers reflecting current depressive symptoms. Using a two-stage meta-analytic design to remove confounds, we tested the association between features representing vocal pitch and MDD in a multisite case-control cohort study of Chinese women with recurrent depression. Sixteen features were replicated in an independent cohort, with absolute association coefficients (beta values) from the combined analysis ranging from 0.24 to 1.07, indicating moderate to large effects. The statistical significance of these associations remained robust, with P values ranging from 7.2 × 10-6 to 6.8 × 10-58. Eleven features were significantly associated with current depressive symptoms. Using genotype data, we found that this association was driven in part by a genetic correlation with MDD. Significant voice features, reflecting a slower pitch change and a lower pitch, achieved an AUC-ROC of 0.90 (sensitivity of 0.85 and specificity of 0.81) in MDD classification. Our results return vocal features to a more central position in clinical and research work on MDD.

Genome-Wide Association Study of Age-Related Hearing Loss in CFW Mice Identifies Multiple Genes and Loci, Including Prkag2

PURPOSE

Age-related hearing loss (ARHL) is one of the most prevalent conditions affecting the elderly. ARHL is influenced by a combination of environmental and genetic factors; the identification of the genes that confer risk will aid in the prevention and treatment of ARHL. The mouse and human inner ears are functionally and genetically homologous. We used Carworth Farms White (CFW) mice to study the genetic basis of ARHL because they are genetically diverse and exhibit variability in the age of onset and severity of ARHL.

METHODS

Hearing at a range of frequencies was measured using auditory brainstem response (ABR) thresholds in 946 male and female CFW mice at the age of 1, 6, and 10 months. We genotyped the mice using low-coverage (mean coverage 0.27 ×) whole-genome sequencing (lcWGS) followed by imputation using STITCH. To determine the accuracy of the genotypes, we sequenced 8 samples at > 30 × coverage and used those data to estimate the accuracy of lcWGS genotyping, which was > 99.5%. We performed a genome-wide association study (GWAS) for the ABR thresholds for each frequency at each age, and we also performed a GWAS for age at deafness.

RESULTS

We obtained genotypes at 4.18 million single nucleotide polymorphisms (SNP). The SNP heritability for traits ranged from 0 to 42%. GWAS identified 10 significant associations with ARHL that contained potential candidate genes, including Dnah11, Rapgef5, Cpne4, Prkag2, and Nek11. Genetic ablation of Prkag2 caused ARHL at high frequencies, strongly suggesting that Prkag2 is the causal gene for one of the associations.

CONCLUSIONS

GWAS for ARHL in CFW outbred mice identified genetic risk factors for ARHL, including Prkag2. Our results will help to define novel therapeutic targets for the treatment and prevention of this common disorder.

Differentiation of executive functions during adolescence: Converging evidence from behavioral, genetic and neural data

Executive functions (EF) have been found to differentiate from a single component to three distinct components (i.e., updating, shifting, and inhibition) during development. However, there is still much debate regarding when such differentiation takes place and biological evidence is needed. Here we used the longitudinal and multimodality data from the ABCD study to address this question at two age groups (9-10 and 13-14). Three tasks (i.e., List, Card and Flanker tasks) were used to represent the three EF components respectively at baseline, and two tasks (Flanker and List) at 4th year follow up. Genes associated with each task were identified by whole genome and transcriptome association analyses and were then used for genetic similarity calculation; structural and functional brain indices related to each task were identified and used to assess neural similarity. We found that at baseline (9-10 years old), the three EF components were behaviorally highly inter-correlated and were associated with many of the same genes and the same brain regions. Four years later, the follow-up data (with Flanker and List tasks only) still showed significant but smaller behavioral/genetic/neural similarity. This study is the first to chart the path of EF differentiation during adolescence by combining behavioral, genetic, and neural data, and this approach may be relevant to the study of development of other cognitive abilities.

Unraveling the genetics of gulf war illness in diverse participants enrolled in the million veteran program

Gulf War Illness (GWI) is a multi-symptom chronic condition that affects Veterans who served in the 1990-1991 Gulf War (GW). To generate novel information about GWI pathogenesis, we used genome-wide data available from 33 523 Veterans of diverse ancestral backgrounds who served during the 1990-1991 Gulf War era (34% deployed). Polygenic score (PGS) analysis showed GWI pleiotropy for several traits with the strongest evidence for type-2 diabetes (T2D), anxiety, and depression. While T2D PGS was associated with higher GWI odds in GW Veterans, anxiety and depression PGSs were associated with higher odds of GWI in non-deployed GW-era Veterans. Seven independent variants were identified (P < 5 × 10-8). Two of them were supported by independent transcriptomic and phenome-wide analyses. Rs4675853 was associated with AGXT, MAB21L4, and ATG4Btranscriptomic regulation and with sex hormone-binding globulin levels. Rs138168412 was associated with AOPEPtranscriptomic regulation and with respiratory function and physical strength. The TWAS identified five additional loci such as CEMIPin the cerebellum and SNCGin the adrenal gland. The results provide a comprehensive assessment of the polygenic architecture of GWI research definitions, identifying mechanisms potentially relevant to the disease pathogenesis.

A latent outcome variable approach for Mendelian randomization using the stochastic expectation maximization algorithm

Mendelian randomization (MR) is a widely used tool to uncover causal relationships between exposures and outcomes. However, existing MR methods can suffer from inflated type I error rates and biased causal effects in the presence of invalid instruments. Our proposed method enhances MR analysis by augmenting latent phenotypes of the outcome, explicitly disentangling horizontal and vertical pleiotropy effects. This allows for explicit assessment of the exclusion restriction assumption and iteratively refines causal estimates through the expectation-maximization algorithm. This approach offers a unique and potentially more precise framework compared to existing MR methods. We rigorously evaluate our method against established MR approaches across diverse simulation scenarios, including balanced and directional pleiotropy, as well as violations of the Instrument Strength Independent of Direct Effect (InSIDE) assumption. Our findings consistently demonstrate superior performance of our method in terms of controlling type I error rates, bias, and robustness to genetic confounding, regardless of whether individual-level or summary data is used. Additionally, our method facilitates testing for directional horizontal pleiotropy and outperforms MR-Egger in this regard, while also effectively testing for violations of the InSIDE assumption. We apply our method to real data, demonstrating its effectiveness compared to traditional MR methods. This analysis reveals the causal effects of body mass index (BMI) on metabolic syndrome (MetS) and a composite MetS score calculated by the weighted sum of its component factors. While the causal relationship is consistent across most methods, our proposed method shows fewer violations of the exclusion restriction assumption, especially for MetS scores where horizontal pleiotropy persists and other methods suffer from inflation.

Transcriptome-wide analysis of differential expression in perturbation atlases

Single-cell CRISPR screens such as Perturb-seq enable transcriptomic profiling of genetic perturbations at scale. However, the data produced by these screens are noisy, and many effects may go undetected. Here we introduce transcriptome-wide analysis of differential expression (TRADE)-a statistical model for the distribution of true differential expression effects that accounts for estimation error appropriately. TRADE estimates the 'transcriptome-wide impact', which quantifies the total effect of a perturbation across the transcriptome. Analyzing several large Perturb-seq datasets, we show that many transcriptional effects remain undetected in standard analyses but emerge in aggregate using TRADE. A typical gene perturbation affects an estimated 45 genes, whereas a typical essential gene affects over 500. We find moderate consistency of perturbation effects across cell types, identify perturbations where transcriptional responses vary qualitatively across dosage levels and clarify the relationship between genetic and transcriptomic correlations across neuropsychiatric disorders.

Genome-Wide Association Studies of Body Weight and Average Daily Gain in Chinese Dongliao Black Pigs

In the domain of swine production, body weight (BW) and average daily gain (ADG) are recognized as the primary performance indicators. Nevertheless, the genetic architecture of ADG and BW in Dongliao black (DLB) pigs remains to be fully elucidated. In this study, we performed a genome-wide association analysis of BW, ADG, and body mass index (BMI) in 358 DLB pigs of different days of age. The genome-wide association study (GWAS) showed the following: (1) The most significant single nucleotide polymorphism (SNP) detected for BW was on Sus scrofa chromosome (SSC) 11:100,808 (p-value = 1.16 × 10-6) that was also the most significant SNP for ADG. (2) The most significant SNP associated with BMI was SSC17:51,463,521 (p-value = 5.16 × 10-8). (3) SNPs SSC10:6,523,844 and SSC17:23,852,682 were identified in both BW and ADG. A meta-analysis was conducted on BW at different days and demonstrated SSC5:39,028,335 (p-value = 8.37 × 10-6) which was not identified in the results of each single trait. The regions of two SNPs (SSC11:100,808, SSC4:10,703,277) exhibited considerable influence on both BW and ADG and the related regions were selected for linkage disequilibrium (LD) analyses that exhibited a notable linkage. In addition, several genes were identified that are associated with obesity and play roles in lipid metabolism, including MACROD2, PHLPP2, CYP2E1, and STT3B.

Quantifying pleiotropy through directed signaling networks: A synchronous Boolean network approach and in-silico pleiotropic scoring

Pleiotropy refers to a gene's ability to influence multiple phenotypes or traits. In the context of human genetic diseases, pleiotropy manifests as different pathological effects resulting from mutations in the same gene. This phenomenon plays a crucial role in understanding gene-gene interactions in system-level biological diseases. Previous studies have largely focused on pleiotropy within undirected molecular correlation networks, leaving a gap in examining pleiotropy induced by directed signaling networks, which can better explain dynamic gene-gene interactions. In this study, we utilized a synchronous Boolean network model to explore pleiotropic dynamics induced by various mutations in large-scale networks. We introduced an in-silico Pleiotropic Score (sPS) to quantify the impact of gene mutations and validated the model against observational pleiotropy data from the Human Phenotype Ontology (HPO). Our results indicate a significant correlation between sPS and network structural characteristics, including degree centrality and feedback loop involvement. The highest correlation was observed between closeness centrality and sPS (0.6), suggesting that genes more central in the network exhibit higher pleiotropic potential. Furthermore, genes involved in feedback loops demonstrated higher sPS values (p < 0.0001), supporting the role of feedback loops in amplifying pleiotropic behavior. Our model provides a novel approach for quantifying pleiotropy through directed network dynamics, complementing traditional observational methods.

Pierre CL, Missfeldt Sanches T, Bimschleger HV, Gao J, Cheng R, Nguyen K, Holl KL, Polesskaya O, Ishiwari K, Chen H, Robinson TE, Flagel SB, Solberg Woods LC, Palmer AA, Meyer PJ. Genetic Loci Influencing Cue-Reactivity in Heterogeneous Stock Rats

Addiction vulnerability is associated with the tendency to attribute incentive salience to reward predictive cues. Both addiction and the attribution of incentive salience are influenced by environmental and genetic factors. To characterize the genetic contributions to incentive salience attribution, we performed a genome-wide association study (GWAS) in a cohort of 1596 heterogeneous stock (HS) rats. Rats underwent a Pavlovian conditioned approach task that characterized the responses to food-associated stimuli ("cues"). Responses ranged from cue-directed "sign-tracking" behavior to food-cup directed "goal-tracking" behavior (12 measures, SNP heritability: 0.051-0.215). Next, rats performed novel operant responses for unrewarded presentations of the cue using the conditioned reinforcement procedure. GWAS identified 14 quantitative trait loci (QTLs) for 11 of the 12 traits across both tasks. Interval sizes of these QTLs varied widely. Seven traits shared a QTL on chromosome 1 that contained a few genes (e.g., Tenm4, Mir708) that have been associated with substance use disorders and other psychiatric disorders in humans. Other candidate genes (e.g., Wnt11, Pak1) in this region had coding variants and expression-QTLs in mesocorticolimbic regions of the brain. We also conducted a Phenome-Wide Association Study (PheWAS) on addiction-related behaviors in HS rats and found that the QTL on chromosome 1 was also associated with nicotine self-administration in a separate cohort of HS rats. These results provide a starting point for the molecular genetic dissection of incentive motivational processes and provide further support for a relationship between the attribution of incentive salience and drug abuse-related traits.

Mendelian randomisation to uncover causal associations between conformation, metabolism, and production as potential exposure to reproduction in German Holstein dairy cattle

BACKGROUND

Reproduction is vital to welfare, health, and economics in animal husbandry and breeding. Health and reproduction are increasingly being considered because of the observed genetic correlations between reproduction, health, conformation, and performance traits in dairy cattle. Understanding the detailed genetic architecture underlying these traits would represent a major step in comprehending their interplay. Identifying known, putative or novel associations in genomics could improve animal health, welfare, and performance while allowing further adjustments in animal breeding.

RESULTS

We conducted genome-wide association studies for 25 different traits belonging to four different complexes, namely reproduction (n = 13), conformation (n = 6), production (n = 3), and metabolism (n = 3), using a cohort of over 235,000 dairy cows. As a result, we identified genome-wide significant signals for all the studied traits. The obtained summary statistics collected served as the input for a Mendelian randomisation approach (GSMR) to infer causal associations between putative exposure and reproduction traits. The study considered conformation, production, and metabolism as exposure and reproduction as outcome. A range of 139 to 252 genome-wide significant SNPs per combination were identified as instrumental variables (IVs). Out of 156 trait combinations, 135 demonstrated statistically significant effects, thereby enabling the identification of the responsible IVs. Combinations of traits related to metabolism (38 out of 39), conformation (68 out of 78), or production (29 out of 39) were found to have significant effects on reproduction. These relationships were partially non-linear. Moreover, a separate variance component estimation supported these findings, strongly correlating with the GSMR results and offering suggestions for improvement. Downstream analyses of selected representative traits per complex resulted in identifying and investigating potential physiological mechanisms. Notably, we identified both trait-specific SNPs and genes that appeared to influence specific traits per complex, as well as more general SNPs that were common between exposure and outcome traits.

CONCLUSIONS

Our study confirms the known genetic associations between reproduction traits and the three complexes tested. It provides new insights into causality, indicating a non-linear relationship between conformation and reproduction. In addition, the downstream analyses have identified several clustered genes that may mediate this association.

Validation of loci and genes associated with fertility in Holstein cows using gene-set enrichment analysis-SNP and genotype-by-sequencing

BACKGROUND

The financial strain fertility issues cause the dairy cattle industry is substantial, with over $7 billion in lost revenue accrued annually due to a relatively low cow conception rate (CCR; 30-43%) for US dairy cows. While CCR has been improving through genomic selection, identification of causal mutations would help improve the rate of genetic progress with genomic selection and provide a better understanding of infertility. The objectives of this study were to: (1) identify genes and gene-sets associated with CCR to the first breeding (CCR1) and the number of breedings required to conceive (TBRD) in Holstein cows and (2) identify putative functional variants associated with CCR1 and TBRD through a custom genotype-by-sequencing array. The study consisted of 1,032 cows (494 pregnant to first breeding, 472 pregnant to subsequent [2-20] services, and 66 that never conceived). Cows were artificially inseminated, and pregnancy was determined 35d later by rectal palpation of uterine contents. Gene-set enrichment analyses with SNP data (GSEA-SNP) were conducted for CCR1 and TBRD with a normalized enrichment score (NES) ≥ 3.0 required for significance. Leading edge genes (LEG) and positional candidate genes from this and 26 additional studies were used to validate 100 loci associated (P < 1 × 10- 5) with cow fertility using a custom sequencing genotyping array of putative functional variants (exons, promoters, splice sites, and conserved regions).

RESULTS

GSEA-SNP identified 95 gene-sets (1,473 LEG) enriched for CCR1 and 67 gene sets enriched (1,438 LEG) for TBRD (NES ≥ 3). Thirty-four gene-sets were shared between CCR1 and TBRD along with 788 LEG. The association analysis for TBRD identified three loci: BTA1 at 83 Mb, BTA1 at 145 Mb, and BTA 20 at 46 Mb (P < 1 × 10- 5). The loci associated with TBRD contained candidate genes with functions relating to implantation and uterine receptivity. No loci were associated with CCR1, however a single locus on BTA1 at 146 Mb trended toward significance with an FDR of 0.04.

CONCLUSIONS

The validation of three loci associated with CCR and TBRD in Holsteins can be used to improve fertility through genomic selection and provide insight into understanding infertility.

Genomic links between symptoms of eating disorders and suicidal ideation

Eating disorders, including anorexia nervosa, bulimia nervosa and binge eating disorder, are psychiatric conditions associated with high mortality rates, particularly due to suicide. Although eating disorders are strongly associated with suicidal ideation, attempts, and fatalities, the precise relationship between these conditions remains poorly understood. While substantial genetic influences have been identified for both eating disorders and suicidality, the shared genetics contributing to their co-occurrence remain unclear. In this study, we utilized a multivariate approach to examine the shared genetic architecture of eating disorder symptoms, suicidal thoughts and behaviors in ~20,000 participants from the COVID-19 Psychiatry and Neurological Genetics (COPING) study. We applied individual-level structural equation modeling to explore the factor structure underlying eating disorder symptoms and suicidal ideation, followed by genetic correlation analyses. We modeled the general factor of susceptibility to eating disorders and suicidal ideation that was as strongly genetically influenced as both conditions, with mean SNP heritability of 9%. Importantly, despite the frequent co-occurrence of eating disorders with other psychiatric conditions, our findings highlight the specificity of the relationship between eating disorders and suicidality, independent of other co-occurring psychopathology, such as depression and anxiety. This specificity highlights the need for targeted approaches in understanding the shared susceptibility factors.

Genetic and metabolic characterization of individual differences in liver fat accumulation in Atlantic salmon

Introduction

Lipid accumulation in the liver can negatively impact liver function and health, which is well-described for humans and other mammals, but relatively unexplored in Atlantic salmon. This study investigates the phenotypic, genetic, and transcriptomic variations related to individual differences in liver fat content within a group of slaughter-sized Atlantic salmon reared under the same conditions and fed the same feed. The objective was to increase the knowledge on liver fat deposition in farmed salmon and evaluate the potential for genetic improvement of this trait.

Methods

The study involved measuring liver fat content in a group of slaughter-sized Atlantic salmon. Genetic analysis included estimating heritability and conducting genome-wide association studies (GWAS) to identify quantitative trait loci (QTLs). Transcriptomic analysis was performed to link liver fat content to gene expression, focusing on genes involved in lipid metabolic processes.

Results

There was a large variation in liver fat content, ranging from 3.6% to 18.8%, with frequent occurrences of high liver fat. Livers with higher levels of fat had higher proportions of the fatty acids 16:1 n-7, 18:2 n-6, and 18:1 n-9, and less of the long-chain omega-3 fatty acids. The heritability of liver fat was estimated at 0.38, and the genetic coefficient of variation was 20%, indicating substantial potential for selective breeding to reduce liver fat deposition in Atlantic salmon. Liver fat deposition appears to be a polygenic trait, with no large QTLs detected by GWAS. Gene expression analysis linked liver fat content to numerous genes involved in lipid metabolic processes, including key transcription factors such as LXR, SREBP1, and ChREBP.

Discussion

The results indicated a connection between liver fat and increased cholesterol synthesis in Atlantic salmon, with potentially harmful free cholesterol accumulation. Further, the gene expression results linked liver fat accumulation to reduced peroxisomal β-oxidation, increased conversion of carbohydrates to lipids, altered phospholipid synthesis, and possibly increased de novo lipogenesis. It is undetermined whether these outcomes are due to high fat levels or if they are caused by underlying metabolic differences that result in higher liver fat levels in certain individuals. Nonetheless, the results provide new insights into the metabolic profile of livers in fish with inherent differences in liver fat content.

A novel smoking cessation behavior based on quit attempts may identify new genes associated with long-term abstinence

BACKGROUND

Smoking cessation at any age has been shown to improve quality of life, decrease illness, and reduce mortality. About half of smokers attempt to quit each year, but only ∼ 7 % maintain long-term abstinence unaided. Few genetic factors have been consistently associated with smoking cessation, possibly due to poor phenotype definition.

METHODS

We performed a genome-wide association study (GWAS) with an alternative phenotype based on the difficulty of quitting smoking (DQS) in the Cancer Prevention Study-3 cohort. Difficult quitters were defined as having made at least ten quit attempts, whether successful or not, and easy quitters as having quit after only one attempt. Only individuals of European ancestry were selected for the study. Among 10,004 smokers (5,071 difficult quitters, 4,933 easy quitters), we assessed the genetic heritability of DQS and evaluated associations between DQS and each genome-wide variant using logistic regression while adjusting for confounders, including smoking intensity (cigarettes per day).

RESULTS

The genetic heritability of the DQS phenotype was 13 %, comparable to, or higher than, the reported heritability of other smoking behaviors (e.g., smoking intensity, cessation). Although no variants were genome-wide significant, several genes were identified at a subthreshold level (p < 10-4). A variant in MEGF9 (rs149760032), a transmembrane protein largely expressed in the central nervous system, showed the strongest association with DQS (OR = 0.60, p = 1.3x10-7). Additional variants associated with DQS independently by smoking intensity were also detected in GLRA3 (rs73006492, OR = 0.77, p = 5.6x10-7) and FOCAD (rs112251973, OR = 1.96, p = 1.8x10-6) and are plausibly related to smoking cessation through pathways in the brain and respiratory system.

CONCLUSIONS

The use of an alternative cessation phenotype based on difficulty quitting smoking facilitated the identification of new pathways that could lead to unique smoking treatments.

Leveraging genome-wide association studies to better understand the etiology of cancers

Genome-wide association studies (GWAS) statistically assess the association between tens of millions of genetic variants in the whole genome and a phenotype of interest. Genome-wide association studies enable the elucidation of polygenic inheritance of cancer, in which myriad low-penetrance genetic variants collectively contribute to a substantial proportion of the heritable susceptibility. In addition to the robust genotype-phenotype associations provided by GWAS, combining GWAS data with functional genomic datasets or sophisticated statistical genetic methods unlocks deeper insights. Integrating genotype and molecular phenotyping data facilitates functional characterization of GWAS association signals through molecular quantitative trait loci mapping and transcriptome-wide association studies. Furthermore, aggregating genome-wide polygenic signals, including subthreshold associations, enables one to estimate genetic correlations across diverse phenotypes and helps in clinical risk predictions by evaluating polygenic risk scores. In this review, we begin by summarizing the rationale for GWAS of cancer, introduce recent methodological updates in the GWAS-derived downstream analyses, and demonstrate their applications to GWAS of cancers.

Genomic regions associated with Holstein heifer times bred to artificial insemination and embryo transfer services

This study aimed to identify loci (p < 1 × 10-5) and gene sets (normalized enrichment score (NES) ≥ 3.0) associated with the number of times a heifer is bred to attain a successful pregnancy (TBRD) for Holstein heifers bred by artificial insemination (AI, n = 2754) or that were embryo transfer (ET, n = 1566) recipients. Eight loci were associated (p < 1 × 10-5) with TBRD in AI bred heifers and four loci were associated with TBRD in ET recipients. The gene set enrichment analysis with SNP data identified one gene set enriched (NES ≥ 3.0) with TBRD in AI bred heifers and two gene sets that were enriched with TBRD in ET recipients. The estimated pseudo-heritability for times bred to AI was 0.063 and 0.043 for ET. The identification of loci associated with embryonic loss aids in the selection of Holstein heifers with higher reproductive efficiencies that are AI bred or that are ET recipients.

Allele frequency impacts the cross-ancestry portability of gene expression prediction in lymphoblastoid cell lines

Population-level genetic studies are overwhelmingly biased toward European ancestries. Transferring genetic predictions from European ancestries to other ancestries results in a substantial loss of accuracy. Yet, it remains unclear how much various genetic factors, such as causal effect differences, linkage disequilibrium (LD) differences, or allele frequency differences, contribute to the loss of prediction accuracy across ancestries. In this study, we used gene expression levels in lymphoblastoid cell lines to understand how much each genetic factor contributes to lowered portability of gene expression prediction from European to African ancestries. We found that cis-genetic effects on gene expression are highly similar between European and African individuals. However, we found that allele frequency differences of causal variants have a striking impact on prediction portability. For example, portability is reduced by more than 32% when the causal cis-variant is common (minor allele frequency, MAF >5%) in European samples (training population) but is rarer (MAF <5%) in African samples (prediction population). While large allele frequency differences can decrease portability through increasing LD differences, we also determined that causal allele frequency can significantly impact portability when the impact from LD is substantially controlled. This observation suggests that improving statistical fine-mapping alone does not overcome the loss of portability resulting from differences in causal allele frequency. We conclude that causal cis-eQTL effects are highly similar in European and African individuals, and allele frequency differences have a large impact on the accuracy of gene expression prediction.

Controlling for polygenic genetic confounding in epidemiologic association studies

Epidemiologic associations estimated from observational data are often confounded by genetics due to pervasive pleiotropy among complex traits. Many studies either neglect genetic confounding altogether or rely on adjusting for polygenic scores (PGS) in regression analysis. In this study, we unveil that the commonly employed PGS approach is inadequate for removing genetic confounding due to measurement error and model misspecification. To tackle this challenge, we introduce PENGUIN, a principled framework for polygenic genetic confounding control based on variance component estimation. In addition, we present extensions of this approach that can estimate genetically unconfounded associations using GWAS summary statistics alone as input and between multiple generations of study samples. Through simulations, we demonstrate superior statistical properties of PENGUIN compared to the existing approaches. Applying our method to multiple population cohorts, we reveal and remove substantial genetic confounding in the associations of educational attainment with various complex traits and between parental and offspring education. Our results show that PENGUIN is an effective solution for genetic confounding control in observational data analysis with broad applications in future epidemiologic association studies.

Multiomic integration analysis identifies atherogenic metabolites mediating between novel immune genes and cardiovascular risk

BACKGROUND

Understanding genetic-metabolite associations has translational implications for informing cardiovascular risk assessment. Interrogating functional genetic variants enhances our understanding of disease pathogenesis and the development and optimization of targeted interventions.

METHODS

In this study, a total of 187 plasma metabolite levels were profiled in 4974 individuals of European ancestry of the GCAT| Genomes for Life cohort. Results of genetic analyses were meta-analysed with additional datasets, resulting in up to approximately 40,000 European individuals. Results of meta-analyses were integrated with reference gene expression panels from 58 tissues and cell types to identify predicted gene expression associated with metabolite levels. This approach was also performed for cardiovascular outcomes in three independent large European studies (N = 700,000) to identify predicted gene expression additionally associated with cardiovascular risk. Finally, genetically informed mediation analysis was performed to infer causal mediation in the relationship between gene expression, metabolite levels and cardiovascular risk.

RESULTS

A total of 44 genetic loci were associated with 124 metabolites. Lead genetic variants included 11 non-synonymous variants. Predicted expression of 53 fine-mapped genes was associated with 108 metabolite levels; while predicted expression of 6 of these genes was also associated with cardiovascular outcomes, highlighting a new role for regulatory gene HCG27. Additionally, we found that atherogenic metabolite levels mediate the associations between gene expression and cardiovascular risk. Some of these genes showed stronger associations in immune tissues, providing further evidence of the role of immune cells in increasing cardiovascular risk.

CONCLUSIONS

These findings propose new gene targets that could be potential candidates for drug development aimed at lowering the risk of cardiovascular events through the modulation of blood atherogenic metabolite levels.

Unraveling the Associations Between Voice Pitch and Major Depressive Disorder: A Multisite Genetic Study

Major depressive disorder (MDD) often goes undiagnosed due to the absence of clear biomarkers. We sought to identify voice biomarkers for MDD and separate biomarkers indicative of MDD predisposition from biomarkers reflecting current depressive symptoms. Using a two-stage meta-analytic design to remove confounds, we tested the association between features representing vocal pitch and MDD in a multisite case-control cohort study of Chinese women with recurrent depression. Sixteen features were replicated in an independent cohort, with absolute association coefficients (beta values) from the combined analysis ranging from 0.24 to 1.07, indicating moderate to large effects. The statistical significance of these associations remained robust, with P-values ranging from 7.2 × 10-6 to 6.8 × 10-58. Eleven features were significantly associated with current depressive symptoms. Using genotype data, we found that this association was driven in part by a genetic correlation with MDD. Significant voice features, reflecting a slower pitch change and a lower pitch, achieved an AUC-ROC of 0.90 (sensitivity of 0.85 and specificity of 0.81) in MDD classification. Our results return vocal features to a more central position in clinical and research work on MDD.

Impact of negative emotions and insomnia on sepsis: A mediation Mendelian randomization study

BACKGROUND

Negative emotions and insomnia (NEI) can lead to inflammation, which is a characteristic of sepsis. However, the interaction among NEI and sepsis has not yet been proven. Therefore, Mendelian mediation was used to explore this relationship in this study.

METHODS

The genetic correlation NEI and sepsis was assessed by via linkage disequilibrium scores (LDSC). A two-sample Mendelian randomization (MR) study design was performed to examine the causal association between NEI and sepsis using the inverse variance weighted (IVW) method. The reliability of the results was estimated by weighted median and MR-Egger methods, but heterogeneity was evaluated via Radial and Cochran's Q tests. Biases in gene polymorphisms were checked by MR-Egger regression and MR-PRESSO. Mendelian mediation analyses were applied to quantify the intermediary effect and proportional contribution.

RESULTS

A genetic link between sepsis and depression was determined via LDSC analysis. The relationship between depression and sepsis was revealed through MR analysis [odds ratio (OR) = 1.21, 95 % confidence interval (CI) = 1.08-1.36, p = 1.07 × 10-3)]. The results were not influenced by heterogeneity or pleiotropy biases. Chitinase 3 Like 1 (CHI3L1) was a mediator with a mediation effect size of 0.12. The ratio of the intermediated effect to total effect was 10.31 %.

CONCLUSION

CHI3L1 is a key factor which mediates the interaction between NEI and sepsis.

Genetic architecture of brain morphology and overlap with neuropsychiatric traits

Uncovering the genetic architectures of brain morphology offers valuable insights into brain development and disease. Genetic association studies of brain morphological phenotypes have discovered thousands of loci. However, interpretation of these loci presents a significant challenge. One potential solution is exploring the genetic overlap between brain morphology and disorders, which can improve our understanding of their complex relationships, ultimately aiding in clinical applications. In this review, we examine current evidence on the genetic associations between brain morphology and neuropsychiatric traits. We discuss the impact of these associations on the diagnosis, prediction, and treatment of neuropsychiatric diseases, along with suggestions for future research directions.

Genetic determinants of obesity in Korean populations: exploring genome-wide associations and polygenic risk scores

East Asian populations exhibit a genetic predisposition to obesity, yet comprehensive research on these traits is limited. We conducted a genome-wide association study (GWAS) with 93,673 Korean subjects to uncover novel genetic loci linked to obesity, examining metrics such as body mass index, waist circumference, body fat ratio, and abdominal fat ratio. Participants were categorized into non-obese, metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) groups. Using advanced computational methods, we developed a multifaceted polygenic risk scores (PRS) model to predict obesity. Our GWAS identified significant genetic effects with distinct sizes and directions within the MHO and MUO groups compared with the non-obese group. Gene-based and gene-set analyses, along with cluster analysis, revealed heterogeneous patterns of significant genes on chromosomes 3 (MUO group) and 11 (MHO group). In analyses targeting genetic predisposition differences based on metabolic health, odds ratios of high PRS compared with medium PRS showed significant differences between non-obese and MUO, and non-obese and MHO. Similar patterns were seen for low PRS compared with medium PRS. These findings were supported by the estimated genetic correlation (0.89 from bivariate GREML). Regional analyses highlighted significant local genetic correlations on chromosome 11, while single variant approaches suggested widespread pleiotropic effects, especially on chromosome 11. In conclusion, our study identifies specific genetic loci and risks associated with obesity in the Korean population, emphasizing the heterogeneous genetic factors contributing to MHO and MUO.

A parametric bootstrap approach for computing confidence intervals for genetic correlations with application to genetically determined protein-protein networks

Genetic correlation refers to the correlation between genetic determinants of a pair of traits. When using individual-level data, it is typically estimated based on a bivariate model specification where the correlation between the two variables is identifiable and can be estimated from a covariance model that incorporates the genetic relationship between individuals, e.g., using a pre-specified kinship matrix. Inference relying on asymptotic normality of the genetic correlation parameter estimates may be inaccurate when the sample size is low, when the genetic correlation is close to the boundary of the parameter space, and when the heritability of at least one of the traits is low. We address this problem by developing a parametric bootstrap procedure to construct confidence intervals for genetic correlation estimates. The procedure simulates paired traits under a range of heritability and genetic correlation parameters, and it uses the population structure encapsulated by the kinship matrix. Heritabilities and genetic correlations are estimated using the close-form, method of moment, Haseman-Elston regression estimators. The proposed parametric bootstrap procedure is especially useful when genetic correlations are computed on pairs of thousands of traits measured on the same exact set of individuals. We demonstrate the parametric bootstrap approach on a proteomics dataset from the Jackson Heart Study.

Transcriptome-wide characterization of genetic perturbations

Single cell CRISPR screens such as Perturb-seq enable transcriptomic profiling of genetic perturbations at scale. However, the data produced by these screens are often noisy due to cost and technical constraints, limiting power to detect true effects with conventional differential expression analyses. Here, we introduce TRanscriptome-wide Analysis of Differential Expression (TRADE), a statistical framework which estimates the transcriptome-wide distribution of true differential expression effects from noisy gene-level measurements. Within TRADE, we derive multiple novel, interpretable statistical metrics, including the "transcriptome-wide impact", an estimator of the overall transcriptional effect of a perturbation which is stable across sampling depths. We analyze new and published large-scale Perturb-seq datasets to show that many true transcriptional effects are not statistically significant, but detectable in aggregate with TRADE. In a genome-scale Perturb-seq screen, we find that a typical gene perturbation affects an estimated 45 genes, whereas a typical essential gene perturbation affects over 500 genes. An advantage of our approach is its ability to compare the transcriptomic effects of genetic perturbations across contexts and dosages despite differences in power. We use this ability to identify perturbations with cell-type dependent effects and to find examples of perturbations where transcriptional responses are not only larger in magnitude, but also qualitatively different, as a function of dosage. Lastly, we expand our analysis to case/control comparison of gene expression for neuropsychiatric conditions, finding that transcriptomic effect correlations are greater than genetic correlations for these diagnoses. TRADE lays an analytic foundation for the systematic comparison of genetic perturbation atlases, as well as differential expression experiments more broadly.

Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease

The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R2 of 0.43 and 0.39 in the BIDMC cohort and UK Biobank, respectively for AI-ECG BMI vs. measured BMI. We found delta-BMI, the difference between measured BMI and AI-ECG-predicted BMI (AI-ECG-BMI), to be a biomarker of cardiometabolic health. The top tertile of delta-BMI showed increased risk of future cardiometabolic disease (BIDMC: HR 1.15, p < 0.001; UKB: HR 1.58, p < 0.001) and diabetes mellitus (BIDMC: HR 1.25, p < 0.001; UKB: HR 2.28, p < 0.001) after adjusting for covariates including measured BMI. Significant enhancements in model fit, reclassification and improvements in discriminatory power were observed with the inclusion of delta-BMI in both cohorts. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits, including SCN10A, SCN5A, EXOG and RXRG. In summary, our AI-ECG-BMI model accurately predicts BMI and introduces delta-BMI as a non-invasive biomarker for cardiometabolic risk stratification.

Rare variants analyses suggest novel cleft genes in the African population

Non-syndromic orofacial clefts (NSOFCs) are common birth defects with a complex etiology. While over 60 common risk loci have been identified, they explain only a small proportion of the heritability for NSOFCs. Rare variants have been implicated in the missing heritability. Thus, our study aimed to identify genes enriched with nonsynonymous rare coding variants associated with NSOFCs. Our sample included 814 non-syndromic cleft lip with or without palate (NSCL/P), 205 non-syndromic cleft palate only (NSCPO), and 2150 unrelated control children from Nigeria, Ghana, and Ethiopia. We conducted a gene-based analysis separately for each phenotype using three rare-variants collapsing models: (1) protein-altering (PA), (2) missense variants only (MO); and (3) loss of function variants only (LOFO). Subsequently, we utilized relevant transcriptomics data to evaluate associated gene expression and examined their mutation constraint using the gnomeAD database. In total, 13 genes showed suggestive associations (p = E-04). Among them, eight genes (ABCB1, ALKBH8, CENPF, CSAD, EXPH5, PDZD8, SLC16A9, and TTC28) were consistently expressed in relevant mouse and human craniofacial tissues during the formation of the face, and three genes (ABCB1, TTC28, and PDZD8) showed statistically significant mutation constraint. These findings underscore the role of rare variants in identifying candidate genes for NSOFCs.

Genomic analyses of withers height and linear conformation traits in German Warmblood horses using imputed sequence-level genotypes

BACKGROUND

Body conformation, including withers height, is a major selection criterion in horse breeding and is associated with other important traits, such as health and performance. However, little is known about the genomic background of equine conformation. Therefore, the aim of this study was to use imputed sequence-level genotypes from up to 4891 German Warmblood horses to identify genomic regions associated with withers height and linear conformation traits. Furthermore, the traits were genetically characterised and putative causal variants for withers height were detected.

RESULTS

A genome-wide association study (GWAS) for withers height confirmed the presence of a previously known quantitative trait locus (QTL) on Equus caballus (ECA) chromosome 3 close to the LCORL/NCAPG locus, which explained 16% of the phenotypic variance for withers height. An additional significant association signal was detected on ECA1. Further investigations of the region on ECA3 identified a few promising candidate causal variants for withers height, including a nonsense mutation in the coding sequence of the LCORL gene. The estimated heritability for withers height was 0.53 and ranged from 0 to 0.34 for the conformation traits. GWAS identified significantly associated variants for more than half of the investigated conformation traits, among which 13 showed a peak on ECA3 in the same region as withers height. Genetic parameter estimation revealed high genetic correlations between these traits and withers height for the QTL on ECA3.

CONCLUSIONS

The use of imputed sequence-level genotypes from a large study cohort led to the discovery of novel QTL associated with conformation traits in German Warmblood horses. The results indicate the high relevance of the QTL on ECA3 for various conformation traits, including withers height, and contribute to deciphering causal mutations for body size in horses.

Genome-wide association study for age-related hearing loss in CFW mice

Age-related hearing impairment is the most common cause of hearing loss and is one of the most prevalent conditions affecting the elderly globally. It is influenced by a combination of environmental and genetic factors. The mouse and human inner ears are functionally and genetically homologous. Investigating the genetic basis of age-related hearing loss (ARHL) in an outbred mouse model may lead to a better understanding of the molecular mechanisms of this condition. We used Carworth Farms White (CFW) outbred mice, because they are genetically diverse and exhibit variation in the onset and severity of ARHL. The goal of this study was to identify genetic loci involved in regulating ARHL. Hearing at a range of frequencies was measured using Auditory Brainstem Response (ABR) thresholds in 946 male and female CFW mice at the age of 1, 6, and 10 months. We obtained genotypes at 4.18 million single nucleotide polymorphisms (SNP) using low-coverage (mean coverage 0.27x) whole-genome sequencing followed by imputation using STITCH. To determine the accuracy of the genotypes we sequenced 8 samples at >30x coverage and used calls from those samples to estimate the discordance rate, which was 0.45%. We performed genetic analysis for the ABR thresholds for each frequency at each age, and for the time of onset of deafness for each frequency. The SNP heritability ranged from 0 to 42% for different traits. Genome-wide association analysis identified several regions associated with ARHL that contained potential candidate genes, including Dnah11, Rapgef5, Cpne4, Prkag2, and Nek11. We confirmed, using functional study, that Prkag2 deficiency causes age-related hearing loss at high frequency in mice; this makes Prkag2 a candidate gene for further studies. This work helps to identify genetic risk factors for ARHL and to define novel therapeutic targets for the treatment and prevention of ARHL.

Genome-wide identification of quantitative trait loci and candidate genes for seven carcass traits in a four-way intercross porcine population

BACKGROUND

Carcass traits are essential economic traits in the commercial pig industry. However, the genetic mechanism of carcass traits is still unclear. In this study, we performed a genome-wide association study (GWAS) based on the specific-locus amplified fragment sequencing (SLAF-seq) to study seven carcass traits on 223 four-way intercross pigs, including dressing percentage (DP), number of ribs (RIB), skin thinkness (ST), carcass straight length (CSL), carcass diagonal length (CDL), loin eye width (LEW), and loin eye thickness (LET).

RESULTS

A total of 227,921 high-quality single nucleotide polymorphisms (SNPs) were detected to perform GWAS. A total of 30 SNPs were identified for seven carcass traits using the mixed linear model (MLM) (p < 1.0 × 10- 5), of which 9 SNPs were located in previously reported quantitative trait loci (QTL) regions. The phenotypic variation explained (PVE) by the significant SNPs was from 2.43 to 16.32%. Furthermore, 11 candidate genes (LYPLAL1, EPC1, MATN2, ZFAT, ZBTB10, ZNF704, INHBA, SMYD3, PAK1, SPTBN2, and ACTN3) were found for carcass traits in pigs.

CONCLUSIONS

The GWAS results will improve our understanding of the genetic basis of carcass traits. We hypothesized that the candidate genes associated with these discovered SNPs would offer a biological basis for enhancing the carcass quality of pigs in swine breeding.

Mendelian randomization: causal inference leveraging genetic data

Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples - the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.

Genetic parameter estimation and molecular foundation of chicken beak shape

The bird beak is mainly functioned as feeding and attacking, and its shape has extremely important significance for survival and reproduction. In chickens, since beak shape could lead to some disadvantages including pecking and waste of feed, it is important to understand the inheritance of chicken beak shape. In the present study, we firstly established 4 indicators to describe the chicken beak shapes, including upper beak length (UL), lower beak length (LL), distance between upper and lower beak tips (DB) and upper beak curvature (BC). And then, we measured the 4 beak shape indicators as well as some production traits including body weight (BW), shank length (SL), egg weight (EW), eggshell strength (ES) of a layer breed, Rhode Island Red (RIR), in order to estimate genetic parameters of chicken beak shape. The heritabilities of UL and LL were 0.41 and 0.37, and the heritabilities of DB and BC were 0.22 and 0.21, indicating that beak shape was a highly or mediumly heritable. There were significant positive genetic and phenotypic correlations among UL, LL, and DB. And UL was positively correlated with body weight (BW18) and shank length (SL18) at 18 weeks of age in genetics, and DB was positively correlated with BC in terms of genetics and phenotype. We also found that layers of chicken cages played a role on beak shape, which could be attributed to the difference of lightness in different cage layers. By a genome-wide association study (GWAS) for the chicken UL, we identified 9 significant candidate genes associated with UL in RIR. For the variants with low minor allele frequencies (MAF <0.01) and outside of high linkage disequilibrium (LD) regions, we also conducted rare variant association studies (RVA) and GWAS to find the association between genotype and phenotype. We also analyzed transcriptomic data from multiple tissues of chicken embryos and revealed that all of the 9 genes were highly expressed in beak of chicken embryos, indicating their potential function for beak development. Our results provided the genetic foundation of chicken beak shape, which could help chicken breeding on beak related traits.

Accelerating Genome- and Phenome-Wide Association Studies using GPUs - A case study using data from the Million Veteran Program

The expansion of biobanks has significantly propelled genomic discoveries yet the sheer scale of data within these repositories poses formidable computational hurdles, particularly in handling extensive matrix operations required by prevailing statistical frameworks. In this work, we introduce computational optimizations to the SAIGE (Scalable and Accurate Implementation of Generalized Mixed Model) algorithm, notably employing a GPU-based distributed computing approach to tackle these challenges. We applied these optimizations to conduct a large-scale genome-wide association study (GWAS) across 2,068 phenotypes derived from electronic health records of 635,969 diverse participants from the Veterans Affairs (VA) Million Veteran Program (MVP). Our strategies enabled scaling up the analysis to over 6,000 nodes on the Department of Energy (DOE) Oak Ridge Leadership Computing Facility (OLCF) Summit High-Performance Computer (HPC), resulting in a 20-fold acceleration compared to the baseline model. We also provide a Docker container with our optimizations that was successfully used on multiple cloud infrastructures on UK Biobank and All of Us datasets where we showed significant time and cost benefits over the baseline SAIGE model.

Cross-ancestry genetic architecture and prediction for cholesterol traits

While cholesterol is essential, a high level of cholesterol is associated with the risk of cardiovascular diseases. Genome-wide association studies (GWASs) have proven successful in identifying genetic variants that are linked to cholesterol levels, predominantly in white European populations. However, the extent to which genetic effects on cholesterol vary across different ancestries remains largely unexplored. Here, we estimate cross-ancestry genetic correlation to address questions on how genetic effects are shared across ancestries. We find significant genetic heterogeneity between ancestries for cholesterol traits. Furthermore, we demonstrate that single nucleotide polymorphisms (SNPs) with concordant effects across ancestries for cholesterol are more frequently found in regulatory regions compared to other genomic regions. Indeed, the positive genetic covariance between ancestries is mostly driven by the effects of the concordant SNPs, whereas the genetic heterogeneity is attributed to the discordant SNPs. We also show that the predictive ability of the concordant SNPs is significantly higher than the discordant SNPs in the cross-ancestry polygenic prediction. The list of concordant SNPs for cholesterol is available in GWAS Catalog. These findings have relevance for the understanding of shared genetic architecture across ancestries, contributing to the development of clinical strategies for polygenic prediction of cholesterol in cross-ancestral settings.

Estimating the overall fraction of phenotypic variance attributed to high-dimensional predictors measured with error

In prospective genomic studies (e.g., DNA methylation, metagenomics, and transcriptomics), it is crucial to estimate the overall fraction of phenotypic variance (OFPV) attributed to the high-dimensional genomic variables, a concept similar to heritability analyses in genome-wide association studies (GWAS). Unlike genetic variants in GWAS, these genomic variables are typically measured with error due to technical limitation and temporal instability. While the existing methods developed for GWAS can be used, ignoring measurement error may severely underestimate OFPV and mislead the design of future studies. Assuming that measurement error variances are distributed similarly between causal and noncausal variables, we show that the asymptotic attenuation factor equals to the average intraclass correlation coefficients of all genomic variables, which can be estimated based on a pilot study with repeated measurements. We illustrate the method by estimating the contribution of microbiome taxa to body mass index and multiple allergy traits in the American Gut Project. Finally, we show that measurement error does not cause meaningful bias when estimating the correlation of effect sizes for two traits.

Genetic associations of risk behaviours and educational achievement

Risk behaviours are common in adolescent and persist into adulthood, people who engage in more risk behaviours are more likely to have lower educational attainment. We applied genetic causal inference methods to explore the causal relationship between adolescent risk behaviours and educational achievement. Risk behaviours were phenotypically associated with educational achievement at age 16 after adjusting for confounders (-0.11, 95%CI: -0.11, -0.09). Genomic-based restricted maximum likelihood (GREML) results indicated that both traits were heritable and have a shared genetic architecture (Riskh 2  = 0.18, 95% CI: -0.11,0.47; educationh 2  = 0.60, 95%CI: 0.50,0.70). Consistent with the phenotypic results, genetic variation associated with risk behaviour was negatively associated with education (r g  = -0.51, 95%CI: -1.04,0.02). Lastly, the bidirectional MR results indicate that educational achievement or a closely related trait is likely to affect risk behaviours PGI (β=-1.04, 95% CI: -1.41, -0.67), but we found little evidence that the genetic variation associated with risk behaviours affected educational achievement (β=0.00, 95% CI: -0.24,0.24). The results suggest engagement in risk behaviour may be partly driven by educational achievement or a closely related trait.

Genomic Loci Influencing Cue-Reactivity in Heterogeneous Stock Rats

Addiction vulnerability is associated with the tendency to attribute incentive salience to reward predictive cues; both addiction and the attribution of incentive salience are influenced by environmental and genetic factors. To characterize the genetic contributions to incentive salience attribution, we performed a genome-wide association study (GWAS) in a cohort of 1,645 genetically diverse heterogeneous stock (HS) rats. We tested HS rats in a Pavlovian conditioned approach task, in which we characterized the individual responses to food-associated stimuli ("cues"). Rats exhibited either cue-directed "sign-tracking" behavior or food-cup directed "goal-tracking" behavior. We then used the conditioned reinforcement procedure to determine whether rats would perform a novel operant response for unrewarded presentations of the cue. We found that these measures were moderately heritable (SNP heritability, h2 = .189-.215). GWAS identified 14 quantitative trait loci (QTLs) for 11 of the 12 traits we examined. Interval sizes of these QTLs varied widely. 7 traits shared a QTL on chromosome 1 that contained a few genes (e.g. Tenm4, Mir708) that have been associated with substance use disorders and other mental health traits in humans. Other candidate genes (e.g. Wnt11, Pak1) in this region had coding variants and expression-QTLs in mesocorticolimbic regions of the brain. We also conducted a Phenome-Wide Association Study (PheWAS) on other behavioral measures in HS rats and found that regions containing QTLs on chromosome 1 were also associated with nicotine self-administration in a separate cohort of HS rats. These results provide a starting point for the molecular genetic dissection of incentive salience and provide further support for a relationship between attribution of incentive salience and drug abuse-related traits.

Rare Variants Analyses Suggest Novel Cleft Genes in the African Population

Non-syndromic orofacial clefts (NSOFCs) are common birth defects with a complex etiology. While over 60 common risk loci have been identified, they explain only a small proportion of the heritability for NSOFC. Rare variants have been implicated in the missing heritability. Thus, our study aimed to identify genes enriched with nonsynonymous rare coding variants associated with NSOFCs. Our sample included 814 non-syndromic cleft lip with or without palate (NSCL/P), 205 non-syndromic cleft palate only (NSCPO), and 2150 unrelated control children from Nigeria, Ghana, and Ethiopia. We conducted a gene-based analysis separately for each phenotype using three rare-variants collapsing models: (1) protein-altering (PA), (2) missense variants only (MO); and (3) loss of function variants only (LOFO). Subsequently, we utilized relevant transcriptomics data to evaluate associated gene expression and examined their mutation constraint using the gnomeAD database. In total, 13 genes showed suggestive associations (p = E-04). Among them, eight genes (ABCB1, ALKBH8, CENPF, CSAD, EXPH5, PDZD8, SLC16A9, and TTC28) were consistently expressed in relevant mouse and human craniofacial tissues during the formation of the face, and three genes (ABCB1, TTC28, and PDZD8) showed statistically significant mutation constraint. These findings underscore the role of rare variants in identifying candidate genes for NSOFCs.

Structural models of genome-wide covariance identify multiple common dimensions in autism

Common genetic variation has been associated with multiple phenotypic features in Autism Spectrum Disorder (ASD). However, our knowledge of shared genetic factor structures contributing to this highly heterogeneous phenotypic spectrum is limited. Here, we developed and implemented a structural equation modelling framework to directly model genomic covariance across core and non-core ASD phenotypes, studying autistic individuals of European descent with a case-only design. We identified three independent genetic factors most strongly linked to language performance, behaviour and developmental motor delay, respectively, studying an autism community sample (N = 5331). The three-factorial structure was largely confirmed in independent ASD-simplex families (N = 1946), although we uncovered, in addition, simplex-specific genetic overlap between behaviour and language phenotypes. Multivariate models across cohorts revealed novel associations, including links between language and early mastering of self-feeding. Thus, the common genetic architecture in ASD is multi-dimensional with overarching genetic factors contributing, in combination with ascertainment-specific patterns, to phenotypic heterogeneity.

Genetic risk for hospitalization of African American patients with severe mental illness reveals HLA loci

Background

Mood disorders such as major depressive and bipolar disorders, along with posttraumatic stress disorder (PTSD), schizophrenia (SCZ), and other psychotic disorders, constitute serious mental illnesses (SMI) and often lead to inpatient psychiatric care for adults. Risk factors associated with increased hospitalization rate in SMI (H-SMI) are largely unknown but likely involve a combination of genetic, environmental, and socio-behavioral factors. We performed a genome-wide association study in an African American cohort to identify possible genes associated with hospitalization due to SMI (H-SMI).

Methods

Patients hospitalized for psychiatric disorders (H-SMI; n=690) were compared with demographically matched controls (n=4467). Quality control and imputation of genome-wide data were performed following the Psychiatric Genetic Consortium (PGC)-PTSD guidelines. Imputation of the Human Leukocyte Antigen (HLA) locus was performed using the HIBAG package.

Results

Genome-wide association analysis revealed a genome-wide significant association at 6p22.1 locus in the ubiquitin D (UBD/FAT10) gene (rs362514, p=9.43x10-9) and around the HLA locus. Heritability of H-SMI (14.6%) was comparable to other psychiatric disorders (4% to 45%). We observed a nominally significant association with 2 HLA alleles: HLA-A*23:01 (OR=1.04, p=2.3x10-3) and HLA-C*06:02 (OR=1.04, p=1.5x10-3). Two other genes (VSP13D and TSPAN9), possibly associated with immune response, were found to be associated with H-SMI using gene-based analyses.

Conclusion

We observed a strong association between H-SMI and a locus that has been consistently and strongly associated with SCZ in multiple studies (6p21.32-p22.1), possibly indicating an involvement of the immune system and the immune response in the development of severe transdiagnostic SMI.

Genome-wide association study implicates the role of TBXAS1 in the pathogenesis of depressive symptoms among the Korean population

Although depression is an emerging disorder affecting many people worldwide, most genetic studies have been performed in European descent populations. Herein, a genome-wide association study (GWAS) was conducted in Korean population to elucidate the genomic loci associated with depressive symptoms. Two independent cohorts were used as discovery datasets, which consisted of 6474 (1484 cases and 4990 controls) and 1654 (557 cases and 1097 controls) Korean participants, respectively. The participants were divided into case and control groups based on the Beck Depression Inventory (BDI). Meta-analysis using the two cohorts revealed that rs6945590 was significantly associated with the risk of depressive symptoms [P = 2.83 × 10-8; odds ratio (OR) = 1.23; 95% confidence interval (CI): 1.15-1.33]. This association was validated in other independent cohorts which were another Korean cohort (258 cases and 1757 controls) and the East Asian study of the Psychiatric Genomics Consortium (PGC) (12,455 cases and 85,548 controls). The predicted expression levels of thromboxane A synthase 1 gene (TBXAS1), which encodes the enzyme thromboxane A synthase 1 and participates in the arachidonic acid (AA) cascade, was significantly decreased in the whole blood tissues of the participants with depressive symptoms. Furthermore, Mendelian randomization (MR) analysis showed a causal association between TBXAS1 expression and the risk of depressive symptoms. In conclusion, as the number of risk alleles (A) of rs6945590 increased, TBXAS1 expression decreased, which subsequently caused an increase in the risk of depressive symptoms.

Intracranial Volume Is Driven by Both Genetics and Early Life Exposures: The SOL-INCA-MRI Study

Intracranial volume (ICV) reflects maximal brain development and is associated with later-life cognitive abilities. We quantified ICV among first- and second-generation Hispanic and Latino adults from the Study of Latinos-Investigation of Cognitive Aging - MRI (SOL-INCA-MRI), estimated ICV heritability, and tested its associations with previously reported genetic variants, both individually and as a genetic risk score (GRS). We also estimated the association of ICV with early life environmental measures: nativity or age of immigration and parental education. The estimated heritability of ICV was 19% (95% CI, 0.1%-56%) in n=1781 unrelated SOL-INCA-MRI individuals. Four of 10 tested genetic variants were associated with ICV and an increase of 1 SD of the ICV-GRS was associated with an increase of 10.37 cm3 in the ICV (95% CI, 5.29-15.45). Compared to being born in the continental United States, immigrating to the United States at age 11 years or older was associated with 24 cm3 smaller ICV (95% CI, -39.97 to -8.06). Compared to both parents having less than high-school education, at least 1 parent completing high-school education was associated with 15.4 cm3 greater ICV (95% CI, 4.46-26.39). These data confirm the importance of early life health on brain development.

Genome-wide association with footrot in hair and wool sheep

Ovine footrot is an infectious disease with important contributions from Dichelobacter nodosus and Fusobacterium necrophorum. Footrot is characterized by separation of the hoof from underlying tissue, and this causes severe lameness that negatively impacts animal wellbeing, growth, and profitability. Large economic losses result from lost production as well as treatment costs, and improved genetic tools to address footrot are a valuable long-term goal. Prior genetic studies had examined European wool sheep, but hair sheep breeds such as Katahdin and Blackbelly have been reported to have increased resistance to footrot, as well as to intestinal parasites. Thus, footrot condition scores were collected from 251 U.S. sheep including Katahdin, Blackbelly, and European-influenced crossbred sheep with direct and imputed genotypes at OvineHD array (>500,000 single nucleotide polymorphism) density. Genome-wide association was performed with a mixed model accounting for farm and principal components derived from animal genotypes, as well as a random term for the genomic relationship matrix. We identified three genome-wide significant associations, including SNPs in or near GBP6 and TCHH. We also identified 33 additional associated SNPs with genome-wide suggestive evidence, including a cluster of 6 SNPs in a peak near the genome-wide significance threshold located near the glutamine transporter gene SLC38A1. These findings suggest genetic susceptibility to footrot may be influenced by genes involved in divergent biological processes such as immune responses, nutrient availability, and hoof growth and integrity. This is the first genome-wide study to investigate susceptibility to footrot by including hair sheep and also the first study of any kind to identify multiple genome-wide significant associations with ovine footrot. These results provide a foundation for developing genetic tests for marker-assisted selection to improve resistance to ovine footrot once additional steps like fine mapping and validation are complete.

Korea4K: whole genome sequences of 4,157 Koreans with 107 phenotypes derived from extensive health check-ups

BACKGROUND

Phenome-wide association studies (PheWASs) have been conducted on Asian populations, including Koreans, but many were based on chip or exome genotyping data. Such studies have limitations regarding whole genome-wide association analysis, making it crucial to have genome-to-phenome association information with the largest possible whole genome and matched phenome data to conduct further population-genome studies and develop health care services based on population genomics.

RESULTS

Here, we present 4,157 whole genome sequences (Korea4K) coupled with 107 health check-up parameters as the largest genomic resource of the Korean Genome Project. It encompasses most of the variants with allele frequency >0.001 in Koreans, indicating that it sufficiently covered most of the common and rare genetic variants with commonly measured phenotypes for Koreans. Korea4K provides 45,537,252 variants, and half of them were not present in Korea1K (1,094 samples). We also identified 1,356 new genotype-phenotype associations that were not found by the Korea1K dataset. Phenomics analyses further revealed 24 significant genetic correlations, 14 pleiotropic associations, and 127 causal relationships based on Mendelian randomization among 37 traits. In addition, the Korea4K imputation reference panel, the largest Korean variants reference to date, showed a superior imputation performance to Korea1K across all allele frequency categories.

CONCLUSIONS

Collectively, Korea4K provides not only the largest Korean genome data but also corresponding health check-up parameters and novel genome-phenome associations. The large-scale pathological whole genome-wide omics data will become a powerful set for genome-phenome level association studies to discover causal markers for the prediction and diagnosis of health conditions in future studies.

Unraveling phenotypic variance in metabolic syndrome through multi-omics

Complex multi-omics effects drive the clustering of cardiometabolic risk factors, underscoring the imperative to comprehend how individual and combined omics shape phenotypic variation. Our study partitions phenotypic variance in metabolic syndrome (MetS), blood glucose (GLU), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and blood pressure through genome, transcriptome, metabolome, and exposome (i.e., lifestyle exposome) analyses. Our analysis included a cohort of 62,822 unrelated individuals with white British ancestry, sourced from the UK biobank. We employed linear mixed models to partition phenotypic variance using the restricted maximum likelihood (REML) method, implemented in MTG2 (v2.22). We initiated the analysis by individually modeling omics, followed by subsequent integration of pairwise omics in a joint model that also accounted for the covariance and interaction between omics layers. Finally, we estimated the correlations of various omics effects between the phenotypes using bivariate REML. Significant proportions of the MetS variance were attributed to distinct data sources: genome (9.47%), transcriptome (4.24%), metabolome (14.34%), and exposome (3.77%). The phenotypic variances explained by the genome, transcriptome, metabolome, and exposome ranged from 3.28% for GLU to 25.35% for HDL-C, 0% for GLU to 19.34% for HDL-C, 4.29% for systolic blood pressure (SBP) to 35.75% for TG, and 0.89% for GLU to 10.17% for HDL-C, respectively. Significant correlations were found between genomic and transcriptomic effects for TG and HDL-C. Furthermore, significant interaction effects between omics data were detected for both MetS and its components. Interestingly, significant correlation of omics effect between the phenotypes was found. This study underscores omics' roles, interaction effects, and random-effects covariance in unveiling phenotypic variation in multi-omics domains.

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.

Genome-Wide Association Study for Somatic Skeletal Traits in Duroc × (Landrace × Yorkshire) Pigs

The pig bone weight trait holds significant economic importance in southern China. To expedite the selection of the pig bone weight trait in pig breeding, we conducted molecular genetic research on these specific traits. These traits encompass the bone weight of the scapula (SW), front leg bone weight (including humerus and ulna) (FLBW), hind leg bone weight (including femur and tibia) (HLBW), and spine bone weight (SBW). Up until now, the genetic structure related to these traits has not been thoroughly explored, primarily due to challenges associated with obtaining the phenotype data. In this study, we utilized genome-wide association studies (GWAS) to discern single nucleotide polymorphisms (SNPs) and genes associated with four bone weight traits within a population comprising 571 Duroc × (Landrace × Yorkshire) hybrid pigs (DLY). In the analyses, we employed a mixed linear model, and for the correction of multiple tests, both the false discovery rate and Bonferroni methods were utilized. Following functional annotation, candidate genes were identified based on their proximity to the candidate sites and their association with the bone weight traits. This study represents the inaugural application of GWAS for the identification of SNPs associated with individual bone weight in DLY pigs. Our analysis unveiled 26 SNPs and identified 12 promising candidate genes (OPRM1, SLC44A5, WASHC4, NOPCHAP1, RHOT1, GLP1R, TGFB3, PLCB1, TLR4, KCNJ2, ABCA6, and ABCA9) associated with the four bone weight traits. Furthermore, our findings on the genetic mechanisms influencing pig bone weight offer valuable insights as a reference for the genetic enhancement of pig bone traits.

A Regression-based Approach to Robust Estimation and Inference for Genetic Covariance

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex traits, and some variants are shown to be associated with multiple complex traits. Genetic covariance between two traits is defined as the underlying covariance of genetic effects and can be used to measure the shared genetic architecture. The data used to estimate such a genetic covariance can be from the same group or different groups of individuals, and the traits can be of different types or collected based on different study designs. This paper proposes a unified regression-based approach to robust estimation and inference for genetic covariance of general traits that may be associated with genetic variants nonlinearly. The asymptotic properties of the proposed estimator are provided and are shown to be robust under certain model mis-specification. Our method under linear working models provides a robust inference for the narrow-sense genetic covariance, even when both linear models are mis-specified. Numerical experiments are performed to support the theoretical results. Our method is applied to an outbred mice GWAS data set to study the overlapping genetic effects between the behavioral and physiological phenotypes. The real data results reveal interesting genetic covariance among different mice developmental traits.

A parametric bootstrap approach for computing confidence intervals for genetic correlations with application to genetically-determined protein-protein networks

Genetic correlation refers to the correlation between genetic determinants of a pair of traits. When using individual-level data, it is typically estimated based on a bivariate model specification where the correlation between the two variables is identifiable and can be estimated from a covariance model that incorporates the genetic relationship between individuals, e.g., using a pre-specified kinship matrix. Inference relying on asymptotic normality of the genetic correlation parameter estimates may be inaccurate when the sample size is low, when the genetic correlation is close to the boundary of the parameter space, and when the heritability of at least one of the traits is low. We address this problem by developing a parametric bootstrap procedure to construct confidence intervals for genetic correlation estimates. The procedure simulates paired traits under a range of heritability and genetic correlation parameters, and it uses the population structure encapsulated by the kinship matrix. Heritabilities and genetic correlations are estimated using the close-form, method of moment, Haseman-Elston regression estimators. The proposed parametric bootstrap procedure is especially useful when genetic correlations are computed on pairs of thousands of traits measured on the same exact set of individuals. We demonstrate the parametric bootstrap approach on a proteomics dataset from the Jackson Heart Study.