Genome to phenome analysis is necessary in livestock areas because of its various and complex phenotypes. Pork belly is a favorable part of meat worldwide, including East Asia. A previous study has suggested that the three key transcription factors (ZNF444, NFYA and PPARG) affecting pork belly traits include total volume, the volume of total fat and muscle, and component muscles of the corresponding slice. However, other transcription factor genes affecting each slice other than pork belly component traits still needed to be identified. Thus, we aimed to analyze pork belly components at the genome to phenome level for identifying key transcription factor genes and their co-associated networks. The range of node numbers against each component trait via the association weight matrix was from 598 to 3020. Premised on the result, an in silico functional approach was performed. Each co-association network enriched three key transcription factors in adipogenesis and skeletal muscle proliferation, mesoderm development, metabolism, and gene transcription. The three key transcription factors and their related genes may be useful in comprehending their effect of pork belly construction.

The applicability of multivariate approaches for the joint analysis of genomics and phenomics information is currently limited by the lack of scalability, and by the difficulty of interpreting the related findings from a biological perspective. To tackle these limitations, we present Bayesian Genome-to-Phenome Sparse Regression (G2PSR), a novel multivariate regression method based on sparse SNP-gene constraints. The statistical framework of G2PSR is based on a Bayesian neural network, were constraints on SNPs-genes associations are integrated by incorporating a priori knowledge linking variants to their respective genes, to then reconstruct the phenotypic data in the output layer. Interpretability is promoted by inducing sparsity on the genes through variational dropout, allowing to estimate the uncertainty associated with each gene, and related SNPs, in the reconstruction task. Ultimately, G2PSR is conceived to prevent multiple testing correction and to assess the combined effect of SNPs, thus increasing the statistical power in detecting genome-to-phenome associations. The effectiveness of G2PSR was demonstrated on synthetic and real data, with respect to state-of-the-art methods based on group-wise sparsity constraints. The application on real data consisted in an imaging-genetics analysis on the Alzheimer\'s Disease Neuroimaging Initiative data, relating SNPs from more than 3,500 genes to clinical and multi-variate brain volumetric information. The experimental results show that our method can provide accurate selection of relevant genes in dataset with large SNPs-to-samples ratio, thus overcoming the main limitations of current genome-to-phenome association methods.

The tree shrew ( Tupaia belangeri) has long been proposed as a suitable alternative to non-human primates (NHPs) in biomedical and laboratory research due to its close evolutionary relationship with primates. In recent years, significant advances have facilitated tree shrew studies, including the determination of the tree shrew genome, genetic manipulation using spermatogonial stem cells, viral vector-mediated gene delivery, and mapping of the tree shrew brain atlas. However, the limited availability of tree shrews globally remains a substantial challenge in the field. Additionally, determining the key questions best answered using tree shrews constitutes another difficulty. Tree shrew models have historically been used to study hepatitis B virus (HBV) and hepatitis C virus (HCV) infection, myopia, and psychosocial stress-induced depression, with more recent studies focusing on developing animal models for infectious and neurodegenerative diseases. Despite these efforts, the impact of tree shrew models has not yet matched that of rodent or NHP models in biomedical research. This review summarizes the prominent advancements in tree shrew research and reflects on the key biological questions addressed using this model. We emphasize that intensive dedication and robust international collaboration are essential for achieving breakthroughs in tree shrew studies. The use of tree shrews as a unique resource is expected to gain considerable attention with the application of advanced techniques and the development of viable animal models, meeting the increasing demands of life science and biomedical research.
树鼩（ Tupaia belangeri）是灵长类动物的近亲，长期以来一直被提议作为生物医学和实验研究中灵长类动物的合适替代品。近年来，树鼩的研究取得了重大进展，如树鼩基因组测定、使用精原干细胞成功实现树鼩基因操作、病毒载体介导的基因传递以及树鼩脑图谱绘制等。然而，全球范围内的实验树鼩供应有限，仍然是该领域面临的重大挑战。此外，确定最适合使用树鼩来研究的关键问题，也是一个困难。树鼩模型在历史上被用于研究乙型肝炎病毒（HBV）和丙型肝炎病毒（HCV）感染、近视和心理社会压力引发的抑郁症，最近的研究重点是开发感染性疾病和神经退行性疾病的树鼩动物模型。尽管有这些努力与成效，树鼩模型在生物医学研究中的影响力，还远未达到啮齿动物或灵长类动物模型的水平。该综述总结了树鼩研究中的重要进展，并对使用树鼩模型解决的关键生物学问题进行了反思。该文强调，为了在树鼩研究中取得突破，需要更广泛和深度的国际合作与投入。随着先进技术的应用和动物模型的开发，树鼩作为一种独特资源，有望受到越来越多的关注，以满足生命科学和生物医学研究中日益增长的需求。.

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OBJECTIVE: The causes and triggering factors of epilepsy are still unknown. The results of genome-wide association studies can be utilized for a phenome-wide association study using Mendelian randomization (MR) to identify potential risk factors for epilepsy.
METHODS: This study utilizes two-sample MR analysis to investigate whether 316 phenotypes, including lifestyle, environmental factors, blood biomarker, and more, are causally associated with the occurrence of epilepsy. The primary analysis employed the inverse variance weighted (IVW) model, while complementary MR analysis methods (MR Egger, Wald ratio) were also employed. Sensitivity analyses were also conducted to evaluate heterogeneity and pleiotropy.
RESULTS: There was no evidence of a statistically significant causal association between the examined phenotypes and epilepsy following Bonferroni correction (p < 1.58 × 10-4) or false discovery rate correction. The results of the MR analysis indicate that the frequency of tiredness or lethargy in the last 2 weeks (p = 0.042), blood uridine (p = 0.003), blood propionylcarnitine (p = 0.041), and free cholesterol (p = 0.044) are suggestive causal risks for epilepsy. Lifestyle choices, such as sleep duration and alcohol consumption, as well as biomarkers including steroid hormone levels, hippocampal volume, and amygdala volume were not identified as causal factors for developing epilepsy (p > 0.05).
CONCLUSIONS: Our study provides additional insights into the underlying causes of epilepsy, which will serve as evidence for the prevention and control of epilepsy. The associations observed in epidemiological studies may be partially attributed to shared biological factors or lifestyle confounders.

The neglect of Moso bamboo\'s phenotype variations hinders its broader utilization, despite its high economic value globally. Thus, this study investigated the morphological variations of 16 Moso bamboo populations. The analysis revealed the culm heights ranging from 9.67 m to 17.5 m, with average heights under the first branch ranging from 4.91 m to 7.67 m. The total internode numbers under the first branch varied from 17 to 36, with internode lengths spanning 2.9 cm to 46.4 cm, diameters ranging from 5.10 cm to 17.2 cm, and wall thicknesses from 3.20 mm to 33.3 mm, indicating distinct attributes among the populations. Furthermore, strong positive correlations were observed between the internode diameter, thickness, length, and volume. The coefficient of variation of height under the first branch showed strong positive correlations with several parameters, indicating variability in their contribution to the total culm height. A regression analysis revealed patterns of covariation among the culm parameters, highlighting their influence on the culm height and structural characteristics. Both the diameter and thickness significantly contribute to the internode volume and culm height, and the culm parameters tend to either increase or decrease together, influencing the culm height. Moreover, this study also identified a significant negative correlation between monthly precipitation and the internode diameter and thickness, especially during December and January, impacting the primary thickening growth and, consequently, the internode size.

What distinguishes vulnerability and resilience to posttraumatic stress disorder (PTSD) remains unclear. Levering traumatic experiences reporting, genetic data, and electronic health records (EHR), we investigated and predicted the clinical comorbidities (co-phenome) of PTSD vulnerability and resilience in the UK Biobank (UKB) and All of Us Research Program (AoU), respectively. In 60,354 trauma-exposed UKB participants, we defined PTSD vulnerability and resilience considering PTSD symptoms, trauma burden, and polygenic risk scores. EHR-based phenome-wide association studies (PheWAS) were conducted to dissect the co-phenomes of PTSD vulnerability and resilience. Significant diagnostic endpoints were applied as weights, yielding a phenotypic risk score (PheRS) to conduct PheWAS of PTSD vulnerability and resilience PheRS in up to 95,761 AoU participants. EHR-based PheWAS revealed three significant phenotypes positively associated with PTSD vulnerability (top association \"Sleep disorders\") and five outcomes inversely associated with PTSD resilience (top association \"Irritable Bowel Syndrome\"). In the AoU cohort, PheRS analysis showed a partial inverse relationship between vulnerability and resilience with distinct comorbid associations. While PheRSvulnerability associations were linked to multiple phenotypes, PheRSresilience showed inverse relationships with eye conditions. Our study unveils phenotypic differences in PTSD vulnerability and resilience, highlighting that these concepts are not simply the absence and presence of PTSD.

OBJECTIVE: To develop recommendations regarding the use of weights to reduce selection bias for commonly performed analyses using electronic health record (EHR)-linked biobank data.
METHODS: We mapped diagnosis (ICD code) data to standardized phecodes from 3 EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n = 244 071), Michigan Genomics Initiative (MGI; n = 81 243), and UK Biobank (UKB; n = 401 167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to represent the US adult population more. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted 4 common analyses comparing unweighted and weighted results.
RESULTS: For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted phenome-wide association study for colorectal cancer, the strongest associations remained unaltered, with considerable overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates.
CONCLUSIONS: Weighting had a limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation. When interested in estimating effect size, specific signals from untargeted association analyses should be followed up by weighted analysis.
CONCLUSIONS: EHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly.

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.
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.
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.

UNASSIGNED: To explore the role of selection bias adjustment by weighting electronic health record (EHR)-linked biobank data for commonly performed analyses.
UNASSIGNED: We mapped diagnosis (ICD code) data to standardized phecodes from three EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n=244,071), Michigan Genomics Initiative (MGI; n=81,243), and UK Biobank (UKB; n=401,167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to be more representative of the US adult population. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted four common descriptive and analytic tasks comparing unweighted and weighted results.
UNASSIGNED: For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB\'s estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted PheWAS for colorectal cancer, the strongest associations remained unaltered and there was large overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates.
UNASSIGNED: Weighting had limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation more. Results from untargeted association analyses should be followed by weighted analysis when effect size estimation is of interest for specific signals.
UNASSIGNED: EHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly.