暂无摘要。

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.

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.

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.

BACKGROUND: APOL1 variants G1 and G2 are common in populations with recent African ancestry. They are associated with protection from African sleeping sickness, however homozygosity or compound heterozygosity for these variants is associated with chronic kidney disease (CKD) and related conditions. What is not clear is the extent of associations with non-kidney-related disorders, and whether there are clusters of diseases associated with individual APOL1 genotypes.
METHODS: Using a cohort of 7462 UK Biobank participants with recent African ancestry, we conducted a phenome-wide association study investigating associations between individual APOL1 genotypes and conditions identified by the International Classification of Disease phenotypes.
RESULTS: We identified 27 potential associations between individual APOL1 genotypes and a diverse range of conditions. G1/G2 compound heterozygotes were specifically associated with 26 of these conditions (all deleteriously), with an over-representation of infectious diseases (including hospitalisation and death resulting from COVID-19). The analysis also exposed complexities in the relationship between APOL1 and CKD that are not evident when risk variants are grouped together: G1 homozygosity, G2 homozygosity, and G1/G2 compound heterozygosity were each shown to be associated with distinct CKD phenotypes. The multi-locus nature of the G1/G2 genotype means that its associations would go undetected in a standard genome-wide association study.
CONCLUSIONS: Our findings have implications for understanding health risks and better-targeted detection, intervention, and therapeutic strategies, particularly in populations where APOL1 G1 and G2 are common such as in sub-Saharan Africa and its diaspora.
BACKGROUND: This study was funded by the Wellcome Trust (209511/Z/17/Z) and H3Africa (H3A/18/004).

Progeroid disorders are a heterogenous group of rare and complex hereditary syndromes presenting with pleiotropic phenotypes associated with normal aging. Due to the large variation in clinical presentation the diseases pose a diagnostic challenge for clinicians which consequently restricts medical research. To accommodate the challenge, we compiled a list of known progeroid syndromes and calculated the mean prevalence of their associated phenotypes, defining what we term the \'progeria phenome\'. The data were used to train a support vector machine that is available at https://www.mitodb.com and able to classify progerias based on phenotypes. Furthermore, this allowed us to investigate the correlation of progeroid syndromes and syndromes with various pathogenesis using hierarchical clustering algorithms and disease networks. We detected that ataxia-telangiectasia like disorder 2, spastic paraplegia 49 and Meier-Gorlin syndrome display strong association to progeroid syndromes, thereby implying that the syndromes are previously unrecognized progerias. In conclusion, our study has provided tools to evaluate the likelihood of a syndrome or patient being progeroid. This is a considerable step forward in our understanding of what constitutes a premature aging disorder and how to diagnose them.

A central goal of biology is to understand how genetic variation produces phenotypic variation, which has been described as a genotype to phenotype (G to P) map. The plant form is continuously shaped by intrinsic developmental and extrinsic environmental inputs, and therefore plant phenomes are highly multivariate and require comprehensive approaches to fully quantify. Yet a common assumption in plant phenotyping efforts is that a few pre-selected measurements can adequately describe the relevant phenome space. Our poor understanding of the genetic basis of root system architecture is at least partially a result of this incongruence. Root systems are complex 3D structures that are most often studied as 2D representations measured with relatively simple univariate traits. In prior work, we showed that persistent homology, a topological data analysis method that does not pre-suppose the salient features of the data, could expand the phenotypic trait space and identify new G to P relations from a commonly used 2D root phenotyping platform. Here we extend the work to entire 3D root system architectures of maize seedlings from a mapping population that was designed to understand the genetic basis of maize-nitrogen relations. Using a panel of 84 univariate traits, persistent homology methods developed for 3D branching, and multivariate vectors of the collective trait space, we found that each method captures distinct information about root system variation as evidenced by the majority of non-overlapping QTL, and hence that root phenotypic trait space is not easily exhausted. The work offers a data-driven method for assessing 3D root structure and highlights the importance of non-canonical phenotypes for more accurate representations of the G to P map.