Principals about principal components in statistical genetics

Computational study of the potential impact of WHRN protein missense SNPs on WHRN-MYO15A protein complex interaction and their association with Usher syndrome

Usher syndrome is a rare genetic condition characterized by both hearing and vision impairment that occurs through mutations of multiple genes, including WHRN and MYO15A. In this computational work, we intend to explore how missense SNPs within the WHRN protein affect its interaction with the MYO15A protein, a crucial component of the Usher interactome. Therefore, the identification of missense SNPs that has a potential effect on the function of the WHRN protein was realized using various computational prediction tools, including VEP, SIFT, PolyPhen-2, CADD, REVEL, and Mutation Assessor. Further evaluation of the stability of mutated proteins was conducted through SDM2, MCSM, DeepDDG and CUP-SAT. We used ConSurf web server to identify conserved regions in the WHRN protein. Yasara and Haddock analysis tools were used to minimize the energy of protein 3D structures and to dock protein-protein complexes, respectively. and then the binding energy of the complexes was calculated through PRODIGY. Mutation pathogenicity prediction tools showed that in total, 18 missense SNPs, predicted as deleterious. However, a comprehensive analysis revealed that only SIX single nucleotide polymorphisms were predicted to be the most deleterious with high conservation and less stability. Furthermore, we conducted molecular dynamics analysis to fully comprehend the impact of these variations on the dynamic behavior of the WHRN-MYO15A protein complex, which revealed significant insights into the destabilizing effects of the deleterious SNPs impacting the protein's binding affinity and stability that occurs during the binding process of the WHRN-MYO15A protein complex.

Alzheimer disease is (sometimes) highly heritable: Drivers of variation in heritability estimates for binary traits, a systematic review

Estimating heritability has been fundamental in understanding the genetic contributions to complex disorders like late-onset Alzheimer's disease (LOAD), and provides a rationale for identifying genetic factors associated with disease susceptibility. While numerous studies have established substantial genetic contribution for LOAD, the interpretation of heritability estimates remains challenging. These challenges are further complicated by the binary nature of LOAD status, where estimation and interpretation require additional considerations. Through a systematic review, we identified LOAD heritability estimates from 6 twin studies and 17 genome-wide association studies, all conducted in populations of European ancestry. We demonstrate that these heritability estimates for LOAD vary considerably. The variation reflects not only differences in study design and methodological approaches but also the underlying study population characteristics. Our findings indicate that commonly cited heritability estimates, often treated as universal values, should be interpreted within specific population contexts and methodological frameworks.

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.

Adjusting for principal components can induce collider bias in genome-wide association studies

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

Association of Metal Cations with the Anti-PF4/Heparin Antibody Response in Heparin-Induced Thrombocytopenia

Heparin-induced thrombocytopenia (HIT) is an antibody-mediated immune response against complexes of heparin and platelet factor 4 (PF4). The electrostatic interaction between heparin and PF4 is critical for the anti-PF4/heparin antibody response seen in HIT. The binding of metal cations to heparin induces conformational changes and charge neutralization of the heparin molecule, and cation-heparin binding can modulate the specificity and affinity for heparin-binding partners. However, the effects of metal cation binding to heparin in the context of anti-PF4/heparin antibody response have not been determined. Here, we utilized inductively coupled plasma mass spectrometry (ICP-MS) to quantify 16 metal cations in patient plasma and tested for correlation with anti-PF4/heparin IgG levels and platelet count after clinical suspicion of HIT in a cohort of heparin-treated patients. The average age of the cohort (n = 32) was 60.53 (SD = 14.31) years old, had a mean anti-PF4/heparin antibody optical density [OD405] of 0.93 (SD = 1.21) units, and was primarily female (n = 23). Patients with positive anti-PF4/heparin antibody test results (OD405 ≥ 0.5 units) were younger, had increased weight and BMI, and were more likely to have a positive serotonin release assay (SRA) result compared to antibody-negative patients. We observed statistical differences between antibody-positive and -negative groups for sodium and aluminum and significant correlations of anti-PF4/heparin antibody levels with sodium and silver. While differences in sodium concentrations were associated with antibody-positive status and correlated with antibody levels, no replication was performed. Additional studies are warranted to confirm our observed association, including in vitro binding studies and larger observational cohorts.

Raman spectroscopic deep learning with signal aggregated representations for enhanced cell phenotype and signature identification

Feature representation is critical for data learning, particularly in learning spectroscopic data. Machine learning (ML) and deep learning (DL) models learn Raman spectra for rapid, nondestructive, and label-free cell phenotype identification, which facilitate diagnostic, therapeutic, forensic, and microbiological applications. But these are challenged by high-dimensional, unordered, and low-sample spectroscopic data. Here, we introduced novel 2D image-like dual signal and component aggregated representations by restructuring Raman spectra and principal components, which enables spectroscopic DL for enhanced cell phenotype and signature identification. New ConvNet models DSCARNets significantly outperformed the state-of-the-art (SOTA) ML and DL models on six benchmark datasets, mostly with >2% improvement over the SOTA performance of 85-97% accuracies. DSCARNets also performed well on four additional datasets against SOTA models of extremely high performances (>98%) and two datasets without a published supervised phenotype classification model. Explainable DSCARNets identified Raman signatures consistent with experimental indications.

Isolation by environment and its consequences for range shifts with global change: Landscape genomics of the invasive plant common tansy

Invasive species are a growing global economic and ecological problem. However, it is not well understood how environmental factors mediate invasive range expansion. In this study, we investigated the recent and rapid range expansion of common tansy across environmental gradients in Minnesota, USA. We densely sampled individuals across the expanding range and performed reduced representation sequencing to generate a dataset of 3071 polymorphic loci for 176 individuals. We used non-spatial and spatially explicit analyses to determine the relative influences of geographic distance and environmental variation on patterns of genomic variation. We found no evidence for isolation by distance but strong evidence for isolation by environment, indicating that environmental factors may have modulated patterns of range expansion. Land use classification and soils were particularly important variables related to population structure although they operated on different spatial scales; land use classification was related to broad-scale patterns and soils were related to fine-scale patterns. All analyses indicated a distinctive genetic cluster in the most recently invaded portion of the range. Individuals from the far northwestern range margin were separated from the remainder of the range by reduced migration, which was associated with environmental resistance. This portion of the range was invaded primarily in the last 15 years. Ecological niche models also indicated that this cluster was associated with the expansion of the niche. While invasion is often assumed to be primarily influenced by dispersal limitation, our results suggest that ongoing invasion and range shifts with climate change may be strongly affected by environmental heterogeneity.

Comprehensive lipidomic analysis revealed the effects of fermented Morus alba L. intake on lipid profile in backfat and muscle tissue of Yuxi black pigs

Mulberry leaf is a widely used protein feed and is often used as a strategy to reduce feed costs and improve meat quality in the livestock industry. However, to date, there is a lack of research on the improvement of meat quality using mulberry leaves, and the exact mechanisms are not yet known. The results showed that fermented mulberry leaves significantly reduced backfat content but had no significant effect on intramuscular fat (IMF). Lipidomic analysis showed that 98 and 303 differential lipid molecules (p < 0.05) were identified in adipose and muscle tissues, respectively, including triglycerides (TG), phosphatidylcholine, phosphatidylethanolamine, sphingolipids, and especially TG; therefore, we analysed the acyl carbon atom number of TG. The statistical results of acyl with different carbon atom numbers of TG in adipose tissue showed that the acyl group containing 13 carbon atoms (C13) in TG was significantly upregulated, whereas C15, C16, C17, and C23 were significantly downregulated, whereas in muscle tissue, the C12, C19, C23, C25, and C26 in TG were significantly downregulated. Acyl changes in TG were different for different numbers of carbon atoms in different tissues. We found that the correlations of C (14-18) in adipose tissue were higher, but in muscle tissue, the correlations of C (18-26) were higher. Through pathway enrichment analysis, we identified six and four metabolic pathways with the highest contributions of differential lipid metabolites in adipose and muscle tissues respectively. These findings suggest that fermented mulberry leaves improve meat quality mainly by inhibiting TG deposition by downregulating medium- and short-chain fatty acids in backfat tissue and long-chain fatty acids in muscle tissue.

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

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

Bayesian LASSO for population stratification correction in rare haplotype association studies

Population stratification (PS) is one major source of confounding in both single nucleotide polymorphism (SNP) and haplotype association studies. To address PS, principal component regression (PCR) and linear mixed model (LMM) are the current standards for SNP associations, which are also commonly borrowed for haplotype studies. However, the underfitting and overfitting problems introduced by PCR and LMM, respectively, have yet to be addressed. Furthermore, there have been only a few theoretical approaches proposed to address PS specifically for haplotypes. In this paper, we propose a new method under the Bayesian LASSO framework, QBLstrat, to account for PS in identifying rare and common haplotypes associated with a continuous trait of interest. QBLstrat utilizes a large number of principal components (PCs) with appropriate priors to sufficiently correct for PS, while shrinking the estimates of unassociated haplotypes and PCs. We compare the performance of QBLstrat with the Bayesian counterparts of PCR and LMM and a current method, haplo.stats. Extensive simulation studies and real data analyses show that QBLstrat is superior in controlling false positives while maintaining competitive power for identifying true positives under PS.

Population stratification correction using Bayesian shrinkage priors for genetic association studies

INTRODUCTION

Population stratification (PS) is a major source of confounding in population-based genetic association studies of quantitative traits. Principal component regression (PCR) and linear mixed model (LMM) are two commonly used approaches to account for PS in association studies. Previous studies have shown that LMM can be interpreted as including all principal components (PCs) as random-effect covariates. However, including all PCs in LMM may dilute the influence of relevant PCs in some scenarios, while including only a few preselected PCs in PCR may fail to fully capture the genetic diversity.

MATERIALS AND METHODS

To address these shortcomings, we introduce Bayestrat-a method to detect associated variants with PS correction under the Bayesian LASSO framework. To adjust for PS, Bayestrat accommodates a large number of PCs and utilizes appropriate shrinkage priors to shrink the effects of nonassociated PCs.

RESULTS

Simulation results show that Bayestrat consistently controls type I error rates and achieves higher power compared to its non-shrinkage counterparts, especially when the number of PCs included in the model is large. As a demonstration of the utility of Bayestrat, we apply it to the Multi-Ethnic Study of Atherosclerosis (MESA). Variants and genes associated with serum triglyceride or HDL cholesterol are identified in our analyses.

DISCUSSION

The automatic and self-selection features of Bayestrat make it particularly suited in situations with complex underlying PS scenarios, where it is unknown a priori which PCs are potential confounders, yet the number that needs to be considered could be large in order to fully account for PS.

Identification of atrial fibrillation phenotypes at low risk of stroke in patients with CHA2DS2-VASc ≥2: Insight from the China-AF study

OBJECTIVE

Patients with atrial fibrillation (AF) are highly heterogeneous, and current risk stratification scores are only modestly good at predicting an individual's stroke risk. We aim to identify distinct AF clinical phenotypes with cluster analysis to optimize stroke prevention practices.

METHODS

From the prospective Chinese Atrial Fibrillation Registry cohort study, we included 4337 AF patients with CHA2 DS2 -VASc≥2 for males and 3 for females who were not treated with oral anticoagulation. We randomly split the patients into derivation and validation sets by a ratio of 7:3. In the derivation set, we used outcome-driven patient clustering with metric learning to group patients into clusters with different risk levels of ischemic stroke and systemic embolism, and identify clusters of patients with low risks. Then we tested the results in the validation set, using the clustering rules generated from the derivation set. Finally, the survival decision tree was applied as a sensitivity analysis to confirm the results.

RESULTS

Up to the follow-up of 1 year, 140 thromboembolic events (ischemic stroke or systemic embolism) occurred. After supervised metric learning from six variables involved in CHA2 DS2 -VASc scheme, we identified a cluster of patients (255/3035, 8.4%) at an annual thromboembolism risk of 0.8% in the derivation set. None of the patients in the low-risk cluster had prior thromboembolism, heart failure, diabetes, or age older than 70 years. After applying the regularities from metric learning on the validation set, we also identified a cluster of patients (137/1302, 10.5%) with an incident thromboembolism rate of 0.7%. Sensitivity analysis based on the survival decision tree approach selected a subgroup of patients with the same phenotypes as the metric-learning algorithm.

CONCLUSIONS

Cluster analysis identified a distinct clinical phenotype at low risk of stroke among high-risk [CHA2 DS2 -VASc≥2 (3 for females)] patients with AF. The use of the novel analytic approach has the potential to prevent a subset of AF patients from unnecessary anticoagulation and avoid the associated risk of major bleeding.

Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy

The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.

Guidelines for standardizing the application of discriminant analysis of principal components to genotype data

Despite the popularity of discriminant analysis of principal components (DAPC) for studying population structure, there has been little discussion of best practice for this method. In this work, I provide guidelines for standardizing the application of DAPC to genotype data sets. An often overlooked fact is that DAPC generates a model describing genetic differences among a set of populations defined by a researcher. Appropriate parameterization of this model is critical for obtaining biologically meaningful results. I show that the number of leading PC axes used as predictors of among-population differences, p_axes , should not exceed the k-1 biologically informative PC axes that are expected for k effective populations in a genotype data set. This k-1 criterion for p_axes specification is more appropriate compared to the widely used proportional variance criterion, which often results in a choice of p_axes  ≫ k-1. DAPC parameterized with no more than the leading k-1 PC axes: (i) is more parsimonious; (ii) captures maximal among-population variation on biologically relevant predictors; (iii) is less sensitive to unintended interpretations of population structure; and (iv) is more generally applicable to independent sample sets. Assessing model fit should be routine practice and aids interpretation of population structure. It is imperative that researchers articulate their study goals, that is, testing a priori expectations vs. studying de novo inferred populations, because this has implications on how their DAPC results should be interpreted. The discussion and practical recommendations in this work provide the molecular ecology community with a roadmap for using DAPC in population genetic investigations.

Genetic variations in relation to bleeding and pharmacodynamics of dabigatran in Chinese patients with nonvalvular atrial fibrillation: A nationwide multicentre prospective cohort study

INTRODUCTION

To identify the potential factors responsible for the individual variability of dabigatran, we investigated the genetic variations associated with clinical outcomes and pharmacodynamics (PD) in Chinese patients with nonvalvular atrial fibrillation (NVAF).

MATERIALS AND METHODS

Chinese patients with NVAF taking dabigatran etexilate with therapeutic doses were enrolled. The primary (bleeding events) and secondary (thromboembolic and major adverse cardiac events) outcomes for a 2-year follow-up were evaluated. Peak and trough PD parameters (anti-FIIa activity, activated partial thromboplastin time and prothrombin time) were detected. Whole-exome sequencing, genome-wide sequencing and candidate gene association analyses were performed.

RESULTS

There were 170 patients with NVAF treated with dabigatran (110 mg twice daily) who were finally included. Two single-nucleotide polymorphisms (SNPs) were significantly related with bleeding, which include UBASH3B rs2276408 (odds ratio [OR] = 8.79, 95% confidence interval [CI]: 2.99-25.83, p = 7.77 × 10-5 at sixth month visit) and FBN2 rs3805625 (OR = 8.29, 95% CI: 2.87-23.89, p = 9.08 × 10-5 at 12th month visit), as well as with increased trends at other visits (p < .05). Furthermore, minor allele carriers of 16 new SNPs increased PD levels, and those of one new SNP decreased PD values (p < 1.0 × 10-5 ). Lastly, 33 new SNPs were found to be associated with bleeding and PD among 14 candidate genes. Unfortunately, the low number of secondary outcomes precluded further association analyses.

CONCLUSIONS

Genetic variations indeed affected bleeding and PD in Chinese patients with NVAF treated with dabigatran. The functions of these suggestive genes and SNPs might further be explored and verified in more in vivo and in vitro investigations.

Multi-omics data integration approaches for precision oncology

Next-generation sequencing (NGS) has been pivotal to enhance the molecular characterization of human malignancies, allowing multiple omics data types to be available for cancer researchers and practitioners. In this context,...

Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure

Background

In genome-wide association studies the extent and impact of confounding due to population structure have been well recognized. Inadequate handling of such confounding is likely to lead to spurious associations, hampering replication, and the identification of causal variants. Several strategies have been developed for protecting associations against confounding, the most popular one is based on Principal Component Analysis. In contrast, the extent and impact of confounding due to population structure in gene-gene interaction association epistasis studies are much less investigated and understood. In particular, the role of nonlinear genetic population substructure in epistasis detection is largely under-investigated, especially outside a regression framework.

Methods

To identify causal variants in synergy, to improve interpretability and replicability of epistasis results, we introduce three strategies based on a model-based multifactor dimensionality reduction approach for structured populations, namely MBMDR-PC, MBMDR-PG, and MBMDR-GC.

Results

Simulation results comparing the performance of various approaches show that in the presence of population structure MBMDR-PC and MBMDR-PG consistently better control type I error rate at the nominal level than MBMDR-GC. Moreover, our proposed three methods of population structure correction outperform MDR-SP in terms of statistical power.

Conclusion

We demonstrate through extensive simulation studies the effect of various degrees of genetic population structure and relatedness on epistasis detection and propose appropriate remedial measures based on linear and nonlinear sample genetic similarity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13040-021-00247-w.

Simultaneous determination of phenolic metabolites in Chinese citrus and grape cultivars

Background

As the major bioactive compounds in citrus and grape, it is significant to use the contents of flavonoids and phenolic acids as quality evaluation criteria to provide a better view of classifying the quality and understanding the potential health benefits of each fruit variety.

Methods

A total of 15 varieties of citrus and 12 varieties of grapes were collected from Fujian, China. High-performance liquid chromatography method was used for the simultaneous determination of 17 phenolic compounds, including gallic acid, chlorogenic acid, caffeic acid, syringic acid, ρ-coumaric acid, ferulic acid, benzoic acid, salicylic acid, catechin, epicatechin, resveratrol, rutin, naringin, hesperidin, quercetin, nobiletin and tangeritin in the peels of citrus and grape cultivars. Further, the cultivars of citrus and grape were classified using principal component analysis (PCA) and hierarchical cluster analysis (HCA).

Results

A thorough separation of the 17 compounds was achieved within 100 min. The tested method exhibited good linearity (the limits of detection and limits of quantification were in the range of 0.03–1.83 µg/mL and 0.09–5.55 µg/mL, respectively), precision (the relative standard deviations of repeatability were 1.02–1.97%), and recovery (92.2–102.82%) for all the compounds, which could be used for the simultaneous determination of phenolic compounds in citrus and grape. Hesperidin (12.93–26,160.98 µg/g DW) and salicylic acid (5.35–751.02 µg/g DW) were the main flavonoids and phenolic acids in 15 citrus varieties, respectively. Besides, the hesperidin (ND to 605.48 µg/g DW) and salicylic acid (ND to 1,461.79 µg/g DW) were found as the highest flavonoid and the most abundant phenolic acid in grapes, respectively. A total of 15 citrus and 12 grape samples were classified into two main groups by PCA and HCA with strong consistency.

Educational attainment polygenic scores are associated with cortical total surface area and regions important for language and memory

It is well established that higher cognitive ability is associated with larger brain size. However, individual variation in intelligence exists despite brain size and recent studies have shown that a simple unifactorial view of the neurobiology underpinning cognitive ability is probably unrealistic. Educational attainment (EA) is often used as a proxy for cognitive ability since it is easily measured, resulting in large sample sizes and, consequently, sufficient statistical power to detect small associations. This study investigates the association between three global (total surface area (TSA), intra-cranial volume (ICV) and average cortical thickness) and 34 regional cortical measures with educational attainment using a polygenic scoring (PGS) approach. Analyses were conducted on two independent target samples of young twin adults with neuroimaging data, from Australia (N ​= ​1097) and the USA (N ​= ​723), and found that higher EA-PGS were significantly associated with larger global brain size measures, ICV and TSA (R² ​= ​0.006 and 0.016 respectively, p ​< ​0.001) but not average thickness. At the regional level, we identified seven cortical regions-in the frontal and temporal lobes-that showed variation in surface area and average cortical thickness over-and-above the global effect. These regions have been robustly implicated in language, memory, visual recognition and cognitive processing. Additionally, we demonstrate that these identified brain regions partly mediate the association between EA-PGS and cognitive test performance. Altogether, these findings advance our understanding of the neurobiology that underpins educational attainment and cognitive ability, providing focus points for future research.

A different view on fine-scale population structure in Western African populations

Due to its long genetic evolutionary history, Africans exhibit more genetic variation than any other population in the world. Their genetic diversity further lends itself to subdivisions of Africans into groups of individuals with a genetic similarity of varying degrees of granularity. It remains challenging to detect fine-scale structure in a computationally efficient and meaningful way. In this paper, we present a proof-of-concept of a novel fine-scale population structure detection tool with Western African samples. These samples consist of 1396 individuals from 25 ethnic groups (two groups are African American descendants). The strategy is based on a recently developed tool called IPCAPS. IPCAPS, or Iterative Pruning to CApture Population Structure, is a genetic divisive clustering strategy that enhances iterative pruning PCA, is robust to outliers and does not require a priori computation of haplotypes. Our strategy identified in total 12 groups and 6 groups were revealed as fine-scale structure detected in the samples from Cameroon, Gambia, Mali, Southwest USA, and Barbados. Our finding helped to explain evolutionary processes in the analyzed West African samples and raise awareness for fine-scale structure resolution when conducting genome-wide association and interaction studies.

Population Structure and Genetic Diversity of Italian Beef Breeds as a Tool for Planning Conservation and Selection Strategies

The aim was to investigate the population structure of eight beef breeds: three local Tuscan breeds under extinction, Calvana (CAL), Mucca Pisana (MUP), and Pontremolese (PON); three local unselected breeds reared in Sardinia, Sarda (SAR), Sardo Bruna (SAB), and Sardo Modicana (SAM); and two cosmopolitan breeds, Charolais (CHA) and Limousine (LIM), reared in the same regions. An effective population size ranges between 14.62 (PON) to 39.79 (SAM) in local breeds, 90.29 for CHA, and 135.65 for LIM. The average inbreeding coefficients were higher in Tuscan breeds (7.25%, 5.10%, and 3.64% for MUP, CAL, and PON, respectively) compared to the Sardinian breeds (1.23%, 1.66%, and 1.90% in SAB, SAM, and SAR, respectively), while for CHA and LIM they were <1%. The highest rates of mating between half-siblings were observed for CAL and MUP (~9% and 6.5%, respectively), while the highest rate of parent-offspring mating was ~8% for MUP. Our findings describe the urgent situation of the three Tuscan breeds and support the application of conservation measures and/or the development of breeding programs. Development of breeding strategies is suggested for the Sardinian breeds.